Multi-temporal image analysis of wetland dynamics using machine learning algorithms.

Autor: Aslam RW; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China. Electronic address: ranawaqaraslam@whu.edu.cn., Naz I; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China., Shu H; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China., Yan J; 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., Tariq A; Department of Wildlife, Fisheries and Aquaculture, College of the Forest Resources, Mississippi State University, Starkville, MS, 39762-9690, USA., Davis JB; Department of Wildlife, Fisheries and Aquaculture, College of the Forest Resources, Mississippi State University, Starkville, MS, 39762-9690, USA., Al-Saif AM; Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi Arabia., Soufan W; Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi Arabia.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Dec; Vol. 371, pp. 123123. Date of Electronic Publication: 2024 Nov 10.
DOI: 10.1016/j.jenvman.2024.123123
Abstrakt: Wetlands play a crucial role in enhancing groundwater quality, mitigating natural hazards, controlling erosion, and providing essential habitats for unique flora and wildlife. Despite their significance, wetlands are facing decline in various global locations, underscoring the need for effective mapping, monitoring, and predictive modeling approaches. Recent advances in machine learning, time series earth observation data, and cloud computing have opened up new possibilities to address the challenges of large-scale wetlands mapping and dynamics forecasting. This research conducts a comprehensive analysis of wetland dynamics in the Thatta region, encompassing Haleji & Kinjhar Lake in Pakistan, and evaluates the efficacy of different classification systems. Leveraging Google Earth Engine, Landsat imagery, and various spectral indices, we assess four classification techniques to derive accurate wetland mapping results. Our findings demonstrate that Random Forest emerged as the most efficient and accurate method, achieving 87% accuracy across all time periods. Change detection analysis reveals a significant and alarming decline in Haleji & Kinjhar Lake wetlands over 1990-2020, primarily driven by agricultural expansion, urbanization, groundwater extraction, and climate change impacts like rising temperatures and reduced precipitation. If left unaddressed, this continued wetland loss could have severe implications for aquatic and terrestrial species, water and soil quality, wildlife populations, and local livelihoods. The study predicts future wetland dynamics under different scenarios - enhancing drainage for farmland conversion (10-20% increase), increasing urbanization (10-20% expansion), escalating groundwater extraction (7.2m annual decline), and climate change (up to 5 °C warming and 54% precipitation deficit by 2050). These scenarios forecast sustained long-term wetland deterioration driven by anthropogenic pressures and climate change. To guide conservation strategies, the research integrates satellite data analytics, machine learning algorithms, and spatial modeling to generate actionable insights into multifaceted wetland vulnerabilities. Findings provide a robust baseline to inform policies ensuring sustainable management and preservation of these vital ecosystems amidst escalating human and climate threats. Over 1990-2020, the Thatta region witnessed a 352.8 sq.km loss of wetlands, necessitating urgent restoration efforts to safeguard their invaluable ecosystem services.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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