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Remote Sens., Volume 11, Issue 10 (May-2 2019) – 115 articles

Cover Story (view full-size image): With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species, owing to its spatial, spectral, and temporal resolution. We evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We used 18 Sentinel-2 images from 2018. Different combinations of Sentinel-2 imagery were selected based on the mean decrease in accuracy and mean decrease in Gini measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the random forest classification algorithm. Our results show that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination. View [...] Read more.
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26 pages, 9011 KiB  
Article
Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
by Ovidiu Csillik, Mariana Belgiu, Gregory P. Asner and Maggi Kelly
Remote Sens. 2019, 11(10), 1257; https://doi.org/10.3390/rs11101257 - 27 May 2019
Cited by 76 | Viewed by 11310
Abstract
The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series [...] Read more.
The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products. Full article
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20 pages, 4762 KiB  
Article
Analysis of Factors Affecting Asynchronous RTK Positioning with GNSS Signals
by Bao Shu, Hui Liu, Yanming Feng, Longwei Xu, Chuang Qian and Zhixin Yang
Remote Sens. 2019, 11(10), 1256; https://doi.org/10.3390/rs11101256 - 27 May 2019
Cited by 4 | Viewed by 4745
Abstract
For short baseline real-time kinematic (RTK) positioning, the atmosphere and broadcast ephemeris errors can be usually eliminated in double-differenced (DD) processing for synchronous observations. However, in the case of possible communication latency time, these errors may not be eliminated in DD treatments due [...] Read more.
For short baseline real-time kinematic (RTK) positioning, the atmosphere and broadcast ephemeris errors can be usually eliminated in double-differenced (DD) processing for synchronous observations. However, in the case of possible communication latency time, these errors may not be eliminated in DD treatments due to their variations during latency time. In addition, the time variation of these errors may present different characteristics among GPS, GLONASS, BDS, and GALILEO due to different satellite orbit and clock types. In this contribution, the formulas for studying the broadcast orbit and clock offset errors and atmosphere error in asynchronous RTK (ARTK) model is proposed, and comprehensive experimental analysis is performed to numerically show time variations of these errors and their impacts on RTK results from short-baselines among four systems. Compared with synchronous RTK, the degradation of position precision for ARTK can reach a few centimeters, but the accuracy degradation to a different degree by different systems. BDS and Galileo usually outperform GPS and GLONASS in ARTK due to the smaller variation of broadcast ephemeris error. The variation of broadcast orbit error is generally negligible compared with the variation of broadcast clock offset error for GPS, BDS, and Galileo. Specifically, for a month of data, the root mean square (RMS) values for the variation of broadcast ephemeris error over 15 seconds are 11.2, 16.9, 7.3, and 3.0 mm for GPS, GLONASS, BDS, and Galileo, respectively. The variation of ionosphere error for some satellites over 15 seconds can reach a few centimeters during active sessions under a normal ionosphere day. In addition, compared with other systems, BDS ARTK shows an advantage under high ionosphere activity, and such advantage may be attributed to five GEO satellites in the BDS constellation. Full article
(This article belongs to the Special Issue High-precision GNSS: Methods, Open Problems and Geoscience Applications)
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13 pages, 20118 KiB  
Article
Improvement of Full Waveform Airborne Laser Bathymetry Data Processing based on Waves of Neighborhood Points
by Tomasz Kogut and Krzysztof Bakuła
Remote Sens. 2019, 11(10), 1255; https://doi.org/10.3390/rs11101255 - 27 May 2019
Cited by 15 | Viewed by 3567
Abstract
Measurements of the topography of the sea floor are one of the main tasks of hydrographic organizations worldwide. The occurrence of any disaster in maritime traffic can contaminate the environment for many years. Therefore, increasing attention is being paid to the development of [...] Read more.
Measurements of the topography of the sea floor are one of the main tasks of hydrographic organizations worldwide. The occurrence of any disaster in maritime traffic can contaminate the environment for many years. Therefore, increasing attention is being paid to the development of effective methods for the detection and monitoring of possible obstacles on the transport route. Bathymetric laser scanners record the full waveform reflected from the object (target). Its transformation allows to obtain information about the water surface, water column, seabed, and the objects on it. However, it is not possible to identify subsequent returns among all waves, leading to a loss of information about the situation under the water. On the basis of the studies conducted, it was concluded that the use of a secondary analysis of a full waveform of the airborne laser bathymetry allowed for the identification of objects on the seabed. It allowed us to detect further points in the point cloud, which are necessary in the identification of objects on the seabed. The results of the experiment showed that, among the area of experiment where objects on the seabed were located, the number of points increased between 150 and 550% and the altitude accuracy of the seabed elevation model even by 50% to the level of 0.30 m with reference to sonar data depending of types of objects. Full article
(This article belongs to the Special Issue Applications of Full Waveform Lidar)
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26 pages, 13368 KiB  
Article
Transition Characteristics of the Dry-Wet Regime and Vegetation Dynamic Responses over the Yarlung Zangbo River Basin, Southeast Qinghai-Tibet Plateau
by Liu Liu, Qiankun Niu, Jingxia Heng, Hao Li and Zongxue Xu
Remote Sens. 2019, 11(10), 1254; https://doi.org/10.3390/rs11101254 - 27 May 2019
Cited by 18 | Viewed by 4536
Abstract
The dry-wet transition is of great importance for vegetation dynamics, however the response mechanism of vegetation variations is still unclear due to the complicated effects of climate change. As a critical ecologically fragile area located in the southeast Qinghai-Tibet Plateau, the Yarlung Zangbo [...] Read more.
