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Remote Sens., Volume 9, Issue 3 (March 2017)

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Cover Story Climatic changes and increasing water demands are threatening water resources in many regions of [...] Read more.
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Editorial

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Open AccessEditorial Earth Observations for Geohazards: Present and Future Challenges
Remote Sens. 2017, 9(3), 194; doi:10.3390/rs9030194
Received: 22 February 2017 / Revised: 22 February 2017 / Accepted: 22 February 2017 / Published: 24 February 2017
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Abstract
Earth Observations (EO) encompasses different types of sensors (e.g., Synthetic Aperture Radar, Laser Imaging Detection and Ranging, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in
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Earth Observations (EO) encompasses different types of sensors (e.g., Synthetic Aperture Radar, Laser Imaging Detection and Ranging, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in which ground observations may not be possible due to physical and/or political constraints. EO can provide high spatial, temporal and spectral resolution, stereo-mapping and all-weather-imaging capabilities, but not by a single satellite at a time. Improved satellite and sensor technologies, increased frequency of satellite measurements, and easier access and interpretation of EO data have all contributed to the increased demand for satellite EO data. EO, combined with complementary terrestrial observations and with physical models, have been widely used to monitor geohazards, revolutionizing our understanding of how the Earth system works. This Special Issue presents a collection of scientific contributions focusing on innovative EO methods and applications for monitoring and modeling geohazards, consisting of four Sections: (1) earthquake hazards; (2) landslide hazards; (3) land subsidence hazards; and (4) new EO techniques and services. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Research

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Open AccessFeature PaperArticle Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits
Remote Sens. 2017, 9(3), 300; doi:10.3390/rs9030300
Received: 21 January 2017 / Revised: 15 March 2017 / Accepted: 17 March 2017 / Published: 22 March 2017
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Abstract
Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources
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Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources with variable qualities, from dispersed time instances, or with different spatial resolutions. Consequently, terrain representation used for orthorectifying satellite images is often in disagreement with reality at image acquisition. Normally, the lateral orthoprojection offsets resulting from vertical DEM errors are taken into account in the geolocation error budget of the corrected images, or may even be neglected. The largest offsets of this type are often found over glaciers, as these may show strong elevation changes over time and thus large elevation errors in the reference DEM with respect to image acquisition. The detection and correction of such orthorectification offsets is further complicated by ice flow which adds a second offset component to the displacement vectors between orthorectified data. Vice versa, measurement of glacier flow is complicated by the inherent superposition of ice movement vectors and orthorectification offset vectors. In this study, we try to estimate these orthorectification offsets in the presence of terrain movement and translate them to elevation biases in the reference surface. We demonstrate our method using three different sites which include very dynamic glaciers. For the Oriental Glacier, an outlet of the Southern Patagonian icefield, Landsat 7 and 8 data from different orbits enabled the identification of trends related to elevation change. For the Aletsch Glacier, Swiss Alps, we assess the terrain offsets of both Landsat 8 and Sentinel-2A: a superior DEM appears to be used for Landsat in comparison to Sentinel-2, however a systematic bias is observed in the snow covered areas. Lastly, we demonstrate our methodology in a pipeline structure; displacement estimates for the Helheim-glacier, in Greenland, are mapped and corrected for orthorectification offsets between data from different orbits, which enables a twice as dense a temporal resolution of velocity data, as compared to the standard method of measuring velocities from repeat-orbit data only. In addition, we introduce and implement a novel matching method which uses image triplets. By formulating the three image displacements as a convolution, a geometric constraint can be exploited. Such a constraint enhances the reliability of the displacement estimations. Furthermore the implementation is simple and computationally swift. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
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Open AccessArticle Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter
Remote Sens. 2017, 9(3), 201; doi:10.3390/rs9030201
Received: 20 December 2016 / Accepted: 21 February 2017 / Published: 24 February 2017
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Abstract
The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting
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The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank–Kennard–Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods. Full article
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Open AccessArticle Evaluation of Satellite Retrievals of Chlorophyll-a in the Arabian Gulf
Remote Sens. 2017, 9(3), 301; doi:10.3390/rs9030301
Received: 12 December 2016 / Revised: 18 February 2017 / Accepted: 15 March 2017 / Published: 22 March 2017
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Abstract
The Arabian Gulf is a highly turbid, shallow sedimentary basin whose coastal areas have been classified as optically complex Case II waters (where ocean colour sensors have been proved to be unreliable). Yet, there is no such study assessing the performance and quality
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The Arabian Gulf is a highly turbid, shallow sedimentary basin whose coastal areas have been classified as optically complex Case II waters (where ocean colour sensors have been proved to be unreliable). Yet, there is no such study assessing the performance and quality of satellite ocean-colour datasets in relation to ground truth data in the Gulf. Here, using a unique set of in situ Chlorophyll-a measurements (Chl-a; an index of phytoplankton biomass), collected from 24 locations in four transects in the central Gulf over six recent research cruises (2015–2016), we evaluated the performance of VIIRS and other merged satellite datasets, for the first time in the region. A highly significant relationship was found (r = 0.795, p < 0.001), though a clear overestimation in satellite-derived Chl-a concentrations is evident. Regardless of this constant overestimation, the remotely sensed Chl-a observations illustrated adequately the seasonal cycles. Due to the optically complex environment, the first optical depth was calculated to be on average 6–10 m depth, and thus the satellite signal is not capturing the deep chlorophyll maximum (DCM at ~25 m). Overall, the ocean colour sensors’ performance was comparable to other Case II waters in other regions, supporting the use of satellite ocean colour in the Gulf. Yet, the development of a regional-tuned algorithm is needed to account for the unique environmental conditions of the Gulf, and ultimately provide a better estimation of surface Chl-a in the region. Full article
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Open AccessArticle Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar
Remote Sens. 2017, 9(3), 302; doi:10.3390/rs9030302
Received: 4 January 2017 / Revised: 8 March 2017 / Accepted: 20 March 2017 / Published: 22 March 2017
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Abstract
Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the
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Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG. Full article
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Open AccessArticle Spectral-Spatial Response for Hyperspectral Image Classification
Remote Sens. 2017, 9(3), 203; doi:10.3390/rs9030203
Received: 6 December 2016 / Accepted: 18 February 2017 / Published: 24 February 2017
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Abstract
This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special
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This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification. Full article
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Open AccessArticle A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions
Remote Sens. 2017, 9(3), 205; doi:10.3390/rs9030205
Received: 8 December 2016 / Revised: 22 February 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
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Abstract
Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface
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Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface and height models (CSMs and CHMs, respectively) over different forest types. The primary aim of this study is to investigate the applicability of image-based CHMs at different spatial resolutions (1 m, 2 m, 5 m) for use in stand-level forest inventory, with a special focus on estimation of stand-level merchantable volume of even-aged pedunculate oak (Quercus robur L.) forests. CHMs are generated by subtracting digital terrain models (DTMs), derived from the national digital terrain database, from corresponding digital surface models (DSMs), derived by the process of image matching of digital aerial images. Two types of stand-level volume regression models are developed for each CHM resolution. The first model is based solely on stand-level CHM metrics, whereas in the second model, easily obtainable variables from forest management databases are included in addition to CHM metrics. The estimation accuracies of the stand volume estimates based on stand-level metrics (relative root mean square error RMSE% = 12.53%–13.28%) are similar or slightly higher than those obtained from previous studies in which stand volume estimates were based on plot-level metrics. The inclusion of stand age as an independent variable in addition to CHM metrics improves the accuracy of the stand volume estimates. Improvements are notable for young and middle-aged stands, and negligible for mature and old stands. Results show that CHMs at the three different resolutions are capable of providing reasonably accurate volume estimates at the stand level. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle An Improved Automatic Algorithm for Global Eddy Tracking Using Satellite Altimeter Data
Remote Sens. 2017, 9(3), 206; doi:10.3390/rs9030206
Received: 3 November 2016 / Revised: 21 February 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract
In this paper, we propose a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data. This method integrates both physical and geometric eddy properties (including the distance between eddies, the area and amplitude of eddy,
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In this paper, we propose a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data. This method integrates both physical and geometric eddy properties (including the distance between eddies, the area and amplitude of eddy, and the shape of the eddy edge) via the output of detection and the calculation of Hausdorff distance, which could describe the similarity between eddy boundaries. We applied the proposed hybrid method to several previously reported eddies and compared the results with those from two traditional tracking methods. A quantitative comparison indicates that the hybrid algorithm can better reveal eddy signals in terms of their spatial scale, amplitude, lifespan, and splitting. The hybrid method was used for global mesoscale eddies tracking from 1993 to 2015. Global distributions of net eddy numbers revealed that the sources of eddies are located along the eastern boundaries of the world oceans, while the sinks of eddies are located along the western boundaries. The lifespan distribution of eddies exhibited steep growth from high and low latitudes to middle latitudes. A clear divergent pathway demonstrates that cyclonic/anticyclonic eddies tend to travel poleward/equatorward in the world oceans. Full article
