Work

Bayesian geostatistical models for predicting above-ground biomass from remote sensing data

Year: 2025

Type: article

Abstract: Monitoring and quantifying the amount of carbon stored in forests is crucial for shaping global policies, understanding climate change, and supporting emerging carbon credit markets. One key indicator that effectively describes carbon quantity is the Above-Ground Biomass (AGB), that has been traditionally estimated using allometric equations and data collected in forest inventory data. Recently, advances in technology have enabled the integration of remote sensing data in this process. In this study, Bayesian geostatistical models (using INLA and SPDE approaches) were applied to estimate AGB in two areas of the Atlantic Forest in northern Spain, Sierra de la Culebra and Montaña Palentina . Field data from the Cuarto Inventario Forestal Nacional (IFN4) of Spain were combined with satellite data from GEOSAT-2. Measurements such as texture, vegetation indices, and spectral variables were extracted from satellite reflectance bands and used as covariates in the models. The models incorporated a spatial random field to capture spatial autocorrelation, which was found to be significant in both areas. In Sierra de la Culebra , NDVI (Normalized Difference Vegetation Index), homogeneity, and Forest Type were statistically significant covariates in modelling AGB. In contrast, in Montaña Palentina , the Red and Blue reflectance bands were the main contributors to AGB estimation. Predictive results in Sierra de la Culebra were satisfactory (RMSE = 47.06 (t/ha); MSE = 2214.68 (t 2 /ha); MAE = 36.84 (t/ha); R 2 = 0.52; rRMSE = 39%), while in Montaña Palentina , predictive performance was even better (RMSE = 24.41 (t 0 . 75 /ha); MSE = 505.71 (t 1 . 5 /ha); MAE = 20.02 (t 0 . 75 /ha); R 2 = 0.5; rRMSE = 36%).

Source: Remote Sensing Applications Society and Environment

DOI: 10.1016/j.rsase.2025.101855

Authors: Ricardo Coelho, Isabel Natário, Silvia Fraile

Institutions: University of Lisbon, NOVAMath Center for Mathematics and Applications, Universidade Nova de Lisboa

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Climate action

Open Access status: hybrid

Estimated APC paid: $2,640