scholarly journals Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

2019 ◽  
Vol 11 (5) ◽  
pp. 516 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Newfoundland And Labrador ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Conservation Strategies ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Discrimination Capability ◽  
Sar Data

Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2.

scholarly journals Aspect Entropy Extraction Using Circular SAR Data and Scattering Anisotropy Analysis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 346 ◽  
Author(s):  
Fei Teng ◽  
Wen Hong ◽  
Yun Lin
Keyword(s):  
Anisotropic Scattering ◽  
Synthetic Aperture ◽  
Viewing Angle ◽  
Denoising Method ◽  
Discrimination Capability ◽  
X Band ◽  
Different Types ◽  
Degree Of Anisotropy ◽  
Sar Data ◽  
Scattering Anisotropy

In conventional synthetic aperture radar (SAR) working modes, targets are assumed isotropic because the viewing angle is small. However, most man-made targets are anisotropic. Therefore, anisotropy should be considered when the viewing angle is large. From another perspective, anisotropy is also a useful feature. Circular SAR (CSAR) can detect the scattering variation under different azimuthal look angles by a 360-degree observation. Different targets usually have varying degrees of anisotropy, which aids in target discrimination. However, there is no effective method to quantify the degree of anisotropy. In this paper, aspect entropy is presented as a descriptor of the scattering anisotropy. The range of aspect entropy is from 0 to 1, which corresponds to anisotropic to isotropic. First, the method proposed extracts aspect entropy at the pixel level. Since the aspect entropy of pixels can discriminate isotropic and anisotropic scattering, the method prescreens the target from the isotropic clutters. Next, the method extracts aspect entropy at the target level. The aspect entropy of targets can discriminate between different types of targets. Then, the effect of noise on aspect entropy extraction is analyzed and a denoising method is proposed. The Gotcha public release dataset, an X-band circular SAR data, is used to validate the method and the discrimination capability of aspect entropy.

Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada

2017 ◽  
Vol 43 (4) ◽  
pp. 360-373 ◽  
Author(s):  
Meisam Amani ◽  
Bahram Salehi ◽  
Sahel Mahdavi ◽  
Jean Elizabeth Granger ◽  
Brian Brisco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Newfoundland And Labrador ◽  
Remote Sensing Data ◽  
Wetland Classification ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

scholarly journals Comparison Between Surface Melt Days Estimation from a Regional Climate Model and Near-Daily Synthetic Aperture Radar Backscattering

2020 ◽  
Author(s):  
Quentin Glaude ◽  
Christoph Kittel
Keyword(s):  
Remote Sensing ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Polar Regions ◽  
Radar Backscattering ◽  
Surface Melt ◽  
Wide Swath

<p> </p><p>Remote sensing has long been used as a powerful tool for the observation in cryospheric sciences. With the advances brought by the ESA Copernicus program, Earth observation goes a step further in its ability to get acquisitions at very high temporal rate. This is even amplified in polar regions due to heliosynchronism of satellites’ orbits. Earth observation shifts from sporadic observations to Earth monitoring.</p><p>Observations are a critical aspect for the assessment of geophysical models. The ability of a model to replicate observations is crucial as a benchmark. It also allows to refine our comprehension of Earth systems, such as in cryospheric sciences.</p><p>In this work, we are using the regional climate model MAR to compute the surface melt on a domain focusing on the Roi Baudouin Ice Shelf, Queen Maud Land, East Antarctica. From the results, we extract the number of days with surface melt in a region. In parallel, we employ remote sensing to obtain comparison data. Synthetic aperture radar appears as a solution of choice thanks to its day-and-night (critical in polar regions) and atmospheric-free capabilities. Radar backscattering anomalies between different dates are witnesses of substantial increase of soil moisture. Using Sentinel-1 in its wide-swath modes (namely Interferometric Wide Swath and Extra Wide Swath modes) and multiple satellite paths, near-daily acquisitions can be obtained. By comparing the two independent results, we better constraint model’s outputs while also better interpret SAR acquisitions. </p>

scholarly journals Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

2021 ◽  
Vol 13 (20) ◽  
pp. 4025
Author(s):  
S. Mohammad Mirmazloumi ◽  
Armin Moghimi ◽  
Babak Ranjgar ◽  
Farzane Mohseni ◽  
Arsalan Ghorbanian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Meta Analysis ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Sensing Technology ◽  
Status And Trends ◽  
The Status ◽  
Supervised Classifiers

