Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery

Abstract

Private forests have a crucial role in maintaining the functioning of the Indonesian forest ecosystem especially because of the continuous degradation of natural forests. Private forests are a part of social forestry which becomes a tool for the Indonesian government to reduce carbon dioxide (CO2) emission by 26% by 2030. The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation has encouraged the Indonesian government to establish a forest monitoring system by estimating forest carbon stock using a combination of forest inventory and remote sensing. This study is aimed at assessing the potential of vegetation indices derived from Sentinel-2 for estimating aboveground biomass (AGB) of private forests. We used 45 sample plots and 7 vegetation indices to evaluate the ability of Sentinel-2 in estimating AGB on private forests. Normalised difference index (NDI) 45 exhibited a strong correlation with AGB compared to other indices (r = 0.89; R2 = 0.79). Stepwise linear regression fitted for establishing the model between field AGB and vegetation indices (R2 = 0.81). We also found that AGB in the study area based on spatial analysis was 72.54 Mg/ha. A root mean square error (RMSE) value from predicted and observed AGB was 27 Mg/ha. The AGB value in the study area is higher than the AGB value from some of forest types, and it indicates that private forests are good for biomass storage. Overall, vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests.

Ranking the Key Forest Habitats in Ecosystem Function Provision: Case Study from Morava River Basin

“…The fact that all analysed services are evaluated as dependent on plant production, i.e., the amount and condition of biomass produced, also speaks for this direction of expansion. This is also a key feature for the necessary remote sensing methods (spectral analyses and vegetation indices) [52,53]. Algorithms for determining vegetation indices, which can be used to assess the health of vegetation, are well known today, and are optimized for different types of multispectral data (Landsat, Sentinel .…”

Energy Production from Forest Biomass: An Overview

“…The nonparametric models include regression k-nearest neighbor [88][89][90], artificial neural network [91], regression tree [35,92,93], random forest [18,[94][95][96], support vector machine [94], and maximum entropy [97]. These functions have been developed with satellite imagery of low [98][99][100], medium [100][101][102][103], and high [73,[75][76][77][104][105][106] spatial resolution. In the fifth approach, biomass is modeled with data from Synthetic Aperture Radar (SAR) with bagging stochastic gradient boosting algorithms, backscattering amplitudes, and multivariate linear regression [107][108][109].…”

Vegetation structure, aboveground biomass, and carbon storage of wono¸ a local forest management in Gunungkidul, Yogyakarta, Indonesia, across three geomorphological zones

“…Research related to biomass and carbon storage of community forests in Gunungkidul District has been carried out by scholars (Askar et al 2018;Indrajaya and Sudomo, 2016;Purwanto et al 2012;Sulistyo et al 2010). However, research on similar themes that focused specifically on wono is rarely found.…”