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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
Published on: October 24, 2025
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.
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.
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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.