Spatio-Temporal Mapping and Monitoring of Mangrove Forests Changes from 1991 to 2021 in Panay Island, Philippines using Machine Learning

Abstract

Mangrove forests hold a crucial role in our social, economic, and ecological activities. Despite this immense importance, they are constantly threatened by reclamation, deforestation, and climate change. In order to forward conservation and restoration efforts, accurate and cost-effective mangrove mapping and monitoring must be done. This paper explores the use of a supervised learning algorithm called Random Forest (RF) in mapping mangrove extent in Panay Island, Philippines from 1991-to 2021. Using land cover data from Landsat, maps of the mangrove extent from 1991to 2021 were developed. Results revealed that there has been an 8% decline from 1991 to 1996; 24% decrease in 1996 to 2001; 6% increase in 2001 to 2006; 21% decline from 2006 to 2011; 17% increase in 2011-2016; and 16% increase in 2016 to 2021. Over the past three (3) decades, the Philippines has lost 20% of its mangrove forests. From 31713 ha in 1991 to only 25313 ha in 2021. Through a confusion matrix, the model was evaluated and it showed a specificity, sensitivity, and AUC(Area Under the ROC Curve) above 70%. This suggests that, machine learning, when integrated with remote sensing, can provide an effective yet low-cost approach to mapping mangrove extent at a large-scale.