A Multi-Platform LiDAR-Based Method for Co-optimization of Stand Structure and Biomass

By using multi-platform LiDAR point cloud technology and grey relational weighted Voronoi diagram partitioning, combined with structural equation modeling and multi-objective simulated selective logging algorithm, the problem of multi-source data fusion in forest ecological remote sensing was solved, realizing the synergistic optimization of forest stand spatial structure and biomass, and improving forest productivity and carbon sink potential.

CN122089753APending Publication Date: 2026-05-26EAST CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
EAST CHINA UNIV OF TECH
Filing Date
2025-12-02
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies in forest ecological remote sensing suffer from problems such as difficulty in multi-source data fusion, low accuracy in individual tree segmentation, unreasonable division of spatial structural units, unclear driving mechanisms between parameters, and lack of quantitative support for management decisions, leading to a decline in forest productivity and a weakening of carbon sequestration potential.

Method used

Using multi-platform LiDAR point cloud technology, combined with a single-tree segmentation algorithm that integrates voxel clustering and region growth, spatial structural units are divided using grey relational weighted Voronoi diagrams, six core spatial structural parameters are quantified, and a comprehensive stand spatial index Q is constructed. By combining structural equation modeling and multi-objective simulated selective logging algorithm, the synergistic optimization of stand spatial structure and biomass is achieved.

Benefits of technology

It achieves high-precision extraction of individual tree parameters and construction of spatial structure units, quantifies the nonlinear relationship between forest stand spatial structure and aboveground biomass, improves the optimization efficiency of forest stand spatial structure and carbon sink function, and provides quantifiable management decision support.

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Abstract

This invention discloses a method for synergistic optimization of forest stand structure and biomass based on multi-platform LiDAR. Using airborne, vehicle-mounted, and backpack LiDAR point clouds and synchronously measured data, the method first performs denoising, registration, and ground filtering preprocessing. Voxel clustering and region growing algorithms are then used to extract trunk position, diameter at breast height (DBH), tree height, and crown width. Spatial structural units are divided based on a weighted Voronoi diagram of DBH-tree height-crown width grey relational degree. Combining parameter extraction algorithms and species-specific allometric growth equations, six parameters—angular scale (W), DBH-to-size ratio (U), aggregation index (R), openness (K), competition index (CI), and forest layer index (S)—are quantified, and the forest topography index (AGB) of the sample plots is estimated. A complete technical chain is constructed, from data acquisition—structural unit construction—parameter quantification—mechanism analysis—precision management, providing quantifiable support for the sustainable management and carbon sequestration enhancement of mixed coniferous and broadleaf forests.
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