Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains.
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| Title: | Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains. |
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| Authors: | Zhou, Wenqiang1,2 (AUTHOR), Deng, Shiwen2,3 (AUTHOR) dengswen@gmail.com, Zang, Shuying1,2,3 (AUTHOR), Guo, Dianfan1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1627. 28p. |
| Subjects: | Data augmentation, Remote sensing, Feature selection, Supervised learning, Remote-sensing images, Forest measurement |
| Abstract: | Highlights: What are the main findings? Significantly enhanced tree above-ground biomass (AGB) estimation precision can be achieved using an innovative semi-supervised ensemble learning framework. Active sample augmentation is achieved under sparse sampling conditions through a refined Query-by-Committee strategy. What are the implications of the main findings? Precision and stability of AGB remote sensing estimation were improved in scenarios with limited samples. This study provides a novel paradigm for addressing the challenge of sparse ground-truth samples in remote sensing retrieval. 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 ( R 2 = 0.80) and peak accuracy ( R 2 = 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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