International Journal of Applied Earth Observations and Geoinformation (Sep 2020)
Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia
Abstract
Accurate estimation of forest aboveground biomass (AGB) using remote sensing is a requisite for monitoring, reporting and verification (MRV) system of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation. However, attaining high accuracy remains a great challenge in the diverse tropical forests. Among available technologies, l-band Synthetic Aperture Radar (SAR) estimates AGB with reasonably high accuracy in the terrestrial tropical forests. Nevertheless, the accuracy is relatively low in the mangrove forests. In this context, the study was carried out to model and map AGB using backscatter coefficients of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array l-band SAR-2 (PALSAR-2) in part of the restored mangrove forest at Mahakam Delta, Indonesia. PALSAR-2 data was acquired with image scene observation during the peak low tide on 30 July 2018 from Japan Aerospace Exploration Agency. The forest parameters namely tree height and diameter at breast height were measured from 71 field plots in September-October 2018. The parameters were used in mangrove allometry to calculate the field AGB. Finally, HV polarized backscatter coefficients of PALSAR-2 were used to model AGB using linear regression. The model demonstrated a comparatively high performance using three distinct methods viz. independent validation (R2 of 0.89 and RMSE of 23.16 tons ha−1), random k-fold cross validation (R2 of 0.89 and RMSE of 24.59 tons ha−1) and leave location out cross validation (LLO CV) (R2 of 0.88 and RMSE of 24.05 tons ha−1). The high accuracy of the LLO CV indicates no spatial overfitting in the model. Thus, the model based on LLO CV was used to map AGB in the study area. This is the first study that successfully obtains high accuracy in modeling AGB in the mangrove forest. Therefore, it offers a significant contribution to the MRV mechanism for monitoring mangrove forests in the tropics and sub-tropics.
