Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Rana Waqar Aslam; Hong Shu; Iram Naz; Abdul Quddoos; Andaleeb Yaseen; Khansa Gulshad; Saad Saud Alarifi

doi:10.3390/rs16050928

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Abstrakt

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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Autorzy (7)

Rana Waqar Aslam
- Wuhan University, China
Hong Shu
- Wuhan University, China
Iram Naz
- University of Engineering and Technology, Pakistan
Abdul Quddoos
- Wuhan University, China
Andaleeb Yaseen
- Italian Institute of Technology / Ca’ Foscari University of Venice
Khansa Gulshad
Saad Saud Alarifi
- King Saud University, Saudi Arabia

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Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs16050928
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Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Remote Sensing nr 16,
ISSN: 2072-4292

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

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:

10.3390/rs16050928

Źródła finansowania:

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

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