Special Issue "Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2020).

Special Issue Editor

Prof. Dr. John Trinder
Website
Guest Editor
School of Civil and Environmental Engineering, University of New South Wales (UNSW) Australia, Sydney, NSW 2052, Australia
Interests: 3D information extraction from image, SAR and lidar data; environmental assessment of urban areas using remote sensing data
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Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is a unique technology that provides new opportunities for the interpretation of remote sensing data derived from space and airborne (piloted and remote piloted) data acquisition systems. In particular, the increasing number of satellites equipped with SAR data acquisition systems that are being launched with a range of characteristics are enabling a better understanding of the earth’s environment, such as for vegetation analysis, forest inventories, land subsidence, and urban analysis. As well, airborne systems for remote piloted systems and ground-based systems are available. This Special Issue is intended to present high-quality scientific review papers of existing achievements in the development of SAR technologies and applications of SAR, or research papers that describe new developments of SAR technologies, including wide band multi-polarized arrays; digital beamforming; bistatic and multi-static modes;  improved methods of interpretation of SAR data for soil moisture monitoring, crop growth, forest biomass and forest characteristics, and urban analysis; as well as improved methods interferometry and integrated polarimetry and differential interferometry for elevation determinations and land subsidence. In car SAR systems for vehicle navigation will also be included in this Special Issue.

Prof. Dr. John Trinder
Guest Editor

Manuscript Submission Information

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Keywords

  • SAR technologies
  • SAR remote sensing
  • SAR polarimetry
  • Biomass measurement
  • Vegetation monitoring
  • Forest characteristics

Published Papers (7 papers)

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Editorial

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Open AccessEditorial
Editorial for Special Issue “Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis”
Remote Sens. 2020, 12(15), 2428; https://doi.org/10.3390/rs12152428 - 29 Jul 2020
Abstract
Synthetic aperture radar (SAR) imaging systems derive microwave data, from space or airborne (piloted and remote piloted), that provide opportunities for the interpretation of many characteristics of the terrain surface. The increasing number of satellites equipped with SAR data acquisition systems that are [...] Read more.
Synthetic aperture radar (SAR) imaging systems derive microwave data, from space or airborne (piloted and remote piloted), that provide opportunities for the interpretation of many characteristics of the terrain surface. The increasing number of satellites equipped with SAR data acquisition systems that are being launched with a range of wavelengths, polarizations, and operating characteristics are enabling a better understanding of the earth’s environment, for such activities as vegetation analysis, forest inventories, land subsidence, and urban analysis. In addition, airborne systems for remote piloted systems and ground-based systems are available. This Special Issue presents six quality scientific papers on typical applications of SAR technologies. They include methods for the determination of above ground biomass (AGB), crop mapping using data from an advanced X-band system developed in Japan, analysis of natural and human-induced slow-rate ground deformations in the region of Campania, in Italy, the location of landslides caused by natural phenomena based on SAR images derived from the Japanese high-resolution Advanced Land Observing Satellite-2 (ALOS-2), and monitoring the size of refugee camps and their environmental impacts caused by the displacement of people from Myanmar to the Cox’s Bazar District, around Kutupalong, in Bangladesh. The paper concludes with some comments on the future directions of developments in SAR systems. Full article

