Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada

Abstract

Abstract:In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate 8566(dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

“…Potential for this application has also been demonstrated in other regions [15,16]. Additional, relevant research has shown that it is possible to map more general Arctic land cover types [17].…”

“…With their hierarchical object-based classifier, Demers et al [13] observed that some classes were better detected with either SAR or optical data (e.g., vegetated and un-vegetated features were better differentiated with NDVI values; Freeman-Durden double bounce and HH/HV values were better for detecting wetlands). Souza-Filho et al [15,16] found that fusing optical and SAR imagery together improved their ability to visually discriminate features, and Ullmann et al [17] also found that the combination of SAR and optical imagery produced higher classification accuracies. Based on these results we chose to use both SAR and optical imagery in this research.…”

“…Ullmann et al [17] assessed the potential to classify five land cover types along the outer Mackenzie Delta, Northwest Territories, Canada, including: water, bare substrate, low/grass and herb dominated tundra, medium/herb dominated tundra, high/shrub dominated tundra, and wetlands. The authors compared results for supervised and unsupervised classification methods using different combinations of Dual Pol TerraSAR-X, Quad Pol RADARSAT-2, and Landsat 8 imagery.…”

Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?

“…In order to distinguish the different scattering types, polarimetric SAR data as used, for example, in Ullmann et al (2014) would be required. Such data are, however, not available from ENVISAT ASAR GM.…”

Random Forest Classification of Sediments on Exposed Intertidal Flats Using Alos-2 Quad-Polarimetric Sar Data

“…Former studies have thrown light on the potentials of the features extracted from optical and SAR data source and abilities of discrimination of tidal water, sediment and other biotic community. (Baghdadi et al, 2013;Banks et al, 2014a;Banks et al, 2014b;Brockmann and Stelzer, 2008;Gade et al, 2008;Koch et al, 2012;Ullmann et al, 2014). But there are rarely studies focus on subdividing sediment types which can promote economic development and environmental protection.…”