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Open AccessArticle Estimation of Downwelling Surface Longwave Radiation under Heavy Dust Aerosol Sky
Remote Sens. 2017, 9(3), 207; doi:10.3390/rs9030207
Received: 12 December 2016 / Revised: 13 February 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract
The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, which makes it difficult to estimate downwelling surface longwave
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The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, which makes it difficult to estimate downwelling surface longwave radiation (DSLR) with satellite data. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we have simulated the outgoing longwave radiation (OLR) and DSLR under different land surface types and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols; that aerosol longwave radiative forcing (ALRF) increased with the increasing of aerosol optical depth (AOD); and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model yielding the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it can estimate the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/m2 and 24.2 W/m2 for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/m2 and 9.6 W/m2, respectively. For the Arvaikheer site, the RMSEs are 23.2 W/m2 and 19.8 W/m2 for Terra and Aqua, respectively, and the biases are 7.8 W/m2 and 10.5 W/m2, respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data. Full article
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Open AccessArticle Investigation of Simultaneous Effects of Aerosol Properties and Aerosol Peak Height on the Air Mass Factors for Space-Borne NO2 Retrievals
Remote Sens. 2017, 9(3), 208; doi:10.3390/rs9030208
Received: 3 January 2017 / Revised: 13 February 2017 / Accepted: 23 February 2017 / Published: 25 February 2017
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Abstract
We investigate the simultaneous effects of aerosol peak height (APH), aerosol properties, measurement geometry, and other factors on the air mass factor for NO2 retrieval at sites with high NO2 concentration. A comparison of the effects of high and low surface
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We investigate the simultaneous effects of aerosol peak height (APH), aerosol properties, measurement geometry, and other factors on the air mass factor for NO2 retrieval at sites with high NO2 concentration. A comparison of the effects of high and low surface reflectance reveals that NO2 air mass factor (AMF) values over a snowy surface (surface reflectance 0.8) are generally higher than those over a deciduous forest surface (surface reflectance 0.05). Under high aerosol optical depth (AOD) conditions, the aerosol shielding effect over a high-albedo surface is revealed to reduce the path-length of light at the surface, whereas high single scattering albedo (SSA) conditions (e.g., SSA = 0.95) lead to an increase in the aerosol albedo effect, which results in an increased AMF over areas with low surface reflectance. We also conducted an in-depth study of the APH effect on AMF. For an AOD of 0.1 and half width (HW) of 5 km, NO2 AMF decreases by 29% from 1.36 to 0.96 as APH changes from 0 to 2 km. In the case of high-AOD conditions (0.9) and HW of 5 km, the NO2 AMF decreases by 240% from 1.85 to 0.54 as APH changes from 0 to 2 km. The AMF variation due to error in the model input parameters (e.g., AOD, SSA, aerosol shape, and APH) is also examined. When APH is 0 km with an AOD of 0.4, SSA of 0.88, and surface reflectance of 0.05, a 30% error in AOD induces an AMF error of between 4.85% and −3.67%, an SSA error of 0.04 leads to NO2 VCD errors of between 4.46% and −4.77%, and a 30% error in AOD induces an AMF error of between −9.53% and 8.35% with an APH of 3 km. In addition to AOD and SSA, APH is an important factor in calculating AMF, due to the 2 km error in APH under high-SZA conditions, which leads to an NO2 VCD error of over 60%. Aerosol shape is also found to have a measureable effect on AMF under high-AOD and small relative azimuth angle (RAA) conditions. The diurnal effect of the NO2 profile is also examined and discussed. Full article
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Open AccessArticle SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model
Remote Sens. 2017, 9(3), 209; doi:10.3390/rs9030209
Received: 19 December 2016 / Revised: 23 February 2017 / Accepted: 23 February 2017 / Published: 25 February 2017
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Abstract
Edge detection in Synthetic Aperture Radar (SAR) images has been a challenging task due to the speckle noise. Ratio-based edge detectors are robust operators for SAR images that provide constant false alarm rates, but they are only optimal for step edges. Edge detectors
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Edge detection in Synthetic Aperture Radar (SAR) images has been a challenging task due to the speckle noise. Ratio-based edge detectors are robust operators for SAR images that provide constant false alarm rates, but they are only optimal for step edges. Edge detectors developed by the phase congruency model provide the identification of different types of edge features, but they suffer from speckle noise. By combining the advantages of the two edge detectors, we propose a SAR phase congruency detector (SAR-PC). Firstly, an improved local energy model for SAR images is obtained by replacing the convolution of raw image and the quadrature filters by the ratio responses. Secondly, a new noise level is estimated for the multiplicative noise. Substituting the SAR local energy and the new noise level into the phase congruency model, SAR-PC is derived. Edge response corresponds to the max moment of SAR-PC. We compare the proposed detector with the ratio-based edge detectors and the phase congruency edge detectors. Receiver Operating Characteristic (ROC) curves and visual effects are used to evaluate the performance. Experimental results of simulated images and real-world images show that the proposed edge detector is robust to speckle noise and it provides a consecutive edge response. Full article
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Open AccessArticle Fluorescence-Based Approach to Estimate the Chlorophyll-A Concentration of a Phytoplankton Bloom in Ardley Cove (Antarctica)
Remote Sens. 2017, 9(3), 210; doi:10.3390/rs9030210
Received: 23 September 2016 / Revised: 20 February 2017 / Accepted: 20 February 2017 / Published: 25 February 2017
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Abstract
A phytoplankton bloom occurred in Ardley Cove, King George Island in January 2016, during which maximum chlorophyll-a reached 9.87 mg/m3. Records show that blooms have previously not occurred in this area prior to 2010 and the average chlorophyll-a concentration between 1991
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A phytoplankton bloom occurred in Ardley Cove, King George Island in January 2016, during which maximum chlorophyll-a reached 9.87 mg/m3. Records show that blooms have previously not occurred in this area prior to 2010 and the average chlorophyll-a concentration between 1991 and 2009 was less than 2 mg/m3. Given the lack of in situ measurements and the poor performance of satellite algorithms in the Southern Ocean and Antarctic waters, we validate and assess several chlorophyll-a algorithms and apply an improved baseline fluorescence approach to examine this bloom event. In situ water properties including in vivo fluorescence, water leaving radiance, and solar irradiance were collected to evaluate satellite algorithms and characterize chlorophyll-a concentration, as well as dominant phytoplankton groups. The results validated the nFLH fluorescence baseline approach, resulting in a good agreement at this high latitude, high chlorophyll-a region with correlation at 59.46%. The dominant phytoplankton group within the bloom was micro-phytoplankton, occupying 79.58% of the total phytoplankton community. Increasing sea ice coverage and sea ice concentration are likely responsible for increasing phytoplankton blooms in the recent decade. Given the profound influence of climate change on sea-ice and phytoplankton dynamics in the region, it is imperative to develop accurate methods of estimating the spatial distribution and concentrations of the increasing occurrence of bloom events. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas
Remote Sens. 2017, 9(3), 211; doi:10.3390/rs9030211
Received: 19 August 2016 / Revised: 19 January 2017 / Accepted: 21 February 2017 / Published: 25 February 2017
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Abstract
Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to
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Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas. Full article
(This article belongs to the collection Editor's Choice)
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Open AccessArticle A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems
Remote Sens. 2017, 9(3), 212; doi:10.3390/rs9030212
Received: 30 August 2016 / Revised: 17 January 2017 / Accepted: 20 February 2017 / Published: 25 February 2017
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Abstract
Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among
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Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas
Remote Sens. 2017, 9(3), 213; doi:10.3390/rs9030213
Received: 21 October 2016 / Revised: 8 February 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract
Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent’s AGB map) by using a map
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Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent’s AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent’s AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km2. Second, the difference between the AGB from the Vieilledent’s AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent’s AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent’s AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R2 = 0.62) to 74.1 t/ha (R2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach). Full article
(This article belongs to the collection Editor's Choice)
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Open AccessArticle Direct Digital Surface Model Generation by Semi-Global Vertical Line Locus Matching
Remote Sens. 2017, 9(3), 214; doi:10.3390/rs9030214
Received: 22 January 2017 / Revised: 21 February 2017 / Accepted: 23 February 2017 / Published: 25 February 2017
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Abstract
As the core issue for Digital Surface Model (DSM) generation, image matching is often implemented in photo space to get disparity or depth map. However, DSM is generated in object space with additional processes such as reference image selection, disparity maps fusion or