Research

Jump to: Editorial

Open AccessArticle
Analysis and Classification of Natural and Human-Induced Ground Deformations at Regional Scale (Campania, Italy) Detected by Satellite Synthetic-Aperture Radar Interferometry Archive Datasets
Remote Sens. 2019, 11(23), 2822; https://doi.org/10.3390/rs11232822 - 28 Nov 2019
Cited by 3
Abstract
The high levels of geo-hydrological, seismic, and volcanic hazards in the Campania region prompted full data collection from C-band satellites ERS-1/2, ENVISAT, and RADARSAT within regional (TELLUS) and national (PST-A) projects. The quantitative analysis, interpretation, and classification of natural and human-induced slow-rate ground [...] Read more.
The high levels of geo-hydrological, seismic, and volcanic hazards in the Campania region prompted full data collection from C-band satellites ERS-1/2, ENVISAT, and RADARSAT within regional (TELLUS) and national (PST-A) projects. The quantitative analysis, interpretation, and classification of natural and human-induced slow-rate ground deformations across a span of two decades (1992–2010) was performed at regional scale (Campania, Italy) by using interferometric archive datasets, based on the Persistent Scatterer Interferometry approach. As radar satellite sensors have a side-looking view, the post-processing of the interferometric datasets allows for the evaluation of two spatial components (vertical and E-W horizontal ones) of ground deformation, while the N-S horizontal component cannot be detected. The ground deformation components have been analyzed across 89.5% of the Campania territory within a variety of environmental, topographical, and geological conditions. The main part (57%) of the regional territory was characterized during 1992–2010 by stable areas, where SAR signals do not have recorded significant horizontal and vertical components of ground deformation with an average annual rate greater than +1 mm/yr or lower than −1 mm/yr. Within the deforming areas, the coastal plains are characterized by widespread and continuous strong subsidence signals due to sediment compaction locally enhanced by human activity, while the inner plain sectors show mainly scattered spots with locally high subsidence in correspondence of urban areas, sinkholes, and groundwater withdrawals. The volcanic sectors show interplaying horizontal and vertical trends due to volcano-tectonic processes, while in the hilly and mountain inner sectors the ground deformation is mainly controlled by large-scale tectonic activity and by local landslide activity. The groundwater-related deformation is the dominant cause of human-caused ground deformation. The results confirm the importance of using Persistent Scatterer Interferometry data for a comprehensive understanding of rates and patterns of recent ground deformation at regional scale also within tectonically active areas as in Campania region. Full article
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Open AccessArticle
Study on the Intensity and Coherence Information of High-Resolution ALOS-2 SAR Images for Rapid Massive Landslide Mapping at a Pixel Level
Remote Sens. 2019, 11(23), 2808; https://doi.org/10.3390/rs11232808 - 27 Nov 2019
Cited by 2
Abstract
A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick [...] Read more.
A rapid mapping of landslides following a disaster is important for coordinating emergency response and limiting rescue delays. A synthetic aperture radar (SAR) can provide a solution even in harsh weather and at night, due to its independence of weather and light, quick response, no contact and broad coverage. This study aimed to conduct a comprehensive exploration on the intensity and coherence information of three Advanced Land Observing Satellite-2 (ALOS-2) SAR images, for rapid massive landslide mapping in a pixel level, in order to provide a reference for future applications. Applied data were two pre-event and one post-event high-resolution ALOS-2 products. Studied area was in the east of Iburi, Hokkaido, Japan, where massive shallow landslides were triggered in the 2018 Hokkaido Eastern Iburi Earthquake. Potential parameters, including intensity difference (d), co-event correlation coefficient (r), correlation coefficient difference ( r ), co-event coherence ( γ ), and coherence difference ( γ ), were first selected and calculated based on a radar reflection mechanism, to facilitate rapid detection. Qualitative observation was then performed by overlapping ground truth landslides to calculated parameter images. Based on qualitative observation, an absolute value of d ( d a b s 1 ) was applied to facility analyses, and a new parameter ( d a b s 2 ) was proposed to avoid information loss in the calculation. After that, quantitative analyses of the six parameters ( d a b s 1 , d a b s 2 , r, r , γ and γ ) were performed by receiver operating characteristic. d a b s 2 and r were found to be favorable parameters, which had the highest AUC values of 0.82 and 0.75, and correctly classified 69.36% and 64.57% landslide and non-landslide pixels by appropriate thresholds. Finally, a discriminant function was developed, combining three relatively favorable parameters ( d a b s 2 , r , and γ ) with one in each type, and achieved an overall accuracy of 74.31% for landslide mapping. Full article
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Open AccessArticle
PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation
Remote Sens. 2019, 11(19), 2287; https://doi.org/10.3390/rs11192287 - 30 Sep 2019
Cited by 3
Abstract
Polarimetric synthetic aperture radar (PolSAR) remote sensing has been widely used for forest mapping and monitoring. PolSAR data has the capability to provide scattering information that is contributed by different scatterers within a single SAR resolution cell. A methodology for a PolSAR-based extended [...] Read more.
Polarimetric synthetic aperture radar (PolSAR) remote sensing has been widely used for forest mapping and monitoring. PolSAR data has the capability to provide scattering information that is contributed by different scatterers within a single SAR resolution cell. A methodology for a PolSAR-based extended water cloud model (EWCM) has been proposed and evaluated in this study. Fully polarimetric phased array type L-band synthetic aperture radar (PALSAR) data of advanced land observing satellite (ALOS) was used in this study for forest aboveground biomass (AGB) retrieval of Dudhwa National Park, India. The shift in the polarization orientation angle (POA) is a major problem that affects the PolSAR-based scattering information. The two sources of POA shift are Faraday rotation angle (FRA) and structural properties of the scatterer. Analysis was carried out to explore the effect of FRA in the SAR data. Deorientation of PolSAR data was implemented to minimize any ambiguity in the scattering retrieval of model-based decomposition. After POA compensation of the coherency matrix, a decrease in the power of volume scattering elements was observed for the forest patches. This study proposed a framework to extend the water cloud model for AGB retrieval. The proposed PolSAR-based EWCM showed less dependency on field data for model parameters retrieval. The PolSAR-based scattering was used as input model parameters to derive AGB for the forest area. Regression between PolSAR-decomposition-based volume scattering and AGB was performed. Without deorientation of the PolSAR coherency matrix, EWCM showed a modeled AGB of 92.90 t ha−1, and a 0.36 R2 was recorded through linear regression between the field-measured AGB and the modeled output. After deorientation of the PolSAR data, an increased R2 (0.78) with lower RMSE (59.77 t ha−1) was obtained from EWCM. The study proves the potential of a PolSAR-based semiempirical model for forest AGB retrieval. This study strongly recommends the POA compensation of the coherency matrix for PolSAR-scattering-based semiempirical modeling for forest AGB retrieval. Full article
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Open AccessArticle
Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data
Remote Sens. 2019, 11(18), 2105; https://doi.org/10.3390/rs11182105 - 09 Sep 2019
Cited by 1
Abstract
Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support [...] Read more.
Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects. Full article
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Open AccessArticle
Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2
Remote Sens. 2019, 11(17), 2047; https://doi.org/10.3390/rs11172047 - 30 Aug 2019
Cited by 6
Abstract
Approximately one million refugees of the Rohingya minority population in Myanmar crossed the border to Bangladesh on 25 August 2017, seeking shelter from systematic oppression and persecution. This led to a dramatic expansion of the Kutupalong refugee camp within a couple of months [...] Read more.
Approximately one million refugees of the Rohingya minority population in Myanmar crossed the border to Bangladesh on 25 August 2017, seeking shelter from systematic oppression and persecution. This led to a dramatic expansion of the Kutupalong refugee camp within a couple of months and a decrease of vegetation in the surrounding forests. As many humanitarian organizations demand frameworks for camp monitoring and environmental impact analysis, this study suggests a workflow based on spaceborne radar imagery to measure the expansion of settlements and the decrease of forests. Eleven image pairs of Sentinel-1 and ALOS-2, as well as a digital elevation model, were used for a supervised land cover classification. These were trained on automatically-derived reference areas retrieved from multispectral images to reduce required user input and increase transferability. Results show an overall decrease of vegetation of 1500 hectares, of which 20% were used to expand the camp and 80% were deforested, which matches findings from other studies of this case. The time-series analysis reduced the impact of seasonal variations on the results, and accuracies between 88% and 95% were achieved. The most important input variables for the classification were vegetation indices based on synthetic aperture radar (SAR) backscatter intensity, but topographic parameters also played a role. Full article
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Open AccessArticle
Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping
Remote Sens. 2019, 11(16), 1920; https://doi.org/10.3390/rs11161920 - 16 Aug 2019
Cited by 6
Abstract
The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of [...] Read more.
The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM. Full article
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