Exploring new mangrove horizons: A scalable remote sensing approach with Planet-NICFI and Sentinel-2 images

Abstract

Mangroves offer massive ecosystem services ranging from coastal protection, and wildlife habitat to carbon sequestration. This makes them an integral part of tropical developing countries' strategies to pursue climate neutrality targets. In this respect, the advanced development of big data, machine learning, and cloud computing in remote sensing provides a huge opportunity to explore this ecosystem and to provide scalable monitoring solutions. This study aims to discover new potential mangrove areas, focusing on far-off and under-monitored locations along the coasts and rivers of Indonesia, using a precise, practical, and scalable remote sensing approach via Google Earth Engine. To demonstrate the potential of our approach, we selected Lampung province, Indonesia as the study area, which has varied taxonomic, topographical, bathymetrical, and oceanographical characteristics. The methodology includes defining mapping zones using coastline, river, and elevation data. The satellite image processing is based on integrating Planet-NICFI and Sentinel-2 images using the Simple Non-Iterative Clustering (SNIC) segmentation and Random Forest (RF) classifier. Our classification with F1 score of 0.95 successfully mapped 10,290 ha of mangroves, with coastal mangroves contributing 6058 ha and riverine mangroves another 4250 ha. Importantly, this study discovered 1714 ha of previously unknown mangroves, equivalent to 18.55 % of the official area. These new areas are dominated by nypa palm, a native species that contributes to bioeconomy. This study contributes to refining carbon sequestration baselines and highlights the scalability of a national-level implementation to support progress towards net-zero emissions goals. The method can readily be deployed to other mangrove areas.