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

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 fulland 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.

[1]  Heather McNairn,et al.  The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Timo Balz,et al.  Rational function modeling for spaceborne SAR datasets , 2011 .

[3]  Mohammed Dabboor,et al.  Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization , 2018, Remote. Sens..

[4]  Megan W. Lang,et al.  Remote Sensing of Wetlands : Applications and Advances , 2015 .

[5]  Juan M. Lopez-Sanchez,et al.  Time Series of Hybrid-Polarity Parameters Over Agricultural Crops , 2012, IEEE Geoscience and Remote Sensing Letters.

[6]  Brian Brisco,et al.  Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands , 2018 .

[7]  E. Pottier,et al.  Polarimetric Radar Imaging: From Basics to Applications , 2009 .

[8]  Brian Brisco,et al.  An Assessment of Simulated Compact Polarimetric SAR Data for Wetland Classification Using Random Forest Algorithm , 2017 .

[9]  Yun Zhang,et al.  Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Nicolas Baghdadi,et al.  Evaluation of C-band SAR data for wetlands mapping , 2001 .

[11]  Hurtado Abril,et al.  Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat. , 2020 .

[12]  Anna Wendleder,et al.  TerraSAR-X and Wetlands: A Review , 2018, Remote. Sens..

[13]  R. Raney,et al.  The m‐chi decomposition of hybrid dual‐polarimetric radar data with application to lunar craters , 2012 .

[14]  Emanuele Santi,et al.  Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation , 2013 .

[15]  Alisa L. Gallant,et al.  The Challenges of Remote Monitoring of Wetlands , 2015, Remote. Sens..

[16]  Zhaohua Chen,et al.  Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic , 2017, Remote. Sens..

[17]  Brian Brisco,et al.  An efficient feature optimization for wetland mapping by synergistic use of SAR intensity, interferometry, and polarimetry data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Mohammed Dabboor,et al.  Towards sea ice classification using simulated RADARSAT Constellation Mission compact polarimetric SAR imagery , 2014 .

[19]  Stuart R. Phinn,et al.  Floodplain inundation and vegetation dynamics in the Alligator Rivers region (Kakadu) of northern Australia assessed using optical and radar remote sensing , 2014 .

[20]  Lisa-Maria Rebelo,et al.  Eco-Hydrological Characterization of Inland Wetlands in Africa Using L-Band SAR , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Masoud Mahdianpari,et al.  Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry, object-based classification, and geological features: a case study from Mayo, Yukon Territory, Canada , 2018, GIScience & Remote Sensing.

[22]  Joong-Sun Won,et al.  Interferometric Coherence Analysis of the Everglades Wetlands, South Florida , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[23]  M. Mahdianpari,et al.  Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery , 2017 .

[24]  Masoud Mahdianpari,et al.  The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method , 2017 .

[25]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[26]  Mohammed Dabboor,et al.  Mapping and monitoring flooded vegetation and soil moisture using simulated compact polarimetry , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[27]  T. L. Toan,et al.  Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data , 2007 .

[28]  Hao Chen,et al.  Compact Decomposition Theory , 2012, IEEE Geoscience and Remote Sensing Letters.

[29]  Ridha Touzi,et al.  Wetland characterization using polarimetric RADARSAT-2 capability , 2007 .

[30]  Ting Yuan,et al.  Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields , 2015, Remote. Sens..

[31]  Rudolf Ressel,et al.  Arctic Sea Ice Characterization Using RISAT-1 Compact-Pol SAR Imagery and Feature Evaluation: A Case Study Over Northeast Greenland , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Eric Pottier,et al.  Estimation of Soil Moisture and Faraday Rotation From Bare Surfaces Using Compact Polarimetry , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Evlyn Márcia Leão de Moraes Novo,et al.  Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon várzea wetlands. , 2016 .

