Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
Abstract
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’s 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–Markov (CA–Markov) 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.
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/rs16050928
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Remote Sensing
no. 16,
ISSN: 2072-4292 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Aslam R. W., Shu H., Naz I., Quddoos A., Yaseen A., Gulshad K., Saud Alarifi S.: Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data// Remote Sensing -,iss. 5 (2024), s.928-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/rs16050928
- Sources of funding:
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- COST_FREE This work is supported by Major Program of National Natural Science Foundation of China (No. 42394065). This research is also supported by Researchers Supporting Project number (RSP2024R496), King Saud University, Riyadh, Saudi Arabia
- Verified by:
- Gdańsk University of Technology
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