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Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between lan...

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Autores principales: Yu, Tao, Bao, Anming, Xu, Wenqiang, Guo, Hao, Jiang, Liangliang, Zheng, Guoxiong, Yuan, Ye, NZABARINDA, Vincent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982001/
https://www.ncbi.nlm.nih.gov/pubmed/31861894
http://dx.doi.org/10.3390/ijerph17010079
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author Yu, Tao
Bao, Anming
Xu, Wenqiang
Guo, Hao
Jiang, Liangliang
Zheng, Guoxiong
Yuan, Ye
NZABARINDA, Vincent
author_facet Yu, Tao
Bao, Anming
Xu, Wenqiang
Guo, Hao
Jiang, Liangliang
Zheng, Guoxiong
Yuan, Ye
NZABARINDA, Vincent
author_sort Yu, Tao
collection PubMed
description Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD’s downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran’s I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.
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spelling pubmed-69820012020-02-07 Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta Yu, Tao Bao, Anming Xu, Wenqiang Guo, Hao Jiang, Liangliang Zheng, Guoxiong Yuan, Ye NZABARINDA, Vincent Int J Environ Res Public Health Article Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD’s downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran’s I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection. MDPI 2019-12-20 2020-01 /pmc/articles/PMC6982001/ /pubmed/31861894 http://dx.doi.org/10.3390/ijerph17010079 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yu, Tao
Bao, Anming
Xu, Wenqiang
Guo, Hao
Jiang, Liangliang
Zheng, Guoxiong
Yuan, Ye
NZABARINDA, Vincent
Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta
title Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta
title_full Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta
title_fullStr Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta
title_full_unstemmed Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta
title_short Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta
title_sort exploring variability in landscape ecological risk and quantifying its driving factors in the amu darya delta
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982001/
https://www.ncbi.nlm.nih.gov/pubmed/31861894
http://dx.doi.org/10.3390/ijerph17010079
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