ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1629146

This article is part of the Research TopicAccurate Measurement and Dynamic Monitoring of Forest ParametersView all 8 articles

Forest Canopy Closure Estimation in Mountainous Southwest China Using Multi-source Remote Sensing Data

Provisionally accepted
  • 1Guangyuan Forestry Workstation, Guang yuan, China
  • 2College of Forestry, Southwest Forestry University, Kunming, China
  • 3Faculty of College of Soil and Water Conservation, Southwest Forestry University, Kunming, China

The final, formatted version of the article will be published soon.

Forest canopy closure (FCC) is an important biological parameter to evaluate forest resources and biodiversity, and the use of multi-source remote sensing synergy to achieve high-accuracy estimate regional FCC at low-cost is a current research hotspot. In this study, Shangri-La City, a mountainous area in southwest China, was taken as the research area. The satellite-borne LiDAR ICESat-2/ATLAS data was used as the main information source. Combined with 54 measured plot data, the improved machine learning model of Bayesian optimization (BO) algorithm was used to obtain the FCC in the footprintspot-scale ATLAS footprintspot. Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that: (1) Among the 50 extracted ATLAS LiDAR feature indexes, the best spot footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness and n_touc_photons after random forest (RF) RF feature variable optimization. (2) Among the BO-RFR, BO-KNN and BO-GBRT models developed at the footprint-spot scale, the FCC results estimated by the BO-GBRT model were the best (R 2 = 0.65, RMSE = 0.10, RS = 0.079, P = 79.2%), which was used as the FCC estimation model for 74808 spots footprints in the study area. (3) Taking the FCC value of ATLAS spot footprintscale in forest land as the training sample data of regional-scale GWR model, the model accuracy was R 2 = 0.70, RMSE = 0.06, P = 88.27%. (4) The R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. Additionally, the average FCC is 0.50, predominantly distributed between 0.3 and 0.6, comprising 68.43%. These findings highlight the advantages of mountain FCC estimation using ICESat-2/ATLAS high-density, high-precision footprintlight spots, and that small-sample estimation results at the footprint-light spot scale can serve as training data for the regional-scale GWR model, offering a reference for low-cost, high-precision FCC estimation from footprint-scale light spot to regional-scale.

Keywords: ICESat-2/ATLAS1, Bayesian optimization algorithm2, Machine learning method3, Geographically weighted regression4, Multi-source remote sensing data5, Forest canopy closure6

Received: 15 May 2025; Accepted: 10 Jul 2025.

Copyright: © 2025 Zhou, Shu, Xia, Xu, Xiang, Fu, Zhengdao Yang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Qingtai Shu, College of Forestry, Southwest Forestry University, Kunming, China

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