The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4/W8-2023
https://doi.org/10.5194/isprs-archives-XLVIII-4-W8-2023-313-2024
https://doi.org/10.5194/isprs-archives-XLVIII-4-W8-2023-313-2024
25 Apr 2024
 | 25 Apr 2024

DEFORESTATION AND FOREST DEGRADATION ANALYSIS OF SOUTHERN SIERRA MADRE, PHILIPPINES USING GOOGLE EARTH ENGINE AND COMMUNITY MAPPING

K. P. R. Israel, M. J. C. Gabriel, W. P. Hintural, and M. M. M. Baldonado

Keywords: remote sensing, google earth engine, deforestation, forest degradation, community mapping

Abstract. Google Earth Engine (GEE) has empowered researchers to map and monitor vegetation cover through the utilization of satellite imagery and cloud-computing resources. This study employed such platform to evaluate deforestation and forest degradation (DFD) in the Southern Sierra Madre Mountain range, where historical and current evidence underscores ongoing degradation. This research endeavors to capitalize on GEE to generate land cover maps for the Southern Sierra Madre region, specifically in General Nakar, Quezon, and in Rodriguez, Rizal and to present the changes that occurred during three specific years: 2016, 2019, and 2022. Community mapping activities were also conducted in these municipalities to further validate the results from GEE. The findings indicate that forested lands predominantly encompassed both municipalities, albeit exhibiting a declining pattern for successive years. Analysis of land cover maps revealed that in General Nakar, most of the forest areas converted to shrublands are found in the barangays of Umiray and Pagsangahan while for Rodiguez, most of the forest areas converted to shrublands were found to be in Marikina Watershed Forest Reserve and in Puray. Community mapping unveiled that DFD factors such as kaingin, infrastructure extension, timber poaching, small-scale mining, charcoal making, and natural hazards are also present in these areas. With the availability of these openly available cloud-computing platforms, aided by inputs from members of the communities, efficient monitoring and implementation of interventions may be realized.