Land use changes in Zhangjiakou from 2005 to 2025 and the importance of ecosystem services

Xu, Kaipeng; Chi, Yanyan; Ge, Rongfeng; Wang, Xiahui; Liu, Siyang

doi:10.7717/peerj.12122

Cited by 10 publications

(5 citation statements)

References 64 publications

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“…Numerous current land use simulation models, such as CA-Markov [9,10], ANN-CA [11], CLUS-S [12], Dyna-CLUE [13] and FLUS models [14], are widely used by scholars. However, these models have some limitations; they cannot identify effectively the drivers affecting land use change, especially the inability to simulate multiple land use panels dynamically and spatially, which limits the simulation of land under future climate change.…”

Section: Introductionmentioning

confidence: 99%

Coupling PLUS–InVEST Model for Ecosystem Service Research in Yunnan Province, China

et al. 2022

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In efforts to improve regional ecosystem service functions, coordinate land development and ecological conservation, and establish a reference for optimizing land resource allocation and policy formulation to cope with climate change, it is critical to investigate the spatial distribution of land use/cover change (LUCC) and ecosystem services (ESs) under future climate change. This study proposes a framework based on the Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP), integrating the patch-generating land use simulation (PLUS) model and the integrated valuation of ecosystem services and tradeoffs (InVEST) model to analyze the spatial agglomeration of ESs, to analyze the importance of each driving factors. The results of the study show as follows: (1) the combination of CMIP6 and PLUS models can effectively simulate land use with an overall accuracy of 0.9379. (2) In spatial correlation, ESs show good clustering in all three future scenarios, with similar distribution of cold hotspots in the SSP126 and SSP245 scenarios. Hotspots are more dispersed and cold spots are shifted to the west in the SSP585 scenario. (3) GDP is an important factor in carbon storage and habitat quality, and precipitation has a greater impact on soil retention and water production. Overall, ESs can be increased by appropriately controlling population and economic development, balancing economic development and ecological protection, promoting energy transition, maintaining ecological hotspot areas, and improving cold spot areas.

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Section: Introductionmentioning

confidence: 99%

Coupling PLUS–InVEST Model for Ecosystem Service Research in Yunnan Province, China

et al. 2022

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“…At present, the existing land-use change prediction models mainly include the CA-Markov model [22], CLUS-S model [23,24], and FLUS-Markov model [25,26]. The first two models have good spatial extensibility and can predict the spatial distribution pattern of future land use, but the CA-Markov model lacks a cellular state transition restriction module and can simulate only a single land-use type [27], while the CLUS-S model easily ignores the possibility of the conversion of nondominant land-use types and has shortcomings regarding the land-use allocation process [26].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Prediction of Land-Use Changes and Ecosystem Service Values in the Lhasa River Basin Based on the FLUS-Markov Model

Qi,

Yu,

2024

Land

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The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused mainly on static assessment and evolution analysis based on historical data, and have not considered future development trends. Moreover, most of the current driving factors selected in land use and ESV prediction studies are homogeneous, and do not reflect the geographical and cultural characteristics of the study area well. With the Lhasa River Basin as the research focus, 20 driving factors were selected according to the characteristics of the plateau alpine area, and the land-use changes under three developmental orientations, namely, natural evolution, ecological protection, and agricultural development, were predicted for the year 2030 with the FLUS-Markov model. Based on these predictions, the values of ecosystem services were calculated, and their spatiotemporal dynamic characteristics were analyzed. The results show that the model has high accuracy in simulating land-use change in the Lhasa River Basin, with a kappa coefficient of 0.989 and an overall accuracy of 99.33%, indicating a high applicability. The types of land use in the Lhasa River basin are dominated by the existence of grassland, unused land, and forest, with a combined proportion of 94.3%. The change trends of each land-use type in the basin under the three scenarios differ significantly, with grassland, cropland, and building land showing the most significant changes. The area of cropland increased only in the agricultural development scenario; the areas of forest and grassland increased only in the ecological protection scenario; and the expansion of building land was most effectively controlled in the ecological protection scenario. The ESV increased in all three scenarios, and the spatial distribution of the ESV per unit area in the middle and lower reaches was greater than that in the upper reaches. The ESV was the greatest in the ecological protection scenario, with grasslands, forests, and water bodies contributing more to the ESV of the basin. This study provides decision-making references for the effective utilization of land resources, ecological environmental protection planning, and urban construction in the Lhasa River Basin in the future.

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“…Scholars at home and abroad conduct research and exploration to analyze the evolution of landscape patterns based on land use cover change (LUCC) and predict the future trend of landscape pattern changes [36]. Currently, the commonly used prediction models include the Gray prediction model, the CLUS-S model [37], the SLEUTH model [38], the FLUS model [19], and the CA-Markov model [39]. Among them, the CA-Markov model couples the respective advantages of the CA model and the Markov model, which can effectively reduce simulation errors when landscape types are transformed into each other and better predict the spatial and temporal evolution of complex nonlinear landscape patterns.…”

Section: Introductionmentioning

confidence: 99%

Exploring and Predicting Landscape Changes and Their Driving Forces within the Mulan River Basin in China from the Perspective of Production–Living–Ecological Space

Zhou,

Wen,

Wang

et al. 2024

Sustainability

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With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km2 of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km2 by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km2. Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin.

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Land use changes in Zhangjiakou from 2005 to 2025 and the importance of ecosystem services

Cited by 10 publications

References 64 publications

Coupling PLUS–InVEST Model for Ecosystem Service Research in Yunnan Province, China

Coupling PLUS–InVEST Model for Ecosystem Service Research in Yunnan Province, China

Multi-Scenario Prediction of Land-Use Changes and Ecosystem Service Values in the Lhasa River Basin Based on the FLUS-Markov Model

Exploring and Predicting Landscape Changes and Their Driving Forces within the Mulan River Basin in China from the Perspective of Production–Living–Ecological Space

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