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Related Experiment Video

Updated: Sep 9, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

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Published on: August 29, 2019

Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data.

Wenwu Zhou^1,2, Qingtai Shu², Cuifen Xia²

¹Guangyuan Forestry Workstation, Guangyuan, China.

Frontiers in Plant Science

|August 28, 2025

Summary

This study demonstrates a novel approach for estimating forest canopy closure (FCC) using satellite-borne LiDAR and machine learning. The findings offer a cost-effective method for high-accuracy, large-scale FCC assessment in mountainous regions.

Keywords:

Bayesian optimization algorithm ICESat-2/ATLAS forest canopy closure geographically weighted regression machine learning method multi-source remote sensing data

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Area of Science:

Forestry
Remote Sensing
Biodiversity Assessment

Background:

Forest canopy closure (FCC) is crucial for evaluating forest resources and biodiversity.
Accurate and cost-effective regional FCC estimation is a research hotspot.
Multi-source remote sensing synergy is key for improving FCC estimates.

Purpose of the Study:

To develop a high-accuracy, low-cost method for regional FCC estimation in mountainous areas.
To utilize satellite-borne LiDAR (ICESat-2/ATLAS) data for footprint-scale FCC modeling.
To integrate multi-source remote sensing data (Sentinel-1/2) and terrain factors for regional FCC mapping.

Main Methods:

Implemented Bayesian optimization (BO) with Random Forest (RF) for footprint-scale FCC modeling using ICESat-2/ATLAS data.
Selected optimal LiDAR feature indices (e.g., Landsat_perc, h_dif_canopy) for FCC estimation.
Employed a geographically weighted regression (GWR) model for regional-scale FCC estimation using footprint-scale data as training samples.

Main Results:

The BO-GBRT model achieved the best footprint-scale FCC estimation (R² = 0.65).
The regional-scale GWR model demonstrated high accuracy (R² = 0.70) using footprint-scale data.
Regional FCC estimates showed strong agreement with measured values (R² = 0.70, correlation coefficient = 0.784).

Conclusions:

Satellite-borne LiDAR (ICESat-2/ATLAS) provides high-density, high-precision data for mountain FCC estimation.
Footprint-scale FCC estimates can effectively train regional-scale GWR models.
The developed methodology offers a valuable reference for low-cost, high-precision FCC estimation from local to regional scales.