Mapping Plant Functional Types in Floodplain Wetlands: An Analysis of C-Band Polarimetric SAR Data from RADARSAT-2

Abstract

Abstract:The inclusion of functional approaches on wetland characterizations and on biodiversity assessments improves our understanding of ecosystem functioning. In the Lower Paraná River floodplain, we assessed the ability of C-band polarimetric SAR data of contrasting incidence angles to discriminate wetland areas dominated by different plant functional types (PFTs). Unsupervised H/α and H/A/α Wishart classifications were implemented on two RADARSAT-2 images differing in their incidence angles (FQ24 and FQ08). Obtained classes were assigned to the information classes (open water, bare soil and PFTs) by a priori labeling criteria that involved the expected interaction mechanisms between SAR signal and PFTs as well as the relative values of H and α. The product obtained with the shallow incidence angle scene had a higher accuracy than the one obtained with the steep incidence angle product (61.5% vs. 46.2%). We show how a systematic analysis of the H/A/α space can be used to improve the knowledge about the radar polarimetric response of herbaceous vegetation. The map obtained provides novel ecologically relevant information about plant strategies dominating the floodplain. Since the obtained classes can be interpreted in terms of their functional features, the approach is a valuable tool for predicting vegetation response to floods, anthropic impacts and climate change.

Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series

“…Attenuation by the vegetation also decreases with longer wavelengths like L-band [9], which is why a considerable number of studies has been carried out using data acquired at that wavelength e.g., [12][13][14][15][16]. It should be noted that we consider only techniques for single-polarized data here although, more recently, specialized algorithms for wetland detection from polarimetric SAR data have become available e.g., [17][18][19].…”

Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery

“…Another useful index for monitoring vegetation over wetlands is SARVI2 (Soil and Atmosphere Resistant Vegetation Index) (Huete, et al, 1997). As an addition, other data such as LiDAR data (Huang, Peng, Lang, Yeo, & McCarty, 2014), DEM (Li & Chen, 2005), and microwave data (Morandeira, Grings, Facchinetti, & Kandus, 2016;Moser, Schmitt, Wendleder, & Roth, 2016;White, Brisco, Dabboor, Schmitt, & Pratt, 2015) have been used for wetlands monitoring.…”

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

“…Furthermore, more advanced approaches based on machine learning [23][24][25], fuzzy logic [11,26], Markov Random Field modelling [14], or wishart classifications [27][28][29] are applied in dependency on the task, the availability of polarization modes, and phase information, as well as spatial or temporal resolution [6].…”