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Extracting Forest Parameters based on Stand Automatic Segmentation Algorithm.

Pengxiang Zhao1, Linghan Gao2, Ting Gao1

  • 1College of forestry, Northwest A&F University, Yangling, 712100, China.

Scientific Reports
|February 2, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic forest stand segmentation algorithm using ArboLiDAR data. The optimized algorithm provides accurate forest parameter extraction for improved forest inventory and management.

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Area of Science:

  • Forestry
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Accurate forest stand segmentation is crucial for effective forest management and inventory.
  • The quality of forest stand level parameters directly depends on segmentation accuracy.

Purpose of the Study:

  • To develop and optimize an automatic forest stand segmentation algorithm using ArboLiDAR.
  • To evaluate the algorithm's performance in the Dayekou forest area, Qilian Mountain, China.
  • To extract key forest parameters at the stand level.

Main Methods:

  • Developed an automatic forest stand segmentation algorithm utilizing ArboLiDAR software for processing Light Detection and Ranging (LiDAR) point cloud data.
  • Optimized algorithm parameters specifically for the Dayekou forest area to achieve precise automatic stand segmentation.
  • Extracted forest parameters including mean height (H), average diameter at breast height (D), basal area (G), and stand volume (V) using the Bysh method.

Main Results:

  • The limited region growing method based on the gradient was identified as the most suitable approach for automatic stand segmentation in the study area.
  • The fifth group of tested parameters yielded optimal results for automatic stand segmentation.
  • The coefficient of determination (R²) for H, D, G, and V were 0.744, 0.720, 0.562, and 0.696, respectively, with corresponding RMSE values of 5.24%, 28.57%, 19.93%, and 17.66%.

Conclusions:

  • The developed ArboLiDAR-based algorithm and optimized parameters provide a reliable method for automatic forest stand segmentation.
  • The study demonstrates the effectiveness of the approach for extracting essential forest parameters, offering a technical basis for future forest resource inventory in China.