Exploring forest disturbance detection using Sentinel-2 imagery: A case study at FRIM campus

Abstract Monitoring forest disturbances is crucial for the preservation of biodiversity, climate change mitigation, and overall ecosystem health. Remote sensing offers a powerful tool for monitoring forest disturbances as it can cover large areas, detect changes in near real-time, and cost effective as compared to survey method. Thus, this study tries to utilize the use of Sentinel-2 imagery to detect forest disturbances in Forest Research Institute Malaysia (FRIM) campus. Continuous change detection and classification (CCDC) algorithm was employed to identify disturbances that occurred between 2019 and 2023. NDVI, NDMI, and NBR were also used to improve the accuracy of disturbance detection. The study returned a promising result, achieving a precision of 85 percent in detecting forest disturbances. The effective utilization of Sentinel-2 imagery and the CCDC method in this study highlights not only the efficiency of remote sensing in precisely detecting forest disturbances but also underscores its potential to contribute significantly to sustainable conservation initiatives.