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Remote Sens., Volume 10, Issue 9 (September 2018)

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Open AccessFeature PaperArticle Irrigation Mapping Using Sentinel-1 Time Series at Field Scale
Remote Sens. 2018, 10(9), 1495; https://doi.org/10.3390/rs10091495 (registering DOI)
Received: 6 August 2018 / Revised: 7 September 2018 / Accepted: 15 September 2018 / Published: 18 September 2018
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Abstract
The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time
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The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change. Full article
(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)
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Open AccessArticle Tracking Fine-Scale Structural Changes in Coastal Dune Morphology Using Kite Aerial Photography and Uncertainty-Assessed Structure-from-Motion Photogrammetry
Remote Sens. 2018, 10(9), 1494; https://doi.org/10.3390/rs10091494 (registering DOI)
Received: 2 August 2018 / Revised: 11 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
Coastal dunes are globally-distributed dynamic ecosystems that occur at the land-sea interface. They are sensitive to disturbance both from natural forces and anthropogenic stressors, and therefore require regular monitoring to track changes in their form and function ultimately informing management decisions. Existing techniques
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Coastal dunes are globally-distributed dynamic ecosystems that occur at the land-sea interface. They are sensitive to disturbance both from natural forces and anthropogenic stressors, and therefore require regular monitoring to track changes in their form and function ultimately informing management decisions. Existing techniques employing satellite or airborne data lack the temporal or spatial resolution to resolve fine-scale changes in these environments, both temporally and spatially whilst fine-scale in-situ monitoring (e.g., terrestrial laser scanning) can be costly and is therefore confined to relatively small areas. The rise of proximal sensing-based Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetric techniques for land surface surveying offers an alternative, scale-appropriate method for spatially distributed surveying of dune systems. Here we present the results of an inter- and intra-annual experiment which utilised a low-cost and highly portable kite aerial photography (KAP) and SfM-MVS workflow to track sub-decimetre spatial scale changes in dune morphology over timescales of between 3 and 12 months. We also compare KAP and drone surveys undertaken at near-coincident times of the same dune system to test the KAP reproducibility. Using a Monte Carlo based change detection approach (Multiscale Model to Model Cloud Comparison (M3C2)) which quantifies and accounts for survey uncertainty, we show that the KAP-based survey technique, whilst exhibiting higher x, y, z uncertainties than the equivalent drone methodology, is capable of delivering data describing dune system topographical change. Significant change (according to M3C2); both positive (accretion) and negative (erosion) was detected across 3, 6 and 12 months timescales with the majority of change detected below 500 mm. Significant topographic changes as small as ~20 mm were detected between surveys. We demonstrate that portable, low-cost consumer-grade KAP survey techniques, which have been employed for decades for hobbyist aerial photography, can now deliver science-grade data, and we argue that kites are well-suited to coastal survey where winds and sediment might otherwise impede surveys by other proximal sensing platforms, such as drones. Full article
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Open AccessArticle Potential of Cost-Efficient Single Frequency GNSS Receivers for Water Vapor Monitoring
Remote Sens. 2018, 10(9), 1493; https://doi.org/10.3390/rs10091493 (registering DOI)
Received: 17 August 2018 / Revised: 12 September 2018 / Accepted: 13 September 2018 / Published: 18 September 2018
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Abstract
Dual-frequency Global Navigation Satellite Systems (GNSSs) enable the estimation of Zenith Tropospheric Delay (ZTD) which can be converted to Precipitable Water Vapor (PWV). The density of existing GNSS monitoring networks is insufficient to capture small-scale water vapor variations that are especially important for
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Dual-frequency Global Navigation Satellite Systems (GNSSs) enable the estimation of Zenith Tropospheric Delay (ZTD) which can be converted to Precipitable Water Vapor (PWV). The density of existing GNSS monitoring networks is insufficient to capture small-scale water vapor variations that are especially important for extreme weather forecasting. A densification with geodetic-grade dual-frequency receivers is not economically feasible. Cost-efficient single-frequency receivers offer a possible alternative. This paper studies the feasibility of using low-cost receivers to increase the density of GNSS networks for retrieval of PWV. We processed one year of GNSS data from an IGS station and two co-located single-frequency stations. Additionally, in another experiment, the Radio Frequency (RF) signal from a geodetic-grade dual-frequency antenna was split to a geodetic receiver and two low-cost receivers. To process the single-frequency observations in Precise Point Positioning (PPP) mode, we apply the Satellite-specific Epoch-differenced Ionospheric Delay (SEID) model using two different reference network configurations of 50–80 km and 200–300 km mean station distances, respectively. Our research setup can distinguish between the antenna, ionospheric interpolation, and software-related impacts on the quality of PWV retrievals. The study shows that single-frequency GNSS receivers can achieve a quality similar to that of geodetic receivers in terms of RMSE for ZTD estimations. We demonstrate that modeling of the ionosphere and the antenna type are the main sources influencing the ZTD precision. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation)
