Article
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Can SAR Data Predict Harvesting Residues Mass?
Version 1
: Received: 14 August 2024 / Approved: 14 August 2024 / Online: 15 August 2024 (07:29:55 CEST)
How to cite: Udali, A.; Persson, H. J.; Talbot, B.; Grigolato, S. Can SAR Data Predict Harvesting Residues Mass?. Preprints 2024, 2024081102. https://doi.org/10.20944/preprints202408.1102.v1 Udali, A.; Persson, H. J.; Talbot, B.; Grigolato, S. Can SAR Data Predict Harvesting Residues Mass?. Preprints 2024, 2024081102. https://doi.org/10.20944/preprints202408.1102.v1
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 through SAR techniques. Two models, generalized linear model (GLM) and random forest (RF), were used to predict the mass of residues using interfer-ometric 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 acceptable bias and RMSE. We concluded that SAR data are feasible to predict the mass of harvesting residues and the findings could lead to improved monitoring and management of forest residues, contributing to sustainable forestry practices and enhanced bioenergy resource utilization.
Keywords
Interferometry; harvesting residues; SAR; sustainable forest management
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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