The dry-wet transition is of great importance for vegetation dynamics, however the response mechanism of vegetation variations is still unclear due to the complicated effects of climate change. As a critical ecologically fragile area located in the southeast Qinghai-Tibet Plateau, the Yarlung Zangbo River (YZR) basin, which was selected as the typical area in this study, is significantly sensitive and vulnerable to climate change. The standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) based on the GLDAS-NOAH products and the GIMMS-NDVI remote sensing data from 1982 to 2015 were employed to investigate the spatio-temporal characteristics of the dry-wet regime and the vegetation dynamic responses. The results showed that: (1) The spatio-temporal patterns of the precipitation and temperature simulated by the GLDAS-NOAH fitted well with those of the in-situ data. (2) During the period of 1982–2015, the whole YZR basin exhibited an overall wetting tendency. However, the spatio-temporal characteristics of the dry-wet regime exhibited a reversal phenomenon before and after 2000, which was jointly identified by the SPEI and runoff. That is, the YZR basin showed a wetting trend before 2000 and a drying trend after 2000; the arid areas in the basin showed a tendency of wetting whereas the humid areas exhibited a trend of drying. (3) The region where NDVI was positively correlated with SPEI accounted for approximately 70% of the basin area, demonstrating a similar spatio-temporal reversal phenomenon of the vegetation around 2000, indicating that the dry-wet condition is of great importance for the evolution of vegetation. (4) The SPEI showed a much more significant positive correlation with the soil water content which accounted for more than 95% of the basin area, implying that the soil water content was an important indicator to identify the dry-wet transition in the YZR basin. Full article
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)
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17 pages, 5542 KiB  
Article
Data Processing and Interpretation of Antarctic Ice-Penetrating Radar Based on Variational Mode Decomposition
by Siyuan Cheng, Sixin Liu, Jingxue Guo, Kun Luo, Ling Zhang and Xueyuan Tang
Remote Sens. 2019, 11(10), 1253; https://doi.org/10.3390/rs11101253 - 27 May 2019
Cited by 10 | Viewed by 3971
Abstract
In the Arctic and Antarctic scientific expeditions, ice-penetrating radar is an effective method for studying the bedrock under the ice sheet and ice information within the ice sheet. Because of the low conductivity of ice and the relatively uniform composition of ice sheets [...] Read more.
In the Arctic and Antarctic scientific expeditions, ice-penetrating radar is an effective method for studying the bedrock under the ice sheet and ice information within the ice sheet. Because of the low conductivity of ice and the relatively uniform composition of ice sheets in the polar regions, ice-penetrating radar is able to obtain deeper and more abundant data than other geophysical methods. However, it is still necessary to suppress the noise in radar data to obtain more accurate and plentiful effective information. In this paper, the entirely non-recursive Variational Mode Decomposition (VMD) is applied to the data noise reduction of ice-penetrating radar. VMD is a decomposition method of adaptive and quasi-orthogonal signals, which decomposes airborne radar data into multiple frequency-limited quasi-orthogonal Intrinsic Mode Functions (IMFs). The IMFs containing noise are then removed according to the information distribution in the IMF’s components and the remaining IMFs are reconstructed. This paper employs this method to process the real ice-penetrating radar data, which effectively eliminates the interference noise in the data, improves the signal-to-noise ratio and obtains the clearer inner layer structure of ice. It is verified that the method can be applied to the noise reduction processing of polar ice-penetrating radar data very well, which provides a better basis for data interpretation. At last, we present fine ice structure within the ice sheet based on VMD denoised radar profile. Full article
(This article belongs to the Special Issue Recent Progress in Ground Penetrating Radar Remote Sensing)
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19 pages, 10362 KiB  
Article
Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images
by Erich Seifert, Stefan Seifert, Holger Vogt, David Drew, Jan van Aardt, Anton Kunneke and Thomas Seifert
Remote Sens. 2019, 11(10), 1252; https://doi.org/10.3390/rs11101252 - 27 May 2019
Cited by 130 | Viewed by 11982
Abstract
Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the [...] Read more.
Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications. Full article
(This article belongs to the Section Forest Remote Sensing)
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37 pages, 7160 KiB  
Article
Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission
by Carlos Granero-Belinchon, Aurelie Michel, Jean-Pierre Lagouarde, Jose A. Sobrino and Xavier Briottet
Remote Sens. 2019, 11(10), 1251; https://doi.org/10.3390/rs11101251 - 27 May 2019
Cited by 13 | Viewed by 5193
Abstract
This work is linked to the future Indian–French high spatio-temporal TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) mission, which includes shortwave and thermal infrared bands, and is devoted amongst other things to the monitoring of urban heat island events. In [...] Read more.
This work is linked to the future Indian–French high spatio-temporal TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) mission, which includes shortwave and thermal infrared bands, and is devoted amongst other things to the monitoring of urban heat island events. In this article, the performance of seven empirical thermal unmixing techniques applied on simulated TRISHNA satellite images of an urban scenario is studied across spatial resolutions. For this purpose, Top Of Atmosphere (TOA) images in the shortwave and Thermal InfraRed (TIR) ranges are constructed at different resolutions (20 m, 40 m, 60 m, 80 m, and 100 m) and according to TRISHNA specifications (spectral bands and sensor properties). These images are synthesized by correcting and undersampling DESIREX 2008 Airborne Hyperspectral Scanner (AHS) images of Madrid at 4 m resolution. This allows to compare the Land Surface Temperature (LST) retrieval of several unmixing techniques applied on different resolution images, as well as to characterize the evolution of the performance of each technique across resolutions. The seven unmixing techniques are: Disaggregation of radiometric surface Temperature (DisTrad), Thermal imagery sHARPening (TsHARP), Area-To-Point Regression Kriging (ATPRK), Adaptive Area-To-Point Regression Kriging (AATPRK), Urban Thermal Sharpener (HUTS), Multiple Linear Regressions (MLR), and two combinations of ground classification (index-based classification and K-means classification) with DisTrad. Studying these unmixing techniques across resolutions also allows to validate the scale invariance hypotheses on which the techniques hinge. Each thermal unmixing technique has been tested with several shortwave indices, in order to choose the best one. It is shown that (i) ATPRK outperforms the other compared techniques when characterizing the LST of Madrid, (ii) the unmixing performance of any technique is degraded when the coarse spatial resolution increases, (iii) the used shortwave index does not strongly influence the unmixing performance, and (iv) even if the scale-invariant hypotheses behind these techniques remain empirical, this does not affect the unmixing performances within this range of resolutions. Full article
(This article belongs to the Section Urban Remote Sensing)
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33 pages, 40507 KiB  
Article
Net Cloud Thinning, Low-Level Cloud Diminishment, and Hadley Circulation Weakening of Precipitating Clouds with Tropical West Pacific SST Using MISR and Other Satellite and Reanalysis Data
by Terence L. Kubar and Jonathan H. Jiang
Remote Sens. 2019, 11(10), 1250; https://doi.org/10.3390/rs11101250 - 27 May 2019
Cited by 5 | Viewed by 4304
Abstract
Daily gridded Multi-Angle Imaging Spectroradiometer (MISR) satellite data are used in conjunction with CERES, TRMM, and ERA-Interim reanalysis data to investigate horizontal and vertical high cloud structure, top-of-atmosphere (TOA) net cloud forcing and albedo, and dynamics relationships against local SST and precipitation as [...] Read more.
Daily gridded Multi-Angle Imaging Spectroradiometer (MISR) satellite data are used in conjunction with CERES, TRMM, and ERA-Interim reanalysis data to investigate horizontal and vertical high cloud structure, top-of-atmosphere (TOA) net cloud forcing and albedo, and dynamics relationships against local SST and precipitation as a function of the mean Tropical West Pacific (TWP; 120°E to 155°W; 30°S–30°N) SST. As the TWP warms, the SST mode (~29.5 °C) is constant, but the area of the mode grows, indicating increased kurtosis of SSTs and decreased SST gradients overall. This is associated with weaker low-level convergence and mid-tropospheric ascent (ω500) over the highest SSTs as the TWP warms, but also a broader area of weak ascent away from the deepest convection, albeit stronger when compared to when the mean TWP is cooler. These associated dynamics changes are collocated with less anvil and thick cloud cover over the highest SSTs and similar thin cold cloud fraction when the TWP is warmer, but broadly more anvil and cirrus clouds over lower local SSTs (SST < 27 °C). For all TWP SST quintiles, anvil cloud fraction, defined as clouds with tops > 9 km and TOA albedos between 0.3–0.6, is closely associated with rain rate, making it an excellent proxy for precipitation; but for a given heavier rain rate, cirrus clouds are more abundant with increasing domain-mean TWP SST. Clouds locally over SSTs between 29–30 °C have a much less negative net cloud forcing, up to 25 W m−2 greater, when the TWP is warm versus cool. When the local rain rate increases, while the net cloud fraction with tops < 9 km decreases, mid-level clouds (4 km < Ztop < 9 km) modestly increase. In contrast, combined low-level and mid-level clouds decrease as the domain-wide SST increases (−10% deg−1). More cirrus clouds for heavily precipitating systems exert a stronger positive TOA effect when the TWP is warmer, and anvil clouds over a higher TWP SST are less reflective and have a weaker cooling effect. For all precipitating systems, total high cloud cover increases modestly with higher TWP SST quintiles, and anvil + cirrus clouds are more expansive, suggesting more detrainment when TWP SSTs are higher. Total-domain anvil cloud fraction scales mostly with domain-mean ω500, but cirrus clouds mostly increase with domain-mean SST, invoking an explanation other than circulation. The overall thinning and greater top-heaviness of clouds over the TWP with warming are possible TWP positive feedbacks not previously identified. Full article
(This article belongs to the Special Issue MISR)
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19 pages, 12438 KiB  
Article
Evaluation of Environmental Influences on a Multi-Point Optical Fiber Methane Leak Monitoring System
by Claudio Floridia, Joao Batista Rosolem, João Paulo Vicentini Fracarolli, Fábio Renato Bassan, Rivael Strobel Penze, Larissa Maria Pereira and Maria Angélica Carmona da Motta Resende
Remote Sens. 2019, 11(10), 1249; https://doi.org/10.3390/rs11101249 - 27 May 2019
Cited by 8 | Viewed by 3671
Abstract
A novel system to monitor methane fugitive emissions was developed using passive optical sensors to attend to the natural gas production and transportation industry. The system is based on optical time domain reflectometry and direct optical absorption spectroscopy. The system was tested in [...] Read more.