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Open AccessArticle The Potential of Spectral Indices in Detecting Various Stages of Afforestation over the Loess Plateau Region of China
Remote Sens. 2018, 10(9), 1492; https://doi.org/10.3390/rs10091492 (registering DOI)
Received: 7 August 2018 / Revised: 13 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
China has the greatest afforestation area in the world, mainly due to the implementation of various ecological restoration projects, which have taken place over several decades. However, the progress of these restoration projects has rarely been evaluated through sapling growth monitoring. In this
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China has the greatest afforestation area in the world, mainly due to the implementation of various ecological restoration projects, which have taken place over several decades. However, the progress of these restoration projects has rarely been evaluated through sapling growth monitoring. In this research, we assessed the potential of eighteen spectral indices derived from time-series Landsat data to characterize the different stages of afforestation over the Loess Plateau region. First, we obtained data for the afforestation area from 1997 to 2010. Then we estimated the average year of afforestation that could be uniquely identified and the sensitivity to growth exhibited by each of the indices. The results show that the first shortwave infrared band (SWIR1) of the Landsat Thematic Mapper and the Brightness index from the tasseled cap transformation (TCB) had the fastest response to sapling growth. It takes 4.2 and 4.3 years on average for the saplings to be detected as forest by SWIR1 and TCB, respectively. However, these two indices saturate too soon to allow better distinction of the various stages of sapling growth but are better for monitoring the over-reporting situation. By contrast, the disturbance index (DI), and the normalized burnt ratio (NBR) and the normalized burnt ratio 2 (NBR2) respond slowly to sapling growth and can detect forest at 7.4 years on average. Unlike SWIR1 and TCB, these indices do not saturate early and can provide more detail on the level and structural condition of sapling growth. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm
Remote Sens. 2018, 10(9), 1491; https://doi.org/10.3390/rs10091491 (registering DOI)
Received: 9 August 2018 / Revised: 9 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly
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Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible. Full article
(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)
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Open AccessEditorial Recent Progress in Quantitative Land Remote Sensing in China
Remote Sens. 2018, 10(9), 1490; https://doi.org/10.3390/rs10091490 (registering DOI)
Received: 12 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
During the past forty years, since the first book with a title mentioning quantitative and remote sensing was published [1], quantitative land remote sensing has advanced dramatically, and numerous books have been published since then [26] although
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During the past forty years, since the first book with a title mentioning quantitative and remote sensing was published [1], quantitative land remote sensing has advanced dramatically, and numerous books have been published since then [26] although some of them did not use quantitative land remote sensing in their titles. [...]
Full article
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Open AccessArticle Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery
Remote Sens. 2018, 10(9), 1489; https://doi.org/10.3390/rs10091489 (registering DOI)
Received: 2 July 2018 / Revised: 30 August 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited
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The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications. Full article
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
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Open AccessArticle Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems
Remote Sens. 2018, 10(9), 1488; https://doi.org/10.3390/rs10091488 (registering DOI)
Received: 27 June 2018 / Revised: 27 August 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order
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All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities. Full article
(This article belongs to the Special Issue Google Earth Engine Applications)
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Open AccessArticle Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery
Remote Sens. 2018, 10(9), 1487; https://doi.org/10.3390/rs10091487 (registering DOI)
Received: 25 July 2018 / Revised: 29 August 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is
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The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system. Full article
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Open AccessLetter In-Flight Calibration and Performance of the OSIRIS-REx Visible and IR Spectrometer (OVIRS)
Remote Sens. 2018, 10(9), 1486; https://doi.org/10.3390/rs10091486 (registering DOI)
Received: 1 August 2018 / Revised: 6 September 2018 / Accepted: 16 September 2018 / Published: 18 September 2018
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Abstract
Performance of the Origins, Spectral Interpretation, Resource Identification, Security–Regolith Explorer (OSIRIS-REx) Visible and InfraRed Spectrometer (OVIRS) instrument was validated, showing that it met all science requirements during extensive thermal vacuum ground testing. Preliminary instrument radiometric calibration coefficients and wavelength mapping were also determined