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As the core issue for Digital Surface Model (DSM) generation, image matching is often implemented in photo space to get disparity or depth map. However, DSM is generated in object space with additional processes such as reference image selection, disparity maps fusion or depth maps merging, and interpolation. This difference between photo space and object space leads to process complexity and computation redundancy. We propose a direct DSM generation approach called the semi-global vertical line locus matching (SGVLL), to generate DSM with dense matching in the object space directly. First, we designed a cost function, robust to the pre-set elevation step and projection distortion, and detected occlusion during cost calculation to achieve a sound photo-consistency measurement. Then, we proposed an improved semi-global cost aggregation with guidance of true-orthophoto to obtain superior results at weak texture regions and slanted planes. The proposed method achieves performance very close to the state-of-the-art with less time consumption, which was experimentally evaluated and verified using nadir aerial images and reference data. Full article
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Open AccessArticle Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data
Remote Sens. 2017, 9(3), 215; doi:10.3390/rs9030215
Received: 12 December 2016 / Revised: 17 February 2017 / Accepted: 23 February 2017 / Published: 27 February 2017
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Abstract
In this paper, we propose a method for using mobile laser-scanning data to estimate the green space ratio (GSR), a landscape index that represents the proportion of green area to the whole-view area. The proposed method first classifies and segments vegetation using voxel-based
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In this paper, we propose a method for using mobile laser-scanning data to estimate the green space ratio (GSR), a landscape index that represents the proportion of green area to the whole-view area. The proposed method first classifies and segments vegetation using voxel-based and shape-based approaches. Vertical planar-surface objects are excluded, and randomly distributed objects are extracted as vegetation via multi-spatial-scale analysis. Then, the method generates a map representing occlusion by vegetation, and estimates GSR at an arbitrary location. We applied the method to a data set collected in a residential area in Kyoto, Japan. We compared the results with the ground truth data and obtained a root mean squared error of approximately 4.1%. Although some non-vegetation with rough surfaces was falsely extracted as vegetation, our method seems to estimate GSR to an acceptable accuracy. Full article
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Open AccessArticle Burst Misalignment Evaluation for ALOS-2 PALSAR-2 ScanSAR-ScanSAR Interferometry
Remote Sens. 2017, 9(3), 216; doi:10.3390/rs9030216
Received: 21 December 2016 / Revised: 17 February 2017 / Accepted: 25 February 2017 / Published: 28 February 2017
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Abstract
This paper reports the validation results of burst misalignment for ScanSAR-ScanSAR interferometry of the Phased Array-type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”). After the internal software modification on 8 February 2015, ALOS-2 ScanSAR observation mode archives
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This paper reports the validation results of burst misalignment for ScanSAR-ScanSAR interferometry of the Phased Array-type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”). After the internal software modification on 8 February 2015, ALOS-2 ScanSAR observation mode archives have been available for use in interferometric analysis, as it was planned. However, it has not been reported whether its burst misalignment satisfies the mission requirements: 90% or higher burst overlap ratio. The validation results in this paper confirm that the expected observation misalignment satisfies the required range, including minimal seasonal and orbital dependencies. The results in this paper are obtained directly from the azimuth offset of the single look complex (SLC) data and are not derived from the orbit records. Nine identical orbits and frame numbers are chosen and evaluated to investigate the average burst misalignment and orbital dependency. Full article
(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)
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Open AccessArticle Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors
Remote Sens. 2017, 9(3), 217; doi:10.3390/rs9030217
Received: 24 November 2016 / Revised: 2 February 2017 / Accepted: 18 February 2017 / Published: 28 February 2017
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Abstract
An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical
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An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Role of Climate Variability and Human Activity on Poopó Lake Droughts between 1990 and 2015 Assessed Using Remote Sensing Data
Remote Sens. 2017, 9(3), 218; doi:10.3390/rs9030218
Received: 5 December 2016 / Accepted: 25 February 2017 / Published: 28 February 2017
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Abstract
In 2015, an emergency state was declared in Bolivia when Poopó Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the
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In 2015, an emergency state was declared in Bolivia when Poopó Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopó Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopó Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015. This suggests that other factors than rainfall influenced the recent disappearance of the lake. The regional evapotranspiration increased by 12.8% between 2000 and 2014. Evapotranspiration increase is not homogeneous over the watershed but limited over the main agriculture regions. Agriculture activity is one of the major factors contributing to the regional desertification and recent disappearance of Poopó Lake. Full article
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Open AccessArticle Doppler Spectrum-Based NRCS Estimation Method for Low-Scattering Areas in Ocean SAR Images
Remote Sens. 2017, 9(3), 219; doi:10.3390/rs9030219
Received: 6 December 2016 / Revised: 24 February 2017 / Accepted: 25 February 2017 / Published: 28 February 2017
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Abstract
The image intensities of low-backscattering areas in synthetic aperture radar (SAR) images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity,
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The image intensities of low-backscattering areas in synthetic aperture radar (SAR) images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity, which causes confusion in subsequent image processing or interpretation. In this paper, a method is proposed to estimate the normalized radar cross-section (NRCS) of low-backscattering area by utilizing the differences between noise, azimuthal ambiguity, and signal in the Doppler frequency domain of single-look SAR images; the aim is to eliminate the effect of system noise and azimuthal ambiguity. Analysis shows that, for a spaceborne SAR with a noise equivalent sigma zero (NESZ) of −25 dB and a single-look pixel of 8 m × 5 m, the NRCS-estimation precision of this method can reach −38 dB at a resolution of 96 m × 100 m. Three examples are given to validate the advantages of this method in estimating the low NRCS and the filtering of the azimuthal ambiguity. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Calibration and Validation of a Detailed Architectural Canopy Model Reconstruction for the Simulation of Synthetic Hemispherical Images and Airborne LiDAR Data
Remote Sens. 2017, 9(3), 220; doi:10.3390/rs9030220
Received: 20 December 2016 / Accepted: 23 February 2017 / Published: 28 February 2017
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Abstract
Canopy density measures such as the Leaf Area Index (LAI) have become standardized mapping products derived from airborne and terrestrial Light Detection And Ranging (aLiDAR and tLiDAR, respectively) data. A specific application of LiDAR point clouds is their integration into radiative transfer models
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Canopy density measures such as the Leaf Area Index (LAI) have become standardized mapping products derived from airborne and terrestrial Light Detection And Ranging (aLiDAR and tLiDAR, respectively) data. A specific application of LiDAR point clouds is their integration into radiative transfer models (RTM) of varying complexity. Using, e.g., ray tracing, this allows flexible simulations of sub-canopy light condition and the simulation of various sensors such as virtual hemispherical images or waveform LiDAR on a virtual forest plot. However, the direct use of LiDAR data in RTMs shows some limitations in the handling of noise, the derivation of surface areas per LiDAR point and the discrimination of solid and porous canopy elements. In order to address these issues, a strategy upgrading tLiDAR and Digital Hemispherical Photographs (DHP) into plausible 3D architectural canopy models is suggested. The presented reconstruction workflow creates an almost unbiased virtual 3D representation of branch and leaf surface distributions, minimizing systematic errors due to the object–sensor relationship. The models are calibrated and validated using DHPs. Using the 3D models for simulations, their capabilities for the description of leaf density distributions and the simulation of aLiDAR and DHP signatures are shown. At an experimental test site, the suitability of the models, in order to systematically simulate and evaluate aLiDAR based LAI predictions under various scan settings is proven. This strategy makes it possible to show the importance of laser point sampling density, but also the diversity of scan angles and their quantitative effect onto error margins. Full article
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Open AccessArticle Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China
Remote Sens. 2017, 9(3), 221; doi:10.3390/rs9030221
Received: 15 December 2016 / Revised: 25 January 2017 / Accepted: 27 February 2017 / Published: 1 March 2017
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Abstract
Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM
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Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. Through assembling techniques, including linear mixed effect regressions with a spatial-varying coefficient, a maximum likelihood estimator and the spatiotemporal Kriging together, we develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1° over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.0 μg/m3 and R2 = 0.72) and outperforms either AOD-derived PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-derived PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides spatiotemporally complete predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.7 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81 across China during 2014. Fused estimates will be publicly available for future health-related studies. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle Differences in Rate and Direction of Shifts between Phytoplankton Size Structure and Sea Surface Temperature
Remote Sens. 2017, 9(3), 222; doi:10.3390/rs9030222
Received: 26 December 2016 / Accepted: 21 February 2017 / Published: 1 March 2017
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Abstract
Species distributions are changing with various rates and directions in response to recent global warming. The velocity of sea surface temperature (SST) has been used to predict species migration and persistence as an expectation of how species track their thermal niches; however, several
