Application of geographical detector and geographically weighted regression for assessing landscape ecological risk in the Irtysh River Basin, Central Asia

Li, Mingrui; Abuduwaili, Jilili; Liu, Wen; Feng, Sen; Saparov, Galymzhan; Ma, Long

doi:10.1016/j.ecolind.2023.111540

Cited by 25 publications

(7 citation statements)

References 67 publications

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“…The LER patterns and changes in the study area are the result of the combined effects of nature, human activities and landscape patterns [63]. From a relatively static ground perspective, natural factors such as topography and climate dominate the influence of the LER in the study area [64], especially the NDVI, annual average temperature and annual average precipitation, which all affect the LER at different topographic gradients. Socioeconomic activities resulting from human development and utilization are relatively secondary, but human interference has the greatest impact on LER at small scales [64].…”

Section: Landscape Ecological Risk and Drivers Under Different Topogr...mentioning

confidence: 99%

Landscape Ecological Risk and Drivers of Land-Use Transition under the Perspective of Differences in Topographic Gradient

Zhang,

Yu,

Feng

et al. 2024

Land

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Human activities have caused different degrees of land-use change on different topographic gradients, with impacts on the landscape and ecosystem. Effectively preventing and addressing ecological risk (ER) and achieving harmonious coexistence between humans and nature are important aspects of sustainable development. In this study, we used Gansu Province as an example, adopted five periods of land-use data in 1980, 1990, 2000, 2010 and 2020, and used the geoinformatic Tupu method and the terrain distribution index to study land-use changes under different topographic gradients, and then constructed the landscape ecological risk assessment (LERA) model based on the landscape pattern index to analyze landscape ecological risk (LER) spatiotemporal changes under different topographic gradients, and finally explored the LER driving factors using the geodetector model. The results showed that (1) the dominant land-use types were unused land and grassland, accounting for approximately 74% of the land. The situation of transferring and changing each type was more drastic. The distribution and changes in cropland and built-up land were easily found in low topographic gradient areas with low elevations and small slopes; the distribution and changes in woodland, grassland and water areas were easily found in high topographic gradient areas with high elevations and large slopes. (2) The landscape ecological risk index (LERI) was 0.018, 0.019, 0.019, 0.019 and 0.020, respectively, with spatial expressions of high in the northwest and low in the southeast. Low LER was concentrated in high topographic gradient ecological reserves; high LER was concentrated in low topographic gradient human interference areas and high topographic gradient natural environmental complex areas. (3) Natural factors mainly acted on the LER on moderate and high topographic position gradients; socioeconomic factors mainly acted on the LER on low topographic position gradients. Human interference interacted with natural factors more than human interference alone on LER. This study can provide a scientific basis for ensuring ecological security and sustainable development in areas with complex topography and geomorphology.

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Section: Landscape Ecological Risk and Drivers Under Different Topogr...mentioning

confidence: 99%

Landscape Ecological Risk and Drivers of Land-Use Transition under the Perspective of Differences in Topographic Gradient

Zhang,

Yu,

Feng

et al. 2024

Land

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“…Geodetector, which is a powerful analytical tool, has been widely utilized for the comprehensive exploration of a broad range of geographic phenomena and their influencing factors. This tool has been applied across critical fields, such as environmental science [44,45], public health [46,47], resource management [48], and ecological risk assessment [49]. For instance, Geodetector facilitated assessments of nitrate contamination in the groundwater in California's Central Valley, identifying the principal factors contributing to this issue [50].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Distribution Characteristics and Influential Mechanisms of China’s Industrial Landscape Based on Geodetector

Yan,

Li,

Song

2024

Land

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The industrial landscape constitutes a crucial aspect of a region’s historical and cultural identity, serving as a valuable asset in the development of industrial tourism. Exploring the industrial landscape supports initiatives in industrial tourism, acts as a catalyst for community revitalization, and contributes to sustainable urban progress. The primary objective of this research was to investigate the spatial distribution characteristics and underlying determinants of China’s industrial landscape (CIL) to inform urban planning, cultural heritage preservation, and sustainable development initiatives. This study utilized analytical tools, such as the nearest neighbor index, geographic concentration index, and hot spot analysis, to comprehensively examine the spatial distribution of CIL. Additionally, Geodetector was employed to explore the correlating factors behind this distribution. The findings reveal the following: (1) CIL exhibited a pronounced agglomerative spatial pattern characterized by a high degree of concentration, significant disparities, and substantial spatial autocorrelation. (2) Over time, the agglomeration of CIL varied, intensifying initially and then diminishing, with the center of gravity of its distribution shifting eastward before subsequently moving westward in a directional trend resembling “northeast–southwest”. (3) There was a diverse array of industrial landscape types within China, with notable disparities in the prevalence of different categories. The manufacturing and transportation sectors boasted the highest number of heritage sites. (4) The distribution pattern of CIL was shaped by factors such as the level of economic development, socio-demographic conditions, transportation infrastructure, and cultural milieu. The interplay between these factors had a substantial impact on this distribution pattern.

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“…Ecological conditions are affected by a variety of factors, which can be divided into natural factors such as terrain, soil, and climate, as well as human factors including social economy, among others [17]. The analysis methods for driver identification mainly include correlation analysis, principal component analysis, linear regression analysis, geographical detectors, and spatial regression analysis [18][19][20][21]. Geographical detectors can detect both numerical and continuous data and can avoid the influence of multivariable collinearity [22].…”

Section: Introductionmentioning

confidence: 99%

“…The geographically weighted regression (GWR) model is capable of establishing the spatial heterogeneity of parameters across different regions. Both geographical detectors and GWR take into account the spatial effects of data and have been widely used in driver analysis [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

Zhou,

Li,

Gao

et al. 2024

Remote Sensing

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The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

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Application of geographical detector and geographically weighted regression for assessing landscape ecological risk in the Irtysh River Basin, Central Asia

Cited by 25 publications

References 67 publications

Landscape Ecological Risk and Drivers of Land-Use Transition under the Perspective of Differences in Topographic Gradient

Landscape Ecological Risk and Drivers of Land-Use Transition under the Perspective of Differences in Topographic Gradient

Spatial and Temporal Distribution Characteristics and Influential Mechanisms of China’s Industrial Landscape Based on Geodetector

Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

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