Spatial characteristics of the human factors of soil erosion at the boundary of political divisions: A spatial approach

Meng, Xiao; Cao, Jiannong; Zhang, Chong; Lv, Jinsheng

doi:10.1016/j.catena.2021.105278

Cited by 15 publications

(4 citation statements)

References 57 publications

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“…In addition, the VIF parameter is no more than seven for all independent variables in the eight qualified models. Moreover, the LM and the robust LM test for the eight models were of significance, which meant that the spatial models such as SLM, SEM, and SDM were better than the OLS model to describe the correlation [51]. The Wald test and LR test for the eight models were significant, which showed that the SDM was more appropriate in this study [50].…”

Section: The Relationship Between Urban Population Density and Land Usementioning

confidence: 98%

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

Peng

Liu

Zhang

et al. 2021

IJERPH

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Urban population density distribution contributes towards a deeper understanding of peoples’ activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of “rapid growth-gentle decline-rapid growth-rapid decline”. Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.

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Section: The Relationship Between Urban Population Density and Land Usementioning

confidence: 98%

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

Peng

Liu

Zhang

et al. 2021

IJERPH

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“…The diagonal and the row or column of the non-diagonal terms reflect the direct and indirect effects of the explanatory variables on the ESBI, respectively. The former and latter represent the effects of changes in an explanatory variable on the ESBI in individual and adjacent units, respectively (Meng et al, 2021).…”

Section: Model For Spatial Spillover Effects Of the Determinants Of E...mentioning

confidence: 99%

“…Traditional econometric models assume that observation and explanatory variables are spatially independent, whereas geographic location-based variables are typically spatially correlated, and that this spatial characteristic should be carefully considered in regression analyses to reduce inaccurate results (Meng et al, 2021). Spatial econometric analyses consider spatial dependence and spillover effects, which improves the interpretability of spatial autocorrelations and demonstrates that SECMs can be adopted as useful tools to identify the determinative variables of ES budgets (Chen W et al, 2020;Cai et al, 2021;Meng et al, 2021). In addition, GTWR analysis revealed that the primary determinants presented strong spatial heterogeneity in influencing the patterns of supply-demand balance (Supplementary Figure S5), which has also been proved in existing literatures (e.g., Ayinuer et al, 2018;Funes et al, 2019).…”

Section: Spatial Determinants Of the Ecosystem Service Supply-demand ...mentioning

confidence: 99%

Landscape patterns and their spatial associations with ecosystem service balance: Insights from a rapidly urbanizing coastal region of southeastern China

Chen

Jiang

Wang

et al. 2022

Front. Environ. Sci.

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Assessing ecosystem service (ES) balance and exploring critical drivers are crucial for landscape management. However, a lack of understanding of the determinants of the ecosystem service supply–demand budget, their spillover effects, and spatial variabilities offsets the efficacy of landscape planning and ecosystem conservation. This novel study attempted to close this gap by quantifying ecosystem service budget using an expert knowledge-based supply–demand matrix and explored its dependencies through spatial econometrics and geographically weighted regression approaches instead of using ordinary model simulation and conventional statistical analysis. The overall patterns of ecosystem service balance in the southeastern coast were found to have remained stable in 1980, 2000, and 2017, although remarkable ecosystem service deficits were identified in hotspots of rapid urbanization. The ecosystem service balance was negatively associated with the proportions of built-up land and cropland (p < 0.0001) and exhibited positive associations with the proportions of woodland and grassland (p < 0.0001). Landscape structure and population density were identified as the primary determinants of ecosystem service balance and exhibited spatial variability and spillover effects (i.e., determining ecosystem service balance in both individual and adjacent units). These findings demonstrate the significance of spatial disparities and external effects of determinants of the supply–demand budget in integrative landscape governance. Consequently, localized and targeted strategies for landscape planning are increasingly needed to optimize landscape configuration and alleviate ecosystem service imbalance according to individual socioeconomic conditions and landscape structures. In addition, the spillover effects demonstrate that the maintenance of regional ecosystem service balance and ecosystem sustainability depends not only on individual areas but also on cross-regional collaborations with neighboring regions. These findings have critical implications on strategy formulation for coastal landscape management and ecosystem sustainability.

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“…In Mongolia, issues related to soil erosion and degradation are worsening every year because of the decrease in vegetation cover (Lamchin et al., 2018). Factors causing soil erosion are largely influenced by the interaction between human activities and the natural environment (Gelagay & Minale, 2016; Lal, 2001; Meng et al., 2021). On a national scale, natural factors caused by the terrain, including the elevation and slope of the region, in addition to climatic and physical factors influencing wind and water, cause soil erosion (Safwan et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessment of potential soil erosion in Mongolia based on the RUSLE model and RCP 8.5 scenario

Tuvshinjargal,

Lamchin,

Kim

et al. 2024

Soil Use and Management

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Due to its relatively high altitude and a continental climate, environmental issues related to soil erosion and land degradation more seriously affect the ecosystem and crop production in Mongolia. As detailed soil erosion and land degradation assessments have only been performed in some regions of Mongolia, the purpose of this study is to evaluate the soil vulnerability on a national scale and predict future soil erosion under climate change prior to establishing an appropriate management plan. The revised universal soil loss equation model was chosen for this study, which reflects geographic and climatic characteristics in a vast area as the Mongolian territory. We evaluated soil erosion using historical data from 1993 and 2013 and generated predictions in the near (2041–2060) and far future (2061–2080) periods under the representative concentration pathway 8.5 scenario. The soil erosion rates were divided into six classes: very low, low, medium, high, very high and extreme. The total of 46,039 km2 of the Mongolian territory was classified as very high and extreme risk, which is predicted to increase up to 48,961 and 51,769 km2, in the near and far future periods, respectively. At the national scale, most of the soil erosion appeared in bare area, as it covers more than 60% of the total area. Meanwhile, the highest soil erosion risk was expected in high mountain ranges and ecologically vulnerable regions of the Gobi Desert and steppe area. This study identifies three land cover types with high priority for management and areas that should be considered first in the sustainable national land use and soil management plans considering regional characteristics.

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Spatial characteristics of the human factors of soil erosion at the boundary of political divisions: A spatial approach

Cited by 15 publications

References 57 publications

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

Landscape patterns and their spatial associations with ecosystem service balance: Insights from a rapidly urbanizing coastal region of southeastern China

Assessment of potential soil erosion in Mongolia based on the RUSLE model and RCP 8.5 scenario

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