High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing

Abstract

The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.

Machine Learning Approches for Evaluating the Properties of Materials

“…Classes are represented by their respective colors. Random Forest (RF) is a method for classification and regression that combines numerous DTs into a single "forest," first suggested by Asy'Ari et al in [11]. Each individual tree in the forest is constructed via recursive partitioning.…”