Work

Accurate Subtropical Mixed Forests Volume Estimation Through UAV-LiDAR and Random Forest

Year: 2026

Type: article

Abstract: This study refines the application of unmanned aerial vehicle light detection and ranging (UAV-LiDAR) for forest parameter extraction, focusing on calibration aspects of random forest models within subtropical mixed forests. A notable inverse correlation was identified between high-density LiDAR-derived intensity percentiles and stand volume of 20 m × 20 m plots. Using a random forest model with a 20% validation proportion (30 plots), we achieved a validation coefficient of determination (R2) of 0.61 and root mean square error (RMSE) of 27.2%. Key variables included radar echo intensity and forest gap fraction, emphasizing their importance in volume estimation. Additionally, analyses highlighted forest gap fraction as an important variable in model construction, suggesting careful consideration of its distribution in sample plot selection.

Source: Forests

DOI: 10.3390/f17060632

Authors: Lixing Guo, Zhe Xu, Tianxiang Yue

Institutions: Yichun University, Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, State Key Laboratory of Resources and Environmental Information System

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,137