Quantifying the Relationship between Land Use Intensity and Ecosystem Services’ Value in the Hanjiang River Basin: A Case Study of the Hubei Section

Yang, Hui; Zheng, Liang; Wang, Ying; Li, Jiangfeng; Zhang, Bowen; Bi, Yuzhe

doi:10.3390/ijerph191710950

Cited by 10 publications

(6 citation statements)

References 73 publications

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“…As stressors operate in complex patterns [ 49 , 50 ], our study can assist in assessing the impact of those stressors on riparian health. Several other studies have reached similar conclusions [ 16 , 51 , 52 ], though some specific riparian zones have reached different conclusions. Cluster analysis of the RHIs and stressors revealed differences in their indices and sub-indices ( Figure 6 ).…”

Section: Discussionmentioning

confidence: 59%

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Hira

Arif

Zarif³

et al. 2022

IJERPH

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Riparian zones along rivers and streams provide ecosystem services that may change over time as disturbances increase and deteriorate these buffer zones globally. The effect of stressors on ecosystem services along the rivers in underdeveloped countries is unclear, which impacts the environment directly in the form of riparian health indicators (RHIs). This study fills this gap and measures the impact of stressors on RHIs (parameters of habitat, plant cover, regeneration, exotics, and erosion) in the Indus River basin (IRB) in Pakistan. Data on 11 stressors and 27 RHIs were collected using a field-based approach in 269 transects in the upper and lower Indus basins (UIB and LIB) in 2020 and analyzed using multivariate statistical methods. The Kruskal–Wallis tests (p < 0.05) indicated that RHIs varied significantly under the influence of stressors in the UIB and LIB. However, their highest mean values were found in the UIB. Principal component analysis revealed the key RHIs and stressors, which explained 62.50% and 77.10% of the variance, respectively. The Pearson correlation showed that stressors had greater impacts on RHIs in LIB (with r ranging from −0.42 to 0.56). Our results also showed that stressors affected RHI indices with r ranging from −0.39 to 0.50 (on habitat), −0.36 to 0.46 (on plant cover), −0.34 to 0.35 (on regeneration), −0.34 to 0.56 (on erosion), and −0.42 to 0.23 (on exotics). Furthermore, it was confirmed by the agglomerative hierarchical cluster that indices and sub-indices of RHIs and stressors differ across the UIB and LIB. These findings may serve as guidance for managers of large rivers and ecosystem service providers to minimize the environmental impact of stressors in terms of RHIs.

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Section: Discussionmentioning

confidence: 59%

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Hira

Arif

Zarif³

et al. 2022

IJERPH

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“…The local bivariate Moran’s I was then used to identify spatial autocorrelation patterns and local spatial instability. According to the local Moran’s I index, a high–high type in the calculation result indicates high urbanization and a high ecosystem health-type area, whereas a low–low type indicates low urbanization and a low ecosystem health-type area; likewise, a high–low type indicates high urbanization and a low ecosystem health-type area, whereas a low–high type indicates low urbanization and a high ecosystem health-type area [ 23 , 43 ]. The formula for calculating Moran’s I was as follows:

where n is the number of research units; W ij is the spatial weights matrix; X i and X j are the UL and EHI of units i and j, respectively; and x is the average of the EHI.…”

Section: Methodsmentioning

confidence: 99%

Spatial Relationships and Impact Effects between Urbanization and Ecosystem Health in Urban Agglomerations along the Belt and Road: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

et al. 2022

IJERPH

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A healthy ecosystem is fundamental for sustainable urban development. Rapid urbanization has altered landscape patterns and ecological functions, resulting in disturbances to ecosystem health. Exploring the effects of urbanization on ecosystem health and the spatial relationships between them is significant for cities along the “Belt and Road” aiming to achieve sustainable regional development. This study took the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as an example and measured the urbanization level (UL) and ecosystem health index (EHI) from 2000 to 2020 using multisource data. We used bivariate spatial autocorrelation, the geographically weighted regression model (GWR), and the optimal parameters-based geographical detector (OPGD) model to clarify the impact of urbanization on ecosystem health and the spatial relationship between them from multiple perspectives. The major findings of this study were: (1) the EHI in the GBA decreased significantly during the study period, dropping from 0.282 to 0.255, whereas the UL increased significantly, exhibiting opposite spatial distribution features; (2) there was a significant negative spatial correlation between UL and the EHI and significant spatial heterogeneity between high–low and low–high types in the GBA; (3) the negative effects of urbanization on ecosystem health were predominant and becoming more pronounced in the central GBA. Moreover, urbanization had an increasingly significant negative effect, leading to the deterioration of ecosystem health, in the central GBA. Population urbanization drove land urbanization, which became the main factor affecting ecosystem health in the GBA. Overall, urbanization had a significant negative effect on ecosystem health, with this impact being particularly prominent in the core urban junctions of the GBA, which require urgent attention. The results of the study provide a basis for decision making in the context of the steady urbanization and ecosystem health protection of cities along the “Belt and Road”.

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“…Its principle is based on the assumption that if a variable has a signi cant impact on another variable, then the spatial distribution of the two variables should be similar. Geographical detector mainly includes four detectors: factor detector, interaction detector, risk detector, and ecological detector 53,54,55 . This study uses factor detector to measure the in uence of environmental factors on the distribution of cardiovascular diseases.…”

Section: Geodetectormentioning

confidence: 99%

The influence mechanism of urban built environment on cardiovascular diseases

Deng,

Liang,

Peng

et al. 2024

Preprint

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Cardiovascular diseases (CVDs) are a major public health concern, and their morbidity is influenced by various built environment elements. This paper aims to investigate the influence mechanisms of different built environment elements on CVDs, and to provide a theoretical foundation for health-oriented urban planning and CVD prevention. We selected the Xixiangtang built-up area of Nanning city as the case study area, and used the distribution data of CVDs and urban point of interests (POIs) as the main data sources. We applied spatial autocorrelation analysis, kernel density analysis, and geographic detector methods to examine the spatial correlation and influence of urban built environment elements on CVD samples. The results show that both the built environment elements and the CVD samples have a spatially clustered distribution, and there is a significant positive correlation between the distribution density of each environmental element and the CVD morbidity. Among the environmental elements, medical care has the largest influence on CVDs, followed by shopping consumption, catering and food, and transportation facilities, while parks and squares and the road network have relatively small influence.

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Quantifying the Relationship between Land Use Intensity and Ecosystem Services’ Value in the Hanjiang River Basin: A Case Study of the Hubei Section

Cited by 10 publications

References 73 publications

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Impacts of Stressors on Riparian Health Indicators in the Upper and Lower Indus River Basins in Pakistan

Spatial Relationships and Impact Effects between Urbanization and Ecosystem Health in Urban Agglomerations along the Belt and Road: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

The influence mechanism of urban built environment on cardiovascular diseases

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