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Species distributions are changing with various rates and directions in response to recent global warming. The velocity of sea surface temperature (SST) has been used to predict species migration and persistence as an expectation of how species track their thermal niches; however, several studies have found that evidence for species shifts has deviated from the velocity of SST. This study investigated whether estimation of the velocity of shifts in phytoplankton size structure using remote sensing data could contribute to better prediction of species shifts. A chlorophyll-a (Chla) size distribution (CSD) model was developed by quantifying the relationships between the size structure of the phytoplankton community and the spectral features of the phytoplankton absorption coefficient (aph(λ)), based on the principal component analysis approach. Model validation demonstrated that the exponent of CSD (hereafter, CSD slope), which can describe the synoptic size structure of a phytoplankton community, was derived successfully with a relative root mean square error of 18.5%. The median velocity of CSD slope across the ocean was 485.2 km·decade−1, broadly similar to Chla (531.5 km·decade−1). These values were twice the velocity of SST, and the directions of shifts in CSD slope and Chla were quite different from that of SST. Because Chla is generally covariant with the size structure of a phytoplankton community, we believe that spatiotemporal changes in Chla can explain the variations of phytoplankton size structure. Obvious differences in both rate and direction of shifts were found between the phytoplankton size structure and SST, implying that shifts of phytoplankton size structure could be a powerful tool for assessing the distributional shifts of marine species. Our results will contribute to generate global and regional maps of expected species shifts in response to environmental forcing. Full article
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Open AccessArticle Improved Class-Specific Codebook with Two-Step Classification for Scene-Level Classification of High Resolution Remote Sensing Images
Remote Sens. 2017, 9(3), 223; doi:10.3390/rs9030223
Received: 21 December 2016 / Accepted: 25 February 2017 / Published: 2 March 2017
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Abstract
With the rapid advances in sensors of remote sensing satellites, a large number of highresolution images (HRIs) can be accessed every day. Land use classification using high-resolution images has become increasingly important as it can help to overcome the problems of haphazard, deteriorating
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With the rapid advances in sensors of remote sensing satellites, a large number of highresolution images (HRIs) can be accessed every day. Land use classification using high-resolution images has become increasingly important as it can help to overcome the problems of haphazard, deteriorating environmental quality, loss of prime agricultural lands, and destruction of important wetlands, and so on. Recently, local feature with bag-of-words (BOW) representation has been successfully applied to land-use scene classification with HRIs. However, the BOW representation ignores information from scene labels, which is critical for scene-level land-use classification. Several algorithms have incorporated information from scene labels into BOW by calculating a class-specific codebook from the universal codebook and coding a testing image with a number of histograms. Those methods for mapping the BOW feature to some inaccurate class-specific codebooks may increase the classification error. To effectively solve this problem, we propose an improved class-specific codebook using kernel collaborative representation based classification (KCRC) combined with SPM approach and SVM classifier to classify the testing image in two steps. This model is robust for categories with similar backgrounds. On the standard Land use and Land Cover image dataset, the improved class-specific codebook achieves an average classification accuracy of 93% and demonstrates superiority over other state-of-the-art scene-level classification methods. Full article
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Open AccessArticle Generating Topographic Map Data from Classification Results
Remote Sens. 2017, 9(3), 224; doi:10.3390/rs9030224
Received: 30 December 2016 / Accepted: 25 February 2017 / Published: 2 March 2017
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Abstract
The use of classification results as topographic map data requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic map data of high thematic and geometric quality is subject of
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The use of classification results as topographic map data requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic map data of high thematic and geometric quality is subject of this contribution. After reviewing the existing literature on this topic, a methodology is presented. The extraction of point clouds belonging to line segments is solved by the Hough transform. The mathematics for deriving polygons of orthogonal, parallel and general line segments by least squares adjustment is presented. A unique solution for polylines, where the Hough parameters are optimized, is also given. By means of two data sets land cover maps of six classes were produced and then enhanced by the proposed method. The classification used the decision tree method applying a variety of attributes including object heights derived from imagery. The cartographic enhancement is carried out with two different levels of quality. The user’s accuracies for the classes “impervious surface” and “building” were above 85% in the “Level 1” map of Example 1. The geometric accuracy of building corners at the “Level 2” maps is assessed by means of reference data derived from ortho-images. The obtained root mean square errors (RMSE) of the generated coordinates (x, y) were RMSEx = 1.2 m and RMSEy = 0.7 m (Example 1) and RMSEx = 0.8 m and RMSEy = 1.0 m (Example 2) using 31 and 62 check points, respectively. All processing for Level 1 (raster data) could be carried out with a high degree of automation. Level 2 maps (vector data) were compiled for the classes “building” and “road and parking lot”. For urban areas with numerous classes and of large size, universal algorithms are necessary to produce vector data fully automatically. The recent progress in sensors and machine learning methods will support the generation of topographic map data of high thematic and geometric accuracy. Full article
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Open AccessArticle Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network
Remote Sens. 2017, 9(3), 225; doi:10.3390/rs9030225
Received: 19 December 2016 / Accepted: 25 February 2017 / Published: 2 March 2017
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Abstract
Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained
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Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks. Full article
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Open AccessArticle Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms
Remote Sens. 2017, 9(3), 226; doi:10.3390/rs9030226
Received: 4 January 2017 / Accepted: 22 February 2017 / Published: 2 March 2017
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Abstract
Canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting
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Canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in three irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm reparameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2. Full article
(This article belongs to the collection Editor's Choice)
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Open AccessArticle Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages
Remote Sens. 2017, 9(3), 227; doi:10.3390/rs9030227
Received: 3 January 2017 / Revised: 23 February 2017 / Accepted: 28 February 2017 / Published: 4 March 2017
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Abstract
For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red
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For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760–900 nm from RapidEye or 770–895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690–730 nm or 705–745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery
Remote Sens. 2017, 9(3), 228; doi:10.3390/rs9030228
Received: 9 September 2016 / Revised: 6 February 2017 / Accepted: 27 February 2017 / Published: 4 March 2017
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Abstract
Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand
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Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha−1. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture–AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture–AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km2 in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation. Full article
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Open AccessArticle A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping
Remote Sens. 2017, 9(3), 229; doi:10.3390/rs9030229
Received: 18 November 2016 / Accepted: 27 February 2017 / Published: 3 March 2017
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Abstract
The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform
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The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazard, the CREST River Ice Observing System (CRIOS) produces ice cover maps based on MODIS and VIIRS overpass data at several locations, including the Susquehanna River. CRIOS uses the respective platform’s automatically produced cloud masks to discriminate ice/snow covered grid cells from clouds. However, since cloud masks are produced using each instrument’s data, and owing to differences in detector performance, it is quite possible that identical algorithms applied to even nearly identical instruments may produce substantially different cloud masks. Besides detector performance, cloud identification can be biased due to local (e.g., land cover), viewing geometry, and transient conditions (snow and ice). Snow/cloud confusions and large view angles can result in substantial overestimates of clouds and ice. This impacts algorithms, such as CRIOS, since false cloud cover precludes the determination of whether an otherwise reasonably cloud free grid consists of water or ice. Especially for applications aiming to frequently classify or monitor a location it is important to evaluate cloud masking, including false cloud detections. We present an assessment of three cloud masks via the parameter of effective revisit time. A 100 km stretch of up to 1.6 km wide river was examined with daily data sampled at 500 m resolution, examined over 317 days during winter. Results show that there are substantial differences between each of the cloud mask products, especially while the river bears ice. A contrast-based cloud screening approach was found to provide improved and consistent cloud and ice identification within the reach (95%–99% correlations, and 3%–7% mean absolute differences) between the independently observing platforms. River ice was also detected accurately (proportion correct 95%–100%) and more frequently. Owing to cross-platform compositing, it is possible to obtain an effective revisit time of 2.8 days and further error reductions. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Fast Detection of Oil Spills and Ships Using SAR Images
Remote Sens. 2017, 9(3), 230; doi:10.3390/rs9030230
Received: 29 October 2016 / Revised: 21 February 2017 / Accepted: 1 March 2017 / Published: 6 March 2017
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Abstract
In this paper, we show the capabilities of a new maritime control system based on the processing of COSMO-SkyMed Synthetic Aperture Radar (SAR) images. This system aims at fast detection of ships that may be responsible for illegal oil dumping. In particular, a
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In this paper, we show the capabilities of a new maritime control system based on the processing of COSMO-SkyMed Synthetic Aperture Radar (SAR) images. This system aims at fast detection of ships that may be responsible for illegal oil dumping. In particular, a novel detection algorithm based on the joint use of the significance parameter, wavelet correlator and a two-dimensional Constant False Alarm Rate (2D-CFAR) is designed. Results show the effectiveness of such algorithms, which can be used by the maritime authorities to have a faster although still reliable response. The proposed algorithm, together with the short revisit time of the COSMO-SkyMed constellation, can help with tracking the scenario evolution from one acquisition to the next. Full article
