Multi-Factor Collaborative Analysis of Conservation Effectiveness of Nature Reserves Based on Remote Sensing Data and Google Earth Engine

Zhang, Jin; Ju, Cunyong; Cai, Tijiu; Sheng, Houcai; Jing, Xia

doi:10.3390/rs15184594

Cited by 1 publication

(1 citation statement)

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“…The approach includes the following steps: an in-depth literature review will be conducted to compile and shortlist key natural and anthropogenic factors that have been identified in previous studies as drivers of wetland vulnerability in the study area. This review will encompass research conducted on Khinjhir Lake as well as other regional wetlands, providing a comprehensive foundation for understanding the factors at play [1,54].…”

Section: Identification Of Vulnerability Factorsmentioning

confidence: 99%

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

Aslam,

Shu,

Naz

et al. 2024

Remote Sensing

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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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Section: Identification Of Vulnerability Factorsmentioning

confidence: 99%