The LAND COVER MAPPING OF TROPICAL FOREST AREAS USING SPOT IMAGE IN CENTRAL BORNEO
LAND COVER MAPPING OF TROPICAL FOREST AREAS
DOI:
https://doi.org/10.15544/ageng.2024.56.1Keywords:
SPOT, Land Cover, Classification, Forested area, Central BorneoAbstract
Land cover is fundamental information that could be applied to understand the interaction between human activities and nature. This information could be derived from remotely sensed images. This study analyzes and compares tropical forested areas’ land cover (LC) classification results. In this study, we use a SPOT image as the primary input. The study was conducted in Central Borneo and covered 162.60 km2. The image was processed using three algorithms, including NN-MLP, MLC, and ECHO Classifier, with three treatments (2×2, 4×4, and 6×6) sized block of pixels. The classification produces nine (9) land cover classes, i.e., Sparse vegetation area, Dense Vegetation area, Shrubland, Open Water Body, Open Land, Grassland, Mining Area, Pavement Area, and Palm Oil Area. The three algorithms can produce land cover maps with kappa and an overall accuracy value of more than 85%. However, NN-MLP has better accuracy than other algorithms (MLC and ECHO), with a kappa value of 90.72% and an overall accuracy of 93.88%.
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