LAND COVER CHANGE ANALYSIS OF CARRASCAL, MADRID AND CANTILAN MINING SITES IN SURIGAO DEL SUR USING REMOTE SENSING

Abstract

The Philippines is recognized as amongst the natural resource-rich countries which has been evident in the influx of extractive industry investors, most specifically in the mining industry. An example of this is the case of mining sites in the municipalities of Carrascal, Madrid, and Cantilan (CMC) in Surigao del Sur region of the Philippines. In this region, development of mining areas was significantly noticeable in a span of seven (7) years since 2015. With this being an alarming case due to its possible negative impacts on the surrounding communities, this study developed an open-source monitoring system of mining activities using remote sensing technologies employing supervised image classification using the Random Forest algorithm. This study is mainly motivated by the need for a monitoring system to ensure responsible mining practices in the country, most especially since there has been an increasing global clamour for a more environmental-friendly and climate-sensitive and sustainable practices. The methodology developed for this study utilized a post-classification change detection approach in which multitemporal land cover (LC) models were produced to capture the land cover changes. To model the LC changes, land cover models for 2015 and 2022 were produced obtaining overall accuracies between 0.93 to 0.95, respectively. Using these models, changes due to mining development were delineated through a multitemporal overlay analysis approach. This change detection model obtained an F1-score of 0.93 indicating high accuracy and satisfactory performance of the model and consequently, the its potential as a mining site monitoring system to achieve the goal of ensuring responsible mining industry practice in the country.