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Remote Sens., Volume 10, Issue 1 (January 2018)

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Editorial

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Open AccessEditorial Acknowledgement to Reviewers of Remote Sensing in 2017
Remote Sens. 2018, 10(1), 102; doi:10.3390/rs10010102
Received: 12 January 2018 / Revised: 12 January 2018 / Accepted: 12 January 2018 / Published: 12 January 2018
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Abstract
Peer review is an essential part in the publication process, ensuring that Remote Sensing maintains high quality standards for its published papers.[...] Full article

Research

Jump to: Editorial, Review, Other

Open AccessArticle Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?
Remote Sens. 2018, 10(1), 2; doi:10.3390/rs10010002
Received: 12 October 2017 / Revised: 11 December 2017 / Accepted: 17 December 2017 / Published: 21 December 2017
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Abstract
In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on
[...] Read more.
In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on given color information, hyperspectral information, and shape information. While the different types of information are used to define a variety of features, classification based on these features is performed using a random forest classifier. To draw conclusions about the relevance of different modalities and their combination for scene analysis, we present and discuss results which have been achieved with our framework on the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set. Full article
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Open AccessArticle Time-Continuous Hemispherical Urban Surface Temperatures
Remote Sens. 2018, 10(1), 3; doi:10.3390/rs10010003
Received: 6 October 2017 / Revised: 5 December 2017 / Accepted: 17 December 2017 / Published: 21 December 2017
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Abstract
Traditional methods for remote sensing of urban surface temperatures (Tsurf) are subject to a suite of temporal and geometric biases. The effect of these biases on our ability to characterize the true geometric and temporal nature of urban Tsurf is
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Traditional methods for remote sensing of urban surface temperatures (Tsurf) are subject to a suite of temporal and geometric biases. The effect of these biases on our ability to characterize the true geometric and temporal nature of urban Tsurf is currently unknown, but is certainly nontrivial. To quantify and overcome these biases, we present a method to retrieve time-continuous hemispherical radiometric urban Tsurf (Them, r) from broadband upwelling longwave radiation measured via pyrgeometer. By sampling the surface hemispherically, this measure is postulated to be more representative of the complex, three-dimensional structure of the urban surface than those from traditional remote sensors that usually have a narrow nadir or oblique viewing angle. The method uses a sensor view model in conjunction with a radiative transfer code to correct for atmospheric effects in three-dimensions using in situ profiles of air temperature and humidity along with information about surface structure. A practical parameterization is also included. Using the method, an eight-month climatology of Them, r is retrieved for Basel, Switzerland. Results show the importance of a robust, geometrically representative atmospheric correction routine to remove confounding atmospheric effects and to foster inter-site, inter-method, and inter-instrument comparison. In addition, over a month-long summertime intensive observation period, Them, r was compared to Tsurf retrieved from nadir (Tplan) and complete (Tcomp) perspectives of the surface. Large differences were observed between Tcomp, Them, r, and Tplan, with differences between Tplan and Tcomp of up to 8 K under clear-sky viewing conditions, which are the cases when satellite-based observations are available. In general, Them, r provides a better approximation to Tcomp than Tplan, particularly under clear-sky conditions. The magnitude of differences in remote sensed Tsurf based on sensor-surface-sun geometry varies significantly based on time of day and synoptic conditions and prompts further investigation of methodological and instrument bias in remote sensed urban surface temperature records. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
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Open AccessArticle The Impact of Precipitation Deficit and Urbanization on Variations in Water Storage in the Beijing-Tianjin-Hebei Urban Agglomeration
Remote Sens. 2018, 10(1), 4; doi:10.3390/rs10010004
Received: 31 October 2017 / Revised: 11 December 2017 / Accepted: 13 December 2017 / Published: 22 December 2017
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Abstract
Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS)
[...] Read more.
Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS) change in Jing-Jin-Ji from 1979 to the 2010s were investigated, based on the global land data assimilation system (GLDAS) and the EartH2Observe (E2O) outputs, and we used a night light index as an index of urbanization. The results showed that TWS anomaly varied in three stages: significant increase from 1981 to 1996, rapid decrease from 1996 to 2002 and increase from 2002 to the 2010s. Simultaneously, GWS has decreased with about 41.5 cm (500% of GWS in 1979). Both urbanization and precipitation change influenced urban water resource variability. Urbanization was a relatively important factor to the depletion of TWS (explains 83%) and GWS (explains 94%) since the 1980s and the precipitation deficit explains 72% and 64% of TWS and GWS variabilities. It indicates that urbanization coupled with precipitation deficit has been a more important factor that impacted depletion of both TWS and GWS than climate change only, in the Jing-Jin-Ji region. Moreover, we suggested that the cumulative effect should be considered when discussing the relationship between influence factors and water storage change. Full article
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Open AccessArticle The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands
Remote Sens. 2018, 10(1), 5; doi:10.3390/rs10010005
Received: 16 November 2017 / Revised: 13 December 2017 / Accepted: 15 December 2017 / Published: 21 December 2017
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Abstract
While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas.
[...] Read more.
While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments. Full article
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
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Open AccessArticle Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
Remote Sens. 2018, 10(1), 7; doi:10.3390/rs10010007
Received: 31 August 2017 / Revised: 10 December 2017 / Accepted: 20 December 2017 / Published: 21 December 2017
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Abstract
Since Landsat-1 first started to deliver volumes of pixels in 1972, the volumes of archived data in remote sensing data centers have increased continuously. Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and
[...] Read more.
Since Landsat-1 first started to deliver volumes of pixels in 1972, the volumes of archived data in remote sensing data centers have increased continuously. Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and revisit periods of these archived data are vastly different. In addition, the remote sensing data received continuously by each data center arrives at a faster code rate; it is best to ingest and archive the newly received data to ensure users have access to the latest data retrieval and distribution services. Hence, an excellent data integration, organization, and management program is urgently needed. However, the multi-source, massive, heterogeneous, and distributed storage features of remote sensing data have not only caused difficulties for integration across distributed data center spatial infrastructures, but have also resulted in the current modes of data organization and management being unable meet the rapid retrieval and access requirements of users. Hence, this paper proposes an object-oriented data technology (OODT) and SolrCloud-based remote sensing data integration and management framework across a distributed data center spatial infrastructure. In this framework, all of the remote sensing metadata in the distributed sub-centers are transformed into the International Standardization Organization (ISO) 19115-based unified format, and then ingested and transferred to the main center by OODT components, continuously or at regular intervals. In the main data center, in order to improve the efficiency of massive data retrieval, we proposed a logical segmentation indexing (LSI) model-based data organization approach, and took SolrCloud to realize the distributed index and retrieval of massive metadata. Finally, a series of distributed data integration, retrieval, and comparative experiments showed that our proposed distributed data integration and management program is effective and promises superior results. Specifically, the LSI model-based data organization and the SolrCloud-based distributed indexing schema was able to effectively improve the efficiency of massive data retrieval. Full article
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Open AccessArticle Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning
Remote Sens. 2018, 10(1), 8; doi:10.3390/rs10010008
Received: 4 October 2017 / Revised: 19 December 2017 / Accepted: 20 December 2017 / Published: 21 December 2017
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Abstract
Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose
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Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distance metric. Furthermore, in order to improve the practicability of the proposed method, we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy of Meteorological Sciences (CAMS), Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessFeature PaperArticle The Role of Resolution in the Estimation of Fractal Dimension Maps From SAR Data
Remote Sens. 2018, 10(1), 9; doi:10.3390/rs10010009
Received: 29 September 2017 / Revised: 13 November 2017 / Accepted: 20 December 2017 / Published: 22 December 2017
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Abstract
This work is aimed at investigating the role of resolution in fractal dimension map estimation, analyzing the role of the different surface spatial scales involved in the considered estimation process. The study is performed using a data set of actual Cosmo/SkyMed Synthetic Aperture
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This work is aimed at investigating the role of resolution in fractal dimension map estimation, analyzing the role of the different surface spatial scales involved in the considered estimation process. The study is performed using a data set of actual Cosmo/SkyMed Synthetic Aperture Radar (SAR) images relevant to two different areas, the region of Bidi in Burkina Faso and the city of Naples in Italy, acquired in stripmap and enhanced spotlight modes. The behavior of fractal dimension maps in the presence of areas with distinctive characteristics from the viewpoint of land-cover and surface features is discussed. Significant differences among the estimated maps are obtained in the presence of fine textural details, which significantly affect the fractal dimension estimation for the higher resolution spotlight images. The obtained results show that if we are interested in obtaining a reliable estimate of the fractal dimension of the observed natural scene, stripmap images should be chosen in view of both economic and computational considerations. In turn, the combination of fractal dimension maps obtained from stripmap and spotlight images can be used to identify areas on the scene presenting non-fractal behavior (e.g., urban areas). Along this guideline, a simple example of stripmap-spotlight data fusion is also presented. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Open AccessArticle Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA
Remote Sens. 2018, 10(1), 10; doi:10.3390/rs10010010
Received: 17 November 2017 / Revised: 19 December 2017 / Accepted: 19 December 2017 / Published: 22 December 2017
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Abstract
Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know
[...] Read more.
Light detection and ranging (Lidar) data can be used to create wall-to-wall forest structure and fuel products that are required for wildfire behavior simulation models. We know that Lidar-derived forest parameters have a non-negligible error associated with them, yet we do not know how this error influences the results of fire behavior modeling that use these layers as inputs. Here, we evaluated the influence of error associated with two Lidar data products—canopy height (CH) and canopy base height (CBH)—on simulated fire behavior in a case study in the Sierra Nevada, California, USA. We used a Monte Carlo simulation approach with expected randomized error added to each model input. Model 1 used the original, unmodified data, Model 2 incorporated error in the CH layer, and Model 3 incorporated error in the CBH layer. This sensitivity analysis showed that error in CH and CBH did not greatly influence the modeled conditional burn probability, fire size, or fire size distribution. We found that the expected error associated with CH and CBH did not greatly influence modeled results: conditional burn probability, fire size, and fire size distributions were very similar between Model 1 (original data), Model 2 (error added to CH), and Model 3 (error added to CBH). However, the impact of introduced error was more pronounced with CBH than with CH, and at lower canopy heights, the addition of error increased modeled canopy burn probability. Our work suggests that the use of Lidar data, even with its inherent error, can contribute to reliable and robust estimates of modeled forest fire behavior, and forest managers should be confident in using Lidar data products in their fire behavior modeling workflow. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Automated Sensing of Wave Inundation across a Rocky Shore Platform Using a Low-Cost Camera System
Remote Sens. 2018, 10(1), 11; doi:10.3390/rs10010011
Received: 7 November 2017 / Revised: 9 December 2017 / Accepted: 12 December 2017 / Published: 23 December 2017
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Abstract
Rocky coastlines are frequently used for recreation, however, they are often highly exposed and hazardous environments resulting in high risk to visitors. Traditional approaches to managing human safety in coastal settings (such as the surf lifesaving clubs that have proven effective on beaches)
[...] Read more.
