TerraSAR-X dual-pol time-series for mapping of wetland vegetation

Abstract

International audienceMapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using Support Vector Machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restoration) and biodiversity studies using remote sensing data

Wetland Mapping Using SAR Data from the Sentinel-1A and TanDEM-X Missions: A Comparative Study in the Biebrza Floodplain (Poland)

“…The Shannon Entropy (SE) can be extracted from quad-pol (TSX/TDX) and dual-pol data (S-1 and TSX/TDX). SE, which measures the randomness of the scattering of a pixel [22], was extracted from a co-variance matrix using the PolSARpro 5.0 software. SE is the sum of two contributions: intensity related (SE I ), which depends on total backscattered power, and polarimetry related (SE P ), which depends on the degree of polarization [23].…”

“…SE is the sum of two contributions: intensity related (SE I ), which depends on total backscattered power, and polarimetry related (SE P ), which depends on the degree of polarization [23]. The SE is given by the following formulae [22,23]. For dual polarization data:…”

“…2018, 10, 78 8 of 19 backscattered power, and polarimetry related (SE ), which depends on the degree of polarization [23]. The SE is given by the following formulae [22,23]. For dual polarization data: SE = log π e |C | = SE + SE (4) SE = 2 log πeI 2 = 2 log πeTr(C ) 2 (5)…”

“…The following parameters were identified: minimum number of classes: 8; maximum number of classes: 12; maximum iterations: 30; change threshold: 1%; minimum number of pixels in class: 100; maximum standard deviation: 1; minimum class distance: 3; and maximum merge pairs: 2. The supervised classification was performed using the Supported Vector Machine technique [22,[34][35][36]. The advantage of this technique is that it does not require a large number of training samples [37], and the probability distribution is not assumed a priori [38].…”

Evaluation of single-band snow-patch mapping using high-resolution microwave remote sensing: an application in the maritime Antarctic

“…The German Aerospace Center (DLR) satellite shows a short revisit time (11 days) and an improved radiometric and geometric resolution, which are key factors to detect the evolution of the snow cover, especially during snowmelt when changing moisture content influences the backscattering signal. TerraSAR-X imagery is frequently used for interferometric applications (Venkataraman and Rao, 2005;Alia et al, 2015;Barboux et al, 2015;Betbeder et al, 2015;Reis et al, 2015), glaciology (Braun, 2001;König et al, 2001;Rott et al, 2011;Schubert et al, 2013) and also for snow mapping (Baghdadi et al, 1997;Gunerissen, 2002, 2003;Venkataraman et al, 2008;Falk et al, 2016), but mostly using the coarserresolution StripMap mode. Most research focuses on the retrieval of snow water equivalent (SWE) and not so much on the detailed mapping of snow extent and melt patterns, topics which are very relevant to the geocryological community.…”