A novel system to monitor methane fugitive emissions was developed using passive optical sensors to attend to the natural gas production and transportation industry. The system is based on optical time domain reflectometry and direct optical absorption spectroscopy. The system was tested in a gas compressor station for four months. The system was capable to measure methane concentration at two points showing its correlation with meteorological data, specially wind velocity and local temperature. Methane concentrations varied from 2.5% to 15% in the first monitored point by sensor 1, and from 5% to 30%, in the second point with sensor 2. Both sensors exhibited a moderate negative correlation with wind velocity with a mean Pearson coefficient of −0.61, despite the external cap designed to avoid the influence of wind. Sensor 2 had a modification to its external package that reduced this mean correlation coefficient to −0.30, considered to be weak to negligible. Regarding temperature, a moderate mean correlation of −0.59 was verified for sensor 1 and zero mean correlation was found for sensor 2. Based on these results the system was proven to be robust for installation in gas transportation or processing facilities. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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22 pages, 19582 KiB  
Article
Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds
by Yong Li, Dong Chen, Xiance Du, Shaobo Xia, Yuliang Wang, Sheng Xu and Qiang Yang
Remote Sens. 2019, 11(10), 1248; https://doi.org/10.3390/rs11101248 - 27 May 2019
Cited by 13 | Viewed by 3202
Abstract
This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In [...] Read more.
This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy. Full article
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18 pages, 5806 KiB  
Article
Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data
by Xue Liu, Alex de Sherbinin and Yanni Zhan
Remote Sens. 2019, 11(10), 1247; https://doi.org/10.3390/rs11101247 - 27 May 2019
Cited by 34 | Viewed by 8508
Abstract
Urbanization poses significant challenges on sustainable development, disaster resilience, climate change mitigation, and environmental and resource management. Accurate urban extent datasets at large spatial scales are essential for researchers and policymakers to better understand urbanization dynamics and its socioeconomic drivers and impacts. While [...] Read more.
Urbanization poses significant challenges on sustainable development, disaster resilience, climate change mitigation, and environmental and resource management. Accurate urban extent datasets at large spatial scales are essential for researchers and policymakers to better understand urbanization dynamics and its socioeconomic drivers and impacts. While high-resolution urban extent data products - including the Global Human Settlements Layer (GHSL), the Global Man-Made Impervious Surface (GMIS), the Global Human Built-Up and Settlement Extent (HBASE), and the Global Urban Footprint (GUF) - have recently become available, intermediate-resolution urban extent data products including the 1 km SEDAC’s Global Rural-Urban Mapping Project (GRUMP), MODIS 1km, and MODIS 500 m still have many users and have been demonstrated in a recent study to be more appropriate in urbanization process analysis (around 500 m resolution) than those at higher resolutions (30 m). The objective of this study is to improve large-scale urban extent mapping at an intermediate resolution (500 m) using machine learning methods through combining the complementary nighttime Visible Infrared Imaging Radiometer Suite (VIIRS) and daytime Moderate Resolution Imaging Spectroradiometer (MODIS) data, taking the conterminous United States (CONUS) as the study area. The effectiveness of commonly-used machine learning methods, including random forest (RF), gradient boosting machine (GBM), neural network (NN), and their ensemble (ESB), has been explored. Our results show that these machine learning methods can achieve similar high accuracies across all accuracy metrics (>95% overall accuracy, >98% producer’s accuracy, and >92% user’s accuracy) with Kappa coefficients greater than 0.90, which have not been achieved in the existing data products or by previous studies; the ESB is not able to produce significantly better accuracies than individual machine learning methods; the total misclassifications generated by GBM are more than those generated by RF, NN, and ESB by 14%, 16%, and 11%, respectively, with NN having the least total misclassifications. This indicates that using these machine learning methods, especially NN and RF, with the combination of VIIRS nighttime light and MODIS daytime normalized difference vegetation index (NDVI) data, high accuracy intermediate-resolution urban extent data products at large spatial scales can be achieved. The methodology has the potential to be applied to annual continental-to-global scale urban extent mapping at intermediate resolutions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)