Rocky coastlines are frequently used for recreation, however, they are often highly exposed and hazardous environments resulting in high risk to visitors. Traditional approaches to managing human safety in coastal settings (such as the surf lifesaving clubs that have proven effective on beaches) are not necessarily transferable to rock platforms due to their often remote and fragmented distribution and the different recreational uses. As such, a different approach is required. To address this, we present a low-cost camera system to assess the wave hazard on a high-visitation rocky shore platform: the Figure Eight Pools Rock Platform, New South Wales, Australia. The camera system is shown to be highly effective and allows identification of both the distance and frequency of wave inundation on the platform using a novel pixel analysis technique. Nearshore wave height is shown to be the primary factor driving inundation frequencies along the cross-platform transect investigated with some influence from wave period. The remotely sensed camera data are used to develop a preliminary overwash hazard rating system, and analysis of the first month of data collected suggests that the platform is highly hazardous to visitors. Future work will expand this hazard rating system, developing a predictive tool that estimates the overwash hazard level based on forecast wave and tide conditions to improve visitor safety at the site. Full article
(This article belongs to the Special Issue Instruments and Methods for Ocean Observation and Monitoring)
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Open AccessArticle Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone
Remote Sens. 2018, 10(1), 12; doi:10.3390/rs10010012
Received: 15 November 2017 / Revised: 6 December 2017 / Accepted: 20 December 2017 / Published: 22 December 2017
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Abstract
The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential
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The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. The model assumed a linear relationship that varied with both time and space between soil moisture and backscattering coefficient (SM- σ 0 ). Results showed that three LME models developed with different polarized σ 0 images all meet the European Space Agency (ESA) accuracy requirement for GMES soil moisture product (≤5% in volume), with the vertical transmit and vertical receive (VV) polarized model achieving the best performance. However, the SM- σ 0 relationship was found to depend strongly on space, making it difficult to predict absolute soil moisture for each grid. Therefore, a relative soil moisture index was then proposed to correct for site effect. When compared with those of the linear fixed effects model, the soil moisture indices predicted by the LME model captured the temporal dynamics of measured soil moisture better, with the overall R2 and cross-validated R2 being 0.68 and 0.64, respectively. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones. Full article
(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)
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Open AccessArticle Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest
Remote Sens. 2018, 10(1), 13; doi:10.3390/rs10010013
Received: 9 November 2017 / Revised: 28 November 2017 / Accepted: 28 November 2017 / Published: 22 December 2017
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Abstract
Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such
[...] Read more.
Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R2 = 0.7786) and canopy area (R2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Carbon)
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Open AccessArticle Monitoring Bare Soil Freeze–Thaw Process Using GPS-Interferometric Reflectometry: Simulation and Validation
Remote Sens. 2018, 10(1), 14; doi:10.3390/rs10010014
Received: 10 October 2017 / Revised: 18 December 2017 / Accepted: 21 December 2017 / Published: 22 December 2017
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Abstract
Frozen soil and permafrost affect ecosystem diversity and productivity as well as global energy and water cycles. Although some space-based Radar techniques or ground-based sensors can monitor frozen soil and permafrost variations, there are some shortcomings and challenges. For the first time, we
[...] Read more.
Frozen soil and permafrost affect ecosystem diversity and productivity as well as global energy and water cycles. Although some space-based Radar techniques or ground-based sensors can monitor frozen soil and permafrost variations, there are some shortcomings and challenges. For the first time, we use GPS-Interferometric Reflectometry (GPS-IR) to monitor and investigate the bare soil freeze–thaw process as a new remote sensing tool. The mixed-texture permittivity models are employed to calculate the frozen and thawed soil permittivities. When the soil freeze/thaw process occurs, there is an abrupt change in the soil permittivity, which will result in soil scattering variations. The corresponding theoretical simulation results from the forward GPS multipath simulator show variations of GPS multipath observables. As for the in-situ measurements, virtual bistatic radar is employed to simplify the analysis. Within the GPS-IR spatial resolution, one SNOTEL site (ID 958) and one corresponding PBO (plate boundary observatory) GPS site (AB33) are used for analysis. In 2011, two representative days (frozen soil on Doy of Year (DOY) 318 and thawed soil on DOY 322) show the SNR changes of phase and amplitude. The GPS site and the corresponding SNOTEL site in four different years are analyzed for comparisons. When the soil freeze/thaw process occurred and no confounding snow depth and soil moisture effects existed, it exhibited a good absolute correlation (|R| = 0.72 in 2009, |R| = 0.902 in 2012, |R| = 0.646 in 2013, and |R| = 0.7017 in 2014) with the average detrended SNR data. Our theoretical simulation and experimental results demonstrate that GPS-IR has potential for monitoring the bare soil temperature during the soil freeze–thaw process, while more test works should be done in the future. GNSS-R polarimetry is also discussed as an option for detection. More retrieval work about elevation and polarization combinations are the focus of future development. Full article
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Open AccessArticle Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016
Remote Sens. 2018, 10(1), 17; doi:10.3390/rs10010017
Received: 24 October 2017 / Revised: 13 December 2017 / Accepted: 21 December 2017 / Published: 22 December 2017
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Abstract
Vegetation phenology in temperate grasslands is highly sensitive to climate change. However, it is still unclear how the timing of vegetation phenology events (especially for autumn phenology) is altered in response to climate change across different grassland types. In this study, we investigated
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Vegetation phenology in temperate grasslands is highly sensitive to climate change. However, it is still unclear how the timing of vegetation phenology events (especially for autumn phenology) is altered in response to climate change across different grassland types. In this study, we investigated variations of the growing season start (SOS) and end (EOS), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000–2016), for meadow steppe, typical steppe, and desert steppe in the Inner Mongolian grassland of Northern China. Using gridded climate data (2000–2015), we further analyzed correlations between SOS/EOS and pre-season average air temperature and total precipitation (defined as 90-day period prior to SOS/EOS, i.e., pre-SOS/EOS) in each grid. The results showed that both SOS and EOS occurred later in desert steppe (day of year (doy) 114 and 312) than in meadow steppe (doy 109 and 305) and typical steppe (doy 111 and 307); namely, desert steppe has a relatively late growing season than meadow steppe and typical steppe. For all three grasslands, SOS was mainly controlled by pre-SOS precipitation with the sensitivity being largest in desert steppe. EOS was closely connected with pre-EOS air temperature in meadow steppe and typical steppe, but more closely related to pre-EOS precipitation in desert steppe. During 2000–2015, SOS in typical steppe and desert steppe has significantly advanced by 2.2 days and 10.6 days due to a significant increase of pre-SOS precipitation. In addition, EOS of desert steppe has also significantly advanced by 6.8 days, likely as a result from the combined effects of elevated preseason temperature and precipitation. Our study highlights the diverse responses in the timing of spring and autumn phenology to preceding temperature and precipitation in different grassland types. Results from this study can help to guide grazing systems and to develop policy frameworks for grasslands protection. Full article
(This article belongs to the Special Issue Land Surface Phenology)
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Open AccessArticle Remote Sensing of Coral Bleaching Using Temperature and Light: Progress towards an Operational Algorithm
Remote Sens. 2018, 10(1), 18; doi:10.3390/rs10010018
Received: 3 October 2017 / Revised: 1 December 2017 / Accepted: 19 December 2017 / Published: 22 December 2017
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Abstract
The National Oceanic and Atmospheric Administration’s Coral Reef Watch program developed and operates several global satellite products to monitor bleaching-level heat stress. While these products have a proven ability to predict the onset of most mass coral bleaching events, they occasionally miss events;
[...] Read more.
The National Oceanic and Atmospheric Administration’s Coral Reef Watch program developed and operates several global satellite products to monitor bleaching-level heat stress. While these products have a proven ability to predict the onset of most mass coral bleaching events, they occasionally miss events; inaccurately predict the severity of some mass coral bleaching events; or report false alarms. These products are based solely on temperature and yet coral bleaching is known to result from both temperature and light stress. This study presents a novel methodology (still under development), which combines temperature and light into a single measure of stress to predict the onset and severity of mass coral bleaching. We describe here the biological basis of the Light Stress Damage (LSD) algorithm under development. Then by using empirical relationships derived in separate experiments conducted in mesocosm facilities in the Mexican Caribbean we parameterize the LSD algorithm and demonstrate that it is able to describe three past bleaching events from the Great Barrier Reef (GBR). For this limited example, the LSD algorithm was able to better predict differences in the severity of the three past GBR bleaching events, quantifying the contribution of light to reduce or exacerbate the impact of heat stress. The new Light Stress Damage algorithm we present here is potentially a significant step forward in the evolution of satellite-based bleaching products. Full article
(This article belongs to the collection Sea Surface Temperature Retrievals from Remote Sensing)
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Open AccessArticle An Approach to Improve the Positioning Performance of GPS/INS/UWB Integrated System with Two-Step Filter
Remote Sens. 2018, 10(1), 19; doi:10.3390/rs10010019
Received: 10 October 2017 / Revised: 18 December 2017 / Accepted: 20 December 2017 / Published: 23 December 2017
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Abstract
The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) single-point-positioning (SPP) mode cannot meet the requirements of high-accuracy navigation. Range observation through ultra-wideband (UWB) is an effective means to enhance the reliability and accuracy of GPS/INS integrated navigation, particularly in
[...] Read more.
The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) single-point-positioning (SPP) mode cannot meet the requirements of high-accuracy navigation. Range observation through ultra-wideband (UWB) is an effective means to enhance the reliability and accuracy of GPS/INS integrated navigation, particularly in environments where GPS availability is poor. Because it is difficult for UWB signal to achieve large-scale intervention coverage, an enhanced GPS/INS/UWB integrated scheme with positioning error correction is proposed to improve the position accuracy in the UWB signal outage scenario. The position difference between the GPS/INS integrated solution and the GPS/INS/UWB integrated solution is predicated as the error correction for GPS/INS/UWB integrated navigation in a UWB signal challenging environment. Position correction information in the north and east directions is input to the two-step filter to decrease the error of GPS/INS integrated navigation in single-point-positioning. In order to validate the proposed method, a real experiment is conducted. The results indicate that the enhanced GPS/INS/UWB integrated scheme with positioning error correction is able to improve the position accuracy of GPS/INS/UWB integrated navigation when UWB signal is unavailable. Full article
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Open AccessArticle The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6
Remote Sens. 2018, 10(1), 21; doi:10.3390/rs10010021
Received: 27 November 2017 / Revised: 20 December 2017 / Accepted: 21 December 2017 / Published: 23 December 2017
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Abstract
A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology
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A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared. Full article
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Open AccessArticle Application of Coupled-Wave Wentzel-Kramers-Brillouin Approximation to Ground Penetrating Radar
Remote Sens. 2018, 10(1), 22; doi:10.3390/rs10010022
Received: 5 December 2017 / Revised: 19 December 2017 / Accepted: 20 December 2017 / Published: 23 December 2017
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Abstract
This paper deals with bistatic subsurface probing of a horizontally layered dielectric half-space by means of ultra-wideband electromagnetic waves. In particular, the main objective of this work is to present a new method for the solution of the two-dimensional back-scattering problem arising when
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This paper deals with bistatic subsurface probing of a horizontally layered dielectric half-space by means of ultra-wideband electromagnetic waves. In particular, the main objective of this work is to present a new method for the solution of the two-dimensional back-scattering problem arising when a pulsed electromagnetic signal impinges on a non-uniform dielectric half-space; this scenario is of interest for ground penetrating radar (GPR) applications. For the analytical description of the signal generated by the interaction of the emitted pulse with the environment, we developed and implemented a novel time-domain version of the coupled-wave Wentzel-Kramers-Brillouin approximation. We compared our solution with finite-difference time-domain (FDTD) results, achieving a very good agreement. We then applied the proposed technique to two case studies: in particular, our method was employed for the post-processing of experimental radargrams collected on Lake Chebarkul, in Russia, and for the simulation of GPR probing of the Moon surface, to detect smooth gradients of the dielectric permittivity in lunar regolith. The main conclusions resulting from our study are that our semi-analytical method is accurate, radically accelerates calculations compared to simpler mathematical formulations with a mostly numerical nature (such as the FDTD technique), and can be effectively used to aid the interpretation of GPR data. The method is capable to correctly predict the protracted return signals originated by smooth transition layers of the subsurface dielectric medium. The accuracy and numerical efficiency of our computational approach make promising its further development. Full article
(This article belongs to the Special Issue Recent Advances in GPR Imaging)
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Open AccessArticle Assessing Different Feature Sets’ Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery
Remote Sens. 2018, 10(1), 23; doi:10.3390/rs10010023
Received: 6 November 2017 / Revised: 20 December 2017 / Accepted: 22 December 2017 / Published: 23 December 2017
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Abstract
Land cover classification (LCC) in complex surface-mined landscapes has become very important for understanding the influence of mining activities on the regional geo-environment. There are three characteristics of complex surface-mined areas limiting LCC: significant three-dimensional terrain, strong temporal-spatial variability of surface cover, and
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Land cover classification (LCC) in complex surface-mined landscapes has become very important for understanding the influence of mining activities on the regional geo-environment. There are three characteristics of complex surface-mined areas limiting LCC: significant three-dimensional terrain, strong temporal-spatial variability of surface cover, and spectral-spatial homogeneity. Thus, determining effective feature sets are very important as input dataset to improve detailed extent of classification schemes and classification accuracy. In this study, data such as various feature sets derived from ZiYuan-3 stereo satellite imagery, a feature subset resulting from a feature selection (FS) procedure, training data polygons, and test sample sets were firstly obtained; then, feature sets’ effects on classification accuracy was assessed based on different feature set combination schemes, a FS procedure, and random forest algorithm. The following conclusions were drawn. (1) The importance of feature set could be divided into three grades: the vegetation index (VI), principal component bands (PCs), mean filters (Mean), standard deviation filters (StDev), texture measures (Textures), and topographic variables (TVs) were important; the Gaussian low-pass filters (GLP) was just positive; and none were useless. The descending order of their importance was TVs, StDev, Textures, Mean, PCs, VI, and GLP. (2) TVs and StDev both significantly outperformed VI, PCs, GLP, and Mean; Mean outperformed GLP; all other pairs of feature sets had no difference. In general, the study assessed different feature sets’ effects on LCC in complex surface-mined landscapes. Full article
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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Open AccessArticle Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs
Remote Sens. 2018, 10(1), 24; doi:10.3390/rs10010024
Received: 6 November 2017 / Revised: 16 December 2017 / Accepted: 19 December 2017 / Published: 24 December 2017
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Abstract
Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery.
