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All Journal Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management)
David Anderson Lubis
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Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Environmental Science

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Monitoring Forest Cover Change and Encroachment Risk Mapping Using the Normalized Difference Fraction Index (NDFI): A Case Study of Gunung HalimunSalak National Park, Indonesia Ahmad Fahrur Rizqi; Hartoyo, Adisti Permatasari Putri; Mursalina Nur Buana; Novia Damayanti; David Anderson Lubis; Yurico Bakhri
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 6 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.6.1060

Abstract

Gunung Halimun Salak National Park (GHSNP) is one of the most biodiversity-rich protected areas in Java, yet it remains highly vulnerable to deforestation and forest degradation. This study examines forest cover dynamics from 1994 to 2024 and projects village-level encroachment risk for 2034. Landsat 5 TM, Landsat 7 ETM+, and Landsat 8–9 OLI imagery were processed in Google Earth Engine to generate the Normalized Difference Fraction Index (NDFI) using spectral mixture analysis of GV, NPV, soil, and shade fractions. Changes in NDFI (ΔNDFI) were used to classify degradation, deforestation, regrowth, and intact forest. Encroachment risk mapping was modeled using a 3 × 3 kernel neighborhood with two analytical approaches: the sum of risk weight and the majority of risks around. Forest cover declined by 19,424 ha between 1994 and 2004, largely driven by illegal encroachment linked to governance uncertainty in 2003. An increase of 6,678 ha during 2004–2014 reflects the impact of restoration initiatives and strengthened area protection, although a subsequent decline of 1,992 ha occurred between 2014 and 2024 due to renewed encroachment. Model evaluation indicates low predictive performance for both kernel methods (Precision 4%). Despite this limitation, areas of elevated risk consistently appeared along forest edges near settlements and footpath access routes. Citorek Kidul was identified as the village most susceptible to encroachment in 2034. Improving the accuracy of encroachment prediction will require the integration of socio-economic drivers and advanced machine-learning approaches capable of capturing the complex and non-linear patterns of forest encroachment. 
Co-Authors Adisti Permatasari Putri Hartoyo Ahmad Fahrur Rizqi Mursalina Nur Buana Novia Damayanti Yurico Bakhri