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| DOI | 10.1080/10095020.2024.2311261 |
| Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China | |
| 发表日期 | 2024 |
| ISSN | 1009-5020 |
| EISSN | 1993-5153 |
| 英文摘要 | Accurate estimation of forest aboveground biomass is essential for the assessment of regional carbon cycle and the climate change in the terrestrial ecosystem. Currently, ensemble learning algorithms and cross-validation methods have been widely applied to estimate regional forest Above Ground Biomass (AGB). However, the effects of ensemble learning algorithms, validation methods, and their interactions on forest AGB estimation were rarely investigated. Based on Landsat 8 Operational Land Imager (OLI) imagery, Airborne Laser Scanning (ALS) data and China's National Forest Continuous Inventory data, this study explored the effects of five ensemble learning algorithms, including Simple Averaging (SA), Weighted Averaging (WA), Stacked Generalization (SG), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and two validation approaches (i.e. 10-fold and leave-one-out cross-validation) on the AGB estimation of the Natural Secondary Forests (NSFs) in northeast China. The results revealed that the ensemble learning algorithms that combine heterogenous-based models (i.e. SA, WA, SG) generally produced higher accuracy than the base models (i.e. Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Convolutional Neural Network (CNN)). Among all ensemble learning algorithms, the SG algorithm has the highest accuracy whereas the XGBoost algorithm has the lowest accuracy. Although prediction models considerably impact the accuracy of AGB estimation, the validation approach also plays a non-negligible role in AGB estimation. The leave-one-out cross-validation produced much higher accuracy than the 10-fold cross-validation using the same prediction model and tends to generate over-optimistic AGB estimates compared to 10-fold cross-validation, especially for the averaging and stacking ensemble learning algorithms (i.e. SA, WA, SG). This study highlights the potential challenges of applying a leave-one-out cross-validation approach and provides a scientific foundation for the feasibility of different ensemble learning algorithms and cross-validation approaches for accurate AGB estimation. |
| 英文关键词 | Landsat 8 OLI; Light detection and ranging (LiDAR); ensemble learning; Natural secondary forests (NSFs); Aboveground biomass (AGB); cross-validation |
| 语种 | 英语 |
| WOS研究方向 | Remote Sensing |
| WOS类目 | Remote Sensing |
| WOS记录号 | WOS:001161190000001 |
| 来源期刊 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/304106 |
| 作者单位 | Northeast Forestry University - China; Northeast Forestry University - China; Northeast Forestry University - China |
| 推荐引用方式 GB/T 7714 | . Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China[J],2024. |
| APA | (2024).Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China.GEO-SPATIAL INFORMATION SCIENCE. |
| MLA | "Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China".GEO-SPATIAL INFORMATION SCIENCE (2024). |
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