[34]  R. Keith Raney,et al.  Hybrid-Polarity SAR Architecture , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[35]  Floyd M. Henderson,et al.  Radar detection of wetland ecosystems: a review , 2008 .

[36]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[37]  Jing Wang,et al.  The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands , 2014, Remote. Sens..

[38]  Camilla Brekke,et al.  Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective , 2017, Remote. Sens..

[39]  Th Toutin,et al.  Demystification of IKONOS , 2000 .

[40]  Li Zhang,et al.  Wetlands, carbon, and climate change , 2013, Landscape Ecology.

[41]  François Charbonneau,et al.  All-Season Compact-Polarimetry C-band SAR Observations of Sea Ice , 2015 .

[42]  Hao Yang,et al.  Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion , 2017, Remote. Sens..

[43]  Yun Zhang,et al.  Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery , 2018, Remote. Sens..

[44]  Lori White,et al.  Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands , 2017, Remote. Sens..

[45]  Hahn Chul Jung,et al.  Mapping wetland water depths over the central Congo Basin using PALSAR ScanSAR, Envisat altimetry, and MODIS VCF data , 2015 .

[46]  Kristof Van Tricht,et al.  Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium , 2018, Remote. Sens..

[47]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[48]  Heather McNairn,et al.  Compact polarimetry overview and applications assessment , 2010 .

[49]  Jonathan Li,et al.  Semiautomated Segmentation of Sentinel-1 SAR Imagery for Mapping Sea Ice in Labrador Coast , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[50]  Josef Aschbacher,et al.  The European Earth monitoring (GMES) programme: Status and perspectives , 2012 .

[51]  Zhong Lu,et al.  Radarsat-1 and ERS InSAR Analysis Over Southeastern Coastal Louisiana: Implications for Mapping Water-Level Changes Beneath Swamp Forests , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .

[53]  Alan A. Thompson,et al.  Overview of the RADARSAT Constellation Mission , 2015 .

[54]  Masoud Mahdianpari,et al.  Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle imagery and electromagnetic induction survey data , 2018, Journal of Applied Remote Sensing.

[55]  Brian Brisco,et al.  Mapping and Monitoring Surface Water and Wetlands with Synthetic Aperture Radar , 2015 .

[56]  E. Novo,et al.  Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images , 2013 .

[57]  Qi Gao,et al.  Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution , 2017, Sensors.

[58]  Hiroyoshi Yamada,et al.  Four-component scattering model for polarimetric SAR image decomposition , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[59]  F. Massey.  The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[60]  Brian Brisco,et al.  Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery , 2018 .

[61]  Eric Pottier,et al.  One year wetland survey investigations from quad-pol RADARSAT-2 time-series SAR images , 2012 .

[62]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[63]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[64]  Royal C. Gardner,et al.  The Ramsar Convention , 2011 .

[65]  Saeid Homayouni,et al.  The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2018, Remote. Sens..

[66]  Haydee Karszenbaum,et al.  Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier , 2002 .

[67]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[68]  Zhong Lu,et al.  Monitoring Everglades freshwater marsh water level using L-band synthetic aperture radar backscatter , 2014 .

[69]  Eric S. Kasischke,et al.  Monitoring South Florida Wetlands Using ERS-1 SAR Imagery , 1997 .

[70]  Samuel Corgne,et al.  Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images , 2016, Remote. Sens..

[71]  Eric S. Kasischke,et al.  Using C-Band Synthetic Aperture Radar Data to Monitor Forested Wetland Hydrology in Maryland's Coastal Plain, USA , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[72]  Shao Yun,et al.  Compact polarimetry assessment for rice and wetland mapping , 2013 .

[73]  Brian Brisco,et al.  Wetland Water Level Monitoring Using Interferometric Synthetic Aperture Radar (InSAR): A Review , 2018, Canadian Journal of Remote Sensing.

[74]  Chris Davies,et al.  Object-based image analysis of optical and radar variables for wetland evaluation , 2015 .

[75]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[76]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .