Integration of optical remote sensing and laser point cloud for forest stock estimation in karst mountainous areas

HIGHLIGHTS

• What: The authors propose a method that integrates optical remote sensing data from Sentinel-2 into airborne LiDAR data to estimate forest stock in karst First an Allometric Growth Model correlating tree height and diameter at breast height (DBH) in karst areas was developed based on field measurements. This study evaluated the robustness of three machine learning methods the Random Forest Regression Model K-Nearest Neighbors Regression Model and Backpropagation Neural Network Model in estimating forest stock in karst mountainous This study provides an effective technical tool for estimating forest stock in karst areas and under complex . . .
  

   

  

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