CARTOGRAPHY of MOROCCAN ARGAN TREE USING COMBINED OPTICAL and SAR IMAGERY INTEGRATED with DIGITAL ELEVATION MODEL

Abstract

Forest tree species mapping became easier due to the global availability of high spatio-Temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA Combining double low line 86.87%, K Combining double low line 0.84, from time series of SAR data (VV+VH/VV): OA Combining double low line 45.90%, K Combining double low line 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA Combining double low line 93.01%, K Combining double low line 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA Combining double low line 93.25%, K Combining double low line 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.