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Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper’s overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpaços, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future. Full article
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Open AccessArticle Evaluation of Accuracy and Practical Applicability of Methods for Measuring Leaf Reflectance and Transmittance Spectra
Remote Sens. 2018, 10(1), 25; doi:10.3390/rs10010025
Received: 6 October 2017 / Revised: 13 December 2017 / Accepted: 21 December 2017 / Published: 24 December 2017
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Abstract
Leaf reflectance and transmittance spectra are urgently needed in interpretation of remote sensing data and modeling energy budgets of vegetation. The measurement methods should be fast to operate and preferably portable to enable quick collection of spectral databases and in situ measurements. At
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Leaf reflectance and transmittance spectra are urgently needed in interpretation of remote sensing data and modeling energy budgets of vegetation. The measurement methods should be fast to operate and preferably portable to enable quick collection of spectral databases and in situ measurements. At the same time, the collected spectra must be comparable across measurement campaigns. We compared three different methods for acquiring leaf reflectance and transmittance spectra. These were a single integrating sphere (ASD RTS-3ZC), a small double integrating sphere (Ocean Optics SpectroClip-TR), and a leaf clip (PP Systems UNI501 Mini Leaf Clip). With all methods, an ASD FieldSpec 4 spectrometer was used to measure white paper and tree leaves. Single and double integrating spheres showed comparable within-method variability in the measurements. Variability with leaf clip was slightly higher. The systematic difference in mean reflectance spectra between single and double integrating spheres was only minor (average relative difference of 1%), whereas a large difference (14%) was observed in transmittance. Reflectance measured with leaf clip was on average 14% higher compared to single integrating sphere. The differences between methods influenced also spectral vegetation indices calculated from the spectra, particularly those that were designed to track small changes in spectra. Measurements with double integrating sphere were four, and with leaf clip six times as fast as with single integrating sphere, if slightly reduced signal level (integration time reduced from optimum) was allowed for the double integrating sphere. Thus, these methods are fast alternatives to a conventional single integrating sphere. However, because the differences between methods depended on the measured target and wavelength, care must be taken when comparing the leaf spectra acquired with different methods. Full article
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Open AccessArticle Urban Imperviousness Effects on Summer Surface Temperatures Nearby Residential Buildings in Different Urban Zones of Parma
Remote Sens. 2018, 10(1), 26; doi:10.3390/rs10010026
Received: 25 October 2017 / Revised: 14 December 2017 / Accepted: 21 December 2017 / Published: 24 December 2017
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Abstract
Rapid and unplanned urban growth is responsible for the continuous conversion of green or generally natural spaces into artificial surfaces. The high degree of imperviousness modifies the urban microclimate and no studies have quantified its influence on the surface temperature (ST) nearby residential
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Rapid and unplanned urban growth is responsible for the continuous conversion of green or generally natural spaces into artificial surfaces. The high degree of imperviousness modifies the urban microclimate and no studies have quantified its influence on the surface temperature (ST) nearby residential building. This topic represents the aim of this study carried out during summer in different urban zones (densely urbanized or park/rural areas) of Parma (Northern Italy). Daytime and nighttime ASTER images, the local urban cartography and the Italian imperviousness databases were used. A reproducible/replicable framework was implemented named “Building Thermal Functional Area” (BTFA) useful to lead building-proxy thermal analyses by using remote sensing data. For each residential building (n = 8898), the BTFA was assessed and the correspondent ASTER-LST value (ST_BTFA) and the imperviousness density were calculated. Both daytime and nighttime ST_BTFA significantly (p < 0.001) increased when high levels of imperviousness density surrounded the residential buildings. These relationships were mostly consistent during daytime and in densely urbanized areas. ST_BTFA differences between urban and park/rural areas were higher during nighttime (above 1 °C) than daytime (about 0.5 °C). These results could help to identify “urban thermal Hot-Spots” that would benefit most from mitigation actions. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Agriculture and Land Cover)
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Open AccessArticle Remote Sensing of Hydrological Changes in Tian-e-Zhou Oxbow Lake, an Ungauged Area of the Yangtze River Basin
Remote Sens. 2018, 10(1), 27; doi:10.3390/rs10010027
Received: 28 September 2017 / Revised: 12 December 2017 / Accepted: 20 December 2017 / Published: 25 December 2017
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Abstract
The hydrological pattern changes have a great influence on the wetland environment. However, some important wetland areas often lack historical observations due to economic and physical conditions. The Tian-e-Zhou oxbow lake wetland is an important habitat for two endangered species and also has
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The hydrological pattern changes have a great influence on the wetland environment. However, some important wetland areas often lack historical observations due to economic and physical conditions. The Tian-e-Zhou oxbow lake wetland is an important habitat for two endangered species and also has very little historical hydrological data. Remote sensing images can be used to explore the historical water area fluctuation of lakes. In addition, remote sensing can also be used to obtain historical water levels based on the water boundary elevation integrated with a topographic data (WBET) method or the level-surface area relationship curve (LRC) method. In order to minimize the uncertainty of the derived results, both methods were introduced in the extraction of the water level of Tian-e-Zhou during 1992–2015. The results reveal that the hydrological regime of the oxbow lake has experienced a significant change after the Shatanzi Levee construction in 1998. With the impact of the levee, the mean annual water surface area of the lake was reduced by 5.8 km2 during the flood season, but, during the non-flood season, it was increased by 1.35 km2. For the same period, the water level of the lake during the flood season also showed a 1.47 m (WBET method) or 3.21 m (LRC method) decrease. The mean annual water level increased by 1.12 m (WBET method) or 0.75 m (LRC method). Both results had a good accuracy with RMSE (root-mean-square errors) of less than 0.4 m. Furthermore, the water level differences between the Yangtze River channel and the oxbow lake increased by at least 0.5 m. It is found that the hydrological pattern of the oxbow lake changed significantly after the levee construction, which could bring some disadvantages to the habitats of the two endangered species. Full article
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
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Open AccessArticle Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture
Remote Sens. 2018, 10(1), 28; doi:10.3390/rs10010028
Received: 24 November 2017 / Revised: 13 December 2017 / Accepted: 21 December 2017 / Published: 24 December 2017
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Abstract
Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field.
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Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM–based cluster method with known SM (classifying the VIS–NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)–based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI–based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS–NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS–NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69–0.77, RPD = 1.79–2.08). The estimation of SOM, when using the NSMI–based cluster method with unknown SM (RPD = 1.95–2.04), was similar to the use of the SM–based cluster method with known SM (RPD = 1.79–2.08). The predictive results (RPD = 1.87–2.06) demonstrated that the NSMI-–based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Vertical Accuracy Simulation of Stereo Mapping Using a Small Matrix Charge-Coupled Device
Remote Sens. 2018, 10(1), 29; doi:10.3390/rs10010029
Received: 2 November 2017 / Revised: 18 December 2017 / Accepted: 22 December 2017 / Published: 25 December 2017
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Abstract
At present, without ground control points (GCPs), the positioning accuracy of remote sensing images often fails to meet the growing requirements for mapping accuracy. Multi-load synergy to improve accuracy without GCPs by eliminating the impact of stereo accuracy, which is caused by on-orbit
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At present, without ground control points (GCPs), the positioning accuracy of remote sensing images often fails to meet the growing requirements for mapping accuracy. Multi-load synergy to improve accuracy without GCPs by eliminating the impact of stereo accuracy, which is caused by on-orbit measurement error, is urgently needed to improve large-scale mapping. In this study, we analyzed error sources in stereo imaging mode and found that vertical accuracy depends on the relative accuracy of attitude during symmetric stereoscopic mapping. With the assistance of small matrix charge-coupled device (CCD) images and the block adjustment method, relative accuracy of attitude was improved, allowing for the improvement in vertical accuracy without GCPs. The simulation results show that vertical accuracy in symmetric stereo mode is not affected by attitude system error. After the restoration of imaging attitude processed by a sequence of matrix CCD images, the relative accuracy of the attitude increased, and the accuracy of the elevation without GCPs improved significantly. The results demonstrate the feasibility of small matrix CCD-assisted stereo mapping. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Evaluation of Satellite-Based Precipitation Products from IMERG V04A and V03D, CMORPH and TMPA with Gauged Rainfall in Three Climatologic Zones in China
Remote Sens. 2018, 10(1), 30; doi:10.3390/rs10010030
Received: 9 August 2017 / Revised: 17 December 2017 / Accepted: 22 December 2017 / Published: 25 December 2017
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Abstract
A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates
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A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement) V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique)-CRT and TRMM (the Tropical Rainfall Measuring Mission) 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1) over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (−46.98%) during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%), RMSE (0.44 mm/day) and RRMSE (28.37%). Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2) within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day) during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3) over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day), RRMSE (8.44%) and highest R (0.92) during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4) in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation with RBs (−70.62% vs. −6.47% over the Tibetan Plateau, −46.92% vs. −0.66% over the Weihe River Basin, respectively); and (5) overall, except for IMERG V04A in Tibetan Plateau, all satellite-based precipitation captured the gauge-based precipitation well over the three regions according to RRMSEs, Rs and Rbs during the study period. IMERG V03D performs better than its predecessors-TRMM 3B42 and CMORPH over the Tibetan Plateau region and the Huaihe River Basin, while IMERG V04A only does so over the latter. Between the two IMERG products, IMERG V04A does not show an advantage over IMERG V03D over the Tibetan Plateau region and the Weihe River Basin. In particular, over the former, IMERG V04A performs far worse than IMERG V03D. These findings provide valuable feedback for both IMERG algorithm developers and data users. Full article
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Open AccessArticle Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China
Remote Sens. 2018, 10(1), 31; doi:10.3390/rs10010031
Received: 16 November 2017 / Revised: 20 December 2017 / Accepted: 22 December 2017 / Published: 25 December 2017
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Abstract
Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial
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Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values. Full article
(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)
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Open AccessArticle Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data
Remote Sens. 2018, 10(1), 32; doi:10.3390/rs10010032
Received: 9 November 2017 / Revised: 7 December 2017 / Accepted: 23 December 2017 / Published: 25 December 2017
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Abstract
Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land
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Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984–2016) data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area—Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions. Full article
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Open AccessArticle Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales
Remote Sens. 2018, 10(1), 33; doi:10.3390/rs10010033
Received: 2 November 2017 / Revised: 18 December 2017 / Accepted: 23 December 2017 / Published: 25 December 2017
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Abstract
A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3),
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A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of ~0.25°, and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of ~0.1°. The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m3 m−3, whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m3 m−3. The JAXA product performed much better at 0.25° than at 0.1°, but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m3 m−3). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m3 m−3. The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m3 m−3 for LWW and 0.016 m3 m−3 for REMEDHUS) and SMOS (−0.004 m3 m−3 for LWW and −0.012 m3 m−3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m3 m−3), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the potential in integrating them into the existing long-term ESA CCI product, in order to form a more reliable and useful product. Full article
