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
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| Publicat a: | Geo-Spatial Information Science vol. 28, no. 2 (Apr 2025), p. 609 |
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| Autor principal: | |
| Altres autors: | , , , |
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Taylor & Francis Ltd.
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3224790444 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1009-5020 | ||
| 022 | |a 1993-5153 | ||
| 024 | 7 | |a 10.1080/10095020.2024.2311261 |2 doi | |
| 035 | |a 3224790444 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 100 | 1 | |a Jin, Hungil |u Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, China; Faculty of Forest Science, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea | |
| 245 | 1 | |a 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 | |
| 260 | |b Taylor & Francis Ltd. |c Apr 2025 | ||
| 513 | |a Case Study Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a China | |
| 653 | |a Carbon cycle | ||
| 653 | |a Accuracy | ||
| 653 | |a Landsat | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Climate change | ||
| 653 | |a Biomass | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision trees | ||
| 653 | |a Airborne lasers | ||
| 653 | |a Forest biomass | ||
| 653 | |a Support vector machines | ||
| 653 | |a Prediction models | ||
| 653 | |a National forests | ||
| 653 | |a Algorithms | ||
| 653 | |a Ensemble learning | ||
| 653 | |a Estimation | ||
| 653 | |a Remote sensing | ||
| 653 | |a Regional analysis | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhao, Yinghui |u Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, China | |
| 700 | 1 | |a Pak, Unil |u Center for Ecological Research, Northeast Forestry University, Harbin, China | |
| 700 | 1 | |a Zhen, Zhen |u Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, China | |
| 700 | 1 | |a So, Kumryong |u Faculty of Forest Science, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea; College of Forestry Economics and Management, Northeast Forestry University, Harbin, China | |
| 773 | 0 | |t Geo-Spatial Information Science |g vol. 28, no. 2 (Apr 2025), p. 609 | |
| 786 | 0 | |d ProQuest |t Research Library | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3224790444/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3224790444/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |