Application of Machine Learning and Change Vector Analysis for Monitoring Forest Cover Change in Lore Lindu National Park, Indonesia

Year 2025, Volume: 11 Issue: 2, 160 - 173

Nabila Shaffana Zhafira Suwiji , I Nengah Surati Jaya , Nining Puspaningsih , Tatang Tiryana

https://doi.org/10.33904/ejfe.1675580

Abstract

This study aimed to develop a Decision Tree (DT) algorithm to detect forest cover change and determine its most influential driven variables in Lore Lindu National Park, Indonesia. To achieve these objectives, the study employed NDVI, NDBI, and NGBLUE indices, along with advanced Change Vector Analysis (CVA), which accounted for both the magnitude and direction of changes. These assessments were based on Landsat images acquired in 1990, 2003, and 2020 to identify key patterns in land cover change caused by deforestation, forest degradation, and regrowth. The patterns observed were the transition from shrub or bush to dryland, abandoned agriculture, bare land, settlement, waterbody, paddy, cloud, and land without change. The results showed that change detection accuracies were 94.7% for the overall accuracy and 93.3% for the Kappa Accuracy for 1990–2003, as well as 96.1% and 94.9% for the 2003–2020 period, respectively. Furthermore, the most significant influential variables identified in 1990–2003 and 2003–2020 were the distance from the forest edge and delta NGBLUE, respectively. This study showed that deforestation and forest degradation covered an area of 2,720.9 ha (an average of 209.3 ha per year) and 14,512 ha in 1990–2003. From 2003 to 2020, deforestation increased significantly to 24,559.8 ha (an average of 1,444.7 ha per year) without forest degradation. In line with the results, deforestation was found to be the conversion of primary and secondary forests into agricultural land.

Keywords

Forest cover change , Machine learning , Decision Tree , Change Vector Analysis

Ethical Statement

This study did not involve any human participants or animal subjects. The research was conducted using remote sensing and geospatial data. Field validation was carried out with official permission from the Lore Lindu National Park authority, and all activities complied with institutional and governmental regulations. Therefore, no additional ethical approval was required.

Supporting Institution

This project was collaboration between the Research Consortium (IPB University, Tadulako University, and Göttingen University) Forest Program III with the Ministry of Environment and Forestry (KLHK) 2021-2024 funded by KfW Germany.

Thanks

This study is part of forest monitoring in LLNP, which is carried out in the framework of collaboration between the Research Consortium (IPB University, Tadulako University, and Göttingen University) of Forest Programme III with the Ministry of Environment and Forestry (KLHK) 2021–2024 funded by KfW Germany.

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