(This article belongs to the Special Issue Soil Moisture Remote Sensing Across Scales)
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Open AccessArticle Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China
Remote Sens. 2018, 10(1), 34; doi:10.3390/rs10010034
Received: 13 November 2017 / Revised: 19 December 2017 / Accepted: 20 December 2017 / Published: 25 December 2017
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Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3
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The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Gaussian Half-Wavelength Progressive Decomposition Method for Waveform Processing of Airborne Laser Bathymetry
Remote Sens. 2018, 10(1), 35; doi:10.3390/rs10010035
Received: 31 October 2017 / Revised: 19 December 2017 / Accepted: 22 December 2017 / Published: 26 December 2017
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Abstract
In an airborne laser bathymetry system, the full-waveform echo signal is usually recorded by discrete sampling. The accuracy of signal recognition and the amount of effective information that can be extracted by conventional methods are limited. To improve the validity and reliability of
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In an airborne laser bathymetry system, the full-waveform echo signal is usually recorded by discrete sampling. The accuracy of signal recognition and the amount of effective information that can be extracted by conventional methods are limited. To improve the validity and reliability of airborne laser bathymetry data and to extract more information to better understand the water reflection characteristics, we select the effective portion of the original waveform for further research, suppress random noise, and decompose the selected portion progressively using the half-wavelength Gaussian function with the time sequence of the received echo signals. After parameter optimization, a reasonable and effective reflection component selection mechanism is established to obtain accurate parameters for the reflected components. The processing strategy proposed in this paper reduces the problems of unreasonable decomposition and the reflected pulse peak-position shift caused by echo waveform superposition and offers good precision for waveform decomposition and peak detection. In another experiment, the regional processing result shows an obvious improvement in the shallow water area, and the bottom point cloud is as accurate as the intelligent waveform digitizer (IWD), a subsystem of airborne laser terrain mapping (ALTM). These findings confirm that the proposed method has high potential for application. Full article
(This article belongs to the Special Issue Instruments and Methods for Ocean Observation and Monitoring)
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Open AccessArticle Inter-Calibration of Passive Microwave Satellite Brightness Temperatures Observed by F13 SSM/I and F17 SSMIS for the Retrieval of Snow Depth on Arctic First-Year Sea Ice
Remote Sens. 2018, 10(1), 36; doi:10.3390/rs10010036
Received: 6 November 2017 / Revised: 12 December 2017 / Accepted: 23 December 2017 / Published: 26 December 2017
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Abstract
Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea
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Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea ice was further retrieved. The SDs derived from F17 TB and F13C TB which were calibrated F17 TB using F13 TB as the baseline were then compared and evaluated against in situ SD measurements based on the Operational IceBridge (OIB) airborne observations from 2009 to 2013. Results show that Cavalieri inter-calibration models (CA models) perform smaller root mean square error (RMSE) than Dai inter-calibration models (DA models), and the standard deviation of OIB SDs in the 25 km pixels is around 6 cm on first-year sea ice. Moreover, the SDs derived from the calibrated F17 TB using F13 TB as the baseline were in better agreement than the F17 SDs as compared with OIB SDs, with the biases of −2 cm (RMSE of 5 cm) and −9 cm (RMSE of 10 cm), respectively. We conclude that TB observations from F17 SSMIS calibrated to F13 SSM/I as the baseline should be recommended when performing the sensors’ biases correction for SD purpose based on the existing algorithm. These findings could serve as a reference for generating more consistent and reliable TB, which could help to improve the retrieval and analysis of long-term snow depth on the Arctic first-year sea ice. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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Open AccessArticle Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
Remote Sens. 2018, 10(1), 37; doi:10.3390/rs10010037
Received: 25 October 2017 / Revised: 7 December 2017 / Accepted: 23 December 2017 / Published: 26 December 2017
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Abstract
The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at
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The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring fp. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Quantification of Two-Dimensional Wave Breaking Dissipation in the Surf Zone from Remote Sensing Data
Remote Sens. 2018, 10(1), 38; doi:10.3390/rs10010038
Received: 30 October 2017 / Revised: 20 December 2017 / Accepted: 21 December 2017 / Published: 26 December 2017
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Abstract
A method for obtaining two dimensional fields of wave breaking energy dissipation in the surfzone is presented. The method relies on acquiring geometrical parameters of the wave roller from remote sensing data. These parameters are then coupled with a dissipation model to obtain
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A method for obtaining two dimensional fields of wave breaking energy dissipation in the surfzone is presented. The method relies on acquiring geometrical parameters of the wave roller from remote sensing data. These parameters are then coupled with a dissipation model to obtain time averaged two dimensional maps, but also the wave breaking energy dissipation on a wave-by-wave basis. Comparison of dissipation maps as obtained from the present technique and a results from a numerical model, show very good correlation in both structure and magnitude. The location of a rip current can also be observed from the field data. Though in the present work a combination of optical and microwave data is used, the underlying method is independent of the remote sensor platform. Therefore, it offers the possibility to acquire high quality and synoptic estimates that could contribute to the understanding of the surfzone hydrodynamics. Full article
(This article belongs to the Special Issue Instruments and Methods for Ocean Observation and Monitoring)
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Open AccessArticle Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures
Remote Sens. 2018, 10(1), 39; doi:10.3390/rs10010039
Received: 16 November 2017 / Revised: 22 December 2017 / Accepted: 23 December 2017 / Published: 26 December 2017
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Abstract
A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with
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A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM) using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW) algorithms. Subsequently, the Random forests (RF) and Conditional inference forests (CF) models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak) and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR) using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF) and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR) and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due to the similar characteristics of blue oak and interior live oak. Uncertainty estimates from the BMLR method compensate for this downside by providing classification results in a probabilistic sense and rendering users with more confidence in interpreting and applying classification results to real-world tasks such as forest inventory. Overall, this study recommends the CF method for feature selection and suggests that BMLR could be a superior alternative to classical machining learning methods. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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Open AccessArticle Land Subsidence in Chiayi, Taiwan, from Compaction Well, Leveling and ALOS/PALSAR: Aquaculture-Induced Relative Sea Level Rise
Remote Sens. 2018, 10(1), 40; doi:10.3390/rs10010040
Received: 24 November 2017 / Revised: 22 December 2017 / Accepted: 22 December 2017 / Published: 26 December 2017
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Abstract
Chiayi County is located in the largest alluvial plain of Taiwan with extensive aquaculture and rice farming sustained by water extracted from groundwater wells. Chiayi is a typical aquaculture area affected by land subsidence, yet such lands worldwide combine to provide nearly 90%
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Chiayi County is located in the largest alluvial plain of Taiwan with extensive aquaculture and rice farming sustained by water extracted from groundwater wells. Chiayi is a typical aquaculture area affected by land subsidence, yet such lands worldwide combine to provide nearly 90% of global aquaculture products, greatly reducing oceanic overfishing problems. This study uses precision leveling, multi-layer compaction monitoring well (MLCW) and spaceborne SAR interferometry (InSAR) to examine the cause and effect of land subsidence in Chiayi associated with groundwater extractions and changes. Heights at benchmarks in a leveling network are measured annually and soil compactions at 24–26 layers up to 300-m depths at 7 MLCWs are collected at one-month intervals. Over 2007–2011, 15 ALOS/PALSAR images are processed by the method of TCPInSAR to produce subsidence rates. All sensors show that land subsidence occur in most parts of Chiayi, with rates reaching 4.5 cm/year around its coast, a result of groundwater pumping from shallow to deep aquifers. MLCWs detect mm-accuracy seasonal soil compactions coinciding with groundwater level fluctuations and causing dynamic compactions. Compactions near Taiwan High Speed Rail may reduce the strength of the rail’s supporting columns to degrade its safety. The SAR images yield subsidence rates consistent with those from leveling and compaction wells after corrections for systematic errors by the leveling result. Subsidence in Chiayi’s coastal area leads to relative sea level rises at rates up to 15 times larger than the global eustatic sea level rising rate, a risk typical for world’s aquaculture-rich regions. At the fish pond-covered Budai Township, InSAR identifies subsidence spots not detected by leveling, providing crucial geo-information for a sustainable land management for aquaculture industry. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Can Satellite Precipitation Products Estimate Probable Maximum Precipitation: A Comparative Investigation with Gauge Data in the Dadu River Basin
Remote Sens. 2018, 10(1), 41; doi:10.3390/rs10010041
Received: 30 July 2017 / Revised: 14 December 2017 / Accepted: 23 December 2017 / Published: 27 December 2017
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Abstract
Probable Maximum Precipitation (PMP) is an essential prerequisite in designing dams, spillways, and reservoirs in order to minimize the risk of overtopping infrastructure collapse, especially under today’s changing climate. This study investigates conventional PMP estimation approach by using both scarce in-situ observations and
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Probable Maximum Precipitation (PMP) is an essential prerequisite in designing dams, spillways, and reservoirs in order to minimize the risk of overtopping infrastructure collapse, especially under today’s changing climate. This study investigates conventional PMP estimation approach by using both scarce in-situ observations and mainstream satellite precipitation products in the Dadu River basin, where plenty of reservoirs and dams are being built. The satellite data include Climate Prediction Center (CPC) MORPHing algorithm (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropic Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The evaluation of satellite products shows that CMORPH and 3B42V7 agree well with gauge-based dataset for the period of 1998–2013 at both the grid and basin scales, also capturing the extreme precipitation events, with high Correlation Coefficients (CC) in terms of 0.68 and 0.71, respectively. Also, CMORPH and 3B42V7 show better performance for the magnitude and spatial distribution of 24-h PMP in such complex terrains. PERSIANN-CDR shows an overestimation in the upstream and an underestimation in the downstream. As among the first studies of satellite precipitation-based PMP estimation, this work sheds lights on the suitability of satellite precipitation in PMP estimation and could provide a reference for future extended spatially-distributed PMP estimation in vast ungauged regions. Full article
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Open AccessArticle Ice Velocity Variations of the Polar Record Glacier (East Antarctica) Using a Rotation-Invariant Feature-Tracking Approach
Remote Sens. 2018, 10(1), 42; doi:10.3390/rs10010042
Received: 21 September 2017 / Revised: 17 December 2017 / Accepted: 17 December 2017 / Published: 27 December 2017
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Abstract
In this study, the ice velocity changes from 2004 to 2015 of the Polar Record Glacier (PRG) in East Antarctica were investigated based on a feature-tracking method using Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) images. The flow
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In this study, the ice velocity changes from 2004 to 2015 of the Polar Record Glacier (PRG) in East Antarctica were investigated based on a feature-tracking method using Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) images. The flow field of the PRG curves make it difficult to generate ice velocities in some areas using the traditional normalized cross-correlation (NCC)-based feature-tracking method. Therefore, a rotation-invariant parameter from scale-invariant feature transform (SIFT) is introduced to build a novel rotation-invariant feature-tracking approach. The validation was performed based on multi-source images and the making earth system data records for use in research environments (MEaSUREs) interferometric synthetic aperture radar (InSAR)-based Antarctica ice velocity map data set. The results indicate that the proposed method is able to measure the ice velocity in more areas and performs as well as the traditional NCC-based feature-tracking method. The sequential ice velocities obtained present the variations in the PRG during this period. Although the maximum ice velocity of the frontal margin of the PRG and the frontal iceberg reached about 900 m/a and 1000 m/a, respectively, the trend from 2004 to 2015 showed no significant change. Under the interaction of the Polar Times Glacier and the Polarforschung Glacier, both the direction and the displacement of the PRG were influenced. This impact also led to higher velocities in the western areas of the PRG than in the eastern areas. In addition, elevation changes and frontal iceberg calving also impacted the ice velocity of the PRG. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing II)
