Predicting growth of individual trees directly and indirectly using 20-year bitemporal airborne laser scanning point cloud data

HIGHLIGHTS

  • who: Valtteri Soininen et al. from the Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Espoo, Finland Department of Built Environment, School of Engineering, Aalto University, POBox , have published the Article: Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data, in the Journal: Forests 2022, 13, 2040. of 30/Nov/2022
  • what: The study showed that long-term series growth of height DBH and stem volume are possible to record with a high-to-moderate coefficient of determination (R2 ) of 0.90 0.48 . . .

     

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