Carbon stock estimation based on remote sensing in the northern coast of Java

Abstract

Abstract Mangroves are one of the land covers on the earth’s surface that being the largest storage of carbon reserves compared to other land covers. On the other hand, quick and accurate monitoring of carbon stocks on the earth’s surface is needed. This research was conducted on the northern coast of Java and used a flexible and efficient cloud computing-based remote sensing approach by using satellite imagery data. We identify land cover classification, especially mangrove, uses the Support Vector Machine (SVM) algorithm through the GEE (Google Earth Engine) platform. The estimated value of mangrove carbon was obtained from the NDVI index (Normalized Difference Vegetation Index) analysis on sentinel-2 images. The results showed that the estimated carbon value was 1,232,311.496 tones. Strong relationship is found between NDVI and carbon stocks with R2 of 98%. The study, therefore, strongly suggests the further use of NDVI to assess and monitor carbon stocks from mangroves in the future.