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Open AccessArticle A New Method for Automatically Tracing Englacial Layers from MCoRDS Data in NW Greenland
Remote Sens. 2018, 10(1), 43; doi:10.3390/rs10010043
Received: 4 October 2017 / Revised: 11 December 2017 / Accepted: 23 December 2017 / Published: 27 December 2017
PDF Full-text (18948 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such
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Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such as the Multi-channel Coherent Radar Depth Sounder (MCoRDS) used in NASA’s Operation IceBridge (OIB) mission. Since the 1960s, the accumulation of the RES data has prompted the development of automated methods to extract the englacial layers. In this study, we propose a new automated method that combines peak detection methods, namely the CWT-based peak detection or the Automatic Phase Picker (APP), with a Hough Transform (HT) to trace boundaries of englacial layers. For CWT-based peak detection, we test it using two different wavelets. The proposed method is tested with twelve MCoRDS radio echograms, which are acquired south of the Northern Greenland Eemian (NEEM) ice drilling site, where the folding of ice layers was observed. The method is evaluated in comparison to the isochrones that were extracted in an independent study. In comparison, the proposed new automated method can restore more than 70% of the englacial layers. This new automated layer-tracing method is publicly available on github. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing II)
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Open AccessArticle Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes
Remote Sens. 2018, 10(1), 44; doi:10.3390/rs10010044
Received: 13 October 2017 / Revised: 10 December 2017 / Accepted: 13 December 2017 / Published: 28 December 2017
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Abstract
The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations
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The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations mapping, which might result in broken patches in fragmented landscapes. This study introduces a new paradigm by combining phenology information with object-based classification to map fragmented patches of rubber plantations in Xishuangbanna. This research first delineated the time windows of the defoliation and foliation phases of rubber plantations by acquiring the temporal profile and phenological features of rubber plantations and natural forests through the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. To investigate the ability of finer resolution images at capturing the temporal profile or phenological information, 30 m resolution Landsat image data were used to capture the temporal profile, and a phenology algorithm to separate rubber plantations and natural forests was then defined. The derived phenology algorithm was used by both the object-based and pixel-based classification to investigate whether the object-based approach could improve the mapping accuracy. Whether adding the phenology information to the object-based classification could improve rubber plantation mapping accuracy in mountainous Xishuangbanna was also investigated. This resulted in three approaches: rule-pixel-based phenology, rule-object-based phenology, and nearest-neighbor-object-based phenology. The results showed that the rule-object-based phenology approaches (with overall accuracy 77.5% and Kappa Coefficients of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved a higher accuracy than that of the rule-pixel-based phenology approach (72.7% and 0.59). The results proved that (1) object-based approaches could improve the accuracy of rubber plantation mapping compared to the pixel-based approach and (2) incorporating the phenological information from vegetation improved the overall accuracy of the thematic map. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Structural Assessment via Ground Penetrating Radar at the Consoli Palace of Gubbio (Italy)
Remote Sens. 2018, 10(1), 45; doi:10.3390/rs10010045
Received: 15 November 2017 / Revised: 13 December 2017 / Accepted: 21 December 2017 / Published: 28 December 2017
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Abstract
Ground Penetrating Radar (GPR) is a flexible and cost-effective tool for performing structural integrity assessment and quick damage evaluation of manmade structures, including cultural heritage (CH) assets. In this context, this paper deals with the usefulness of GPR surveys enhanced by the use
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Ground Penetrating Radar (GPR) is a flexible and cost-effective tool for performing structural integrity assessment and quick damage evaluation of manmade structures, including cultural heritage (CH) assets. In this context, this paper deals with the usefulness of GPR surveys enhanced by the use of a Microwave Tomographic data processing approach as a methodology for the diagnosis and monitoring of CH exposed to climate events and natural hazards. Specifically, the paper reports on the results of a measurement campaign carried out at the Loggia of the Consoli Palace of Gubbio (Italy). These results allowed us to increase our knowledge of the architecture of the surveyed zones and their structural hazards. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)
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Open AccessFeature PaperArticle Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes
Remote Sens. 2018, 10(1), 46; doi:10.3390/rs10010046
Received: 11 October 2017 / Revised: 11 December 2017 / Accepted: 20 December 2017 / Published: 28 December 2017
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Abstract
Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and
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Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km2) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar’s chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection—which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes. Full article
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
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Open AccessArticle Assessing Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia Using Time Series of DMSP/OLS Nighttime Light Data
Remote Sens. 2018, 10(1), 47; doi:10.3390/rs10010047
Received: 15 November 2017 / Revised: 20 December 2017 / Accepted: 26 December 2017 / Published: 8 January 2018
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Abstract
Intraregional spatial variations of satellite-derived anthropogenic nighttime light signals are gradually applied to identify different lighting areas with various socioeconomic activity and urbanization levels when characterizing urbanization dynamics. However, most previous partitioning approaches are carried out at local scales, easily leading to multi-standards
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Intraregional spatial variations of satellite-derived anthropogenic nighttime light signals are gradually applied to identify different lighting areas with various socioeconomic activity and urbanization levels when characterizing urbanization dynamics. However, most previous partitioning approaches are carried out at local scales, easily leading to multi-standards of the extracted results from local areas, and this inevitably hinders the comparative analysis on the urbanization dynamics of the large region. Therefore, a partitioning approach considering the characteristics of nighttime light signals at both local and regional scales is necessary for studying spatiotemporal characteristics of urbanization dynamics across the large region using nighttime light imagery. Based on the quadratic relationships between the pixel-level nighttime light brightness and the corresponding spatial gradient for individual cities, we here proposed an improved partitioning approach to quickly identify different types of nighttime lighting areas for the entire region of Southeast Asia. Using the calibrated Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) data with greater comparability, continuity, and intra-urban variability, the annual nighttime light imagery spanning years 1992–2013 were divided into four types of nighttime lighting areas: low, medium, high, and extremely high, associated with different intensity of anthropogenic activity. The results suggest that Southeast Asia has experienced a rapid and diverse urbanization process from 1992 to 2013. Areas with moderate or low anthropogenic activity show a faster growth rate for the spatial expansion than the developed areas with intense anthropogenic activity. Transitions between different nighttime lighting types potentially depict the trajectory of urban development, the darker areas are gradually transitioning to areas with higher lighting, indicating conspicuous trends of gradually intensified anthropogenic activity from central areas to periphery areas, and from megacities to small cities. Additionally, satellite-derived nighttime lighting areas are in good agreement with the radar-derived human settlements, with dense human settlements in extremely high and high nighttime lighting areas, while sparse human settlements in low nighttime lighting areas. Full article
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Open AccessArticle A Land Product Characterization System for Comparative Analysis of Satellite Data and Products
Remote Sens. 2018, 10(1), 48; doi:10.3390/rs10010048
Received: 21 November 2017 / Revised: 18 December 2017 / Accepted: 21 December 2017 / Published: 29 December 2017
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Abstract
A Land Product Characterization System (LPCS) has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the
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A Land Product Characterization System (LPCS) has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature). The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS), and simulated data for the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA) Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
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Open AccessArticle Assessing the Performance of a Low-Cost Method for Video-Monitoring the Water Surface and Bed Level in the Swash Zone of Natural Beaches
Remote Sens. 2018, 10(1), 49; doi:10.3390/rs10010049
Received: 26 October 2017 / Revised: 8 December 2017 / Accepted: 23 December 2017 / Published: 2 January 2018
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Abstract
A method to continuously measure bed and water levels along a cross-shore transect of vertical poles is evaluated. This remote sensing based method uses video imagery of swash flows propagating past an array of vertical poles buried on the beach face. Using datasets
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A method to continuously measure bed and water levels along a cross-shore transect of vertical poles is evaluated. This remote sensing based method uses video imagery of swash flows propagating past an array of vertical poles buried on the beach face. Using datasets collected at two beaches in Chile, the method is compared against measurements obtained with conventional co-localized instruments: LiDAR and ultrasonic distance meters. The present video swash pole technique shows good skill in retrieving swash zone bed level and water levels, while providing the possibility to measure morphological variations at time scales varying from wave groups (tens of seconds) to hours. Discrepancies between video and ultrasonic distance meters are found when short time scales are used, for both depositional and erosion events, but longer duration trends are captured well. Water surface elevations at the wave-by-wave scale proved to be accurate for the backwash phase (root-mean-sqaure-error, RMSE down to 0.028 m, R 2 up to 0.89), when compared against LiDAR. However, discrepancies have been found during the uprush phase (RMSE up to 0.062 m, R 2 down to 0.71), when the influence of the pole on the swash flow generates an overestimation of the water surface. Overall, owing to its simplicity of deployment, low cost and reasonable accuracy, the technique is considered suitable for swash studies. Full article
(This article belongs to the Special Issue Instruments and Methods for Ocean Observation and Monitoring)
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Open AccessArticle Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image
Remote Sens. 2018, 10(1), 50; doi:10.3390/rs10010050
Received: 8 November 2017 / Revised: 24 December 2017 / Accepted: 28 December 2017 / Published: 29 December 2017
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Abstract
There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys–Matusita (JM) distance and random forests
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There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys–Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification. Full article
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Open AccessArticle Monitoring Inter- and Intra-Seasonal Dynamics of Rapidly Degrading Ice-Rich Permafrost Riverbanks in the Lena Delta with TerraSAR-X Time Series
Remote Sens. 2018, 10(1), 51; doi:10.3390/rs10010051
Received: 31 October 2017 / Revised: 21 December 2017 / Accepted: 26 December 2017 / Published: 29 December 2017
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Abstract
Arctic warming is leading to substantial changes to permafrost including rapid degradation of ice and ice-rich coasts and riverbanks. In this study, we present and evaluate a high spatiotemporal resolution three-year time series of X-Band microwave satellite data from the TerraSAR-X (TSX) satellite
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Arctic warming is leading to substantial changes to permafrost including rapid degradation of ice and ice-rich coasts and riverbanks. In this study, we present and evaluate a high spatiotemporal resolution three-year time series of X-Band microwave satellite data from the TerraSAR-X (TSX) satellite to quantify cliff-top erosion (CTE) of an ice-rich permafrost riverbank in the central Lena Delta. We apply a threshold on TSX backscatter images and automatically extract cliff-top lines to derive intra- and inter-annual CTE. In order to examine the drivers of erosion we statistically compare CTE with climatic baseline data using linear mixed models and analysis of variance (ANOVA). Our evaluation of TSX-derived CTE against annual optical-derived CTE and seasonal in situ measurements showed good agreement between all three datasets. We observed continuous erosion from June to September in 2014 and 2015 with no significant seasonality across the thawing season. We found the highest net annual cliff-top erosion of 6.9 m in 2014, in accordance with above-average mean temperatures and thawing degree days as well as low precipitation. We found high net annual erosion and erosion variability in 2015 associated with moderate mean temperatures but above average precipitation. According to linear mixed models, climate parameters alone could not explain intra-seasonal erosional patterns and additional factors such as ground ice content likely drive the observed erosion. Finally, mean backscatter intensity on the cliff surface decreased from −5.29 to −6.69 dB from 2013 to 2015, respectively, likely resulting from changes in surface geometry and properties that could be connected to partial slope stabilization. Overall, we conclude that X-Band backscatter time series can successfully be used to complement optical remote sensing and in situ monitoring of rapid tundra permafrost erosion at riverbanks and coasts by reliably providing information about intra-seasonal dynamics. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle Effective Fusion of Multi-Modal Remote Sensing Data in a Fully Convolutional Network for Semantic Labeling
