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    • Remote sensing inversion of forest aboveground carbon storage based on GAC feature selection and AST regression algorithm

    • “The research team proposed a stacked ensemble learning regression model AST to improve the accuracy of forest above ground carbon stock estimation. This model optimizes the model through mean fusion of the base model and adaptive weighting of the meta model, providing new ideas and technical support for regional scale forest carbon stock inversion.”
      • role：First author第一作者
      • Affiliation：

        School of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China

      • Email：sunkaiping0903@swfu.edu.cn
      • Introduction：孙凯萍，研究方向为资源环境遥感。E-mail： sunkaiping0903@swfu.edu.cn

      SUN Kaiping

      1,
      • role：Corresponding author通信作者
      • Affiliation：

        College of Forestry (Asia-Pacific Forestry College), Southwest Forestry University, Kunming 650224, China

      • Email：jialongzhang@swfu.edu.cn
      • Introduction：张加龙，研究方向为林业遥感。E-mail： jialongzhang@swfu.edu.cn

      ZHANG Jialong

      2 * ,
      • Affiliation：

        College of Forestry (Asia-Pacific Forestry College), Southwest Forestry University, Kunming 650224, China

      TENG Chenkai

      2,
      • Affiliation：

        College of Forestry (Asia-Pacific Forestry College), Southwest Forestry University, Kunming 650224, China

      YANG Kun

      2,
      • Affiliation：

        Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

      HUANG Kai

      3,
      • Affiliation：

        College of Forestry (Asia-Pacific Forestry College), Southwest Forestry University, Kunming 650224, China

      LEI Qiwang

      2,
      • Affiliation：

        Yunnan Provincial Basic Surveying and Mapping Technology Center, Kunming 650034, China

      XIONG Dengliang

      4
    • Vol. 30, Issue 8, Pages: 2463-2485(2026)  

    • DOI：10.11834/jrs.20265246    

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HUANG Kai 昆明理工大学 国土资源工程学院
LEI QiWang 西南林业大学林学院(亚太林学院)
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ZHANG Shengjun 东北大学 资源与土木工程学院

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