Improving total carbon storage estimation using multi-source remote sensing

Korte inhoud:Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving forest management. This study investigates how multi-source remote sensing data can be used to provide accurate estimations of diameter at breast height (DBH) at the plot level, enhancing biomass estimations across 39.41 x 104 km2. The study is focused on Yunnan Province, China, which is characterized by complex terrain and diverse vegetation. Using ground-based survey data from hundreds of plots for model calibration and validation, the methodology combines multi-source remote sensing data, machine learning algorithms, and statistical analysis to develop models for estimating DBH distribution at regional scales. Decision tree showed the best overall performance. The model effectiveness improved when stratified by climatic zones, highlighting the importance of environmental context. Traditional methods based on the kNDVI index had a mean squared error (MSE) of 2575 t/ha and an R2 value of 0.69. In contrast, combining model-estimated DBH values with remote sensing data resulted in a substantially lower MSE of 212 t/ha and a significantly improved R2 value of 0.97. The results demonstrate that incorporating DBH not only reduced prediction errors but also improved the model's ability to explain biomass variability. In addition, climatic region classification further increased model accuracy, suggesting that future efforts should consider environmental zoning. Our analyses indicate that water availability during cool and dry periods in this monsoon-influenced region was especially critical in influencing DBH across different subtropical zones. In summary, the study integrates DBH and high-resolution remote sensing data with advanced algorithms for accurate biomass estimation. The findings suggest that this approach can support regional forest management and contribute to research on carbon balance and ecosystem assessment.

Jaar van publicatie:2025

Trefwoorden:Biology, Plant- en bodemkunde en technologie

Toegankelijkheid:Open