Assessing the dynamic landscape ecological risk and its driving forces in an island city based on optimal spatial scales: Haitan Island, China

Ai, Jingwen; Yu, Kunyong; Zeng, Zhen; Liu-qing, Yang; Liu, Yanfen; Liu, Jian

doi:10.1016/j.ecolind.2022.108771

Cited by 104 publications

(65 citation statements)

References 62 publications

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“…K-means algorithm belongs to one of the common algorithms in the division method, and its theory also belongs to a basic category of iterative optimization algorithm (optimization clustering), which generally requires the algorithm to satisfy a certain equalization function, as a basic criterion of the basic iterative optimization process, and iterate continuously to get each optimal cluster clusters [22]. Although it is said that the K-means algorithm is relatively easy and fast to implement, in fact, there are some limitations in the process of cluster calculation, iteration, and optimization: (1) the algorithm is sensitive to the initial cluster centroid; (2) it is easy to fall into the local optimal solution [23].…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

“…. ., 24; first, the whole sample data set is clustered on the A1 dimensional group using the clustering method to obtain three classes of K m,n PSO (1,4,7,10,13,16,19,22), (2, 5, 8, 11, 14, 17 20, 23), and (3,6,9,12,15,18,21,24), and then apply the same method to cluster (1,4,7,10,13,16,19,22) first in A2 dimension to obtain one class (1,4,7,10,13,16,19,22) and cluster (2,5,8,11,14,17,20,23) in A2 dimension to obtain two classes (2,5,8,11,14,17) and (20,23), and one class (3,…”

Section: Empirical Analysismentioning

confidence: 99%

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Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Sun

Yang²

2022

Security and Communication Networks

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Garden art is the culture of generations since ancient times and is another spiritual carrier of people’s internal admiration for natural landscape, culture, and art as well as their love for living in nature and landscape. This article mainly studies the data clustering algorithm and adopts a research methodology that progresses from simple to complex. It starts by establishing a spatial data clustering model and then clustering the low-dimensional data, and then processing the high-dimensional spatial data in the low-dimensional data set. The original K-means clustering algorithm is then improved, and the new algorithm is created by combining PSO with the K-means algorithm in the high-dimensional spatial data set. And the improved two algorithms are applied to the study of data related to landscape conservation sites, and the powerful superiority of the improved K-means spatial clustering algorithm in this article is verified through the comparison of the algorithms.

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Section: K-means Clustering Algorithmmentioning

confidence: 99%

Section: Empirical Analysismentioning

confidence: 99%

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Sun

Yang²

2022

Security and Communication Networks

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“…With the support of landscape ecology theory, the landscape loss model based on land use data can describe landscape structure quantitatively and explain the evolution mechanism of landscape ecological risk from the perspective of spatial pattern change. Therefore, this model has been widely used [34]. The five Central Asian countries are adjacent to China's Xinjiang province, which is not only an important fulcrum of the BRI, but also the first stop of China's outbound investment.…”

Section: Introductionmentioning

confidence: 99%

Exploring Changes in Land Use and Landscape Ecological Risk in Key Regions of the Belt and Road Initiative Countries

Zhang

Yao

Jun

et al. 2022

Land

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The Belt and Road Initiative (BRI) has revealed that it is necessary to strengthen research on land use and land cover change (LUCC) and ecological risk in key regions of countries around the world. In this study, the spatiotemporal characteristics of LUCC in the five capitals of Central Asian countries within the BRI were analyzed. Based on the grid scale, a landscape pattern index was introduced to quantitatively evaluate the landscape ecological risk levels of the five capitals. The results showed the following: first, the components of land use types in the five capitals have different structural characteristics, which are mainly grassland, unused land, and cultivated land. The landscape types that changed significantly were water and unused land, while the construction land area showed a trend of continuous increase. Second, different capitals have different land-use transfer patterns. Akmola State is mainly converted from cultivated land to grassland; Chuy State is mainly converted from forest land to grassland; Dushanbe and Tashkent City are mainly converted from grassland to forestland; and Ahal State is mainly converted from grassland to unused land. Third, the overall landscape ecological risks of the five capitals were low. Akmola State had the largest proportion of lowest ecological risk areas, whereas Chuy State and Dushanbe City had an increasing trend of highest ecological risk areas. The level of ecological risk in Tashkent remained stable during the study period, and the highest ecological risk areas in Ahal State decreased to 49,227.86 km2. This study has enriched the research results of land use change and landscape ecological risk assessment of countries within the BRI and can provide a research reference for these countries and regions to achieve ecological sustainable development and strengthen ecosystem management.

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“…Early research primarily concentrated on ecological risk assessment [6,7]. The research objects included urban land, watersheds, and islands [8][9][10], and the research methods encompassed ecological models, numerical models, and landscape ecological models [11]. Most ecological risk assessments of landscapes (LER) are currently based on the ecological "source-sink" theory or on assessing the evolution of their landscape patterns.…”

Section: Introductionmentioning

confidence: 99%

Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment

et al. 2022

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The ecological protection and sustainable development of Urumqi have become an important part of the high-quality growth of the urban agglomeration on the northern slope of Tianshan Mountain. Under the impacts of multi-source factors, the ecological landscape pattern of Urumqi has changed due to it being in a fragile eco-environment, so an ecological network is desperately needed to enhance ecological security patterns. Taking Urumqi city as the study area, the ecological risk evaluation model and the minimum cumulative resistance model were integrated to analyze the spatial and temporal features of landscape ecological risk from 2000 to 2020, and the future land use simulation model was used to predict the ecological risk pattern of Urumqi in 2030, construct a landscape ecological network, and propose ecological security protection strategies. Since 2000, land use in Urumqi has undergone drastic changes: the built-up land area has increased significantly, the landscape has diversified, and landscape fragmentation has shown a decreasing trend from the main urban area as the core to the urban fringe. The high-risk landscape ecology shows a decreasing trend from east to west, mainly in the bare land areas with sparse vegetation, whereas the risk is relatively low in woodland, arable land, and built-up areas. The change of risk in the study area is mainly influenced by the typical defective factors of oasis cities such as urban expansion, land desertification, and sparse vegetation. The landscape ecological network is mainly located in the southwest, central, and east of the study area, whereas there is no corridor distribution in the north and southeast, which is mainly caused by the special geographical location and climatic conditions. The ecological network mainly consists of 10 ecological sources and 10 ecological corridors and proposes conservation strategies for the optimization of the landscape pattern and for the construction of the ecological security pattern in Urumqi, providing a guide for the improvement of ecological security.

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Assessing the dynamic landscape ecological risk and its driving forces in an island city based on optimal spatial scales: Haitan Island, China

Cited by 104 publications

References 62 publications

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Exploring Changes in Land Use and Landscape Ecological Risk in Key Regions of the Belt and Road Initiative Countries

Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment

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