Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm

The necessity for extensive historical data, variables, and weight determination still presents challenges and complexity, notwithstanding the growth in research on socio-ecological vulnerability to climate change. In order to fill in these gaps, this study used China's Fujian Province as a cas...

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Vydáno v:Journal of environmental management Ročník 347; s. 119254
Hlavní autoři: Ikhumhen, Harrison Odion, Fang, Qinhua, Lu, Shanlong, Meilana, Lusita, Raimundo Lopes, Namir Domingos
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2023
Témata:
algorithms
autocorrelation
case studies
China
Climate change
coasts
Data ranking algorithm
decision making
environmental management
land use
Remote sensing
Socioecological indicators
Socioecological vulnerability
stakeholders
United Nations Framework Convention on Climate Change
vegetation
China
Climate change
Data ranking algorithm
Socioecological vulnerability
Remote sensing
Socioecological indicators
ISSN:0301-4797, 1095-8630, 1095-8630
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Shrnutí:The necessity for extensive historical data, variables, and weight determination still presents challenges and complexity, notwithstanding the growth in research on socio-ecological vulnerability to climate change. In order to fill in these gaps, this study used China's Fujian Province as a case study to propose a unique strategic approach for studying socio-ecological vulnerability to climate change from 2000 to 2020 by utilizing remote sensing and the framework of the Intergovernmental Panel on Climate Change. In a GIS scenario, this method employs a comprehensive framework with a wide variety of indicators and a data-driven ranking algorithm. The findings of this study revealed a moderate degree of socio-ecological vulnerability throughout the coast, with significant regional heterogeneity in its spatial distribution. Furthermore, throughout the course of the two-decade, the highly vulnerable zones expanded by 6.04%, outpacing the low-risk areas by 1116 km2 (61.41%) and 2066 km2 (123.39%), respectively, with the majority of the increase taking place in Fuzhou and Ningde. These changes in vulnerability were shown to be principally influenced by changes in vegetation, precipitation, GDP, and land use (LULC). The major influence of precipitation was highlighted further in the spatial autocorrelation analysis, which demonstrated a close correlation between growing socio-ecological vulnerability and increased precipitation. To conclude, this study's methodology differs from other socio-ecological vulnerability studies in that it is flexible and self-sufficient, offering users a choice of weight application. It also gives a more useful, accurate, and suggestive model to enable decision-makers or stakeholders build strategies or ideas for constructing more resilient coastal systems. [Display omitted] •A strategic approach for socio-ecological vulnerability assessment is established.•The coast exhibits a moderate level of socio-ecological vulnerability.•In Ningde and Fuzhou, highly vulnerable zones grew by 6.04% between 2000 and 2020.•Variations in vulnerability revealed a high spatial correlation with precipitation.•Data ranking approach is a crucial tool for building resilient coastal system.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2023.119254