Remote Sens. 2018, 10(1), 52; doi:10.3390/rs10010052
Received: 9 November 2017 / Revised: 17 December 2017 / Accepted: 28 December 2017 / Published: 29 December 2017
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Abstract
In recent years, Fully Convolutional Networks (FCN) have led to a great improvement of semantic labeling for various applications including multi-modal remote sensing data. Although different fusion strategies have been reported for multi-modal data, there is no in-depth study of the reasons of
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In recent years, Fully Convolutional Networks (FCN) have led to a great improvement of semantic labeling for various applications including multi-modal remote sensing data. Although different fusion strategies have been reported for multi-modal data, there is no in-depth study of the reasons of performance limits. For example, it is unclear, why an early fusion of multi-modal data in FCN does not lead to a satisfying result. In this paper, we investigate the contribution of individual layers inside FCN and propose an effective fusion strategy for the semantic labeling of color or infrared imagery together with elevation (e.g., Digital Surface Models). The sensitivity and contribution of layers concerning classes and multi-modal data are quantified by recall and descent rate of recall in a multi-resolution model. The contribution of different modalities to the pixel-wise prediction is analyzed explaining the reason of the poor performance caused by the plain concatenation of different modalities. Finally, based on the analysis an optimized scheme for the fusion of layers with image and elevation information into a single FCN model is derived. Experiments are performed on the ISPRS Vaihingen 2D Semantic Labeling dataset (infrared and RGB imagery as well as elevation) and the Potsdam dataset (RGB imagery and elevation). Comprehensive evaluations demonstrate the potential of the proposed approach. Full article
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Open AccessArticle Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent: A Tale of Three Continents
Remote Sens. 2018, 10(1), 53; doi:10.3390/rs10010053
Received: 2 November 2017 / Revised: 8 December 2017 / Accepted: 12 December 2017 / Published: 30 December 2017
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Abstract
Accurate, consistent and timely cropland information over large areas is critical to solve food security issues. To predict and respond to food insecurity, global cropland products are readily available from coarse and medium spatial resolution earth observation data. However, while the use of
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Accurate, consistent and timely cropland information over large areas is critical to solve food security issues. To predict and respond to food insecurity, global cropland products are readily available from coarse and medium spatial resolution earth observation data. However, while the use of satellite imagery has great potential to identify cropland areas and their specific types, the full potential of this imagery has yet to be realized due to variability of croplands in different regions. Despite recent calls for statistically robust and transparent accuracy assessment, more attention regarding the accuracy assessment of large area cropland maps is still needed. To conduct a valid assessment of cropland maps, different strategies, issues and constraints need to be addressed depending upon various conditions present in each continent. This study specifically focused on dealing with some specific issues encountered when assessing the cropland extent of North America (confined to the United States), Africa and Australia. The process of accuracy assessment was performed using a simple random sampling design employed within defined strata (i.e., Agro-Ecological Zones (AEZ’s) for the US and Africa and a buffer zone approach around the cropland areas of Australia. Continent-specific sample analysis was performed to ensure that an appropriate reference data set was used to generate a valid error matrix indicative of the actual cropland proportion. Each accuracy assessment was performed within the homogenous regions (i.e., strata) of different continents using different sources of reference data to produce rigorous and valid accuracy results. The results indicate that continent-specific modified assessments performed for the three selected continents demonstrate that the accuracy assessment can be easily accomplished for a large area such as the US that has extensive availability of reference data while more modifications were needed in the sampling design for other continents that had little to no reference data and other constraints. Each continent provided its own unique challenges and opportunities. Therefore, this paper describes a tale of these three continents providing recommendations to adapt accuracy assessment strategies and methodologies for validating global cropland extent maps. Full article
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
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Open AccessArticle LakeTime: Automated Seasonal Scene Selection for Global Lake Mapping Using Landsat ETM+ and OLI
Remote Sens. 2018, 10(1), 54; doi:10.3390/rs10010054
Received: 1 November 2017 / Revised: 13 December 2017 / Accepted: 26 December 2017 / Published: 31 December 2017
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Abstract
The Landsat series of satellites provide a nearly continuous, high resolution data record of the Earth surface from the early 1970s through to the present. The public release of the entire Landsat archive, free of charge, along with modern computing capacity, has enabled
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The Landsat series of satellites provide a nearly continuous, high resolution data record of the Earth surface from the early 1970s through to the present. The public release of the entire Landsat archive, free of charge, along with modern computing capacity, has enabled Earth monitoring at the global scale with high spatial resolution. With the large data volume and seasonality varying across the globe, image selection is a particularly important challenge for regional and global multitemporal studies to remove the interference of seasonality from long term trends. This paper presents an automated method for selecting images for global scale lake mapping to minimize the influence of seasonality, while maintaining long term trends in lake surface area dynamics. Using historical meteorological data and a simple water balance model, we define the most stable period after the rainy season, when inflows equal outflows, independently for each Landsat tile and select images acquired during that ideal period for lake surface area mapping. The images selected using this method provide nearly complete global area coverage at decadal episodes for circa 2000 and circa 2014 from Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors, respectively. This method is being used in regional and global lake dynamics mapping projects, and is potentially applicable to any regional/global scale remote sensing application. Full article
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
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Open AccessArticle Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland
Remote Sens. 2018, 10(1), 55; doi:10.3390/rs10010055
Received: 6 September 2017 / Revised: 24 November 2017 / Accepted: 21 December 2017 / Published: 31 December 2017
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Abstract
Efficient methods to monitor forested areas help us to better understand their processes. To date, only a few studies have assessed the usability of multitemporal synthetic aperture radar (SAR) datasets in this context. Here we present an analysis of an unprecedented set of
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Efficient methods to monitor forested areas help us to better understand their processes. To date, only a few studies have assessed the usability of multitemporal synthetic aperture radar (SAR) datasets in this context. Here we present an analysis of an unprecedented set of C-band observations of mixed temperate forests. We demonstrate the potential of using multitemporal C-band VV and VH polarisation data for monitoring phenology and classifying forests in northern Switzerland. Each SAR acquisition was first radiometrically terrain corrected using digital elevation model-based image simulations of the local illuminated area. The flattened backscatter values and the local area values were input to a temporal compositing process integrating backscatter values from ascending and descending tracks. The process used local resolution weighting of each input, producing composite backscatter values that strongly mitigated terrain-induced distortions. Several descriptors were calculated to show the seasonal variation of European beech (Fagus sylvatica), oak (Quercus robur, Quercus petraea) and Norway spruce (Picea abies) in C-band data. Using their distinct seasonal signatures, the timing of leaf emergence and leaf fall of the deciduous species were estimated and compared to available ground observations. Furthermore, classifications for the forest types ‘deciduous’ and ‘coniferous’ and the investigated species were implemented using random forest classifiers. The deciduous species backscatter was about 1 dB higher than spruce throughout the year in both polarisations. The forest types showed opposing seasonal backscatter behaviours. At VH, deciduous species showed higher backscatter in winter than in summer, whereas spruce showed higher backscatter in summer than in winter. In VV, this pattern was similar for spruce, while no distinct seasonal behaviour was apparent for the deciduous species. The time differences between the estimations and the ground observations of the phenological events were approximately within the error margin ( ± 12 days) of the temporal resolution. The classification performances were promising, with higher accuracies achieved for the forest types (OA of 86% and κ = 0.73) than for individual species (OA of 72% and κ = 0.58). These results show that multitemporal C-band backscatter data have significant potential to supplement optical remote sensing data for ecological studies and mapping of mixed temperate forests. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Sensitivity of BRDF, NDVI and Wind Speed to the Aerodynamic Roughness Length over Sparse Tamarix in the Downstream Heihe River Basin
Remote Sens. 2018, 10(1), 56; doi:10.3390/rs10010056
Received: 18 October 2017 / Revised: 22 December 2017 / Accepted: 28 December 2017 / Published: 1 January 2018
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Abstract
The aerodynamic roughness length (z0m) is an important parameter that affects the momentum and energy exchange between the earth surface’s and the atmosphere. In this paper, the gradient wind speed data that were observed from May to October, 2014, at the Si Daoqiao
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The aerodynamic roughness length (z0m) is an important parameter that affects the momentum and energy exchange between the earth surface’s and the atmosphere. In this paper, the gradient wind speed data that were observed from May to October, 2014, at the Si Daoqiao station, which is located in an area of sparse Tamarix in the downstream region of the Heihe River Basin (HRB), are used to evaluate the sensitivity of the moderate-resolution imaging spectroradiometer (MODIS) near-infrared (NIR) bi-directional reflectance distribution function (BRDF) R, the MODIS/Landsat 8 normalized difference vegetation index (NDVI), and the wind speed at 5 m in comparison with the field-measured z0m. The results indicate that the NIR BRDF_R_MODIS and the NDVIMODIS/NDVILandsat8 are less sensitive indicators of the z0m over sparse Tamarix areas (R2: 0.0045 for NIR BRDF_RMODIS; R2: 0.0342 for NDVIMODIS; and, R2: 0.1646 for NDVILandsat8), which differs significantly from the results obtained by previous studies for farmlands and grasslands. However, there is a nearly linear correlation between the wind speed at 5 m and the z0m at the time scale of the NDVILandsat8 acquisitions (R2: 0.3696). Furthermore, the combination of the NDVI and wind speed at 5 m can significantly improve this correlation (R2: 0.7682 for NDVIMODIS; R2: 0.6304 for NDVILandsat8), whereas the combination of the NIR BRDF_RMODIS and wind speed at 5 m still has a low correlation (R2: 0.0886). Finally, the regional z0m of the oasis in the downstream region of the HRB was determined using Landsat 8 surface reflectance data and the wind speed data at the Si Daoqiao station, which properly reflect the temporal evolution of the z0m in that region. The parameterization scheme proposed in this paper has great potential to be applied to evapotranspiration, land surface, and hydrologic model simulations of sparse Tamarix at Si Daoqiao site in the future. Full article
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Open AccessArticle Early Detection of Vitality Changes of Multi-Temporal Norway Spruce Laboratory Needle Measurements—The Ring-Barking Experiment
Remote Sens. 2018, 10(1), 57; doi:10.3390/rs10010057
Received: 8 November 2017 / Revised: 22 December 2017 / Accepted: 28 December 2017 / Published: 3 January 2018
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Abstract
The focus of this analysis is on the early detection of forest health changes, specifically that of Norway spruce (Picea abies L. Karst.). In this analysis, we planned to examine the time (degree of early detection), spectral wavelengths and appropriate method for