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1 pages, 156 KiB  
Correction
Correction: Zhang, M., et al. Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China. Remote Sens. 2019, 11, 133
by Meng Zhang, Hui Lin, Guangxin Wang, Hua Sun and Yaotong Cai
Remote Sens. 2019, 11(10), 1246; https://doi.org/10.3390/rs11101246 - 27 May 2019
Cited by 2 | Viewed by 2370
Abstract
The authors wish to make the following corrections to this paper [...] Full article
16 pages, 6849 KiB  
Article
Comparison of Normalized Difference Vegetation Index Derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes Between 2002 and 2018
by Reyadh Albarakat and Venkataraman Lakshmi
Remote Sens. 2019, 11(10), 1245; https://doi.org/10.3390/rs11101245 - 25 May 2019
Cited by 52 | Viewed by 7193
Abstract
The Mesopotamian marshes are a group of water bodies located in southern Iraq, in the shape of a triangle, with the cities Amarah, Nasiriyah, and Basra located at its corners. The marshes are appropriate habitats for a variety of birds and most of [...] Read more.
The Mesopotamian marshes are a group of water bodies located in southern Iraq, in the shape of a triangle, with the cities Amarah, Nasiriyah, and Basra located at its corners. The marshes are appropriate habitats for a variety of birds and most of the commercial fisheries in the region. The normalized difference vegetation index (NDVI) has been derived using observations from various satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very-High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for the 17-year period between 2002 and 2018. We have chosen this time series (2002–2018) to monitor the change in vegetation of the study area since it is considered as a period of rehabilitation for the marshes (following a period when there was little to no water flowing into the marshes). Statistical analyses were performed to monitor the variability of the maximum biomass time (month of June). The results illustrated a strong positive correlation between the NDVI derived from Landsat, MODIS, and AVHRR. The statistical correlations were 0.79, 0.77, and 0.96 between Landsat and AVHRR, MODIS and AVHRR, and Landsat and MODIS, respectively. The linear slope of NDVI (Landsat, MODIS, and AVHRR) for each pixel over the period 2002–2018 displays a long-term trend of green biomass (NDVI) change in the study area, and the slope is slightly negative over most of the area. Slope values (−0.002 to −0.05) denote a slight decrease in the observed vegetation index over 17 years. The green biomass of the marshlands increased by 33.2% of the total area over 17 years. The areas of negative and positive slopes correspond to the same areas in slope map when calculated from Landsat, MODIS, and AVHRR, although they are different in spatial resolution (30 m, 1 km, and 5 km, respectively). The time series of the average NDVI (2002–2018) for three different sensors shows the highest and lowest NDVI values during the same years (for the month of June each year). The highest values were 0.19, 0.22, and 0.22 for Landsat, MODIS, and AVHRR, respectively, in 2006, and the lowest values were 0.09, 0.14, and 0.09 for Landsat, MODIS, and AVHRR, respectively, in 2003. Full article
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25 pages, 4707 KiB  
Article
UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat
by Adrian Gracia-Romero, Shawn C. Kefauver, Jose A. Fernandez-Gallego, Omar Vergara-Díaz, María Teresa Nieto-Taladriz and José L. Araus
Remote Sens. 2019, 11(10), 1244; https://doi.org/10.3390/rs11101244 - 25 May 2019
Cited by 75 | Viewed by 8039
Abstract
Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial [...] Read more.
Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red–green–blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield. Full article
(This article belongs to the Special Issue Advances of Multi-Temporal Remote Sensing in Vegetation and Agriculture Research)
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20 pages, 9161 KiB  
Article
A Multi-Primitive-Based Hierarchical Optimal Approach for Semantic Labeling of ALS Point Clouds
by Xuming Ge, Bo Wu, Yuan Li and Han Hu
Remote Sens. 2019, 11(10), 1243; https://doi.org/10.3390/rs11101243 - 24 May 2019
Cited by 8 | Viewed by 4009
Abstract
There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and [...] Read more.
There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and the third is to introduce a classifier to label the point clouds. This paper investigates multiple primitives to effectively represent scenes and exploit their geometric relationships. Relationships are graded according to the properties of related primitives. Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. The proposed approach was tested using two sets of representative ALS point clouds, namely the Vaihingen datasets and Hong Kong’s Central District dataset. The results were compared with those generated by other typical methods in previous work. Quantitative assessments for the two experimental datasets showed that the performance of the proposed approach was superior to reference methods in both datasets. The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. The results reveal that our approach of labeling different classes in terms of ALS point clouds is robust and bears significance for future applications, such as 3D modeling and change detection from point clouds. Full article
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
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9 pages, 1298 KiB  
Communication
Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars
by Giovanni Avola, Salvatore Filippo Di Gennaro, Claudio Cantini, Ezio Riggi, Francesco Muratore, Calogero Tornambè and Alessandro Matese
Remote Sens. 2019, 11(10), 1242; https://doi.org/10.3390/rs11101242 - 24 May 2019
Cited by 44 | Viewed by 4793
Abstract
The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance [...] Read more.
The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green520–600, red630–690 and near-infrared760–900 bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis—PCA and linear discriminant analysis—LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases. Full article
(This article belongs to the Special Issue Remote Sensing for Agroforestry)
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28 pages, 25877 KiB  
Article
An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos
by Jing Li, Shuo Chen, Fangbing Zhang, Erkang Li, Tao Yang and Zhaoyang Lu
Remote Sens. 2019, 11(10), 1241; https://doi.org/10.3390/rs11101241 - 24 May 2019
Cited by 40 | Viewed by 6921
Abstract
With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic [...] Read more.
With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV. Then, we use the deep learning detection algorithm to detect the vehicle in the UAV field of view and obtain the trajectory in the image through the tracking-by-detection algorithm. Thereafter, we present a motion compensation method based on homography. This method obtains matching feature points by an optical flow method and eliminates the influence of the detected target to accurately calculate the homography matrix to determine the real motion trajectory in the current frame. Finally, vehicle speed is estimated based on the mapping relationship between the pixel distance and the actual distance. The method regards the actual size of the car as prior information and adaptively recovers the pixel scale by estimating the vehicle size in the image; it then calculates the vehicle speed. In order to evaluate the performance of the proposed system, we carry out a large number of experiments on the AirSim Simulation platform as well as real UAV aerial surveillance experiments. Through quantitative and qualitative analysis of the simulation results and real experiments, we verify that the proposed system has a unique ability to detect, track, and estimate the speed of ground vehicles simultaneously even with a single downward-looking camera. Additionally, the system can obtain effective and accurate speed estimation results, even in various complex scenes. Full article
(This article belongs to the Special Issue Drone Remote Sensing)
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24 pages, 661 KiB  
Review
Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review
by Max Gerhards, Martin Schlerf, Kaniska Mallick and Thomas Udelhoven
Remote Sens. 2019, 11(10), 1240; https://doi.org/10.3390/rs11101240 - 24 May 2019
Cited by 183 | Viewed by 15779
Abstract
Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor [...] Read more.
Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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22 pages, 6698 KiB  
Article
Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter
by Yang Song and Jinfei Wang
Remote Sens. 2019, 11(10), 1239; https://doi.org/10.3390/rs11101239 - 24 May 2019
Cited by 39 | Viewed by 5902
Abstract
Plant height can be used as an indicator to estimate crop phenology and biomass. The Unmanned Aerial Vehicle (UAV)-based point cloud data derived from photogrammetry methods contains the structural information of crops which could be used to retrieve crop height. However, removing noise [...] Read more.
Plant height can be used as an indicator to estimate crop phenology and biomass. The Unmanned Aerial Vehicle (UAV)-based point cloud data derived from photogrammetry methods contains the structural information of crops which could be used to retrieve crop height. However, removing noise and outliers from the UAV-based crop point cloud data for height extraction is challenging. The objective of this paper is to develop an alternative method for canopy height determination from UAV-based 3D point cloud datasets using a statistical analysis method and a moving cuboid filter to remove outliers. In this method, first, the point cloud data is divided into many 3D columns. Secondly, a moving cuboid filter is applied in each column and moved downward to eliminate noise points. The threshold of point numbers in the filter is calculated based on the distribution of points in the column. After applying the moving cuboid filter, the crop height is calculated from the highest and lowest points in each 3D column. The proposed method achieved high accuracy for height extraction with low Root Mean Square Error (RMSE) of 6.37 cm and Mean Absolute Error (MAE) of 5.07 cm. The canopy height monitoring window for winter wheat using this method starts from the beginning of the stem extension stage to the end of the heading stage (BBCH 31 to 65). Since the height of wheat has limited change after the heading stage, this method could be used to retrieve the crop height of winter wheat. In addition, this method only requires one operation of UAV in the field. It could be an effective method that can be widely used to help end-user to monitor their crops and support real-time decision making for farm management. Full article
(This article belongs to the Special Issue Advances of Multi-Temporal Remote Sensing in Vegetation and Agriculture Research)
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22 pages, 7074 KiB  
Article
Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
by Giandomenico De Luca, João M. N. Silva, Sofia Cerasoli, João Araújo, José Campos, Salvatore Di Fazio and Giuseppe Modica
Remote Sens. 2019, 11(10), 1238; https://doi.org/10.3390/rs11101238 - 24 May 2019
Cited by 102 | Viewed by 12908
Abstract
This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central [...] Read more.
