An upscaling framework for estimating regional-scale fractional cover of Chinese fir (FCCF) by integrating UAV and satellite images
Year: 2025
Type: article
Abstract: Accurate estimation of fractional cover of Chinese fir (Cunninghamialanceolata (Lamb.) Hook.) (FCCF) is crucial for forest management and carbon sequestration assessment. Traditional field inventory is time-consuming, labor-intensive, prone to sampling bias, and often mismatched with satellite image resolutions, hindering accurate regional FCCF mapping. In this study, we propose an upscaling framework for regional estimation of FCCF integrating unmanned aerial vehicle (UAV) and satellite imagery. High-precision classification labels were generated from UAV visible imagery using three methods: object-based image analysis with random forest (OBIA-RF), maximum likelihood, and minimum distance classifiers. The resulting binary classification maps were then aggregated to different spatial resolutions and linked with Sentinel-2A (10 m and 20 m) and Landsat 8 OLI (30 m) data to develop FCCF estimation models. We evaluated the performance of four models, including linear regression (LR), RF, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). SHapley Additive exPlanations (SHAP) values were employed to quantify the influence of vegetation indices (VIs) derived from satellite images on model accuracy. Results showed that OBIA-RF achieved the highest accuracy for Chinese fir classification based on UAV images, with an average overall accuracy and recall of 0.919 and 0.909, respectively. At the satellite scale, the LightGBM, RF, and XGBoost models had the best FCCF estimates at 10 m, 20 m, and 30 m spatial resolutions with R2 of 0.569, 0.762, and 0.731, respectively. Notably, models trained on coarse-resolution data achieve higher predictive accuracy and exhibit lower uncertainty. In addition, VIs related to the near-infrared and shortwave infrared have a higher importance in FCCF estimation. This framework demonstrates the effectiveness of integrating UAV and satelite data for regional-scale FCCF estimation and provides practical foundation for improving forest monitoring, management decision and accurate carbon sequestration assessment in Chinese fir plantations.
Authors: Houxi Zhang, Jianwei Cai, Minghui Cai, Xunlong Chen, Yiming Sun, Kun Chen, Shijiang Cao, Jianling Ren
Institution: Fujian Agriculture and Forestry University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: gold
Estimated APC paid: $2,390
OpenAlex ID: W4410889352