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The focus of this analysis is on the early detection of forest health changes, specifically that of Norway spruce (Picea abies L. Karst.). In this analysis, we planned to examine the time (degree of early detection), spectral wavelengths and appropriate method for detecting vitality changes. To accomplish this, a ring-barking experiment with seven subsequent laboratory needle measurements was carried out in 2013 and 2014 in an area in southeastern Germany near Altötting. The experiment was also accompanied by visual crown condition assessment. In total, 140 spruce trees in groups of five were ring-barked with the same number of control trees in groups of five that were selected as reference trees in order to compare their development. The laboratory measurements were analysed regarding the separability of ring-barked and control samples using spectral reflectance, vegetation indices and derivative analysis. Subsequently, a random forest classifier for determining important spectral wavelength regions was applied. Results from the methods are consistent and showed a high importance of the visible (VIS) spectral region, very low importance of the near-infrared (NIR) and minor importance of the shortwave infrared (SWIR) spectral region. Using spectral reflectance data as well as indices, the earliest separation time was found to be 292 days after ring-barking. The derivative analysis showed that a significant separation was observed 152 days after ring-barking for six spectral features spread through VIS and SWIR. A significant separation was detected using a random forest classifier 292 days after ring-barking with 58% separability. The visual crown condition assessment was analysed regarding obvious changes of vitality and the first indication was observed 302 days after ring-barking as bark beetle infestation and yellowing of foliage in the ring-barked trees only. This experiment shows that an early detection, compared with visual crown assessment, is possible using the proposed methods for this specific data set. This study will contribute to ongoing research for early detection of vitality changes that will support foresters and decision makers. Full article
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Open AccessArticle Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand
Remote Sens. 2018, 10(1), 58; doi:10.3390/rs10010058
Received: 13 November 2017 / Revised: 24 December 2017 / Accepted: 26 December 2017 / Published: 3 January 2018
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Abstract
A nationwide model of groundwater recharge for New Zealand (NGRM), as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale
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A nationwide model of groundwater recharge for New Zealand (NGRM), as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014) provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation) in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m 3 /s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future collaborative research associated with the NGRM model include: improvement of rainfall-runoff models, establishment of snowmelt and river recharge modules, further improvement of estimates of rainfall and AET, and satellite-derived AET in irrigated areas. Importantly, the quantification of uncertainty, which should be associated with all future models, should give further impetus to field measurements of rainfall recharge for the purpose of model calibration. Full article
(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)
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Open AccessArticle Wind in Complex Terrain—Lidar Measurements for Evaluation of CFD Simulations
Remote Sens. 2018, 10(1), 59; doi:10.3390/rs10010059
Received: 13 November 2017 / Revised: 21 December 2017 / Accepted: 28 December 2017 / Published: 4 January 2018
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Abstract
Computational Fluid Dynamics (CFD) is widely used to predict wind conditions for wind energy production purposes. However, as wind power development expands into areas of even more complex terrain and challenging flow conditions, more research is needed to investigate the ability of such
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Computational Fluid Dynamics (CFD) is widely used to predict wind conditions for wind energy production purposes. However, as wind power development expands into areas of even more complex terrain and challenging flow conditions, more research is needed to investigate the ability of such models to describe turbulent flow features. In this study, the performance of a hybrid Reynolds-Averaged Navier-Stokes (RANS)/Large Eddy Simulation (LES) model in highly complex terrain has been investigated. The model was compared with measurements from a long range pulsed Lidar, which first were validated with sonic anemometer data. The accuracy of the Lidar was considered to be sufficient for validation of flow model turbulence estimates. By reducing the range gate length of the Lidar a slight additional improvement in accuracy was obtained, but the availability of measurements was reduced due to the increased noise floor in the returned signal. The DES model was able to capture the variations of velocity and turbulence along the line-of-sight of the Lidar beam but overestimated the turbulence level in regions of complex flow. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Conditions for Wind Energy Applications)
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Open AccessArticle An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
Remote Sens. 2018, 10(1), 60; doi:10.3390/rs10010060
Received: 14 November 2017 / Revised: 15 December 2017 / Accepted: 3 January 2018 / Published: 4 January 2018
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Abstract
The satellite clocks used in the BeiDou-2 satellite navigation System (BDS) are Chinese self-developed Rb atomic clocks, and their performances and stabilities are worse than GPS and Galileo satellite clocks. Due to special periodic noises and nonlinear system errors existing in the BDS
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The satellite clocks used in the BeiDou-2 satellite navigation System (BDS) are Chinese self-developed Rb atomic clocks, and their performances and stabilities are worse than GPS and Galileo satellite clocks. Due to special periodic noises and nonlinear system errors existing in the BDS clock offset series, the GPS ultra-rapid clock model, which uses a simple quadratic polynomial plus one periodic is not suitable for BDS. Therefore, an improved prediction model for BDS satellite clocks is proposed in order to enhance the precision of ultra-rapid predicted clock offsets. First, a basic quadratic polynomial model which is fit for the rubidium (Rb) clock is constructed for BDS. Second, the main cyclic terms are detected and identified by the Fast Fourier Transform (FFT) method according to every satellite clock offset series. The detected results show that most BDS clocks have special cyclic terms which are different from the orbit periods. Therefore, two main cyclic terms are added to absorb the periodic effects. Third, after the quadratic polynomial plus two periodic fitting, some evident nonlinear system errors also exist in the model residual, and the Back Propagation (BP) neural network model is chosen to compensate for these nonlinear system errors. The simulation results show that the performance and precision using the improved model are better than that of China iGMAS ultra-rapid prediction (ISU-P) products and the Deutsches GeoForschungsZentrum GFZ BDS ultra-rapid prediction (GBU-P) products. Comparing to ISU-P products, the average improvements using the proposed model in 3 h, 6 h, 12 h and 24 h are 23.1%, 21.3%, 20.2%, and 19.8%, respectively. Meanwhile the accuracy improvements of the proposed model are 9.9%, 13.9%, 17.3%, and 21.2% compared to GBU-P products. In addition, the kinematic Precise Point Positioning (PPP) example using 8 Multi-GNSS Experiment MGEX stations shows that the precision based on the proposed clock model has improved about 16%, 14%, and 38% in the North (N), East (E) and Height (H) components. Full article
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Open AccessArticle Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band
Remote Sens. 2018, 10(1), 61; doi:10.3390/rs10010061
Received: 6 November 2017 / Revised: 23 December 2017 / Accepted: 3 January 2018 / Published: 4 January 2018
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Abstract
Spectral band differences are a critical issue for progressing into an integrated earth observation framework and in particular, in sensor intercalibration. The differences are currently normalized using spectral band adjustment factor (SBAF) that is generated from hyperspectral data. In this context, the current
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Spectral band differences are a critical issue for progressing into an integrated earth observation framework and in particular, in sensor intercalibration. The differences are currently normalized using spectral band adjustment factor (SBAF) that is generated from hyperspectral data. In this context, the current study proposes a method for calculating moderate-resolution imaging spectroradiometer (MODIS)-advanced very high resolution radiometers (AVHRRs) SBAF in the visible band, using the MODIS surface reflectance data. The method involves a uniform ratio index calculated using the MODIS 552-nm and 645-nm bands, and a sensor-specific quadratic equation, producing SBAF data at 500-m spatial resolution. The calculated SBAFs are in good agreement at site scale with literature reported data (relative error < 1.0%), and at local scale with Hyperion-derived data (total uncertainty ≈ 0.001), and significantly improve MODIS-AVHRR surface reflectance data consistency in the visible band (better than 1.0% reflectance units). The calculation is more sensitive to atmospheric effects over the vegetated areas. At global scale, MODIS-AVHRRs SBAFs are generally large (>1.0) over densely vegetated areas and extremely low over deserts and barren lands (0.96–0.98), indicative of large MODIS-AVHRRs differences. Deserts show temporally stable SBAF values, while still suffer from intra-annual BRDF effects and short-term cloud contamination. By means of daily MODIS data, the proposed method can produce ongoing SBAF data at a spatial scale that is comparable to AVHRRs. It increases the sampling of MODIS-AVHRRs image pairs for intercalibration, and offers insight into spectral band conversion, finally contributing to an integrated earth observation at moderate spatial resolutions. Full article
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Open AccessArticle 3-D Water Vapor Tomography in Wuhan from GPS, BDS and GLONASS Observations
Remote Sens. 2018, 10(1), 62; doi:10.3390/rs10010062
Received: 6 December 2017 / Revised: 2 January 2018 / Accepted: 3 January 2018 / Published: 4 January 2018
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Abstract
Three-dimensional water vapor can be reconstructed from Global Navigation Satellite System (GNSS) observations, which can study 3-D profile variations of atmospheric water vapor and climate. However, there is a large uncertainty of water vapor tomography from single GPS system observations due to limited
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Three-dimensional water vapor can be reconstructed from Global Navigation Satellite System (GNSS) observations, which can study 3-D profile variations of atmospheric water vapor and climate. However, there is a large uncertainty of water vapor tomography from single GPS system observations due to limited satellites. The rapid development of multi-GNSS, including China’s Beidou Navigation Satellite System (BDS) and Russia’s GLONASS, has greatly improved the geometric distribution of satellite ray-path signals, which may improve the performance of water vapor tomography by combining multi-GNSS. In this paper, 3-D water vapor tomography results are the first time obtained using multi-GNSS data from Continuously Operating Reference Stations (CORS) network in Wuhan, China, whose performances are validated by radiosonde and the latest ECMWF ERA5 reanalysis products. The results show that the integrated multi-GNSS can pronouncedly increase the number of effective signals, and 3-D water vapor results are better than those from the GPS-only system, improving by 5% with GPS + GLONASS or GPS + GLONASS + BDS, while BDS has results that are not improved too much. Therefore, multi-GNSS will enhance the reliability and accuracy of 3-D water vapor tomography, which has more potential applications in the future. Full article
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Open AccessArticle Improved Modeling of Global Ionospheric Total Electron Content Using Prior Information
Remote Sens. 2018, 10(1), 63; doi:10.3390/rs10010063
Received: 12 December 2017 / Revised: 31 December 2017 / Accepted: 3 January 2018 / Published: 5 January 2018
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Abstract
The Ionosphere Working Group of the International GNSS Service (IGS) has been a reliable source of global ionospheric maps (GIMs) since 1998. Modeling of the global ionospheric total electron content (TEC) is performed daily by several Ionosphere Associate Analysis Centers (IAACs). Four IAACs
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The Ionosphere Working Group of the International GNSS Service (IGS) has been a reliable source of global ionospheric maps (GIMs) since 1998. Modeling of the global ionospheric total electron content (TEC) is performed daily by several Ionosphere Associate Analysis Centers (IAACs). Four IAACs (CODE, ESA, CAS and WHU) use the spherical harmonic (SH) expansion as their primary method for modeling GIMs. The IAACs generally solve a normal equation to obtain the SH coefficients and Differential Code Biases (DCBs) of satellites and receivers by traditional least-squares estimation (LSE) without any prior knowledge. In this contribution, an improved method is proposed and developed for global ionospheric modeling based on utilizing prior knowledge. Prior values of SH coefficients and DCBs of satellites and receivers, as well as the variance factor and covariance matrix, could be obtained from the ionospheric modeling on the previous day. The parameters can subsequently be updated through GNSS measurements to achieve higher accuracy. Comparisons are carried out between WHU products based either on priori information or original LSE and IGS final products, other IAAC products, and JASON data for the year 2014. The results indicate that there is improved consistency between WHU GIMs and IGS final GIMs, other IAAC products, and JASON data, particularly in comparison with ESA and UPC products, with the probabilities of achieving better consistency with these products exceeding 95%. Moreover, WHU-produced DCBs of satellites also have slightly improved consistency with IGS final GIMs and IAAC products. Full article
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Open AccessArticle Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations
Remote Sens. 2018, 10(1), 64; doi:10.3390/rs10010064
Received: 22 November 2017 / Revised: 27 December 2017 / Accepted: 3 January 2018 / Published: 5 January 2018
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Abstract
The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient
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The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time series of remote sensing (T-RS) observations to ensure stability and accuracy. Time series of HJ-1 A/B data was assimilated into the WOFOST model to extrapolate crop growth simulations from a single point to a large area using a specific assimilation method. Because nutrient-limited growth within the growing season is required and the SAN parameters can only be used at the end of the growing season in the original model, the WOFOST model was modified. Notably, the calculation order was changed, and new soil nutrient uptake algorithms were implemented in the model for nutrient-limited growth estimation. Finally, experiments were conducted in the spring maize plots of Hongxing Farm to analyze the effects of nutrient stress on crop growth and the SAN simulation accuracy. The results confirm the differences in crop growth status caused by a lack of soil nutrients. The new approach can take advantage of these differences to provide better SAN estimates. In general, the new approach can overcome the limitations of existing methods and simulate the SAN status with reliable accuracy. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Using an Instrumented Drone to Probe Dust Devils on Oregon’s Alvord Desert
