SAR Data and Harvesting Residues: An Initial Assessment of Estimation Potential

Abstract

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected by SAR techniques. Two models, the generalized linear model (GLM) and random forest (RF) model, were used to predict the mass of residues using interferometric variables (phase, amplitude, and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R2 of 0.48 for GLM and 0.13 for RF), with an acceptable bias and RMSE. It was concluded that it is possible to derive useful indications about the mass of harvesting residues from SAR data and the findings could lead to the improved monitoring and management of forest residues, contributing to sustainable forestry practices and the enhanced utilization of bioenergy resources.