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Open AccessArticle Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands
Remote Sens. 2017, 9(3), 231; doi:10.3390/rs9030231
Received: 5 January 2017 / Accepted: 28 February 2017 / Published: 3 March 2017
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Abstract
Airborne laser scanning (ALS) can be utilised to derive canopy height models (CHMs) for individual tree crown (ITC) delineation. In the case of forest areas subject to defoliation and dieback as a result of disease, increased irregularities across the canopy can add complications
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Airborne laser scanning (ALS) can be utilised to derive canopy height models (CHMs) for individual tree crown (ITC) delineation. In the case of forest areas subject to defoliation and dieback as a result of disease, increased irregularities across the canopy can add complications to the segmentation of ITCs. Research has yet to address this issue in order to suggest appropriate techniques to apply under conditions of forest stands that are infected by phytopathogens. This study aimed to find the best method of ITC delineation for larch canopies affected by defoliation as a result of a Phytophthora ramorum infection. Sample plots from two study sites in Wales, United Kingdom, were selected for ITC segmentation assessment across a range of infection levels and stand characteristics. The performance of two segmentation algorithms (marker-controlled watershed and region growing) were tested for a series of CHMs generated by a standard normalised digital surface model and a pit-free algorithm, across a range of spatial resolutions (0.15 m, 0.25 m and 0.5 m). The results show that the application of a pit-free CHM generation method produced improved segmentation accuracies in moderately and heavily infected larch forest, compared to the standard CHM. The success of ITC delineations was also influenced by CHM resolution. Across all plots the CHMs with a 0.25 m pixel size performed consistently well. However, lower and higher CHM resolutions also provided improved delineation accuracies in plots dominated by larger and smaller canopies respectively. The selected segmentation method also influenced the success of ITC delineations, with the marker-controlled watershed algorithm generating significantly more accurate results than the region growing algorithm (p < 0.10). The results demonstrate that ITCs in forest stands infected with Phytophthora ramorum can be successfully delineated from ALS when a pit-free algorithm is applied to CHM generation. Full article
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Open AccessArticle Radiometric Cross-Calibration of GF-4 in Multispectral Bands
Remote Sens. 2017, 9(3), 232; doi:10.3390/rs9030232
Received: 22 January 2017 / Accepted: 1 March 2017 / Published: 3 March 2017
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Abstract
The GaoFen-4 (GF-4), launched at the end of December 2015, is China’s first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like
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The GaoFen-4 (GF-4), launched at the end of December 2015, is China’s first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like the charge-coupled device (CCD) onboard HuanJing-1 (HJ) or the wide field of view sensor (WFV) onboard GaoFen-1 (GF-1), GF-4 also has a wide field of view, which provides challenges for cross-calibration with narrow field of view sensors, like the Landsat series. A new technique has been developed and used to calibrate HJ-1/CCD and GF-1/WFV, which is verified viable. The technique has three key steps: (1) calculate the surface using the bi-directional reflectance distribution function (BRDF) characterization of a site, taking advantage of its uniform surface material and natural topographic variation using Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery and digital elevation model (DEM) products; (2) calculate the radiance at the top-of-the atmosphere (TOA) with the simulated surface reflectance using the atmosphere radiant transfer model; and (3) fit the calibration coefficients with the TOA radiance and corresponding Digital Number (DN) values of the image. This study attempts to demonstrate the technique is also feasible to calibrate GF-4 multispectral bands. After fitting the calibration coefficients using the technique, extensive validation is conducted by cross-validation using the image pairs of GF-4/PMS and Landsat-8/OLI with similar transit times and close view zenith. The validation result indicates a higher accuracy and frequency than that given by the China Centre for Resources Satellite Data and Application (CRESDA) using vicarious calibration. The study shows that the new technique is also quite feasible for GF-4 multispectral bands as a routine long-term procedure. Full article
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Open AccessArticle Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery
Remote Sens. 2017, 9(3), 233; doi:10.3390/rs9030233
Received: 18 January 2017 / Accepted: 1 March 2017 / Published: 3 March 2017
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Abstract
Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from
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Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season. Full article
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Open AccessArticle Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data
Remote Sens. 2017, 9(3), 234; doi:10.3390/rs9030234
Received: 21 October 2016 / Accepted: 23 February 2017 / Published: 3 March 2017
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Abstract
We estimate two essential sea ice parameters—namely, sea ice concentration (SIC) and sea ice thickness (SIT)—for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare
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We estimate two essential sea ice parameters—namely, sea ice concentration (SIC) and sea ice thickness (SIT)—for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare the SIC and SIT estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of SIC and SIT. Full article
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Open AccessArticle Stay-Green and Associated Vegetative Indices to Breed Maize Adapted to Heat and Combined Heat-Drought Stresses
Remote Sens. 2017, 9(3), 235; doi:10.3390/rs9030235
Received: 25 November 2016 / Revised: 30 January 2017 / Accepted: 28 February 2017 / Published: 8 March 2017
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Abstract
The objective of this study was to assess the importance of stay-green on grain yield under heat and combined heat and drought stress and to identify the associated vegetative indices allowing higher throughput in order to facilitate the identification of climate resilient germplasm.
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The objective of this study was to assess the importance of stay-green on grain yield under heat and combined heat and drought stress and to identify the associated vegetative indices allowing higher throughput in order to facilitate the identification of climate resilient germplasm. Hybrids of tropical and subtropical adaptation were evaluated under heat and combined heat and drought stress in 2014 and 2015. Five weekly measurements with an airplane mounted multispectral camera starting at anthesis were used to estimate the area under the curve (AUC) for vegetation indices during that period; the indices were compared to the AUC (AUCSEN) for three visual senescence scores taken two, four, and six weeks after flowering and a novel stay-green trait (AUC for stay-green; AUCSG) derived from AUCSEN by correcting for the flowering date. Heat and combined heat and drought stress reduced grain yield by 53% and 82% (relative to non-stress trials reported elsewhere) for trials carried out in 2014 and 2015, respectively, going along with lower AUCSG in 2014. The AUCSG was consistently correlated with grain yield across trials and years, reaching correlation coefficients of 0.55 and 0.56 for 2014 and 2015, respectively. The AUC for different vegetative indices, AUCNDVI (rgGY = 0.62; rgAUCSG = 0.72), AUCHBSI (rgGY = 0.64; rgAUCSG = 0.71), AUCGRE (rgGY = 0.57; rgAUCSG = 0.61), and AUCCWMI (rgGY = 0.63; rgAUCSG = 0.75), were associated with grain yield and stay-green across experiments and years. Due to its good correlation with grain yield and stay-green across environments, we propose AUCNDVI for use as an indicator for stay-green and a long grain filling. The trait AUCNDVI can be used in addition to grain yield to identify climate-resilient germplasm in tropical and subtropical regions to increase food security in a changing climate. Full article
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Open AccessArticle Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas
Remote Sens. 2017, 9(3), 236; doi:10.3390/rs9030236
Received: 18 December 2016 / Revised: 8 February 2017 / Accepted: 1 March 2017 / Published: 4 March 2017
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Abstract
The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized
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The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy. Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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Open AccessArticle Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements
Remote Sens. 2017, 9(3), 237; doi:10.3390/rs9030237
Received: 31 December 2016 / Revised: 24 February 2017 / Accepted: 28 February 2017 / Published: 4 March 2017
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Abstract
Wheat grain protein content (GPC) is a key component when evaluating wheat nutrition. It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is
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Wheat grain protein content (GPC) is a key component when evaluating wheat nutrition. It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is spatially variable due to the inherent variability of soil properties and position in the landscape. The objectives of this field study were: (i) to assess the spatial and temporal variability of wheat nitrogen (N) attributes related to the grain quality of winter wheat production through canopy fluorescence sensor measurements; and (ii) to examine the influence of spatial variability of soil N and moisture across different growth stages on the wheat grain quality. A geostatistical approach was used to analyze data collected from 110 georeferenced locations. In particular, Ordinary Kriging Analysis (OKA) was used to produce maps of wheat GPC, GPC yield, and wheat canopy fluorescence parameters, including simple florescence ratio and Nitrogen Balance Indices (NBI). Soil Nitrate-Nitrogen (NO3-N) content and soil Time Domain Reflectometry (TDR) value in the study field were also interpolated through the OKA method. The fluorescence parameter maps, soil NO3-N and soil TDR maps obtained from the OKA output were compared with the wheat GPC and GPC yield maps in order to assess their relationships. The results of this study indicate that the NBI spatial variability map in the late stage of wheat growth can be used to distinguish areas that produce higher GPC. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions
Remote Sens. 2017, 9(3), 238; doi:10.3390/rs9030238
Received: 7 November 2016 / Revised: 20 February 2017 / Accepted: 28 February 2017 / Published: 4 March 2017
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Abstract
Oilseed rape (Brassica napus L.) is one of the three most important oil crops in China, and is regarded as a drought-tolerant oilseed crop. However, it is commonly sensitive to waterlogging, which usually refers to an adverse environment that limits crop development.
