Deep learning-driven land cover monitoring and landscape ecological health assessment: A dynamic study in coastal regions of the China-Pakistan Economic Corridor from 2000 to 2023
文献类型：期刊论文
| 作者 | Xu, Chen1,2; Wang, Juanle1,4,5; Sun, Yamin1,3; Liu, Meng1,2; Liu, Jingxuan1,2; Sajjad, Meer Muhammad1 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 169页码:112860 |
| 关键词 | Deep learning Integrated label Land cover classification Landscape ecological health Pakistan |
| DOI | 10.1016/j.ecolind.2024.112860 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The coastal regions of the China-Pakistan Economic Corridor (CPEC) are crucial links for the 21st Century Maritime Silk Road. Nonetheless, this region is facing significant ecological challenges due to natural disasters and intensive human activity. To effectively monitor and assess the ecological health of these critical coastal zones, this study employed integrated labels and a deep learning model to obtain land cover data spanning from 2000 to 2023. It then constructed a vigour-organisation-resilience (VOR) model with 12 assessment indicators to evaluate the landscape ecological health of this region. The evaluation results showed distinct spatial patterns. Gwadar and Ormara's Bare land areas remained Sick, while Karachi and Lower Indus' Impervious surfaces were Unhealthy with minimal fluctuations. The Lower Indus region saw Sub-healthy expansion with increased Crops areas. Lasbela was Healthy, dominated by shrub-based Other vegetation, and the Indus Delta's mangroves maintained a Very healthy state. Overall, the CPEC coastal regions were rated Unhealthy, with signs of moderate improvement. We recommend that the CPEC coastal areas focus on restoring Sick areas, promoting sustainable agriculture in Sub-healthy regions, and conserving Healthy and Very healthy areas. This study demonstrates the efficacy of deep learning and VOR model in assessing long-term ecological health, providing a valuable framework that can be applied in other coastal regions facing similar challenges. |
| WOS关键词 | ECOSYSTEMS |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:001361996300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210456]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Jiangsu Ocean Univ, Coll Marine Technol & Geomat, Lianyungang 222005, Peoples R China 3.Inst Disaster Prevent Sci & Technol, Sch Geosci, Sanhe 065201, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog, Informat Resource Dev & Applicat, Nanjing 210023, Peoples R China 5.China Pakistan Earth Sci Res Ctr, Islamabad 45320, Pakistan |
| 推荐引用方式 GB/T 7714 | Xu, Chen,Wang, Juanle,Sun, Yamin,et al. Deep learning-driven land cover monitoring and landscape ecological health assessment: A dynamic study in coastal regions of the China-Pakistan Economic Corridor from 2000 to 2023[J]. ECOLOGICAL INDICATORS,2024,169:112860. |
| APA | Xu, Chen,Wang, Juanle,Sun, Yamin,Liu, Meng,Liu, Jingxuan,&Sajjad, Meer Muhammad.(2024).Deep learning-driven land cover monitoring and landscape ecological health assessment: A dynamic study in coastal regions of the China-Pakistan Economic Corridor from 2000 to 2023.ECOLOGICAL INDICATORS,169,112860. |
| MLA | Xu, Chen,et al."Deep learning-driven land cover monitoring and landscape ecological health assessment: A dynamic study in coastal regions of the China-Pakistan Economic Corridor from 2000 to 2023".ECOLOGICAL INDICATORS 169(2024):112860. |
入库方式： OAI收割
来源：地理科学与资源研究所
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