Dynamic projection of ecological risk in the Manas River basin based on terrain gradients

Xue, Lianqing; Zhu, Boli; Wu, Yiping; Wei, Guanghui; Liao, Shumin; Yang, Changbing; Wang, Jing; Zhang, Hui; Ren, Lei; Han, Qiang

doi:10.1016/j.scitotenv.2018.10.382

Cited by 93 publications

(72 citation statements)

References 62 publications

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“…In arid areas where water resources are relatively scarce, over-exploitation of land (mainly including over-expansion of cultivated land and cities) means more water is being consumed for agricultural production and city development. As a consequence, the natural oasis ecosystems may not receive the needed water, and desertification, salinization, groundwater level reduction, and vegetation cover reduction occur easily [69][70][71]. Additionally, with the cities' extension, croplands became occupied by construction land, while the negative impacts of urban sprawl such as a decrease in biodiversity, greenhouse gas emissions, and air pollution also followed.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

Liu

Shi

2019

Sustainability

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Ecological vulnerability assessment increases the knowledge of ecological status and contributes to formulating local plans of sustainable development. A methodology based on remote sensing data and spatial principal component analysis was introduced to discuss ecological vulnerability in the Toutun River Basin (TRB). Exploratory spatial data analysis and a geo-detector were employed to evaluate the spatial and temporal distribution characteristics of ecological vulnerability and detect the driving factors. Four results were presented: (1) During 2003 and 2017, the average values of humidity, greenness, and heat in TRB increased by 49.71%, 11.63%, and 6.51% respectively, and the average values of dryness decreased by 165.24%. However, the extreme differences in greenness, dryness, and heat tended to be obvious. (2) The study area was mainly dominated by a high and extreme vulnerability grade, and the ecological vulnerability grades showed the distribution pattern that the northern desert area was more vulnerable than the central artificial oasis, and the central artificial oasis was more vulnerable than the southern mountainous area. (3) Ecological vulnerability in TRB showed significant spatial autocorrelation characteristics, and the trend was enhanced. The spatial distribution of hot/cold spots presented the characteristics of “hot spot—cold spot—secondary hot spot—cold spot” from north to south. (4) The explanatory power of each factor of ecological vulnerability was temperature (0.5955) > land use (0.5701) > precipitation (0.5289) > elevation (0.4879) > slope (0.3660) > administrative division (0.1541). The interactions of any two factors showed a non-linear strengthening effect, among which, land use type ∩ elevation (0.7899), land use type ∩ precipitation (0.7867), and land use type ∩ temperature (0.7791) were the significant interaction for ecological vulnerability. Overall, remote sensing data contribute to realizing a quick and objective evaluation of ecological vulnerability and provide valuable information for decision making concerning ecology management and region development.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

Liu

Shi

2019

Sustainability

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“…The land-use/land-cover (LULC) dataset for 2000 and 2015 in ArcGIS shapefile format used in this study was developed by the Chinese Academy of Sciences through the Database of Global Change Parameters, with the data interpreted based on Landsat images (http://www.eercasia.com/# [51]). According to previous studies [11,12,15] and the environmental characteristics of the ADD, the land use types were divided into seven categories: farmland, forest land, grassland, construction The ADD has an extremely continental climate characterized by extremely dry conditions throughout the year, with hot summers and cold winters. The annual average temperature is approximately 13 degrees Celsius, and the average frost-free period is 205 days [48].…”

Section: Land Use/land Cover (Lulc)mentioning

confidence: 99%

“…According to previous studies and data availability, digital elevation model (DEM) [15], land surface temperature (LST) [35,52] and normalized difference vegetation index (NDVI) [3] data were selected as the biogeographic factors. DEM data were obtained from NASA with a spatial resolution of 90 m (https://search.earthdata.nasa.gov/search).…”

Section: Biogeographic Variablesmentioning

confidence: 99%

“…The degree and spatial pattern of landscape ecological risk are affected by multiple factors, such as nature, society and the economy [7,13,14]. In addition, studies have shown that topography [15,16], changes in the surface environment [17,18], population [19,20] and crop yield [21] can affect landscape structure and functions of ecosystems [14], which in turn affect the degree and spatial distribution of landscape ecological risk. Therefore, focusing on the relationship between individual driving factors and landscape ecological risk may prevent the identification of factors that have critical impacts on the landscape ecological risk of a regional landscape and lead to incomplete scientific information for regional ecological planning [22,23].…”

Section: Introductionmentioning

confidence: 99%

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

Bao

et al. 2019

IJERPH

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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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“…Ecological vulnerability assessment has become a research focus for promoting ecological civilization. However, most previous studies focused on the unstable areas that are greatly affected by natural factors, such as the Tibetan Plateau [8], the transition zone between desert and oasis [9], the ecotone of farming and husbandry [10], coastal areas [11], and wetland areas [12]. Few people assessed rapidly urbanizing areas, which are mainly affected by human activities.…”

Section: Introductionmentioning

confidence: 99%

Coastal Landscape Vulnerability Analysis in Eastern China—Based on Land-Use Change in Jiangsu Province

Zhou

Liu

Zhu

2020

IJERPH

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The unreasonable land use in rapid urbanization areas induced by large-scale urban construction activities have caused massive ecological issues. In this study, landscape vulnerability index (LVI) and landscape human interference index (LHAI) were originally addressed and calculated using multi-temporal land-use data from 2000 to 2015. Then, the spatial-temporal relationship assessment model of landscape fragility caused by human activities were constructed for each county of Jiangsu Province, China, so as to analyze the spatial distribution of landscape vulnerability and determine the impacts of artificial disturbance on landscape vulnerability. The results showed: (1) The number of counties with middle and high landscape vulnerability increased from 20 in 2000 to 27 in 2015 with a peak value (33) in 2010. (2) Counties with high-intensity human activities showed an upward trend. (3) Land use generally has a significant and diverse impact on landscape vulnerability. At the county level, the LVI was positively correlated with the LHAI before 2010 and was followed by a negative correlation of them. As concluded from this study, a total of four sub-regions (continuous benefit zones, variable benefit zones, continuous harmful zones, and variable harmful zones) have been identified for sustainable landscape management in the future. (4) The LVI suggests that the landscape vulnerability in Jiangsu did not continue to deteriorate in the study period. Further, accelerated land exploitation has produced a positive impact on regional economic development and ecological protection. This study provided an effective method set for analyzing the environmental impacts caused by human activities and promoting future ecosystem management in coastal areas.

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Dynamic projection of ecological risk in the Manas River basin based on terrain gradients

Cited by 93 publications

References 62 publications

Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

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

Coastal Landscape Vulnerability Analysis in Eastern China—Based on Land-Use Change in Jiangsu Province

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