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Performance of the Origins, Spectral Interpretation, Resource Identification, Security–Regolith Explorer (OSIRIS-REx) Visible and InfraRed Spectrometer (OVIRS) instrument was validated, showing that it met all science requirements during extensive thermal vacuum ground testing. Preliminary instrument radiometric calibration coefficients and wavelength mapping were also determined before instrument delivery and launch using NIST-traceable sources. One year after launch, Earth flyby data were used to refine the wavelength map by comparing OVIRS spectra with atmospheric models. Near-simultaneous data from other Earth-orbiting satellites were used to cross-calibrate the OVIRS absolute radiometric response, particularly at visible wavelengths. Trending data from internal calibration sources and the Sun show that instrument radiometric performance has been stable to better than 1% in the 18 months since launch. Full article
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Open AccessArticle Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System
Remote Sens. 2018, 10(9), 1485; https://doi.org/10.3390/rs10091485 (registering DOI)
Received: 23 August 2018 / Revised: 10 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing
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Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing greater numbers of flowers supported significantly higher pollinator populations over that of spontaneous weed plots. Here, we examined the potential of deploying an inexpensive small unmanned aerial vehicle (UAV) as a tool to remotely estimate floral resources and corresponding pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a lightweight digital camera was deployed to capture images of the flowers during the months of June and September 2017. Supervised image classification using a geographic information system (GIS) was carried out on the acquired images, and classified images were used to evaluate the floral area. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, the floral area derived from imagery significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators. Full article
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Open AccessArticle Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape
Remote Sens. 2018, 10(9), 1484; https://doi.org/10.3390/rs10091484
Received: 14 August 2018 / Revised: 8 September 2018 / Accepted: 14 September 2018 / Published: 17 September 2018
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Abstract
Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red
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Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r2 = 0.89). The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 0.91. Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r2 = 0.93 and RMSEP = 16.18), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r2 = 0.95 and RMSEP = 14.13 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI (944, 758)) and FCA. Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape. Full article
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Open AccessArticle Evaluation of SMAP Freeze/Thaw Retrieval Accuracy at Core Validation Sites in the Contiguous United States
Remote Sens. 2018, 10(9), 1483; https://doi.org/10.3390/rs10091483
Received: 9 August 2018 / Revised: 10 September 2018 / Accepted: 14 September 2018 / Published: 17 September 2018
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Abstract
Seasonal freeze-thaw (FT) impacts much of the northern hemisphere and is an important control on its water, energy, and carbon cycle. Although FT in natural environments extends south of 45°N, FT studies using the L-band have so far been restricted to boreal or
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Seasonal freeze-thaw (FT) impacts much of the northern hemisphere and is an important control on its water, energy, and carbon cycle. Although FT in natural environments extends south of 45°N, FT studies using the L-band have so far been restricted to boreal or greater latitudes. This study addresses this gap by applying a seasonal threshold algorithm to Soil Moisture Active Passive (SMAP) data (L3_SM_P) to obtain a FT product south of 45°N (‘SMAP FT’), which is then evaluated at SMAP core validation sites (CVS) located in the contiguous United States (CONUS). SMAP landscape FT retrievals are usually in good agreement with 0–5 cm soil temperature at SMAP grids containing CVS stations (>70%). The accuracy could be further improved by taking into account specific overpass time (PM), the grid-specific seasonal scaling factor, the data aggregation method, and the sampling error. Annual SMAP FT extent maps compared to modeled soil temperatures derived from the Goddard Earth Observing System Model Version 5 (GEOS-5) show that seasonal FT in CONUS extends to latitudes of about 35–40°N, and that FT varies substantially in space and by year. In general, spatial and temporal trends between SMAP and modeled FT were similar. Full article
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Open AccessArticle Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8
Remote Sens. 2018, 10(9), 1482; https://doi.org/10.3390/rs10091482
Received: 27 July 2018 / Revised: 24 August 2018 / Accepted: 14 September 2018 / Published: 17 September 2018
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Abstract
During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure,
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During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series. Full article
(This article belongs to the Special Issue Remote Sensing of Drought Monitoring)
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Open AccessArticle Dynamics of Permafrost Coasts of Baydaratskaya Bay (Kara Sea) Based on Multi-Temporal Remote Sensing Data
Remote Sens. 2018, 10(9), 1481; https://doi.org/10.3390/rs10091481
Received: 8 August 2018 / Revised: 11 September 2018 / Accepted: 11 September 2018 / Published: 16 September 2018
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Abstract
Arctic coasts that are composed of frozen deposits are extremely sensitive to climate change and human impact. They retreat with average rates of 1–2 m per year, depending on climatic and permafrost conditions. In recent decades, retreat rates have shown a tendency to
