ForestResourcesDynamicMonitoringandForestVolumeEstimationUsingLiDARRemoteSensingTechnologies | Views : 0 下载量: 161 CSCD: 0
  • Export

  • Share

  • Collection

  • Album

    • Ground and canopy surface detection method based on direction-adaptive OPTICS in mountainous forest areas

    • Technology news reporters reported that the new generation ICESat-2 satellite faces challenges in signal extraction in mountainous forest areas. To this end, experts have proposed a direction adaptive OPTICS surface and canopy surface detection method. This method first constructs a direction adaptive elliptical search domain to extract fine terrain, and then eliminates terrain influence to extract vegetation signals. The experiment shows that the accuracy of this method is as high as 0.97, and the elevation errors of the surface and canopy are significantly reduced. This method is suitable for areas with large slope changes and provides a reliable data foundation for forest spatial structure inversion.
    • Vol. 29, Issue 10, Pages: 2944-2957(2025)   

      Received:14 September 2023

      Published:07 October 2025

    • DOI: 10.11834/jrs.20243390     

    移动端阅览

  • Xie J F,Yang X M,Xu C P,Lu Y L,Zhang L B,Mo F,Lv X,Liu R and Zeng J Z. 2025. Ground and canopy surface detection method based on direction-adaptive OPTICS in mountainous forest areas. National Remote Sensing Bulletin, 29(10):2944-2957 DOI: 10.11834/jrs.20243390.
  •  
  •  
Alert me when the article has been cited
提交

相关作者

XIE Junfeng 自然资源部国土卫星遥感应用中心
ZHOU Xiaoqing 自然资源部国土卫星遥感应用中心;江苏省地理信息资源开发与利用协同创新中心
LIU Zhao 自然资源部国土卫星遥感应用中心
PENG Jun 自然资源部国土卫星遥感应用中心;山东科技大学
LI Guoyuan 自然资源部国土卫星遥感应用中心;江苏省地理信息资源开发与利用协同创新中心
CHEN Jiyi 自然资源部国土卫星遥感应用中心
Li Guoyuan 自然资源部国土卫星遥感应用中心;江苏省地理信息资源开发与利用协同创新中心
Peng Jun 自然资源部国土卫星遥感应用中心;山东科技大学

相关机构

Shandong University of Science and Technology
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
Aerospace Information Research Institute, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science
School of Resources and Environment, University of Chinese Academy of Sciences
Department of Geographical Sciences, University of Maryland, College Park
0