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Oilseed rape (Brassica napus L.) is one of the three most important oil crops in China, and is regarded as a drought-tolerant oilseed crop. However, it is commonly sensitive to waterlogging, which usually refers to an adverse environment that limits crop development. Moreover, crop growth and soil irrigation can be monitored at a regional level using remote sensing data. High spatial resolution optical satellite sensors are very useful to capture and resist unfavorable field conditions at the sub-field scale. In this study, four different optical sensors, i.e., Pleiades-1A, Worldview-2, Worldview-3, and SPOT-6, were used to estimate the dry above-ground biomass (AGB) of oilseed rape and track the seasonal growth dynamics. In addition, three different soil water content field experiments were carried out at different oilseed rape growth stages from November 2014 to May 2015 in Northern Zhejiang province, China. As a significant indicator of crop productivity, AGB was measured during the seasonal growth stages of the oilseed rape at the experimental plots. Several representative vegetation indices (VIs) obtained from multiple satellite sensors were compared with the simultaneously-collected oilseed rape AGB. Results showed that the estimation model using the normalized difference vegetation index (NDVI) with a power regression model performed best through the seasonal growth dynamics, with the highest coefficient of determination (R2 = 0.77), the smallest root mean square error (RMSE = 104.64 g/m2), and the relative RMSE (rRMSE = 21%). It is concluded that the use of selected VIs and high spatial multiple satellite data can significantly estimate AGB during the winter oilseed rape growth stages, and can be applied to map the variability of winter oilseed rape at the sub-field level under different waterlogging conditions, which is very promising in the application of agricultural irrigation and precision agriculture. Full article
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Open AccessArticle Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping
Remote Sens. 2017, 9(3), 239; doi:10.3390/rs9030239
Received: 30 December 2016 / Revised: 19 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
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Abstract
Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop
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Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle UAV-Based Optical Granulometry as Tool for Detecting Changes in Structure of Flood Depositions
Remote Sens. 2017, 9(3), 240; doi:10.3390/rs9030240
Received: 5 February 2017 / Revised: 26 February 2017 / Accepted: 2 March 2017 / Published: 7 March 2017
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Abstract
This paper presents a new non-invasive technique of granulometric analysis based on the fusion of two imaging techniques, Unmanned Aerial Vehicles (UAV)-based photogrammetry and optical digital granulometry. This newly proposed technique produces seamless coverage of a study site in order to analyze the
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This paper presents a new non-invasive technique of granulometric analysis based on the fusion of two imaging techniques, Unmanned Aerial Vehicles (UAV)-based photogrammetry and optical digital granulometry. This newly proposed technique produces seamless coverage of a study site in order to analyze the granulometric properties of alluvium and observe its spatiotemporal changes. This proposed technique is tested by observing changes along the point bar of a mid-latitude mountain stream. UAV photogrammetry acquired at a low-level flight altitude (at a height of 8 m) is used to acquire ultra-high resolution orthoimages to build high-precision digital terrain models (DTMs). These orthoimages are covered by a regular virtual grid, and the granulometric properties of the grid fields are analyzed using the digital optical granulometric tool BaseGrain. This tested framework demonstrates the applicability of the proposed method for granulometric analysis, which yields accuracy comparable to that of traditional field optical granulometry. The seamless nature of this method further enables researchers to study the spatial distribution of granulometric properties across multiple study sites, as well as to analyze multitemporal changes using repeated imaging. Full article
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Open AccessArticle Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods
Remote Sens. 2017, 9(3), 241; doi:10.3390/rs9030241
Received: 18 October 2016 / Revised: 22 February 2017 / Accepted: 1 March 2017 / Published: 5 March 2017
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Abstract
The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest
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The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements
Remote Sens. 2017, 9(3), 242; doi:10.3390/rs9030242
Received: 5 July 2016 / Revised: 11 February 2017 / Accepted: 22 February 2017 / Published: 5 March 2017
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Abstract
Wake characteristics are of great importance for wind park performance and turbine loads. While wind tunnel experiments provided a solid base for the basic understanding of the structure and dynamics of wind turbine wakes, the consequent step forward to characterize wakes is full-scale
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Wake characteristics are of great importance for wind park performance and turbine loads. While wind tunnel experiments provided a solid base for the basic understanding of the structure and dynamics of wind turbine wakes, the consequent step forward to characterize wakes is full-scale measurements in real atmospheric boundary layer conditions under different stability regimes. Scanning Doppler LiDAR measurements have proven to be a flexible and useful tool for such measurements. However, their advantage of measuring spatial fluctuation is accompanied by the limited temporal resolution of individual sampling volumes within the scanned area. This study presents results from LiDAR Doppler Beam Swing (DBS) measurements and highlights the potential of information retrieved from a spectral analysis of wake measurements. Data originate from three Windcube v1 and sonic anemometers, collected during the Wind Turbine Wake Experiment–Wieringermeer. Despite the ongoing research on the reliability of turbulence retrievals based on DBS data, our results show wake peak frequencies consistent with sonic anemometer measurements. The energy spectra show rather distinct maxima during stable conditions, which broaden during unstable and neutral conditions. Investigations on the effect of blade pitch on downstream wind speed and turbulence intensity profiles indicate the potential for the development of stability-dependent wind farm control strategies. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Open AccessArticle Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
Remote Sens. 2017, 9(3), 243; doi:10.3390/rs9030243
Received: 31 December 2016 / Revised: 21 February 2017 / Accepted: 2 March 2017 / Published: 5 March 2017
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Abstract
Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the
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Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels). Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool) from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC) superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches have the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint. Full article
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Open AccessArticle A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration
Remote Sens. 2017, 9(3), 244; doi:10.3390/rs9030244
Received: 19 January 2017 / Revised: 28 February 2017 / Accepted: 2 March 2017 / Published: 6 March 2017
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Abstract
Compared with prime lenses, zoom lenses have inherent advantages in terms of operational flexibility. Zoom lens camera systems have therefore been extensively adopted in computer vision where precise measurement is not the primary objective. However, the variation of intrinsic camera parameters with respect
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Compared with prime lenses, zoom lenses have inherent advantages in terms of operational flexibility. Zoom lens camera systems have therefore been extensively adopted in computer vision where precise measurement is not the primary objective. However, the variation of intrinsic camera parameters with respect to zoom lens settings poses a series of calibration challenges that have inhibited widespread use in close-range photogrammetry. A flexible zoom lens calibration methodology is therefore proposed in this study, developed with the aim of simplifying the calibration process and promoting practical photogrammetric application. A zoom-dependent camera model that incorporates empirical zoom-related intrinsic parameters into the collinearity condition equations is developed. Coefficients of intrinsic parameters are solved in a single adjustment based on this zoom lens camera model. To validate the approach, experiments on both optical- and digital-zoom lens cameras were conducted using a planar board with evenly distributed circular targets. Zoom lens calibration was performed with images taken at four different zoom settings spread throughout the zoom range of a lens. Photogrammetric accuracies achieved through both mono-focal and multi-focal triangulations were evaluated after calibration. The relative accuracies for mono-focal triangulations ranged from 1: 6300 to 1: 18,400 for the two cameras studied, whereas the multi-focal triangulation accuracies ranged from 1: 11,300 to 1: 16,200. In order to demonstrate the applicability of the approach, calibrated zoom lens imagery was used to render a laser-scanned point cloud of a building façade. Considered alongside experimental results, the successful application demonstrates the feasibility of the proposed calibration method, thereby facilitating the adoption of zoom lens cameras in close range photogrammetry for a wide range of scientific and practical applications. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle A Lookup Table-Based Method for Estimating Sea Surface Hemispherical Broadband Emissivity Values (8–13.5 μm)
Remote Sens. 2017, 9(3), 245; doi:10.3390/rs9030245
Received: 7 December 2016 / Accepted: 3 March 2017 / Published: 6 March 2017
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Abstract
Sea surface hemispherical broadband emissivity (BBE, 8–13.5 μm) is a vital parameter for calculating surface radiation budgets. Such data are currently unavailable. This paper proposes a lookup table-based method for retrieving sea surface hemispherical BBE values. The physicallybased sea surface emissivity model of
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Sea surface hemispherical broadband emissivity (BBE, 8–13.5 μm) is a vital parameter for calculating surface radiation budgets. Such data are currently unavailable. This paper proposes a lookup table-based method for retrieving sea surface hemispherical BBE values. The physicallybased sea surface emissivity model of Wu and Smith, together with the optimal refractive index, were used to generate hemispherical BBE values under wind speeds ranging from zero to 50 m/s. A lookup table of hemispherical BBE values as a function of wind speed was established and used to retrieve sea surface hemispherical BBE values under foam-free conditions. The accuracy of the estimates of hemispherical BBE was 0.003, given a wind speed of zero. The foam effect was explicitly considered. After incorporating the foam effect, hemispherical BBE was expressed as a linear function of the hemispherical BBE values of sea water and foam, weighted by the fraction of foam coverage. With this method, we have produced an hourly sea surface hemispherical BBE product with a resolution of 10 km and global coverage that covers the period from 2003 to 2005, using wind speed data from Modern-Era Retrospective analysis for Research and Applications (MERRA)-2. Full article