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.

scholarly journals SAR interferometry with the RADARSAT Constellation Mission

2022 ◽  
Author(s):  
J P Dudley ◽  
S V Samsonov
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Earth Observation ◽  
Deformation Monitoring ◽  
Automated System ◽  
Synthetic Aperture ◽  
High Temporal Resolution ◽  
Sar Data ◽  
The Impact ◽  
Aperture Radar

The RADARSAT Constellation Mission (RCM) is Canada's latest system of C-band Synthetic Aperture Radar (SAR) Earth observation satellites. The system of three satellites, spaced equally in a common orbit, allows for a rapid four-day repeat interval. The RCM has been designed with a selection of stripmap, spotlight, and ScanSAR beam modes which offer varied combinations of spatial resolution and coverage. Using Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, the growing archive of SAR data gathered by RCM can be used for change detection and ground deformation monitoring for diverse applications in Canada and around the world. In partnership with the Canadian Space Agency (CSA), the Canada Centre for Mapping and Earth Observation (CCMEO) has developed an automated system for generating standard and advanced deformation products and change detection from SAR data acquired by RCM and RADARSAT-2 satellites using DInSAR processing methodology. Using this system, this paper investigates four key interferometric properties of the RCM system which were not available on the RADARSAT-1 or RADARSAT-2 missions: The impact of the high temporal resolution of the four-day repeat cycle of the RCM on temporal decorrelation trends is tested and fitted against simple temporal decay models. The effect of the normalization and the precision of the radiometric calibration on interferometric spatial coherence is investigated. The performance of the RCM ScanSAR mode for wide area interferometric analysis is tested. The performance of the novel RCM Compact-polarization (CP) mode for interferometric analysis is also investigated.

scholarly journals Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1098 ◽  
Author(s):  
Motofumi Arii ◽  
Hiroyoshi Yamada ◽  
Shoichiro Kojima ◽  
Masato Ohki
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Model Simulation ◽  
Synthetic Aperture ◽  
Decomposition Techniques ◽  
Scattering Mechanism ◽  
Radar Backscatter ◽  
Polarimetric Synthetic Aperture Radar ◽  
Scattering Mechanisms ◽  
Sar Data

In a field of polarimetric synthetic aperture radar (SAR) remote sensing, various kinds of polarimetric decomposition techniques have been proposed. However, poor validations prevent them from operational applications. A true composition ratio of scattering mechanisms within a radar backscatter plays a key role. To overcome the issue, a novel comprehensive SAR approach to accurately identify a contribution of each scattering mechanism has been introduced. This is based on multiparametric SAR observation combined with a numerical model simulation. In this article, a comprehensive SAR approach is concisely reviewed to accelerate the research in this field. First, popular model-based polarimetric decompositions are introduced and their limitations are shown. Then, a behavior of scattering mechanisms is analyzed by the discrete scatterer model with some results using real multiparametric SAR data. A comprehensive SAR approach must be essential to realize an operational use of polarimetric SAR data.

scholarly journals Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1756 ◽  
Author(s):  
Xiaojie Liu ◽  
Chaoying Zhao ◽  
Qin Zhang ◽  
Jianbing Peng ◽  
Wu Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Landslide Inventory ◽  
Synthetic Aperture ◽  
Small Scale ◽  
Alos Palsar ◽  
Multi Temporal ◽  
Loess Landslides ◽  
L Band ◽  
Sar Data

The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

scholarly journals Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (21) ◽  
pp. 4444
Author(s):  
Canran Tu ◽  
Peng Li ◽  
Zhenhong Li ◽  
Houjie Wang ◽  
Shuowen Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Coastal Wetlands ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Wetland Classification

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

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