the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model
Abstract. Aboveground biomass (AGB) estimates at the plot level plays a major part in connecting accurate single-tree AGB measurements to relatively difficult regional-scale AGB estimates. However, complex and spatially heterogeneous landscapes, where multiple environmental covariates (such as longitude, latitude, and forest structure) affect the spatial distribution of AGB, make upscaling of plot-level models more challenging. To address this challenge, this study proposes an approach that combines machine learning with spatial statistics to construct a more accurate plot-level AGB model. The study was conducted in a Eucalyptus plantation in Nanjing, China. We developed, evaluated, and compared the accuracy and performance of three different machine learning models [support vector machine (SVM), random forest (RF), and the radial basis function artificial neural network (RBF-ANN)], one spatial statistics model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, RBF-ANN & P-BSHADE) for forest AGB estimates based on AGB data from 30 sample plots and their corresponding environmental covariates. The results show that the performance indices RMSE, nRMSE, MAE, and MRE of all combined models are substantially smaller than those of any individual models, with the RF & P-BSHADE combined method giving the smallest value. These results demonstrate clearly that combined models, especially the RF & P-BSHADE model, can improve the accuracy of plot-level AGB models and reduce uncertainty on plot-level AGB estimates or even on large-forested-landscape AGB estimates. These research results are important because they reduce the uncertainty in estimates of the regional carbon balance.
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Interactive discussion
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SC1: 'review comments on “Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model”', Wenli Huang, 07 Mar 2020
- AC1: 'Response to SC1', Yin Ren, 21 Apr 2020
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SC2: 'Interactive comment on “Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model” by Shaoqing Dai et al.', Wenli Huang, 14 Mar 2020
- AC2: 'Response to SC2', Yin Ren, 21 Apr 2020
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RC1: 'Review of bg-2020-36', Anonymous Referee #1, 25 Mar 2020
- AC3: 'Response to RC1', Yin Ren, 21 Apr 2020
- AC5: 'Additional response to RC1', Yin Ren, 05 Jun 2020
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RC2: 'Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model', Anonymous Referee #2, 16 May 2020
- AC4: 'Response to RC2', Yin Ren, 05 Jun 2020
Interactive discussion
-
SC1: 'review comments on “Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model”', Wenli Huang, 07 Mar 2020
- AC1: 'Response to SC1', Yin Ren, 21 Apr 2020
-
SC2: 'Interactive comment on “Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model” by Shaoqing Dai et al.', Wenli Huang, 14 Mar 2020
- AC2: 'Response to SC2', Yin Ren, 21 Apr 2020
-
RC1: 'Review of bg-2020-36', Anonymous Referee #1, 25 Mar 2020
- AC3: 'Response to RC1', Yin Ren, 21 Apr 2020
- AC5: 'Additional response to RC1', Yin Ren, 05 Jun 2020
-
RC2: 'Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model', Anonymous Referee #2, 16 May 2020
- AC4: 'Response to RC2', Yin Ren, 05 Jun 2020
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Cited
2 citations as recorded by crossref.
- Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale H. Matinfar et al. 10.1016/j.catena.2021.105258
- Estimation of Forest Residual Biomass for Bioelectricity Utilization towards Carbon Neutrality Based on Sentinel-2A Multi-Temporal Images: A Case Study of Aizu Region of Fukushima, Japan T. Qian et al. 10.3390/rs16040706
Shaoqing Dai
Xiaoman Zheng
Chengdong Xu
Shudi Zuo
Qi Chen
Xiaohua Wei
Yin Ren
This preprint has been withdrawn.
- Preprint
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Supplement
(4292 KB) - BibTeX
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