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Arctic coasts that are composed of frozen deposits are extremely sensitive to climate change and human impact. They retreat with average rates of 1–2 m per year, depending on climatic and permafrost conditions. In recent decades, retreat rates have shown a tendency to increase. In this paper, we studied the coastal dynamics of two key sites (Ural and Yamal coasts) of Baydaratskaya Bay, Kara Sea, where a gas pipeline had been constructed. Based on multi-temporal aerial and satellite imagery, we identified coastal erosion rates at several time lapses, in natural conditions and under human impact, and discussed their temporal variability. In addition to planimetric (m/yr), we calculated volumetric (m3/m/yr) retreat rates of erosional coasts using ArcticDEM. We also estimated the influence of geomorphology, lithology, and permafrost structure of the coasts on spatial variations of their dynamics. Erosional coasts of the Ural key site retreat with higher mean rates (1.2 m/yr and 8.7 m3/m/yr) as compared to the Yamal key site (0.3 m/yr and 3.7 m3/m/yr) due to their exposure to higher open sea waves, more complex lithology, higher ice content and lower coastal bluffs. Since the 1960s, coastal retreat rates have been growing on both coasts of Baydaratskaya Bay; we relate this effect with Arctic climate warming. From the 1960s to 2005, such growth was moderate, while in 2005–2016 it became rapid, which may be explained by the enhanced wave and thermal action or by the onset of industrial development. The adjacent coastal segments, originally accumulative, remained relatively stable from the 1960s to 2005. After 2005, a considerable part of them began to retreat as a result of changing weather conditions and/or increasing human impact. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Open AccessArticle Wind Direction Inversion from Narrow-Beam HF Radar Backscatter Signals in Low and High Wind Conditions at Different Radar Frequencies
Remote Sens. 2018, 10(9), 1480; https://doi.org/10.3390/rs10091480
Received: 31 July 2018 / Revised: 11 September 2018 / Accepted: 12 September 2018 / Published: 16 September 2018
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Abstract
Land-based, high-frequency (HF) surface wave radar has the unique capability of monitoring coastal surface parameters, such as current, waves, and wind, up to 200 km off the coast. The Doppler spectrum of the backscattered radar signal is characterized by two strong peaks that
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Land-based, high-frequency (HF) surface wave radar has the unique capability of monitoring coastal surface parameters, such as current, waves, and wind, up to 200 km off the coast. The Doppler spectrum of the backscattered radar signal is characterized by two strong peaks that are caused by the Bragg-resonant scattering from the ocean surface. The wavelength of Bragg resonant waves is exactly half the radio wavelength (grazing incidence), and these waves are located at the higher frequency part of the wave spectral distribution. When HF radar operates at higher frequencies, the resonant waves are relatively shorter waves, which are more sensitive to a change in wind direction, and they rapidly respond to local wind excitation and a change in wind direction. When the radar operates at lower frequencies, the corresponding resonant waves are relatively longer and take longer time to respond to a change in wind direction due to the progress of wave growth from short waves to long waves. For the wind inversion from HF radar backscatter signals, the accuracy of wind measurement is also relevant to radar frequency. In this paper, a pattern-fitting method for extracting wind direction by estimating the wave spreading parameter is presented, and a comparison of the pattern-fitting method and a conventional method is given as well, which concludes that the pattern-fitting method presents better results than the conventional method. In order to analyze the wind direction inversion from radar backscatter signals under different wind conditions and at different radar frequencies, two radar experiments accomplished in Norway and Italy are introduced, and the results of wind direction inversion are presented. In the two experiments, the radar worked at 27.68 MHz and 12 MHz, respectively, and the wind conditions at the sea surface were quite different. In the experiment in Norway, 67.4% of the wind records were higher than 5 m/s, while, in the experiment in Italy, only 18.9% of the wind records were higher than 5 m/s. All these factors affect the accuracy of wind direction inversion. The paper analyzes the radar data and draws a conclusion on the influencing factor of wind direction inversion. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle Economic Assessment of Fire Damage to Urban Forest in the Wildland–Urban Interface Using Planet Satellites Constellation Images
Remote Sens. 2018, 10(9), 1479; https://doi.org/10.3390/rs10091479
Received: 15 August 2018 / Revised: 12 September 2018 / Accepted: 14 September 2018 / Published: 16 September 2018
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Abstract
The wildland-urban interface (WUI)—the area where wildland vegetation and urban buildings intermix—is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it