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Open AccessArticle The SUMO Ship Detector Algorithm for Satellite Radar Images
Remote Sens. 2017, 9(3), 246; doi:10.3390/rs9030246
Received: 25 November 2016 / Revised: 25 January 2017 / Accepted: 13 February 2017 / Published: 7 March 2017
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Abstract
Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR
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Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1–100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU’s Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors. Full article
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Open AccessArticle Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation
Remote Sens. 2017, 9(3), 247; doi:10.3390/rs9030247
Received: 30 November 2016 / Revised: 17 February 2017 / Accepted: 1 March 2017 / Published: 7 March 2017
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Abstract
Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was
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Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%–21% for growth-forms and 3%–30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index
Remote Sens. 2017, 9(3), 248; doi:10.3390/rs9030248
Received: 12 January 2017 / Accepted: 4 March 2017 / Published: 7 March 2017
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Abstract
This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and
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This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring. Full article
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Open AccessArticle Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index
Remote Sens. 2017, 9(3), 249; doi:10.3390/rs9030249
Received: 19 November 2016 / Accepted: 28 February 2017 / Published: 7 March 2017
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Abstract
In recent years, hundreds of Earth observation satellites have been launched to collect massive amounts of remote sensing images. However, the considerable cost and time to process the significant amount of data have become the greatest obstacle between data and knowledge. In order
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In recent years, hundreds of Earth observation satellites have been launched to collect massive amounts of remote sensing images. However, the considerable cost and time to process the significant amount of data have become the greatest obstacle between data and knowledge. In order to accelerate the transformation from remote sensing images to urban thematic maps, a strategy to map the bare land automatically from Landsat imagery was developed and assessed in this study. First, a normalized difference bare land index (NBLI) was presented to maximally differentiate bare land from other land types in Wuhan City, China. Then, an unsupervised classifier was employed to extract the bare land from the NBLI image without training samples or self-assigned thresholds. Experimental results showed good performance on overall accuracy (92%), kappa coefficient (0.84), area ratio (1.1321), and match rate (83.96%), respectively. Results in multiple years disclosed that bare lands in the study site gradually moved from inner loops to the outer loops since 2007, in two main directions. This study demonstrated that the proposed method was an accurate and reliable option to extract the bare land, which led to a promising approach to mapping urban land use/land cover (LULC) automatically with simple indices. Full article
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Open AccessArticle The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities
Remote Sens. 2017, 9(3), 250; doi:10.3390/rs9030250
Received: 30 December 2016 / Revised: 23 February 2017 / Accepted: 6 March 2017 / Published: 8 March 2017
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Abstract
Supported by the rapid economic development in the last few decades, China has become the largest energy consumer in the world. Alongside this, the effect of the anthropogenic heat released from energy consumption is increasingly apparent. We quantified the daytime and nighttime surface
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Supported by the rapid economic development in the last few decades, China has become the largest energy consumer in the world. Alongside this, the effect of the anthropogenic heat released from energy consumption is increasingly apparent. We quantified the daytime and nighttime surface urban heat island intensity (SUHII) for the 32 major cities in mainland China, using MODIS land surface temperature data from 2008 to 2012, and estimated the energy consumption intensity (ECI) based on the correlation between energy consumption and the sum of nighttime lights. On this basis, the impact of energy consumption on the surface urban heat island in China’s 32 major cities was analyzed, by directly examining the relationship between SUHII and the urban-suburban difference in ECI. The results show that energy consumption has a significantly positive correlation with the nighttime SUHII, but no correlation with the daytime SUHII. It indicates that the cities with a larger urban-suburban difference in ECI have a far greater impact on SUHII during the nighttime. Therefore, the statistical analysis of the historical observation data in this study provides evidence for a long-held hypothesis that the anthropogenic heat released from energy consumption is an important contributor to the urban thermal environment. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations
Remote Sens. 2017, 9(3), 251; doi:10.3390/rs9030251
Received: 20 October 2016 / Accepted: 6 March 2017 / Published: 8 March 2017
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Abstract
Using measurements of the column-averaged CO2 dry air mole fraction (XCO2) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from
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Using measurements of the column-averaged CO2 dry air mole fraction (XCO2) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO2. A unique global land mapping dataset of XCO2 with a resolution of 1° by 1° in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO2 retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO2 and NDVI, and implement correlation analyses of seasonal variation between XCO2 and the vegetation parameters. As a result, the spatial distribution of XCO2 SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO2 from CarbonTracker. The spatial pattern of XCO2 SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 ± 0.49 ppm on average) and Central Africa (with SCA of 8.33 ± 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere–atmosphere interactions at local scales. Results from correlation analyses between XCO2 and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO2 and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO2 compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere–atmosphere interactions on the seasonal cycle pattern of atmospheric CO2 columns. Full article
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Open AccessArticle Urban Change Analysis with Multi-Sensor Multispectral Imagery
Remote Sens. 2017, 9(3), 252; doi:10.3390/rs9030252
Received: 5 January 2017 / Revised: 6 March 2017 / Accepted: 6 March 2017 / Published: 9 March 2017
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Abstract
An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is
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An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is generated by characterizing the change probability of image objects based on the proposed change feature analysis. The map is then used to separate the changes from unchanged areas by two threshold selection methods and k-means clustering (k = 2). In order to consider the multi-scale characteristics of ground objects, multi-scale fusion is implemented. The experimental results obtained with QuickBird and IKONOS images show the superiority of the proposed method in detecting urban changes in multi-sensor MS images. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Open AccessArticle SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm
Remote Sens. 2017, 9(3), 253; doi:10.3390/rs9030253
Received: 14 November 2016 / Revised: 1 March 2017 / Accepted: 4 March 2017 / Published: 9 March 2017
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Abstract
The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment,
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The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up-Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite. Full article
(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Data)
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Open AccessArticle Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS)
Remote Sens. 2017, 9(3), 254; doi:10.3390/rs9030254
Received: 2 January 2017 / Revised: 24 February 2017 / Accepted: 6 March 2017 / Published: 9 March 2017
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Abstract
Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable
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Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky–Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations. Full article
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Open AccessArticle Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions
Remote Sens. 2017, 9(3), 255; doi:10.3390/rs9030255
Received: 19 December 2016 / Accepted: 7 March 2017 / Published: 9 March 2017
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Abstract
This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can
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This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations
Remote Sens. 2017, 9(3), 256; doi:10.3390/rs9030256
Received: 1 December 2016 / Revised: 5 March 2017 / Accepted: 9 March 2017 / Published: 10 March 2017
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Abstract
Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest
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Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Qpeak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m3·s−1 for the 12 study years (2003–2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Qpeak. It was found that the temperature variations in the snowmelt season are the principal driver for the Qpeak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Qpeak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
Remote Sens. 2017, 9(3), 257; doi:10.3390/rs9030257
Received: 25 November 2016 / Accepted: 6 March 2017 / Published: 10 March 2017
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Abstract
Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented
