Impacts of Land Use Conversion on Soil Erosion in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains

Guo, Ziqi; Yan, Zhaojin; He, Rong; Yang, Hui; Ci, Hui; Wang, Ran

doi:10.3390/land13040550

Land

2024

DOI: 10.3390/land13040550

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Impacts of Land Use Conversion on Soil Erosion in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains

Ziqi Guo,

Zhaojin Yan,

Rong He

et al.

Abstract: The serious problem of soil erosion not only has a profound impact on people’s lives but also results in a series of ecological and environmental challenges. To determine the impact of changes in land use type on soil erosion in the urban agglomeration on the northern slopes of the Tianshan Mountains, this study commences by employing the InVEST-SDR (integrated valuation of ecosystem services and tradeoffs–sediment delivery ratio) model to calculate soil erosion levels spanning from 2000 to 2020. Subsequently,… Show more

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Cited by 3 publications

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Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Min,

Liu,

et al. 2024

Applied Sciences

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Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used to investigate the spatio-temporal variations of the Loess Plateau’s soil erosion from 2000 to 2050. The results showed that: (1) The primary categories of soil erosion from 2000 to 2020 were moderate, mild, and slight, and the average level of soil erosion exhibited a decreasing and then an increasing tendency during the last 20 years. (2) Soil erosion was directly impacted by changes in land use, with cropland and forest being the primary land use and land cover changes in the study region. Cropland and construction land being turned into woodland between 2000 and 2020 resulted in a significant decrease in the severity of soil erosion. Projected soil erosion is expected to increase significantly between 2020 and 2050 due to arable land being converted into construction land. (3) The key variables impacting the spatial distribution of soil erosion were LUCC (Land-Use and Land-Cover Change), NDVI (Normalized Difference Vegetation Index), and slope, and the interplay of these variables may increase their ability to explain soil erosion. Grasslands with an NDVI ranging from 0.9 to 1, rain ranging from 0.805 to 0.854 m, a slope above 35°, and a terrain elevation ranging from 1595 to 2559 m were identified as having a high risk of soil erosion. Soil erosion prevention and management efforts should focus on the ecological restoration of upland areas in the future.

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Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Min,

Liu,

et al. 2024

Applied Sciences

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Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning

Liu,

Zhang,

Liu

et al. 2024

Land

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Over the past two decades, due to the combined effects of natural and human factors, the ecological environment and resources of the Qinghai–Tibet Plateau (QTP) have faced serious threats, profoundly impacting its ecosystem and the lives of its residents. Therefore, the establishment of the ecological security pattern (ESP) is crucial to cope with climate change, maintain ecosystem function, and sustainable development. Based on the Pressure–State–Response (PSR) model, this study constructed an evaluation index system for the ecological security (ES) of the QTP, evaluated the ES of the QTP during 2000–2020, and predicted the ES of the QTP during 2025–2035 based on the deep learning model. Combined with the residents’ perception of ES, the ES of the QTP was evaluated comprehensively. The results showed that: (1) From 2000 to 2020, the ES value of the QTP continued to rise, the number of dangerous and sensitive counties decreased, and the number of other counties increased. The overall spatial distribution features higher values in the southeast and lower values in the northwest and central regions. (2) From 2000 to 2020, both hot spots and cold spots on the QTP decreased, with the hot spots mainly concentrated in the southeast of the QTP, represented by Yunnan Province, and the cold spots shifting from west to east, mainly concentrated in the central QTP, represented by Qinghai Province. (3) The Long Short-Term Memory (LSTM) model demonstrates high prediction accuracy. Based on the prediction of LSTM, the ES value of the QTP will continue to rise from 2025 to 2035, and the number of safe counties will reach the highest level in history. The spatial distribution is still higher in the southeast and lower in the northwest and central regions. (4) By analyzing residents’ perception of 25 potential factors that may affect the ES of the QTP, the results show that residents generally believe that these factors have an important impact on ES, and their evaluation is between “important” and “very important”. In addition, there is a significant correlation between these factors and the predicted values of ES. The results of the study will help to improve our understanding of the overall ecological environment of the QTP, provide accurate positioning and reasonable help for the government to formulate relevant protection strategies, and lay a methodological and practical foundation for the sustainable development of the QTP.

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Assessing and Predicting Ecosystem Services and Their Trade-Offs/Synergies Based on Land Use Change in Beijing–Tianjin–Hebei Region

Gong,

Zhang,

et al. 2024

Sustainability

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Exploring the spatial and temporal dynamic changes in ecosystem service functions and trade-off/synergistic relationships over a long time series in the Beijing–Tianjin–Hebei region is of great practical significance for regional high-quality development. Taking the Beijing–Tianjin–Hebei region as the research object, PLUS was used to predict the land use distribution in 2030 under three scenarios: business as usual (BAU), cropland protection scenario (CPS), and ecological protection scenario (EPS); the InVEST model was introduced to assess ecosystem services including water yield (WY), carbon stock (CS), habitat quality (HQ), and soil conservation (SC); trade-offs/synergies among ecosystem services were calculated by using R and GeoDa modeling. The results show the following: (1) Between 1980 and 2020, the study area witnessed varying trends in WY and SC, which initially decreased and subsequently increased, showing an overall upward trend. In contrast, CS and HQ consistently declined throughout the period; the HQ, CS, and SC values were high in the northwest and low in the southeast, while the WY value was high in the southeast and low in the northwest. (2) From 1980 to 2020, the relationships SC-HQ and CS-HQ, as well as CS-SC, were characterized by synergy. In contrast, the interactions CS-WY, SC-WY, and WY-HQ demonstrated trade-offs. On the whole, the trade-off/synergy degree showed a trend of fluctuating increase. From the perspective of the spatial scale, CS-HQ, CS-SC, SC-HQ, and WY-HQ were mainly cooperative relationships. CS-WY and SC-WY were trade-off relationships. (3) Compared with 2020, the total ecosystem service of the four types decreased, increased, and increased under business as usual (BAU), the ecological protection scenario (EPS), and the cropland protection scenario (CPS), respectively, and the increase was the highest under the ecological protection scenario. In terms of time scale, there were also differences in the trade-off intensity among ecosystem services under the three scenarios. In the EPS, the trade-off/synergy intensity among various ecosystem services was the highest, followed by the CPS, and the lowest was under BAU. The findings of this research offer theoretical insights and practical guidance for enhancing ecosystem services and zoning functions in Beijing–Tianjin–Hebei, while also providing fundamental support for refining the territorial spatial configuration.

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Impacts of Land Use Conversion on Soil Erosion in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains

Cited by 3 publications

References 68 publications

Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning

Assessing and Predicting Ecosystem Services and Their Trade-Offs/Synergies Based on Land Use Change in Beijing–Tianjin–Hebei Region

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