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The wildland-urban interface (WUI)—the area where wildland vegetation and urban buildings intermix—is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it is unsuitable for WUI fire monitoring due to the low spatial resolution of the images from satellites that provide frequent information which is relevant for timely fire monitoring in WUI. Here, we take advantage of frequent (i.e., ca. daily), high-spatial-resolution (3 m) imagery acquired from a constellation of nano-satellites operated by Planet Labs (“Planet”) to assess fire damage to urban trees in the WUI of a Mediterranean city in Israel (Haifa). The fire occurred at the end of 2016, consuming ca. 17,000 of the trees (152 trees ha−1) within the near-by wildland and urban parts of the city. Three vegetation indices (GNDVI, NDVI and GCC) from Planet satellite images were used to derive a burn severity map for the WUI area after applying a subpixel discrimination method to distinguish between woody and herbaceous vegetation. The produced burn severity map was successfully validated with information acquired from an extensive field survey in the WUI burnt area (overall accuracy and kappa: 87% and 0.75%, respectively). Planet’s vegetation indices were calibrated using in-field tree measurements to obtain high spatial resolution maps of burned trees and consumed woody biomass in the WUI. These were used in conjunction with an ecosystem services valuation model (i-Tree) to estimate spatially-distributed and total economic loss due to damage to urban trees caused by the fire. Results show that nearly half of the urban trees were moderately and severely burned (26% and 22%, respectively). The total damage to the urban forest was estimated at ca. 41 ± 10 M USD. We conclude that using the method developed in this study with high-spatial-resolution Planet images has a great potential for WUI fire economic assessment. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Open AccessArticle Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index
Remote Sens. 2018, 10(9), 1478; https://doi.org/10.3390/rs10091478
Received: 26 July 2018 / Revised: 28 August 2018 / Accepted: 1 September 2018 / Published: 16 September 2018
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Abstract
This work focuses on the detection of tiny macroalgae patches in the eastern parts of the Yellow Sea (YS) using high-resolution Landsat-8 images from 2014 to 2017. In the comparison between floating algae index (FAI) and normalized difference vegetation index (NDVI) better detection
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This work focuses on the detection of tiny macroalgae patches in the eastern parts of the Yellow Sea (YS) using high-resolution Landsat-8 images from 2014 to 2017. In the comparison between floating algae index (FAI) and normalized difference vegetation index (NDVI) better detection by FAI was observed, but many tiny patches still remained undetected. By applying a modification on the FAI around 12% to 27% increased and correct detection of macroalgae is achieved from 35 images compared to the original. Through this method many scattered tiny patches were detected in June or July in Korea Bay and Gyeonggi Bay. Though it was a small-scale phenomenon they occurred in the similar period of macroalgal bloom occurrence in the YS. Thus, by using this modified method we could detect macroalgae in the study areas around one month earlier than the previously used Geostationary Ocean Color Imager NDVI-based detection. Later, more macroalgae patches including smaller ones occupying increased areas were detected. Thus, it seems that those macroalgae started growing locally from tiny patches rather than being transported from the western parts of the YS. Therefore, this modified FAI could be used for the precise detection of macroalgae. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering
Remote Sens. 2018, 10(9), 1477; https://doi.org/10.3390/rs10091477
Received: 22 August 2018 / Revised: 5 September 2018 / Accepted: 13 September 2018 / Published: 16 September 2018
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Abstract
In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to
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In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to 6 m. The investigated data set was acquired between 2011 in bistatic stripmap single polarization (HH) mode. Among the different factors affecting the quality of InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering, which typically occurs in the presence of vegetation, and is a very sensitive indicator for the discrimination of forested from non-forested areas. For this reason, it has been chosen as input observable for performing the classification. In this framework, both standard boxcar and nonlocal filtering methods have been considered for the estimation of the volume correlation factor. The resulting forest/non-forest mosaics have been validated using an accurate vegetation map of the region derived from Lidar-Optic data as external independent reference. Thanks to their outstanding performance in terms of noise reduction, together with spatial features preservation, nonlocal filters show a level of agreement of about 80.5% and we observed a systematic improvement in terms of accuracy with respect to the boxcar filtering at the same resolution of about 4.5 percent points. This approach is therefore of primary importance to achieve a reliable classification at such fine resolution. Finally, the high-resolution forest/non-forest classification product of the State of Pennsylvania presented in this paper demonstrates once again the outstanding capabilities of the TanDEM-X system for a wide spectrum of commercial services and scientific applications in the field of the biosphere. Full article
(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)
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Open AccessArticle Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures
Remote Sens. 2018, 10(9), 1476; https://doi.org/10.3390/rs10091476
Received: 31 July 2018 / Revised: 29 August 2018 / Accepted: 14 September 2018 / Published: 16 September 2018
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Abstract
Quality-control procedures and their impact on data quality are described for the High-Frequency Ocean Radar (HFR) network in Australia, in particular for the commercial phased-array (WERA) HFR type. Threshold-based quality-control procedures were used to obtain radial velocity and signal-to-noise ratio (SNR), however, values
