ArticleFrontiers in plant science2025
A study on the remote sensing estimation and spatiotemporal distribution patterns of aboveground biomass in savanna grasslands of the Yuanmou dry-hot valley.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Savanna grasslands in dry-hot valleys play crucial ecological and productive roles, yet accurate estimation of their aboveground biomass (AGB) remains challenging due to rugged terrain, climatic variability, and intensive human disturbances. To address this, we investigated the Yuanmou dry-hot valley in Yunnan, China, by constructing a multi-source dataset that integrated remote sensing indices, topographic factors, meteorological variables, and biodiversity metrics. Three feature selection techniques were applied to identify key predictors, and the selected variables were used to train ensemble machine learning models. Of all tested model combinations, the Random Forest model with Recursive Feature Elimination achieved the highest predictive accuracy, with a coefficient of determination of 0.6975, a root mean square error of 89.3436 g/m
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