Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
Wuhan University · State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing · +6 more institutions
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…
Citation impact
- FWCI
- 30.78
- Percentile
- 100%
- References
- 94
Authors
7- RWRana Waqar AslamCorresponding
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- HSHong Shu
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- INIram Naz
University of Engineering and Technology Lahore
- AQAbdul Quddoos
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- AYAndaleeb Yaseen
Ca' Foscari University of Venice, Italian Institute of Technology, Center for Cultural Heritage Technology
Topics & keywords
- Remote sensing
- Wetland
- Ramsar site
- Vulnerability (computing)
- Vulnerability assessment
- Environmental science
- Geography
- Computer science
- Life in Land