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Quality-control procedures and their impact on data quality are described for the High-Frequency Ocean Radar (HFR) network in Australia, in particular for the commercial phased-array (WERA) HFR type. Threshold-based quality-control procedures were used to obtain radial velocity and signal-to-noise ratio (SNR), however, values were set through quantitative analyses with independent measurements available within the HFR coverage, when available, or from long-term data statistics. An artifact removal procedure was also applied to the spatial distribution of SNR for the first-order Bragg peaks, under the assumption the SNR is a valid proxy for radial velocity quality and that SNR decays with range from the receiver. The proposed iterative procedure was specially designed to remove anomalous observations associated with strong SNR peaks caused by the 50 Hz sources. The procedure iteratively fits a polynomial along the radial beam (1-D case) or a surface (2-D case) to the SNR associated with the radial velocity. Observations that exceed a detection threshold were then identified and flagged. After removing suspect data, new iterations were run with updated detection thresholds until no additional spikes were found or a maximum number of iterations was reached. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle The Contribution of Terrestrial Laser Scanning to the Analysis of Cliff Slope Stability in Sugano (Central Italy)
Remote Sens. 2018, 10(9), 1475; https://doi.org/10.3390/rs10091475
Received: 13 August 2018 / Revised: 9 September 2018 / Accepted: 14 September 2018 / Published: 15 September 2018
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Abstract
In this work, we describe a comprehensive approach aimed at assessing the slope stability conditions of a tuff cliff located below the village of Sugano (Central Italy) starting from remote geomechanical analysis on high-resolution 3D point clouds collected by terrestrial laser scanner (TLS)
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In this work, we describe a comprehensive approach aimed at assessing the slope stability conditions of a tuff cliff located below the village of Sugano (Central Italy) starting from remote geomechanical analysis on high-resolution 3D point clouds collected by terrestrial laser scanner (TLS) surveys. Firstly, the identification of the main joint systems has been made through both manual and automatic analyses on the 3D slope model resulting from the surveys. Afterwards, the identified joint sets were considered to evaluate the slope stability conditions by attributing safety factor (SF) values to the typical rock blocks whose kinematic was proved as compatible with tests for toppling under two independent triggering conditions: hydrostatic water pressure within the joints and seismic action. The results from the remote investigation of the cliff slope provide geometrical information of the blocks more susceptible to instability and pointed out that limit equilibrium condition can be achieved for potential triggering scenarios in the whole outcropping slope. Full article
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
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Open AccessArticle Using APAR to Predict Aboveground Plant Productivity in Semi-Aid Rangelands: Spatial and Temporal Relationships Differ
Remote Sens. 2018, 10(9), 1474; https://doi.org/10.3390/rs10091474
Received: 5 July 2018 / Revised: 16 August 2018 / Accepted: 30 August 2018 / Published: 14 September 2018
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Abstract
Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be
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Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but rarely both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP. Full article
(This article belongs to the Special Issue Land Surface Phenology)
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Open AccessArticle Multi-Stream Convolutional Neural Network for SAR Automatic Target Recognition
Remote Sens. 2018, 10(9), 1473; https://doi.org/10.3390/rs10091473
Received: 23 July 2018 / Revised: 3 September 2018 / Accepted: 6 September 2018 / Published: 14 September 2018
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Abstract
Despite the fact that automatic target recognition (ATR) in Synthetic aperture radar (SAR) images has been extensively researched due to its practical use in both military and civil applications, it remains an unsolved problem. The major challenges of ATR in SAR stem from
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Despite the fact that automatic target recognition (ATR) in Synthetic aperture radar (SAR) images has been extensively researched due to its practical use in both military and civil applications, it remains an unsolved problem. The major challenges of ATR in SAR stem from severe data scarcity and great variation of SAR images. Recent work started to adopt convolutional neural networks (CNNs), which, however, remain unable to handle the aforementioned challenges due to their high dependency on large quantities of data. In this paper, we propose a novel deep convolutional learning architecture, called Multi-Stream CNN (MS-CNN), for ATR in SAR by leveraging SAR images from multiple views. Specifically, we deploy a multi-input architecture that fuses information from multiple views of the same target in different aspects; therefore, the elaborated multi-view design of MS-CNN enables it to make full use of limited SAR image data to improve recognition performance. We design a Fourier feature fusion framework derived from kernel approximation based on random Fourier features which allows us to unravel the highly nonlinear relationship between images and classes. More importantly, MS-CNN is qualified with the desired characteristic of easy and quick manoeuvrability in real SAR ATR scenarios, because it only needs to acquire real-time GPS information from airborne SAR to calculate aspect differences used for constructing testing samples. The effectiveness and generalization ability of MS-CNN have been demonstrated by extensive experiments under both the Standard Operating Condition (SOC) and Extended Operating Condition (EOC) on the MSTAR dataset. Experimental results have shown that our proposed MS-CNN can achieve high recognition rates and outperform other state-of-the-art ATR methods. Full article
