Optimal Envisat advanced synthetic aperture radar image parameters for mapping and monitoring Sahelian floodplains

Abstract

Floodplains in the Sahel region of Africa are of exceptional socio-economical and ecological importance. Due to their large extent and highly dynamic nature, monitoring these ecosystems can only be performed by means of remote sensing. The capability of the Envisat Advanced Synthetic Aperture Radar (ASAR) sensor to capture radar backscattering at various incident angles and with different polarization combinations, provides opportunities for improved wetland mapping and monitoring. However, little is known of the optimal image parameters, i.e. incident angle, polarization combination, and acquisition time. Backscatter sigma(o) signatures of Land Use and Land Cover (LULC) classes in and around the Waza-Logone floodplain (Cameroon) were analyzed to determine these optimal image parameters. Based on Jeffries-Matusita (JM) distances between all LULC classes it was determined that best separation was obtained with images acquired in the middle of the flooding cycle at a steep incident angle. Furthermore, separability of cross-polarized images was higher than for co-polarized images. The combination of two and three ASAR Alternating Polarization images with highest separability were used as input for a LULC classification. Two methods were evaluated: Pixel-based Maximum Likelihood and object-based Nearest Neighbour (NN) classification. Best results were obtained with the object-based approach

Object-Based Image Analysis in Wetland Research: A Review

“…Radar backscatter may exhibit substantial speckle due to complexity of both horizontal and vertical structure of wetland ecosystems which affects relative contributions of different scattering modes and thus the local variation of the modified signal [37,73]. Averaging of the radar pixel values at the object level often facilitated delineation of wetland units and vegetation types and enhanced their correspondence to semantic objects of interest across the gradients of inundation [27,37,54,74,75,80]. Similar noise-alleviating benefits were also important in studies combining radar and multi-spectral Landsat or MODIS data via hierarchical OBIA to enhance the accuracy of mapping floodplain vegetation types, e.g., in the Amazon River and tributaries [77,78].…”

“…Inland seasonally flooded freshwater [7,17,53,54], river floodplain wetlands [55,56], coastal salt marshes [34,57,58], alpine wetlands of Tibet Plateau [55,59] 2.3-356,000 0.61-32…”

“…Seventeen studies used the data from synthetic aperture radar (SAR) either as the primary input or in combination with Landsat or other sensors [27,37,40,43,47,53,54,71,[73][74][75][76][77][78][79][80]82] Natural log of pixel size in m overall accuracy less than 85% overall accuracy equal or greater than 85% overall accuracy not reported and ALOS PALSAR [40,79,80]; spatial resolution of these datasets varied between 10 and 100 m. Radar images as inputs to OBIA have been especially critical for wetland analyses in humid regions with frequent cloud cover limiting the availability of multi-spectral images and the consistency of seasonal time series [37,75,82]. Several high-resolution studies using multi-spectral imagery additionally incorporated light detection and ranging (lidar) datasets to facilitate the overall wetland classification or discrimination of specific classes and landscape features [6,25,28,32,59,71,77,80,89].…”

“…Wetland types represented in this review were also diverse, including coastal freshwater and tidal marshes, mangroves, freshwater wetlands in river floodplains, riparian ecosystems, diverse wetland types in peatland landscapes, tropical peat swamps and small isolated freshwater wetlands with scattered distribution (Table 1). Several analyses were conducted in spatially extensive and biologically diverse wetland regions of international importance and sites of protected status under Ramsar Convention, such as Poyang Lake [7,17,53,54], Dongtan Nature Reserve [58] and Xianghai and Zhalong wetlands [56] in China, Brazilian Pantanal wetlands in South America [79,80], Kushiro wetland in Japan [5], Koshi Tappu Wildlife Preserve in Nepal [63], and European Eider Treene Sorge Lowland [65], Danube Floodplain [68] and Biebrza River Valley [71], as well as other protected areas including Florida Everglades National Park in USA [8,83] and other nature reserves and wildlife refuge sites [28,34,67].…”

Long-Term Monitoring of Water Dynamics in the Sahel Region Using the Multi-Sar-System

“…Compared to previous studies the use of nine sensors for the monitoring of wetlands or open water bodies is unique. Santoro et al (2015), Westra et al (2010), and Liebe et al (2009) for example used only data of Envisat ASAR. But a combination of two to three different sensors is not exceeded, regardless of whether SAR data alone or combined with optical data were utilized (e.g., Bwangoy et al, 2010;Munyaneza et al, 2009;Li and Chen, 2005).…”