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Related Experiment Video

Updated: Jan 7, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

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Integrating multi-source data for canopy gap detection and distribution modeling in a mixed forest ecosystem.

Petar Donev1, Hong Wang2,3, Shuhong Qin4

  • 1College of Earth Sciences and Engineering, Hohai University, Nanjing, China. petardonev@hhu.edu.cn.

Environmental Monitoring and Assessment
|December 20, 2025
PubMed
Summary

Analyzing forest canopy gaps (CGs) reveals seasonal changes over five years. Aerial LiDAR data provided the highest accuracy for segmenting these crucial forest ecosystem features.

Keywords:
CHMLiDARSegmentation statistical models

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Area of Science:

  • Forestry Science
  • Ecology
  • Remote Sensing

Background:

  • Forest canopy gaps (CGs) are vital for forest dynamics, biodiversity, and ecosystem resilience.
  • Understanding seasonal CG changes is crucial for effective forest management.

Purpose of the Study:

  • To analyze seasonal changes in forest canopy gaps over a five-year period.
  • To evaluate the accuracy of different remote sensing data sources for CG segmentation.
  • To model the spatial and temporal dynamics of CGs.

Main Methods:

  • Utilized multi-source data: Unmanned Aerial Vehicle (UAV) RGB imagery, satellite multispectral imagery, Synthetic Aperture Radar (SAR), and Light Detection and Ranging (LiDAR).
  • Employed statistical models, including Weibull Distribution and Markov Chain, for spatial and temporal CG analysis.
  • Assessed segmentation accuracy, with aerial LiDAR achieving 87% for smaller gaps.

Main Results:

  • Aerial LiDAR demonstrated the highest segmentation accuracy (87%), followed by UAV RGB (84%) and satellite data (70%).
  • Gap size distribution shifted over five years, with smaller gaps prevalent initially and larger gaps increasing later, especially in spring and autumn.
  • Seasonal analysis revealed distinct patterns in CG expansion and contraction.

Conclusions:

  • Combining multi-source remote sensing data and statistical modeling offers a robust approach for CG segmentation and analysis.
  • The findings support flexible monitoring of forest ecosystems, aiding sustainable forest management practices.
  • Understanding seasonal CG dynamics is essential for predicting forest resilience and biodiversity.