SAR image integration for multi-temporal analysis of Lake Manchar Wetland dynamics using machine learning.

Autor: Chaoyong W; Key Laboratory of Coalbed Methane Resources & Reservoir Formation Process, Ministry of Education, Jiangsu, Xuzhou, 221008, China.; School of Resources and Geosciences, China University of Mining and Technology, Jiangsu, Xuzhou, 221116, China., Aslam RW; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China., Quddoos A; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China., Naz I; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China., Tariq A; Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Starkville, MS, 39762-9690, USA., Ullah S; Department of Water Resources and Environmental Engineering, Nangarhar University, Nangarhar, 2600, Afghanistan. sajidjalwan@gmail.com., Sajjad A; Department of Environmental Sciences, Faculty of Biological Sciences, Quaid-I-Azam University, Islamabad, 45320, Pakistan., Soufan W; Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia., Almutairi KF; Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 04; Vol. 14 (1), pp. 26669. Date of Electronic Publication: 2024 Nov 04.
DOI: 10.1038/s41598-024-76730-1
Abstrakt: The Manchar Lake wetland complex, Pakistan's largest freshwater-lake, faces unprecedented ecological challenges amidst climate change and human pressures, necessitating urgent, data-driven conservation strategies. This study employs cutting-edge multi-sensor remote sensing techniques to quantify and analyze the dynamic changes in this critical ecosystem from 2015 to 2023, aiming to provide a comprehensive understanding of wetland dynamics for informed management decisions. Integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral imagery, we assessed changes in wetland extent, vegetation health, and land-use patterns using spectral indices and topographic data. Our methodology achieved classification accuracies exceeding 92% across all study years, revealing significant ecosystem fluctuations. Water body extent exhibited a non-linear trend, expanding from 318.5 km² (5%) in 2015 to 397.0 km² (7%) in 2019, before contracting to 369.9 km² (6%) in 2023. This pattern was corroborated by MNDWI values. Concurrently, vegetation covers dramatically increased from 405.5 km² (7%) in 2019 to 1081.6 km² (18%) in 2023. The Enhanced Vegetation Index (EVI) reflected this trend, decreasing from 0.61 in 2015 to 0.41 in 2019, before recovering to 0.53 in 2023. Land use changes were substantial, with agricultural areas increasing from 118.4 km² (2%) in 2015 to 498.0 km² (8%) in 2023. SAR data consistently supported these observations. Topographic analysis, including the Topographic Wetness Index (TWI), provided crucial insights into wetland distribution and resilience. This comprehensive analysis highlights the complex interplay between natural processes and human influences shaping the Manchar-Lake ecosystem, underscoring the urgent need for adaptive management strategies in the face of rapid environmental change.
(© 2024. The Author(s).)
Databáze: MEDLINE
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