This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands. Full article
(This article belongs to the Special Issue UAV Applications in Forestry)
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15 pages, 1898 KiB  
Article
A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar
by Hyunjae Lee, Byung-Hyun Kim, Jin-Kwan Park and Jong-Gwan Yook
Remote Sens. 2019, 11(10), 1237; https://doi.org/10.3390/rs11101237 - 24 May 2019
Cited by 84 | Viewed by 8465
Abstract
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs [...] Read more.
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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16 pages, 2464 KiB  
Article
Characterizing the Variability of the Structure Parameter in the PROSPECT Leaf Optical Properties Model
by Erik J. Boren, Luigi Boschetti and Dan M. Johnson
Remote Sens. 2019, 11(10), 1236; https://doi.org/10.3390/rs11101236 - 24 May 2019
Cited by 17 | Viewed by 4150
Abstract
Radiative transfer model (RTM) inversion allows for the quantitative estimation of vegetation biochemical composition from satellite sensor data, but large uncertainties associated with inversion make accurate estimation difficult. The leaf structure parameter (Ns) is one of the largest sources of [...] Read more.
Radiative transfer model (RTM) inversion allows for the quantitative estimation of vegetation biochemical composition from satellite sensor data, but large uncertainties associated with inversion make accurate estimation difficult. The leaf structure parameter (Ns) is one of the largest sources of uncertainty in inversion of the widely used leaf-level PROSPECT model, since it is the only parameter that cannot be directly measured. In this study, we characterize Ns as a function of phenology by collecting an extensive dataset of leaf measurements from samples of three dicotyledon species (hard red wheat, soft white wheat, and upland rice) and one monocotyledon (soy), grown under controlled conditions over two full growth seasons. A total of 230 samples were collected: measured leaf reflectance and transmittance were used to estimate Ns from each sample. These experimental data were used to investigate whether Ns depends on phenological stages (early/mid/late), and/or irrigation regime (irrigation at 85%, 75%, 60% of the initial saturated tray weight, and pre-/post-irrigation). The results, supported by the extensive experimental data set, indicate a significant difference between Ns estimated on monocotyledon and dicotyledon plants, and a significant difference between Ns estimated at different phenological stages. Different irrigation regimes did not result in significant Ns differences for either monocotyledon or dicotyledon plant types. To our knowledge, this study provides the first systematic record of Ns as a function of phenology for common crop species. Full article
(This article belongs to the Section Forest Remote Sensing)
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21 pages, 4208 KiB  
Article
Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive
by Aaron M. Shew and Aniruddha Ghosh
Remote Sens. 2019, 11(10), 1235; https://doi.org/10.3390/rs11101235 - 24 May 2019
Cited by 26 | Viewed by 7508
Abstract
In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for [...] Read more.
In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes. Full article
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15 pages, 5123 KiB  
Article
Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
by Lihong Zhu, Xiangnan Liu, Ling Wu, Yibo Tang and Yuanyuan Meng
Remote Sens. 2019, 11(10), 1234; https://doi.org/10.3390/rs11101234 - 24 May 2019
Cited by 59 | Viewed by 7872
Abstract
Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for [...] Read more.
Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or to poplar cultivation areas. This study characterized the resulting long-term (1998–2018) change patterns using the LandTrendr algorithm with Landsat time-series data derived from the Google Earth Engine (GEE). Of the total cropland affected, ~447.48 km2 was converted to lake and 499.9 km2 was converted to poplar cultivation, with overall accuracies of 87.0% and 83.8%, respectively. The former covered a wider range, mainly distributed in the area surrounding Datong Lake, while the latter was more clustered in North and West Dongting Lake. Our methods based on GEE captured cropland change information efficiently, providing data (raster maps, yearly data, and change attributes) that can assist researchers and managers in gaining a better understanding of environmental influences related to the ongoing conversion efforts in this region. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing for Agricultural Monitoring in the Big Data Era)
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20 pages, 6887 KiB  
Article
The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery
by Przemysław Kupidura
Remote Sens. 2019, 11(10), 1233; https://doi.org/10.3390/rs11101233 - 24 May 2019
Cited by 101 | Viewed by 10112
Abstract
The paper presents a comparison of the efficacy of several texture analysis methods as tools for improving land use/cover classification in satellite imagery. The tested methods were: gray level co-occurrence matrix (GLCM) features, Laplace filters and granulometric analysis, based on mathematical morphology. The [...] Read more.
