Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data
The construction and optimization of ecological networks is a crucial strategy to enhance the stability of ecosystems. In arid and semi-arid regions, where vegetation degradation and water stress are prevalent, improving the connectivity of ecological units is crucial for achieving optimal ecologica...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Environmental and Sustainability Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266597272500251X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849240610736701440 |
|---|---|
| author | Ruyi Pan Jie Zhang Wentao Xue Qianqian Xia Jiangxiang Liu Junjie Yan Hongbo Ling Xufan Jin |
| author_facet | Ruyi Pan Jie Zhang Wentao Xue Qianqian Xia Jiangxiang Liu Junjie Yan Hongbo Ling Xufan Jin |
| author_sort | Ruyi Pan |
| collection | DOAJ |
| description | The construction and optimization of ecological networks is a crucial strategy to enhance the stability of ecosystems. In arid and semi-arid regions, where vegetation degradation and water stress are prevalent, improving the connectivity of ecological units is crucial for achieving optimal ecological network design. This study proposes a new methodological framework that integrates Morphological Spatial Pattern Analysis, circuit theory, and machine learning models to explore the spatiotemporal evolution and optimization of ecological networks in Xinjiang from 1990 to 2020. The study presents a refined classification system for ecological units, focusing on the analysis of spatiotemporal patterns of vegetation degradation and drought stress, and proposes specific strategies for ecological restoration, including optimizing ecological corridors by introducing buffer zones and planting drought-resistant species, restoring key ecological areas like forests and wetlands, establishing desert shelter forests and artificial wetlands in desert regions to prevent desertification, and other similar strategies. The results show: 1) The core ecological source regions (Core) area decreased by 10,300 km2, and the secondary core regions decreased by 23,300 km2. 2) After model optimization, the connectivity of ecological sources significantly improved, with the dynamic patch connectivity and dynamic inter-patch connectivity increasing by 43.84 %–62.86 % and 18.84 %–52.94 %, respectively. 3) The proportion of areas with extraordinarily high and high vegetation cover decreased by 4.7 %, while the area of highly arid regions increased by 2.3 %. 4) Change point analysis revealed that TVDI values in the range of 0.35–0.6 and NDVI values in the range of 0.1–0.35 are critical change intervals, with vegetation showing significant threshold effects under drought stress. 5) The area of high resistance increased by 26,438 km2, the total length of ecological corridors increased by 743 km, and the total area of corridors increased by 14677 km2, with significant growth in biological corridors, facilitating smoother species migration paths. This study provides reasonable references for ecological restoration in arid regions and introduces a new approach to optimizing ecological networks. |
| format | Article |
| id | doaj-art-881dfb9366364bccaa07dae1b2a42c1c |
| institution | Kabale University |
| issn | 2665-9727 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj-art-881dfb9366364bccaa07dae1b2a42c1c2025-08-20T04:00:32ZengElsevierEnvironmental and Sustainability Indicators2665-97272025-09-012710083010.1016/j.indic.2025.100830Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing dataRuyi Pan0Jie Zhang1Wentao Xue2Qianqian Xia3Jiangxiang Liu4Junjie Yan5Hongbo Ling6Xufan Jin7Institute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, ChinaInstitute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, ChinaInstitute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, ChinaInstitute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, ChinaInstitute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, ChinaInstitute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, China; Corresponding author. Institute of Resources and Ecology, Yili Normal University, Yining, 835000, China.Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China; State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China; Corresponding author. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China.Institute of Resources and Ecology, Yili Normal University, Yining, 835000, China; College of Resources and Environment, Yili Normal University, Yining, 835000, ChinaThe construction and optimization of ecological networks is a crucial strategy to enhance the stability of ecosystems. In arid and semi-arid regions, where vegetation degradation and water stress are prevalent, improving the connectivity of ecological units is crucial for achieving optimal ecological network design. This study proposes a new methodological framework that integrates Morphological Spatial Pattern Analysis, circuit theory, and machine learning models to explore the spatiotemporal evolution and optimization of ecological networks in Xinjiang from 1990 to 2020. The study presents a refined classification system for ecological units, focusing on the analysis of spatiotemporal patterns of vegetation degradation and drought stress, and proposes specific strategies for ecological restoration, including optimizing ecological corridors by introducing buffer zones and planting drought-resistant species, restoring key ecological areas like forests and wetlands, establishing desert shelter forests and artificial wetlands in desert regions to prevent desertification, and other similar strategies. The results show: 1) The core ecological source regions (Core) area decreased by 10,300 km2, and the secondary core regions decreased by 23,300 km2. 2) After model optimization, the connectivity of ecological sources significantly improved, with the dynamic patch connectivity and dynamic inter-patch connectivity increasing by 43.84 %–62.86 % and 18.84 %–52.94 %, respectively. 3) The proportion of areas with extraordinarily high and high vegetation cover decreased by 4.7 %, while the area of highly arid regions increased by 2.3 %. 4) Change point analysis revealed that TVDI values in the range of 0.35–0.6 and NDVI values in the range of 0.1–0.35 are critical change intervals, with vegetation showing significant threshold effects under drought stress. 5) The area of high resistance increased by 26,438 km2, the total length of ecological corridors increased by 743 km, and the total area of corridors increased by 14677 km2, with significant growth in biological corridors, facilitating smoother species migration paths. This study provides reasonable references for ecological restoration in arid regions and introduces a new approach to optimizing ecological networks.http://www.sciencedirect.com/science/article/pii/S266597272500251XEcological networkEcological sourceEcological corridorMachine learningConnectivity |
| spellingShingle | Ruyi Pan Jie Zhang Wentao Xue Qianqian Xia Jiangxiang Liu Junjie Yan Hongbo Ling Xufan Jin Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data Environmental and Sustainability Indicators Ecological network Ecological source Ecological corridor Machine learning Connectivity |
| title | Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data |
| title_full | Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data |
| title_fullStr | Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data |
| title_full_unstemmed | Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data |
| title_short | Research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi-source remote sensing data |
| title_sort | research on the evolution and optimization of ecological networks in arid areas based on machine learning and multi source remote sensing data |
| topic | Ecological network Ecological source Ecological corridor Machine learning Connectivity |
| url | http://www.sciencedirect.com/science/article/pii/S266597272500251X |
| work_keys_str_mv | AT ruyipan researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT jiezhang researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT wentaoxue researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT qianqianxia researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT jiangxiangliu researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT junjieyan researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT hongboling researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata AT xufanjin researchontheevolutionandoptimizationofecologicalnetworksinaridareasbasedonmachinelearningandmultisourceremotesensingdata |