Abstract
Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.
Highlights
Land cover is a fundamental factor that links and effect with many parts of the human and physical environment (Foody, 2002)
Sentinel-1A/1B Synthetic Aperture Radar (SAR) images are required to be pre-processed before any image classification
In this research, using Sentinel Application Platform (SNAP) software, for image pre-processing a graph is created in GraphBuilder as follows: Figure 2
Summary
Land cover is a fundamental factor that links and effect with many parts of the human and physical environment (Foody, 2002). The change in land cover is considered as an important factor of global change affecting ecological systems (Vitousek, 1994) with an impact on the earth that is linked with climatic change (Skole, 1994). Complex landscapes are difficult to monitor due to sudden changes in environmental gradients (e.g. moisture, elevation, and temperature) and a legacy of past interference (Rogan and Miller, 2006). Such heterogeneous landscapes are defined by land-cover categories that are complicated to be defined spectrally due to low inter-class separability and high intra-class variability
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