Study on land use and landscape pattern change in the Huaihe River Ecological and economic zone from 2000 to 2020

You, Mou; Zou, Zeduo; Wei, Zhao; Zhang, Wenwen; Canfang, Fu

doi:10.1016/j.heliyon.2023.e13430

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…The study area has relatively low ecological risk, primarily corresponding to medium and low risks (Figures 4 and 5). This finding is consistent with the research conducted by You et al [49], who also identified decrease in fragmentation of landscape pattern, and increase in connectivity and landscape diversity in this region. During the period from 1980 to 2020, the ecological risk in the Huaihe River Eco-economic Belt did not increase significantly, indicating that urbanization is not the main cause of ecological deterioration.…”

Section: Discussionsupporting

confidence: 93%

Land Use Change and Its Impact on Ecological Risk in the Huaihe River Eco-Economic Belt

Feng

Yang

et al. 2023

Land

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Exploring the landscape ecological security pattern and its driving mechanisms in key economic zones is of great significance for preventing and resolving landscape ecological risks and promoting regional sustainable development. This study quantitatively analyzed the land use change characteristics in the Huaihe River Eco-economic Belt from 1980 to 2020 using the land use transfer matrix and land use intensity index. Further, the evolution of ecological risks and their driving mechanisms were investigated using the landscape pattern index and hierarchical partitioning analysis. The results show that (1) in terms of absolute area, dryland, grassland, and paddy land decreased by 7075 km2, 2708 km2, and 1874 km2, respectively, while urban–rural land and water area increased by 10,538 km2 and 1336 km2, respectively. In terms of change intensity, grassland, water area, urban–rural land, and unused land exhibited the most dramatic change, whereas forest land, paddy land, and dryland exhibited weaker change. (2) The conversions in the study area were primarily between dryland, paddy land, and urban–rural land. Paddy land and dryland tended to convert to urban–rural land, which is further likely to be transformed into dryland and unused land when converted. (3) The study area mainly presented medium to low ecological risk. Overall, the ecological risk remained stable throughout the study period. Nevertheless, Jining, Zaozhuang, and Bengbu show high ecological risks in the construction of the economic zone. (4) Forest land explained 40% of the variance in landscape risk, whereas urban–rural land and dryland each explained 20% of the variance. An increase in the proportion of urban–rural land and dryland will increase landscape ecological risk. However, after urban–rural land exceeds 15%, the ecological security risk does not increase significantly with increasing proportion of urban–rural land.

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Section: Discussionsupporting

confidence: 93%

Land Use Change and Its Impact on Ecological Risk in the Huaihe River Eco-Economic Belt

Feng

Yang

et al. 2023

Land

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“…Finally, the GRA has been utilized by scholars [ [59] , [60] , [61] , [62] , [63] , [64] , [65] ] in diverse research areas and sectors. The GRA utilization ranges from system restoration, marking inspection, optimization [ 66 ], organization performance [ 58 ], relationship [ 67 ], simulation, and assessment. However, studies that utilized this method in its direct approach or hybrid format for national sustainability performance, grades or ranking are rare in the Gulf region.…”

Section: Methodsmentioning

confidence: 99%

The development of a grey relational analysis-based composite index for environmental sustainability assessment: Towards a net-zero emissions strategy in Saudi Arabia

Alshuwaikhat,

Adenle,

Alotaishan

2023

Heliyon

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“…Moreover, the LPI, PAFRAC, and aggregation degree have a positive impact on the total ESV. Due to population growth and urbanization, the transformation of landscape patterns in this region is primarily driven by human activities [84]. Therefore, across the period, human activities remarkably shape the connection between landscape patterns and ESVs.…”

Section: The Relationships Between Landscape Patterns and Ecosystem S...mentioning

confidence: 99%

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

Han,

Li,

Wei

et al. 2023

Forests

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Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources.

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Study on land use and landscape pattern change in the Huaihe River Ecological and economic zone from 2000 to 2020

Cited by 10 publications

References 21 publications

Land Use Change and Its Impact on Ecological Risk in the Huaihe River Eco-Economic Belt

Land Use Change and Its Impact on Ecological Risk in the Huaihe River Eco-Economic Belt

The development of a grey relational analysis-based composite index for environmental sustainability assessment: Towards a net-zero emissions strategy in Saudi Arabia

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

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