Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach
The Mongolian Plateau, a critical area for global climate change response, faces increasing vulnerability from climate change and human activities impacting its arid ecosystems. This study integrates GeoDetector and machine learning to predict vegetation Carbon Use Efficiency (CUE) dynamics. It util...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/8/1392 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850180014079737856 |
|---|---|
| author | Xinyu Yang Qiang Yu Buyanbaatar Avirmed Yu Wang Jikai Zhao Weijie Sun Huanjia Cui Bowen Chi Ji Long |
| author_facet | Xinyu Yang Qiang Yu Buyanbaatar Avirmed Yu Wang Jikai Zhao Weijie Sun Huanjia Cui Bowen Chi Ji Long |
| author_sort | Xinyu Yang |
| collection | DOAJ |
| description | The Mongolian Plateau, a critical area for global climate change response, faces increasing vulnerability from climate change and human activities impacting its arid ecosystems. This study integrates GeoDetector and machine learning to predict vegetation Carbon Use Efficiency (CUE) dynamics. It utilizes multi-source remote sensing data (MODIS, ERA5-Land) from 2000 to 2020 and incorporates four Shared Socioeconomic Pathways (SSPs) from CMIP6. The results indicate the following: (1) significant spatial variation exists, with high-value CUE areas (≥0.7) in the northwest due to favorable climatic conditions, while low-value areas (<0.6) in the east are affected by decreasing precipitation and overgrazing; (2) CUE increased at an annual rate of 1.03%, with a 43% acceleration after the 2005 climate shift, highlighting the synergistic effects of ecological engineering; (3) our findings reveal that the interaction of evapotranspiration and temperature dominates CUE spatial differentiation, with the random forest model accurately predicting CUE dynamics (root mean square error (RMSE) = 0.0819); (4) scenario simulations show the SSP3-7.0 pathway will peak CUE at 0.6103 by 2050, while the SSP5-8.5 scenario will significantly reduce spatial heterogeneity. The study recommends enhancing water–heat regulation in the northwest and implementing vegetation restoration strategies in the east, alongside establishing a CUE warning system. This research offers valuable insights for improving carbon sequestration and climate resilience in arid ecosystems, with significant implications for carbon management under high-emission scenarios. |
| format | Article |
| id | doaj-art-fe1cf9f6fe454ffebf4a0ddd307895ac |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-fe1cf9f6fe454ffebf4a0ddd307895ac2025-08-20T02:18:20ZengMDPI AGRemote Sensing2072-42922025-04-01178139210.3390/rs17081392Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning ApproachXinyu Yang0Qiang Yu1Buyanbaatar Avirmed2Yu Wang3Jikai Zhao4Weijie Sun5Huanjia Cui6Bowen Chi7Ji Long8College of Forestry, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaSchool of Agroecology, Mongolian University of Life Sciences, Ulaanbaatar 999097, MongoliaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe Mongolian Plateau, a critical area for global climate change response, faces increasing vulnerability from climate change and human activities impacting its arid ecosystems. This study integrates GeoDetector and machine learning to predict vegetation Carbon Use Efficiency (CUE) dynamics. It utilizes multi-source remote sensing data (MODIS, ERA5-Land) from 2000 to 2020 and incorporates four Shared Socioeconomic Pathways (SSPs) from CMIP6. The results indicate the following: (1) significant spatial variation exists, with high-value CUE areas (≥0.7) in the northwest due to favorable climatic conditions, while low-value areas (<0.6) in the east are affected by decreasing precipitation and overgrazing; (2) CUE increased at an annual rate of 1.03%, with a 43% acceleration after the 2005 climate shift, highlighting the synergistic effects of ecological engineering; (3) our findings reveal that the interaction of evapotranspiration and temperature dominates CUE spatial differentiation, with the random forest model accurately predicting CUE dynamics (root mean square error (RMSE) = 0.0819); (4) scenario simulations show the SSP3-7.0 pathway will peak CUE at 0.6103 by 2050, while the SSP5-8.5 scenario will significantly reduce spatial heterogeneity. The study recommends enhancing water–heat regulation in the northwest and implementing vegetation restoration strategies in the east, alongside establishing a CUE warning system. This research offers valuable insights for improving carbon sequestration and climate resilience in arid ecosystems, with significant implications for carbon management under high-emission scenarios.https://www.mdpi.com/2072-4292/17/8/1392carbon use efficiency (CUE)Mongolian Plateaumachine learningCMIP6scenario simulationsclimate change |
| spellingShingle | Xinyu Yang Qiang Yu Buyanbaatar Avirmed Yu Wang Jikai Zhao Weijie Sun Huanjia Cui Bowen Chi Ji Long Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach Remote Sensing carbon use efficiency (CUE) Mongolian Plateau machine learning CMIP6 scenario simulations climate change |
| title | Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach |
| title_full | Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach |
| title_fullStr | Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach |
| title_full_unstemmed | Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach |
| title_short | Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach |
| title_sort | spatiotemporal dynamics and future projections of carbon use efficiency on the mongolian plateau a remote sensing and machine learning approach |
| topic | carbon use efficiency (CUE) Mongolian Plateau machine learning CMIP6 scenario simulations climate change |
| url | https://www.mdpi.com/2072-4292/17/8/1392 |
| work_keys_str_mv | AT xinyuyang spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT qiangyu spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT buyanbaataravirmed spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT yuwang spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT jikaizhao spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT weijiesun spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT huanjiacui spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT bowenchi spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach AT jilong spatiotemporaldynamicsandfutureprojectionsofcarbonuseefficiencyonthemongolianplateauaremotesensingandmachinelearningapproach |