The paper presents a comparison of the efficacy of several texture analysis methods as tools for improving land use/cover classification in satellite imagery. The tested methods were: gray level co-occurrence matrix (GLCM) features, Laplace filters and granulometric analysis, based on mathematical morphology. The performed tests included an assessment of the classification accuracy performed based on spectro-textural datasets: spectral images with the addition of images generated using different texture analysis methods. The class nomenclature was based on spectral and textural differences and included the following classes: water, low vegetation, bare soil, urban, and two (coniferous and deciduous) forest classes. The classification accuracy was assessed using the overall accuracy and kappa index of agreement, based on the reference data generated using visual interpretation of the images. The analysis was performed using very high-resolution imagery (Pleiades, WorldView-2) and high-resolution imagery (Sentinel-2). The results show the efficacy of selected GLCM features and granulometric analysis as tools for providing textural data, which could be used in the process of land use/cover classification. It is also clear that texture analysis is generally a more important and effective component of classification for images of higher resolution. In addition, for classification using GLCM results, the Random Forest variable importance analysis was performed. Full article
(This article belongs to the Special Issue Remote Sensing and GIS for Environmental Analysis and Cultural Heritage)
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16 pages, 4559 KiB  
Article
Characterization of Electromagnetic Properties of In Situ Soils for the Design of Landmine Detection Sensors: Application in Donbass, Ukraine
by Timothy Bechtel, Stanislav Truskavetsky, Gennadiy Pochanin, Lorenzo Capineri, Alexander Sherstyuk, Konstantin Viatkin, Tatyana Byndych, Vadym Ruban, Liudmyla Varyanitza-Roschupkina, Oleksander Orlenko, Pavlo Kholod, Pierluigi Falorni, Andrea Bulletti, Luca Bossi and Fronefield Crawford
Remote Sens. 2019, 11(10), 1232; https://doi.org/10.3390/rs11101232 - 24 May 2019
Cited by 13 | Viewed by 4911
Abstract
To design holographic and impulse ground penetrating radar (GPR) sensors suitable for humanitarian de-mining in the Donbass (Ukraine) conflict zone, we measured critical electromagnetic parameters of typical local soils using simple methods that could be adapted to any geologic setting. Measurements were recorded [...] Read more.
To design holographic and impulse ground penetrating radar (GPR) sensors suitable for humanitarian de-mining in the Donbass (Ukraine) conflict zone, we measured critical electromagnetic parameters of typical local soils using simple methods that could be adapted to any geologic setting. Measurements were recorded along six profiles, each crossing at least two mapped soil types. The parameters selected to evaluate GPR and metal detector sensor performance were magnetic permeability, electrical conductivity, and dielectric permittivity. Magnetic permeability measurements indicated that local soils would be conducive to metal detector performance. Electrical conductivity measurements indicated that local soils would be medium to high loss materials for GPR. Calculation of the expected attenuation as a function of signal frequency suggested that 1 GHz may have optimized the trade-off between resolution and penetration and matched the impulse GPR system power budget. Dielectric permittivity was measured using both time domain reflectometry and impulse GPR. For the latter, a calibration procedure based on an in-situ measurement of reflection coefficient was proposed and the data were analyzed to show that soil conditions were suitable for the reliable use of impulse GPR. A distinct difference between the results of these two suggested a dry (low dielectric) soil surface, grading downward into more moist (higher dielectric) soils. This gradation may provide a matching layer to reduce ground surface reflections that often obscure shallow subsurface targets. In addition, the relatively high dielectric deeper (10 cm–20 cm) subsurface soils should provide a strong contrast with plastic-cased mines. Full article
(This article belongs to the Special Issue Recent Progress in Ground Penetrating Radar Remote Sensing)
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15 pages, 2549 KiB  
Article
Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models
by Linyi Li, Yun Chen, Tingbao Xu, Kaifang Shi, Rui Liu, Chang Huang, Binbin Lu and Lingkui Meng
Remote Sens. 2019, 11(10), 1231; https://doi.org/10.3390/rs11101231 - 24 May 2019
Cited by 10 | Viewed by 3387
Abstract
Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method [...] Read more.
Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands. Full article
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19 pages, 7838 KiB  
Article
A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images
by Jinsong Deng, Yibo Huang, Binjie Chen, Cheng Tong, Pengbo Liu, Hongquan Wang and Yang Hong
Remote Sens. 2019, 11(10), 1230; https://doi.org/10.3390/rs11101230 - 23 May 2019
Cited by 42 | Viewed by 6768
Abstract
Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A [...] Read more.
Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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21 pages, 2306 KiB  
Article
Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation
by Jing Hu, Minghua Zhao and Yunsong Li
Remote Sens. 2019, 11(10), 1229; https://doi.org/10.3390/rs11101229 - 23 May 2019
Cited by 22 | Viewed by 4234 | Correction
Abstract
Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we [...] Read more.
Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method. Full article
(This article belongs to the Special Issue Image Processing and Spatial Neighbourhoods for Remote Sensing Data Analysis)
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19 pages, 5858 KiB  
Article
Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land
by Ying Wu, Bo Qian, Yansong Bao, Meixin Li, George P. Petropoulos, Xulin Liu and Lin Li
Remote Sens. 2019, 11(10), 1228; https://doi.org/10.3390/rs11101228 - 23 May 2019
Cited by 5 | Viewed by 3374
Abstract
A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the [...] Read more.
A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals. Full article
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