首页|Study Findings from Central South University of Forestry and Technology Provide New Insights into Machine Learning (Estimating the Vertical Distribution of Biom ass in Subtropical Tree Species Using an Integrated Random Forest and Least Squa res ...)
Study Findings from Central South University of Forestry and Technology Provide New Insights into Machine Learning (Estimating the Vertical Distribution of Biom ass in Subtropical Tree Species Using an Integrated Random Forest and Least Squa res ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting out of Changsha, Peo ple's Republic of China, by NewsRx editors, research stated, "Accurate quantific ation of forest biomass (FB) is the key to assessing the carbon budget of terres trial ecosystems." Financial supporters for this research include Natural Science Foundation of Hun an Province; Key Project of Hunan Education Department; Scientific Research Proj ect of Hunan Education Department; Key Discipline of The State Forestry Administ ration; "double First-class" Cultivating Subject of Hunan Province. The news journalists obtained a quote from the research from Central South Unive rsity of Forestry and Technology: "Using remote sensing to apply inversion techn iques to the estimation of FBs has recently become a research trend. However, th e limitations of vertical scale analysis methods and the nonlinear distribution of forest biomass stratification have led to significant uncertainties in FB est imation. In this study, the biomass characteristics of forest vertical stratific ation were considered, and based on the integration of random forest and least s quares (RF-LS) models, the FB prediction potential improved. The results indicat ed that compared with traditional biomass estimation methods, the overall R2 of FB retrieval increased by 12.01%, and the root mean square error (R MSE) decreased by 7.50 Mg·hm-2. The RF-LS model we established exhibited better performance in FB inversion and simulation assessments. The indicators of forest canopy height, soil organic matter content, and red-edge chlorophyll vegetation index had greater impacts on FB estimation."
Central South University of Forestry and TechnologyChangshaPeople's Republic of ChinaAsiaCyborgsEmerging Techn ologiesMachine Learning
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