CELEBES Agricultural
Vol. 3 No. 2 (2023): CELEBES Agricultural

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

Rahmat Asy'Ari (Department of Forest Management, IPB University)
Aulia Ranti (Department of Forest Management, IPB University)
Azelia Dwi Rahmawati (Department of Forest Management, IPB University)
Moh Zulfajrin (Department of Soil Science and Land Resource, IPB University)
Lina Lathifah Nurazizah (Department of Agronomy and Horticulture, IPB University)
Made Chandra Aruna Putra (Department of Marine Science and Technology, IPB University)
Zayyaan Nabiila Khairunnisa (Department of Civil and Environmental Engineering, IPB University)
Faradilla Anggit Prameswari (Department of Geography, Faculty of Mathematics and Natural Science (FMIPA), University of Indonesia)
Rahmat Pramulya (Faculty of Agriculture, Teuku Umar University)
Neviaty P. Zamani (Center for Transdisciplinary And Sustainability Sciences, IPB University)
Yudi Setiawan (Center for Environmental Research, IPB University)
Ajat Sudrajat (Ujung Kulon National Park, Ministry of Environment and Forestry, Indonesia)
Anggodo (Ujung Kulon National Park, Ministry of Environment and Forestry, Indonesia)



Article Info

Publish Date
28 Feb 2023

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.

Copyrights © 2023






Journal Info

Abbrev

faperta

Publisher

Subject

Agriculture, Biological Sciences & Forestry

Description

CELEBES Agricultural: The publisher is the Faculty of Agriculture, University of Tompotika Luwuk. The journal article covers the results of research and policy analysis that can be applied in agricultural practices and sciences such as agronomy, soil science, pests, and plant diseases, entomology, ...