跳转到内容
遥感碳汇
  • 网站首页
  • 团队成员
    • 团队概况
    • 骨干成员
      • 张老师
      • 杨婧雯
    • 研究生
      • 博士
      • 2025级
      • 2024级
      • 2023级
      • 2022级
      • 2021级
      • 2020级
      • 2019级
      • 2018级
      • 2017级
      • 2014级
      • 2013级
      • 2012级
      • 基层人才
      • 留学生
  • 研究成果
    • 发表论文
    • 获奖情况
    • 发明专利
    • 软件著作
  • 科研项目
  • 一流课程
    • 遥感与地理信息系统
  • 中文 (中国)
  • English
遥感碳汇

[1]Leyan X, Hongjian T, Zhang J*, et al. Remote Sensing and Machine Learning Uncover Dominant Drivers of Carbon Sink Dynamics in Subtropical Mountain Ecosystems[J]. Remote Sensing, 2025, 17(16): 2843.

[2]Peng M, Xu M, Zhang J*, et al. Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation[J]. Scientific Reports, 2025, 15(1): 17410.

[3]Huang K, Teng C, Zhang J*, et al. A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025.

[4]Yang K, Luo K, Zhang J*, et al. Estimating forest aboveground carbon sink based on landsat time series and its response to climate change[J]. Scientific Reports, 2025, 15(1): 589.

[5]Qiu B, Li S, Cao J, et al. Uncertainty Analysis of Forest Aboveground Carbon Stock Estimation Combining Sentinel-1 and Sentinel-2 Images[J]. Forests, 2024, 15(12): 2134.

[6]Xu M, Han X, Zhang J*, et al. Integrating ward’s clustering stratification and spatially correlated poisson disk sampling to enhance the accuracy of forest aboveground carbon stock estimation[J]. Forests, 2024, 15(12): 2111.

[7]Luo K, Feng Y, Liao Y, et al. Developing a method to estimate above-ground carbon stock of forest tree species Pinus densata using remote sensing and climatic data[J]. Forests, 2024, 15(11): 2023.

下一页

地址:昆明市盘龙区白龙寺300号西南林业大学林学院

  • 邮箱:jialongzhang@swfu.edu.cn
  • 版权归属:遥感碳汇

微信公众号:遥感碳汇

© rscs-zhangjialong.cn 滇ICP备2024029974号