LAND-USE AND LAND-COVER MAPPING USING A COMBINATION OF RADAR AND OPTICAL SENSORS IN RORAIMA - BRAZIL

Land-use and land-cover (LULC) are important environmental properties of the Earth's surface. Satellite platforms and state-of-the-art algorithms enable the mapping of large areas, but they still need to be improved. This study aimed to compare free- and open-access images from radar and optical sensors, using the Google Earth Engine (TM) (GEE) for supervised classification of LULC for five municipalities in Roraima State, Brazil. Sentinel-1 (S1) scenes were used along with Landsat 8 (L8) and Sentinel-2 (S2) ones, resulting in five classification approaches S1 (SD), L8 (ODL), S2 (ODS), S1+L8 (SODL), and S1+S2 (SODS), with an auxiliary ALOS World 3D dataset (DEM approximate to 30m). Accuracy was assessed by an error matrix. The SD approach was significantly different (P <= 0.01) from the others using a mean F1-score of 0.80. ODL and ODS had barely perceptible differences (P <= 0.1), showing F1-scores of 0.95 and 0.92, respectively. When comparing ODL (F1=0.95) and SODL (F1=0.95) no difference was found (P > 0.1). However, by comparing ODS (F1=0.92) and SODS (F1=0.94), there was a significant classification improvement (P <= 0.05). In short, the approaches SODL and SODS had the best pixel-based supervised classification performance.