Assessment of land surface vulnerability using time-series geospatial datasets
Assessing land surface vulnerability is important for understanding ecosystem responses to environmental changes. However, quantitative studies are still lacking, particularly in capturing temporal dynamics. This study proposes a framework for quantitatively assessing land surface vulnerability by i...
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| Language: | English |
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001876 |
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| author | Bo Yuan Shanchuan Guo Haowei Mu Xiaoquan Pan Chunqiang Li Zilong Xia Xingang Zhang Peijun Du |
| author_facet | Bo Yuan Shanchuan Guo Haowei Mu Xiaoquan Pan Chunqiang Li Zilong Xia Xingang Zhang Peijun Du |
| author_sort | Bo Yuan |
| collection | DOAJ |
| description | Assessing land surface vulnerability is important for understanding ecosystem responses to environmental changes. However, quantitative studies are still lacking, particularly in capturing temporal dynamics. This study proposes a framework for quantitatively assessing land surface vulnerability by integrating time-series geospatial datasets from the “water-soil-climate-plant” system, which reflects the dynamics of surface water, soil erosion, drought, and vegetation. Based on dynamic data from these four subsystems during 1990−2022, the spatial heterogeneity of land surface vulnerability and its relationship with both natural and anthropogenic factors were analyzed in the Hohhot-Baotou-Ordos-Yulin urban agglomeration. The results indicate that a significant spatial overlap between areas of high land surface vulnerability and ecological management zones. Specifically, 6.1 % of severely vulnerable regions are concentrated in the Maowusu sandy land, the Kubuqi desert, and the Loess hilly-gully region. Severe vulnerability is also evident in the central part of the urban agglomeration, largely influenced by the compound effects of multiple subsystems. Among these subsystems, the proportion of regions with high and severe vulnerability is highest in drought (33.4 %), followed by soil (16.7 %), vegetation (9.9 %), and surface water (9.3 %). Human activities have facilitated ecosystem recovery in the Yinshan Daqing Mountains and parts of the Kubuqi desert, whereas restoration efforts in the Maowusu sandy land remains limited. In the Loess hilly-gully region, vulnerability intensifies with increasing human activity but is relatively less affected by aridity intensity. By integrating annual fluctuations from key land surface subsystems, this study offers a dynamic vulnerability assessment framework, providing valuable insights for enhancing land surface system resilience in response to ongoing climatic and anthropogenic challenges. |
| format | Article |
| id | doaj-art-fa1d3ca1a4364223beff119d09e58879 |
| institution | DOAJ |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-fa1d3ca1a4364223beff119d09e588792025-08-20T03:19:56ZengElsevierEcological Informatics1574-95412025-11-018910317810.1016/j.ecoinf.2025.103178Assessment of land surface vulnerability using time-series geospatial datasetsBo Yuan0Shanchuan Guo1Haowei Mu2Xiaoquan Pan3Chunqiang Li4Zilong Xia5Xingang Zhang6Peijun Du7Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, ChinaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China; Corresponding author at: Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China.Assessing land surface vulnerability is important for understanding ecosystem responses to environmental changes. However, quantitative studies are still lacking, particularly in capturing temporal dynamics. This study proposes a framework for quantitatively assessing land surface vulnerability by integrating time-series geospatial datasets from the “water-soil-climate-plant” system, which reflects the dynamics of surface water, soil erosion, drought, and vegetation. Based on dynamic data from these four subsystems during 1990−2022, the spatial heterogeneity of land surface vulnerability and its relationship with both natural and anthropogenic factors were analyzed in the Hohhot-Baotou-Ordos-Yulin urban agglomeration. The results indicate that a significant spatial overlap between areas of high land surface vulnerability and ecological management zones. Specifically, 6.1 % of severely vulnerable regions are concentrated in the Maowusu sandy land, the Kubuqi desert, and the Loess hilly-gully region. Severe vulnerability is also evident in the central part of the urban agglomeration, largely influenced by the compound effects of multiple subsystems. Among these subsystems, the proportion of regions with high and severe vulnerability is highest in drought (33.4 %), followed by soil (16.7 %), vegetation (9.9 %), and surface water (9.3 %). Human activities have facilitated ecosystem recovery in the Yinshan Daqing Mountains and parts of the Kubuqi desert, whereas restoration efforts in the Maowusu sandy land remains limited. In the Loess hilly-gully region, vulnerability intensifies with increasing human activity but is relatively less affected by aridity intensity. By integrating annual fluctuations from key land surface subsystems, this study offers a dynamic vulnerability assessment framework, providing valuable insights for enhancing land surface system resilience in response to ongoing climatic and anthropogenic challenges.http://www.sciencedirect.com/science/article/pii/S1574954125001876VulnerabilityComplex systemsPixel scaleArid and semi-arid regionsHohhot-Baotou-Ordos-Yulin urban agglomeration |
| spellingShingle | Bo Yuan Shanchuan Guo Haowei Mu Xiaoquan Pan Chunqiang Li Zilong Xia Xingang Zhang Peijun Du Assessment of land surface vulnerability using time-series geospatial datasets Ecological Informatics Vulnerability Complex systems Pixel scale Arid and semi-arid regions Hohhot-Baotou-Ordos-Yulin urban agglomeration |
| title | Assessment of land surface vulnerability using time-series geospatial datasets |
| title_full | Assessment of land surface vulnerability using time-series geospatial datasets |
| title_fullStr | Assessment of land surface vulnerability using time-series geospatial datasets |
| title_full_unstemmed | Assessment of land surface vulnerability using time-series geospatial datasets |
| title_short | Assessment of land surface vulnerability using time-series geospatial datasets |
| title_sort | assessment of land surface vulnerability using time series geospatial datasets |
| topic | Vulnerability Complex systems Pixel scale Arid and semi-arid regions Hohhot-Baotou-Ordos-Yulin urban agglomeration |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125001876 |
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