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Open AccessArticle Effects of Spatiotemporal Filtering on the Periodic Signals and Noise in the GPS Position Time Series of the Crustal Movement Observation Network of China
Remote Sens. 2018, 10(9), 1472; https://doi.org/10.3390/rs10091472
Received: 3 July 2018 / Revised: 3 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
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Abstract
Analysis of Global Positioning System (GPS) position time series and its common mode components (CMC) is very important for the investigation of GPS technique error, the evaluation of environmental loading effects, and the estimation of a realistic and unbiased GPS velocity field for
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Analysis of Global Positioning System (GPS) position time series and its common mode components (CMC) is very important for the investigation of GPS technique error, the evaluation of environmental loading effects, and the estimation of a realistic and unbiased GPS velocity field for geodynamic applications. In this paper, we homogeneously processed the daily observations of 231 Crustal Movement Observation Network of China (CMONOC) Continuous GPS stations to obtain their position time series. Then, we filtered out the CMC and evaluated its effects on the periodic signals and noise for the CMONOC time series. Results show that, with CMC filtering, peaks in the stacked power spectra can be reduced at draconitic harmonics up to the 14th, supporting the point that the draconitic signal is spatially correlated. With the colored noise suppressed by CMC filtering, the velocity uncertainty estimates for both of the two subnetworks, CMONOC-I (≈16.5 years) and CMONOC-II (≈4.6 years), are reduced significantly. However, the CMONOC-II stations obtain greater reduction ratios in velocity uncertainty estimates with average values of 33%, 38%, and 54% for the north, east, and up components. These results indicate that CMC filtering can suppress the colored noise amplitudes and improve the precision of velocity estimates. Therefore, a unified, realistic, and three-dimensional CMONOC GPS velocity field estimated with the consideration of colored noise is given. Furthermore, contributions of environmental loading to the vertical CMC are also investigated and discussed. We find that the vertical CMC are reduced at 224 of the 231 CMONOC stations and 170 of them are with a root mean square (RMS) reduction ratio of CMC larger than 10%, confirming that environmental loading is one of the sources of CMC for the CMONOC height time series. Full article
(This article belongs to the Special Issue Remote Sensing of Tectonic Deformation)
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Open AccessArticle Land Surface Temperature and Urban Density: Multiyear Modeling and Relationship Analysis Using MODIS and Landsat Data
Remote Sens. 2018, 10(9), 1471; https://doi.org/10.3390/rs10091471
Received: 28 July 2018 / Revised: 5 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
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Abstract
This work aims to model and relate the urban density and land surface temperature (LST) by a straightforward and efficient approach. Although the urban density-LST relation is widely addressed in literature, this study allows for its modeling and parameterization in an accurate way,
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This work aims to model and relate the urban density and land surface temperature (LST) by a straightforward and efficient approach. Although the urban density-LST relation is widely addressed in literature, this study allows for its modeling and parameterization in an accurate way, providing a further scientific support for the city planning policy. The urban density and the LST analysis is carried out in the Bangkok area for the years 2004, 2008, 2012, and 2016; in this time interval, the city exhibited an evident urban expansion. Firstly, by using land cover maps obtained from Landsat reflective observations, the urban land density growth across the years studied is evaluated by applying a ring-based approach, a method employed in urban theory, providing urban density curves as a function of the distance from the city center. For each year, the urban density curve is well modeled by an inverse S-shape function, the parameters of which highlight an urban sprawl over the years studied and an outskirt growth in recent years. Then, employing 237 MODIS LST images, the night-time and daytime mean LST patterns for each year were processed applying the same ring-based analysis, obtaining LST trends versus distance. Albeit the mean LST decreases away from the city core, the daytime and night-time trends are different in both shape and values. The daytime LST exhibits a trend also modeled by an inverse S-shape function, whereas the night-time one is modeled by a quadratic function. Finally, the urban density-LST relationship is inferred across the years: For daytime, the relation is quadratic with a coefficient of determination r2 around 0.98–0.99, whereas for night-time the relation is linear with r2 of the order of 0.95–0.96. The proposed approach allows for reliable modeling and to straightforwardly infer a very accurate urban density-LST relationship. Full article
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Open AccessLetter Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images
Remote Sens. 2018, 10(9), 1470; https://doi.org/10.3390/rs10091470
Received: 24 August 2018 / Revised: 9 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
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Abstract
The region-based convolutional networks have shown their remarkable ability for object detection in optical remote sensing images. However, the standard CNNs are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. To address this, we introduce
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The region-based convolutional networks have shown their remarkable ability for object detection in optical remote sensing images. However, the standard CNNs are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. To address this, we introduce a new module named deformable convolution that is integrated into the prevailing Faster R-CNN. By adding 2D offsets to the regular sampling grid in the standard convolution, it learns the augmenting spatial sampling locations in the modules from target tasks without additional supervision. In our work, a deformable Faster R-CNN is constructed by substituting the standard convolution layer with a deformable convolution layer in the last network stage. Besides, top-down and skip connections are adopted to produce a single high-level feature map of a fine resolution, on which the predictions are to be made. To make the model robust to occlusion, a simple yet effective data augmentation technique is proposed for training the convolutional neural network. Experimental results show that our deformable Faster R-CNN improves the mean average precision by a large margin on the SORSI and HRRS dataset. Full article
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Open AccessArticle Observing Water Vapour in the Planetary Boundary Layer from the Short-Wave Infrared
Remote Sens. 2018, 10(9), 1469; https://doi.org/10.3390/rs10091469
Received: 19 July 2018 / Revised: 11 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
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Abstract
Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global observations of water vapour fields are made from numerous instruments measuring in the ultraviolet/visible, through the infrared bands, to the microwave regions
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Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global observations of water vapour fields are made from numerous instruments measuring in the ultraviolet/visible, through the infrared bands, to the microwave regions of the electromagnetic spectrum. While these observations provide a wealth of information on columnar, free-tropospheric and upper troposphere/lower stratosphere water vapour amounts, there is still an observational gap regarding resolved bulk planetary boundary layer (PBL) concentrations. In this study we demonstrate the ability of the Greenhouse Gases Observing SATellite (GOSAT) to bridge this gap from highly resolved measurements in the shortwave infrared (SWIR). These new measurements of near surface columnar water vapour are free of topographic artefacts and are interpreted as a proxy for bulk PBL water vapour. Validation (over land surfaces only) of this new data set against global radiosondes show low biases that vary seasonally between −2% to 5%. Analysis on broad latitudinal bands show biases between −3% and 2% moving from high latitudes to the equatorial regions. Finally, with the extension of the GOSAT program out to at least 2027, we discuss the potential for a new GOSAT PBL water vapour Climate Data Record (CDR). Full article
(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)
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Open AccessArticle Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
Remote Sens. 2018, 10(9), 1468; https://doi.org/10.3390/rs10091468
Received: 16 July 2018 / Revised: 9 September 2018 / Accepted: 10 September 2018 / Published: 14 September 2018
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Abstract
Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2)
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Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species. Full article
(This article belongs to the collection Sentinel-2: Science and Applications)
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Open AccessArticle Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands
Remote Sens. 2018, 10(9), 1467; https://doi.org/10.3390/rs10091467
Received: 28 July 2018 / Revised: 3 September 2018 / Accepted: 10 September 2018 / Published: 14 September 2018
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Abstract
Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their
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Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristic. When multiple images are used in object-based image analysis, it is often constrained to a specific number of images which are selected because they cover the perceived range of temporal variability of the features of interest. Here, we provide a method to identify wetlands using a time series of Landsat imagery by building a Random Forest model using each image observation as an explanatory variable. We tested our approach in Douglas County, Washington, USA. Our approach exploiting the temporal domain classified wetlands with a high level of accuracy and reduced the number of spectrally similar false positives. We explored how sampling design (i.e., random, stratified, purposive) and temporal resolution (i.e., number of image observations) affected classification accuracy. We found that sampling design introduced bias in different ways, but did not have a substantial impact on overall accuracy. We also found that a higher number of image observations up to a point improved classification accuracy dependent on the selection of images used in the model. While time series analysis has been part of pixel-based remote sensing for many decades, with improved computer processing and increased availability of time series datasets (e.g., Landsat archive), it is now much easier to incorporate time series into object-based image analysis classification. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Mapping Mangrove Extent and Change: A Globally Applicable Approach
Remote Sens. 2018, 10(9), 1466; https://doi.org/10.3390/rs10091466
Received: 30 July 2018 / Revised: 28 August 2018 / Accepted: 8 September 2018 / Published: 14 September 2018
PDF Full-text (1930 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel
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This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.2–93.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers. Full article
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