{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:54:28Z","timestamp":1776120868359,"version":"3.50.1"},"reference-count":88,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Program of National Natural Science Foundation of China","award":["42394065"],"award-info":[{"award-number":["42394065"]}]},{"name":"Major Program of National Natural Science Foundation of China","award":["RSP2024R496"],"award-info":[{"award-number":["RSP2024R496"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["42394065"],"award-info":[{"award-number":["42394065"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSP2024R496"],"award-info":[{"award-number":["RSP2024R496"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model\u2019s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata\u2013Markov (CA\u2013Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.<\/jats:p>","DOI":"10.3390\/rs16050928","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T08:43:14Z","timestamp":1709714594000},"page":"928","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8711-8700","authenticated-orcid":false,"given":"Rana Waqar","family":"Aslam","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Hong","family":"Shu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9509-9914","authenticated-orcid":false,"given":"Iram","family":"Naz","sequence":"additional","affiliation":[{"name":"Centre of Excellence in Water Resources Management, University of Engineering and Technology, Lahore 54000, Pakistan"}]},{"given":"Abdul","family":"Quddoos","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Andaleeb","family":"Yaseen","sequence":"additional","affiliation":[{"name":"Center for Cultural Heritage Technology, Italian Institute of Technology, 30100 Venice, Italy"},{"name":"DAIS, Ca\u2019 Foscari University of Venice, 30100 Venice, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6984-7328","authenticated-orcid":false,"given":"Khansa","family":"Gulshad","sequence":"additional","affiliation":[{"name":"Faculty of Civil and Environmental Engineering, Gda\u0144sk University of Technology, 80-233 Gda\u0144sk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9406-6809","authenticated-orcid":false,"given":"Saad S.","family":"Alarifi","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Igwe, V., Salehi, B., and Mahdianpari, M. (2023). Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing. Remote Sens., 15.","DOI":"10.3390\/rs15204960"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Granger, J.E., Mohammadimanesh, F., Salehi, B., Brisco, B., Homayouni, S., Gill, E., Huberty, B., and Lang, M. (2020). Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America. Remote Sens., 12.","DOI":"10.3390\/rs12111882"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/15481603.2017.1419602","article-title":"Remote Sensing for Wetland Classification: A Comprehensive Review","volume":"55","author":"Mahdavi","year":"2018","journal-title":"GISci. 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