Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data
Aboveground biomass (AGB) is a critical indicator for assessing carbon sequestration and ecosystem health in transboundary ecologically fragile areas. High-precision estimation and spatiotemporal inversion of AGB are the key to investigating transition zones. However, inadequate feature selection an...
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MDPI AG
2025-07-01
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| author | Jiani Ma Chao Zhang Cong Ou Chi Qiu Cuicui Yang Beibei Wang Urtnasan Mandakh |
| author_facet | Jiani Ma Chao Zhang Cong Ou Chi Qiu Cuicui Yang Beibei Wang Urtnasan Mandakh |
| author_sort | Jiani Ma |
| collection | DOAJ |
| description | Aboveground biomass (AGB) is a critical indicator for assessing carbon sequestration and ecosystem health in transboundary ecologically fragile areas. High-precision estimation and spatiotemporal inversion of AGB are the key to investigating transition zones. However, inadequate feature selection and complex parameter tuning limit accuracy and spatiotemporal representation in the estimation model. An AGB estimation model that integrates SHAP-based feature selection with a particle swarm optimization-enhanced random forest model (RF_PSO) was proposed. Then AGB trajectory clustering was used to characterize the grassland change pattern. The method was applied to grasslands across the China–Mongolia–Russia (CMR) border area from 2000 to 2020. The results show that (1) the SHAP-RF_PSO model achieved the highest accuracy (R<sup>2</sup> = 0.87, RMSE = 45.8 g/m<sup>2</sup>), outperforming other estimation models. (2) AGB improvements were observed in 72.13% of the area, mainly in MN_EA, MN_CE, and CN_NMG, while 27.39% showed degradation, concentrated in CN_NMG and MN_CE. The stable area accounts for 0.48%, which is scattered in RU_BU and RU_ZA.CN_NMG. (3) Four change patterns, namely Fluctuating Low, Stable Low, Fluctuating High, and Stable High, were identified, with major shifts in 2007, 2012, and 2014. (4) Projections indicate that 80% of the region may maintain current trends, 13% may reverse, and 7% remain uncertain, requiring targeted interventions. This study offers a robust tool for high-precision AGB estimation and supports dynamic monitoring in the CMR border area. |
| format | Article |
| id | doaj-art-39a165d30eee4da1a7e78aeb18be9e54 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-39a165d30eee4da1a7e78aeb18be9e542025-08-20T03:08:10ZengMDPI AGRemote Sensing2072-42922025-07-011714252710.3390/rs17142527Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial DataJiani Ma0Chao Zhang1Cong Ou2Chi Qiu3Cuicui Yang4Beibei Wang5Urtnasan Mandakh6College of Geographical Sciences, Shanxi Normal University, Taiyuan 030031, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaDivision of GIS and Remote Sensing, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, MongoliaAboveground biomass (AGB) is a critical indicator for assessing carbon sequestration and ecosystem health in transboundary ecologically fragile areas. High-precision estimation and spatiotemporal inversion of AGB are the key to investigating transition zones. However, inadequate feature selection and complex parameter tuning limit accuracy and spatiotemporal representation in the estimation model. An AGB estimation model that integrates SHAP-based feature selection with a particle swarm optimization-enhanced random forest model (RF_PSO) was proposed. Then AGB trajectory clustering was used to characterize the grassland change pattern. The method was applied to grasslands across the China–Mongolia–Russia (CMR) border area from 2000 to 2020. The results show that (1) the SHAP-RF_PSO model achieved the highest accuracy (R<sup>2</sup> = 0.87, RMSE = 45.8 g/m<sup>2</sup>), outperforming other estimation models. (2) AGB improvements were observed in 72.13% of the area, mainly in MN_EA, MN_CE, and CN_NMG, while 27.39% showed degradation, concentrated in CN_NMG and MN_CE. The stable area accounts for 0.48%, which is scattered in RU_BU and RU_ZA.CN_NMG. (3) Four change patterns, namely Fluctuating Low, Stable Low, Fluctuating High, and Stable High, were identified, with major shifts in 2007, 2012, and 2014. (4) Projections indicate that 80% of the region may maintain current trends, 13% may reverse, and 7% remain uncertain, requiring targeted interventions. This study offers a robust tool for high-precision AGB estimation and supports dynamic monitoring in the CMR border area.https://www.mdpi.com/2072-4292/17/14/2527AGBSHAPRF_PSOchange analysisChina–Mongolia–Russia border area |
| spellingShingle | Jiani Ma Chao Zhang Cong Ou Chi Qiu Cuicui Yang Beibei Wang Urtnasan Mandakh Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data Remote Sensing AGB SHAP RF_PSO change analysis China–Mongolia–Russia border area |
| title | Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data |
| title_full | Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data |
| title_fullStr | Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data |
| title_full_unstemmed | Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data |
| title_short | Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data |
| title_sort | estimation and change analysis of grassland agb in the china mongolia russia border area based on multi source geospatial data |
| topic | AGB SHAP RF_PSO change analysis China–Mongolia–Russia border area |
| url | https://www.mdpi.com/2072-4292/17/14/2527 |
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