Work

Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains

Year: 2026

Type: article

Abstract: Accurate estimation of tree Above-Ground Biomass (AGB) is critical for the timely monitoring of forest dynamics. However, the scarcity of high-quality in situ measured data introduces considerable uncertainty into the precise spatial mapping of AGB. In this study, a novel method for AGB mapping is proposed to enhance the accuracy of AGB estimation based on the Semi-Supervised Ensemble Learning (SSEL) strategy. By expanding the sample set via an iterative self-training approach based on an Inverted Query-by-Committee (I-QBC) strategy, the model significantly enhances the accuracy of AGB estimation. Using Sentinel-2 data, the experimental results show that: (1) The I-QBC-driven SSEL model demonstrated significantly higher estimation accuracy for AGB compared to conventional tree-based ensemble models. Optimal stability (R2 = 0.80) and peak accuracy (R2 = 0.88) were achieved at sample increments of 20 and 30, respectively. (2) Among various feature types, Recursive Feature Elimination with Cross-Validation (RFECV) identified GNDVI, PSSRa, slope and texture correlation as the most critical predictors for AGB estimation in the study area. (3) The total AGB stock in the study area is estimated to range from 1.46 × 107 Mg to 1.71 × 107 Mg. The SSEL model provides a valuable reference for AGB estimation under sparse ground-truth sample conditions, while offering a novel approach for large-scale AGB mapping.

Source: Remote Sensing

DOI: 10.3390/rs18101627

Authors: Wenqiang Zhou, Shiwen Deng, Shuying Zang, Dianfan Guo

Institution: Harbin Normal University

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Climate action

Open Access status: gold

Estimated APC paid: $3,258