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Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented by other landuses. Improving rice classification accuracy requires the use of multi-source and multi-temporal high resolution data for operational purposes. In this regard, we first exploited the temporal backscatter of rice fields and background land-cover types at the vertical transmitted and vertical received (VV) and vertical transmitted and horizontal received (VH) polarizations of Sentinel-1A. We observed that the temporal backscatter of rice increased sharply at the early stages of growth, as opposed to the relatively uniform temporal backscatter of the other land-cover classes. However, the increase in rice backscatter is more sustained at the VH polarization, and two-class separability measures further indicated the superiority of VH over VV in discriminating rice fields. We have therefore combined the temporal VH images of Sentinel-1A with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-free Landsat 8 image. The integration of these optical indices with temporal backscatter eliminated all commission errors in the Rice class and increased overall accuracy by 5.3%, demonstrating the complimentary role of optical indices to microwave data in mapping rice fields in subtropical and urban landscapes such as Shanghai. Full article
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Open AccessArticle A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data
Remote Sens. 2017, 9(3), 258; doi:10.3390/rs9030258
Received: 18 January 2017 / Revised: 5 March 2017 / Accepted: 9 March 2017 / Published: 11 March 2017
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Abstract
Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand
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Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models. Full article
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Open AccessArticle A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM)
Remote Sens. 2017, 9(3), 259; doi:10.3390/rs9030259
Received: 30 November 2016 / Revised: 27 February 2017 / Accepted: 8 March 2017 / Published: 11 March 2017
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Abstract
Sentinel-2 images are expected to improve global crop monitoring even in challenging tropical small agricultural systems that are characterized by high intra- and inter-field spatial variability and where satellite observations are disturbed by the presence of clouds. To overcome these constraints, we analyzed
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Sentinel-2 images are expected to improve global crop monitoring even in challenging tropical small agricultural systems that are characterized by high intra- and inter-field spatial variability and where satellite observations are disturbed by the presence of clouds. To overcome these constraints, we analyzed and optimized the performance of a combined Random Forest (RF) classifier/object-based approach and applied it to multisource satellite data to produce land use maps of a smallholder agricultural zone in Madagascar at five different nomenclature levels. The RF classifier was first optimized by reducing the number of input variables. Experiments were then carried out to (i) test cropland masking prior to the classification of more detailed nomenclature levels, (ii) analyze the importance of each data source (a high spatial resolution (HSR) time series, a very high spatial resolution (VHSR) coverage and a digital elevation model (DEM)) and data type (spectral, textural or other), and (iii) quantify their contributions to classification accuracy levels. The results show that RF classifier optimization allowed for a reduction in the number of variables by 1.5- to 6-fold (depending on the classification level) and thus a reduction in the data processing time. Classification results were improved via the hierarchical approach at all classification levels, achieving an overall accuracy of 91.7% and 64.4% for the cropland and crop subclass levels, respectively. Spectral variables derived from an HSR time series were shown to be the most discriminating, with a better score for spectral indices over the reflectances. VHSR data were only found to be essential when implementing the segmentation of the area into objects and not for the spectral or textural features they can provide during classification. Full article
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Open AccessArticle MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar
Remote Sens. 2017, 9(3), 260; doi:10.3390/rs9030260
Received: 1 February 2017 / Revised: 8 March 2017 / Accepted: 10 March 2017 / Published: 12 March 2017
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Abstract
Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human
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Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images
Remote Sens. 2017, 9(3), 261; doi:10.3390/rs9030261
Received: 29 December 2016 / Revised: 3 March 2017 / Accepted: 8 March 2017 / Published: 12 March 2017
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Abstract
Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes
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Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral features or spatial features. The comparative experiments demonstrate the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle Evaluation of Recently Released Open Global Digital Elevation Models of Hubei, China
Remote Sens. 2017, 9(3), 262; doi:10.3390/rs9030262
Received: 8 September 2016 / Revised: 4 February 2017 / Accepted: 2 March 2017 / Published: 12 March 2017
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Abstract
The recent release of worldwide SRTM 1 DEM and AW3D30 adds new members to the open global medium resolution (90–30 m ground spacing) digital elevation models. Together with the previously existing SRTM 3 and ASTER GDEM, their quality is of great interest to
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The recent release of worldwide SRTM 1 DEM and AW3D30 adds new members to the open global medium resolution (90–30 m ground spacing) digital elevation models. Together with the previously existing SRTM 3 and ASTER GDEM, their quality is of great interest to various scientific applications. This paper uses 1:50,000 DEM in Hubei Province of China as a reference to assess their vertical accuracy in terms of terrain types, slopes, and land cover. For ASTER GDEM and AW3D30, we further evaluate their accuracy in terms of the stack number, i.e., the number of scenes used to generate the DEM. It is found out that: (1) all of the DEMs have nearly the same horizontal offset due to the adoption of different datums; (2) the vertical accuracy varies in terms of terrain complexity, from ~5 m for plains, ~10 m for hills to ~20 m for mountains; (3) the vertical accuracy is negatively related to the tangent of terrain slope exponentially in forest areas and linearly in cultivated lands; (4) forest areas have the lowest vertical accuracy, comparing to built-up areas, wetland, and cultivate land areas while SRTM 1 and AW3D30 have the highest accuracy in all land cover classes; (5) the large elevation differences over forest areas are likely due to canopy coverage; and (6) for ASTER GDEM and AW3D30, their accuracy is in general positively related to the stack number. This study provides a practically useful quality specification and comprehensive understanding for these four global DEMs, especially the recently released worldwide SRTM 1 DEM and AW3D30. Full article
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Open AccessArticle Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan
Remote Sens. 2017, 9(3), 264; doi:10.3390/rs9030264
Received: 16 December 2016 / Revised: 6 March 2017 / Accepted: 9 March 2017 / Published: 13 March 2017
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Abstract
Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds
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Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery
Remote Sens. 2017, 9(3), 265; doi:10.3390/rs9030265
Received: 30 November 2016 / Revised: 26 February 2017 / Accepted: 12 March 2017 / Published: 14 March 2017
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Abstract
In recent years, the area of plastic-mulched farmland (PMF) has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus,
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In recent years, the area of plastic-mulched farmland (PMF) has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV) function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1) satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM) algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance. Full article
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Open AccessArticle Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy
Remote Sens. 2017, 9(3), 266; doi:10.3390/rs9030266
Received: 23 January 2017 / Revised: 6 March 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
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Abstract
Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring
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Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in 2006–2007, 24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82–0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user. Full article
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Open AccessArticle Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis
Remote Sens. 2017, 9(3), 267; doi:10.3390/rs9030267
Received: 23 December 2016 / Revised: 8 March 2017 / Accepted: 9 March 2017 / Published: 15 March 2017
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Abstract
An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and
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An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and landslides type definition, have been acquired thanks to geomorphological field investigations and multi-temporal aerial photos interpretation, while satellite SAR archive data (acquired by ERS and Envisat from 1992 to 2010) have been analyzed by means of A-DInSAR (Advanced Differential Interferometric Synthetic Aperture Radar) techniques to evaluate landslides past displacements patterns. Multi-temporal assessment of landslides state of activity has been performed basing on geomorphological evidence criteria and past ground displacement measurements obtained by A-DInSAR. This step has been performed by means of an activity matrix derived from information achieved thanks to double orbital geometry. Thanks to this approach we also achieved more detailed knowledge about the landslides kinematics in time and space. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard
Remote Sens. 2017, 9(3), 268; doi:10.3390/rs9030268
Received: 31 December 2016 / Revised: 2 March 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
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Abstract
The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating
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The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Three-Step Semi-Empirical Radiometric Terrain Correction Approach for PolSAR Data Applied to Forested Areas
Remote Sens. 2017, 9(3), 269; doi:10.3390/rs9030269
Received: 14 October 2016 / Revised: 23 February 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
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Abstract
In recent decades, most methods proposed for radiometric slope correction involved the backscattering intensity values in synthetic aperture radar (SAR) data. However, these methods are not fully applicable to quad-polarimetric SAR (PolSAR) matrix data. In this paper, we propose a three-step semi-empirical radiometric
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In recent decades, most methods proposed for radiometric slope correction involved the backscattering intensity values in synthetic aperture radar (SAR) data. However, these methods are not fully applicable to quad-polarimetric SAR (PolSAR) matrix data. In this paper, we propose a three-step semi-empirical radiometric terrain correction approach for PolSAR forest area data. The three steps of terrain effects correction are: polarisation orientation angle (POA), effective scattering area (ESA), and angular variation effect (AVE) corrections. We propose a novel method to determine adaptively the “n