Remote Sens. 2018, 10(1), 65; doi:10.3390/rs10010065
Received: 9 November 2017 / Revised: 17 December 2017 / Accepted: 3 January 2018 / Published: 5 January 2018
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Abstract
Dust devils are low-pressure, small (many to tens of meters) convective vortices powered by surface heating and rendered visible by lofted dust. Dust devils occur ubiquitously on Mars, where they may dominate the supply of atmospheric dust, and since dust contributes significantly to
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Dust devils are low-pressure, small (many to tens of meters) convective vortices powered by surface heating and rendered visible by lofted dust. Dust devils occur ubiquitously on Mars, where they may dominate the supply of atmospheric dust, and since dust contributes significantly to Mars’ atmospheric heat budget, dust devils probably play an important role in its climate. The dust-lifting capacity of a devil likely depends sensitively on its structure, particularly the wind and pressure profiles, but the exact dependencies are poorly constrained. Thus, the exact contribution to Mars’ atmosphere remains unresolved. Analog studies of terrestrial devils have provided some insights into dust devil dynamics and properties but have been limited to near-surface (few meters) or relatively high altitude (hundreds of meters) sampling. Automated aerial vehicles or drones, combined with miniature, digital instrumentation, promise a novel and uniquely powerful platform from which to sample dust devils at a wide variety of altitudes. In this article, we describe a pilot study using an instrumented quadcopter on an active field site in southeastern Oregon, which (to our knowledge) has not previously been surveyed for dust devils. We present preliminary results from the encounters, including stereo image analysis and encounter footage collected onboard the drone. In spite of some technical difficulties, we show that a quadcopter can successfully navigate in an active dust devil, while collecting time-series data about the dust devil’s structure. Full article
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Open AccessArticle A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy
Remote Sens. 2018, 10(1), 66; doi:10.3390/rs10010066
Received: 16 November 2017 / Revised: 25 December 2017 / Accepted: 2 January 2018 / Published: 5 January 2018
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Abstract
Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index,
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Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models. Full article
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Open AccessArticle Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations
Remote Sens. 2018, 10(1), 67; doi:10.3390/rs10010067
Received: 14 November 2017 / Revised: 24 December 2017 / Accepted: 3 January 2018 / Published: 5 January 2018
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Abstract
Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth’s surface. Unlike monostatic radar, the illuminated
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Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth’s surface. Unlike monostatic radar, the illuminated areas are elliptical regions centered on specular reflection points. Evaluation of the spatiotemporal resolution of the reflections is necessary at the GNSS-R mission design stage for various applications. However, not all specular reflection signals can be received because the size and location of the GNSS-R antenna’s available reflecting ground coverage depends on parameters including the on-board receiver antenna gain, the signal frequency and power, the antenna face direction, and the LEO’s altitude. Additionally, the number of available reflections is strongly related to the number of GNSS-R LEO and GNSS satellites. By 2020, the Galileo and BeiDou Navigation Satellite System (BDS) constellations are scheduled to be fully operational at global scale and nearly 120 multi-GNSS satellites, including Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) satellites, will be available for use as illuminators. In this paper, to evaluate the future capacity for repetitive GNSS-R observations, we propose a GNSS satellite selection method and simulate the orbit of eight-satellite LEO and partial multi-GNSS constellations. We then analyze the spatiotemporal distribution characteristics of the reflections in two cases: (1) When only GPS satellites are available; (2) when multi-GNSS satellites are available separately. Simulation and analysis results show that the multi-GNSS-R system has major advantages in terms of available satellite numbers and revisit times over the GPS-R system. Additionally, the spatial density of the specular reflections on the Earth’s surface is related to the LEO inclination and constellation construction. Full article
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Open AccessArticle Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize
Remote Sens. 2018, 10(1), 68; doi:10.3390/rs10010068
Received: 6 November 2017 / Revised: 25 December 2017 / Accepted: 3 January 2018 / Published: 5 January 2018
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Abstract
In recent years, China has developed and launched several satellites with high spatial resolutions, such as the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX) and 5.8 m spatial resolution, the satellite GaoFen No. 1 (GF-1) with a wide field of
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In recent years, China has developed and launched several satellites with high spatial resolutions, such as the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX) and 5.8 m spatial resolution, the satellite GaoFen No. 1 (GF-1) with a wide field of view (WFV) camera and 16 m spatial resolution, and the environment satellite (HJ-1A/B) with a charge-coupled device (CCD) sensor and 30 m spatial resolution. First, to analyze the potential application of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD to extract the leaf area index (LAI) at the regional scale, this study estimated LAI from the relationships between physical model-based spectral vegetation indices (SVIs) and LAI values that were generated from look-up tables (LUTs), simulated from the combination of the PROSPECT-5B leaf model and the scattering by arbitrarily inclined leaves with the hot-spot effect (SAILH) canopy reflectance model. Second, to assess the surface reflectance quality of these sensors after data preprocessing, the well-processed surface reflectance products of the Landsat-8 operational land imager (OLI) sensor with a convincing data quality were used to compare the performances of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD sensors both in theory and reality. Apart from several reflectance fluctuations, the reflectance trends were coincident, and the reflectance values of the red and near-infrared (NIR) bands were comparable among these sensors. Finally, to analyze the accuracy of the LAI estimated from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, the LAI estimations from these sensors were validated based on LAI field measurements in Huailai, Hebei Province, China. The results showed that the performance of the LAI that was inversed from ZY-3 MUX was better than that from GF-1 WFV, and HJ-1 CCD, both of which tended to be systematically underestimated. In addition, the value ranges and accuracies of the LAI inversions both decreased with decreasing spatial resolution. Full article
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Open AccessArticle Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework
Remote Sens. 2018, 10(1), 69; doi:10.3390/rs10010069
Received: 21 November 2017 / Revised: 27 December 2017 / Accepted: 3 January 2018 / Published: 6 January 2018
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Abstract
This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able
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This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able to build data adaptive classification models using noisy training labels. It is particularly suitable when expert annotated training samples are difficult to obtain as in the case of wild fires in the tropics. This framework has been used to build burned area maps from MODIS surface reflectance data as features and Active Fire hotspots as training labels that are known to have high commission and omission errors due to the prevalence of cloud cover and smoke, especially in the tropics. Using the RAPT framework we report burned areas for 16 MODIS tiles from 2001 to 2014. The total burned area detected in the tropical forests of South America and South-east Asia during these years is 2,071,378 MODIS (500 m) pixels (approximately 520 K sq. km.), which is almost three times compared to the estimates from collection 5 MODIS MCD64A1 (783,468 MODIS pixels). An evaluation using Landsat-based reference burned area maps indicates that our product has an average user’s accuracy of 53% and producer’s accuracy of 55% while collection 5 MCD64A1 burned area product has an average user’s accuracy of 61% and producer’s accuracy of 27%. Our analysis also indicates that the two products can be complimentary and a combination of the two approaches is likely to provide a more comprehensive assessment of tropical fires. Finally, we have created a publicly accessible web-based viewer that helps the community to visualize the burned area maps produced using RAPT and examine various validation sources corresponding to every detected MODIS pixel. Full article
(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)
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Open AccessArticle Mapping Wild Leek through the Forest Canopy Using a UAV
Remote Sens. 2018, 10(1), 70; doi:10.3390/rs10010070
Received: 30 October 2017 / Revised: 2 January 2018 / Accepted: 3 January 2018 / Published: 6 January 2018
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Abstract
Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This
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Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle Monitoring Water Surface and Level of a Reservoir Using Different Remote Sensing Approaches and Comparison with Dam Displacements Evaluated via GNSS
Remote Sens. 2018, 10(1), 71; doi:10.3390/rs10010071
Received: 13 October 2017 / Revised: 23 December 2017 / Accepted: 3 January 2018 / Published: 6 January 2018
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Abstract
Remote sensing allowed monitoring the reservoir water level by estimating its surface extension. Surface extension has been estimated using different approaches, employing both optical (Landsat 5 TM, Landsat 7 ETM+ SLC-Off, Landsat 8 OLI-TIRS and ASTER images) and Synthetic Aperture Radar (SAR) images
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Remote sensing allowed monitoring the reservoir water level by estimating its surface extension. Surface extension has been estimated using different approaches, employing both optical (Landsat 5 TM, Landsat 7 ETM+ SLC-Off, Landsat 8 OLI-TIRS and ASTER images) and Synthetic Aperture Radar (SAR) images (Cosmo SkyMed and TerraSAR-X). Images were characterized by different acquisition modes, geometric and spectral resolutions, allowing the evaluation of alternative and/or complementary techniques. For each kind of image, two techniques have been tested: The first based on an unsupervised classification and suitable to automate the process, the second based on visual matching with contour lines with the aim of fully exploiting the dataset. Their performances were evaluated by comparison with water levels measured in situ (r2 = 0.97 using the unsupervised classification, r2 = 0.95 using visual matching) demonstrating that both techniques are suitable to quantify reservoir surface extension. However ~90% of available images were analyzed using the visual matching method, and just 37 images out of 58 using the other method. The evaluation of the water level from the water surface, using both techniques, could be easily extended to un-gauged reservoirs to manage the variations of the levels during normal operation. In addition, during the period of investigation, the use of Global Navigation Satellite System (GNSS) allowed the estimation of dam displacements. The advantage of using as reference a GNSS permanent station positioned relatively far from the dam, allowed the exclusion of any interaction with the site deformations. By comparing results from both techniques, relationships between the orthogonal displacement component via GNSS, estimated water levels via remote sensing and in situ measurements were investigated. During periods of changing water level (April 2011–September 2011 and October 2011–March 2012), the moving average of displacement time series (middle section on the dam crest) shows a range of variability of ±2 mm. The dam deformation versus reservoir water level behavior differs during the reservoir emptying and filling periods indicating a hysteresis-kind loop. Full article
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Open AccessArticle Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning
Remote Sens. 2018, 10(1), 72; doi:10.3390/rs10010072
Received: 14 November 2017 / Revised: 15 December 2017 / Accepted: 3 January 2018 / Published: 11 January 2018
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Abstract
This paper presents a novel double weight-based synthetic aperture radar (SAR) and infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR). IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather
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This paper presents a novel double weight-based synthetic aperture radar (SAR) and infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR). IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather conditions. On the other hand, SAR-based ATR shows a low recognition rate due to the noisy low resolution but can provide consistent performance regardless of the weather conditions. The fusion of an active sensor (SAR) and a passive sensor (IR) can lead to upgraded performance. This paper proposes a doubly weighted neural network fusion scheme at the decision level. The first weight ( α ) can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight ( β ) can measure the online sensor reliability based on the score distribution for a test target image. The LeNet architecture-based deep convolution network (14 layers) is used as an individual classifier. Doubly weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme ( α β -sum) and neural network-based nonlinear fusion scheme ( α β -NN). The experimental results confirmed the proposed linear fusion method ( α β -sum) to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN, α -sum, β -sum, α β -sum, and Bayesian fusion). In addition, the proposed nonlinear fusion method ( α β -NN) showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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