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Open AccessArticle

Evolution of Landscape Ecological Risk at the Optimal Scale: A Case Study of the Open Coastal Wetlands in Jiangsu, China

by Yongchao Liu 1,2,3, Yongxue Liu 1,3,4,*, Jialin Li 5,6, Wanyun Lu 1, Xianglin Wei 1 and Chao Sun 5
1
Department of Geographic Information Science, Nanjing University, Nanjing 210023, China
2
Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China
3
Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China
4
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
5
Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China
6
Institute for East China Sea, Ningbo University, Ningbo 315211, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(8), 1691; https://doi.org/10.3390/ijerph15081691
Received: 11 June 2018 / Revised: 19 July 2018 / Accepted: 27 July 2018 / Published: 8 August 2018
Detailed analysis of the evolution characteristics of landscape ecological risk is crucial for coastal sustainable management and for understanding the potential environmental impacts of a man-made landform landscapes (MMLL). As a typical open coastal wetland, large-scale human activities (e.g., tidal reclamation, fishery activities, wind farm construction, and port construction) have substantially affected the evolution of the coastal ecological environment. Previous landscape ecological risk assessment studies have documented the effectiveness of assessing the quality of ecological environment processes. However, these studies have either focused on the noncoastal zone, or they have not considered the evolution of the spatial characteristics and ecological risk evolution of the landscape at an optimal scale. Here, we present a landscape ecological risk pattern (LERP) evolution model, based on two successive steps: first, we constructed an optimal scale method with an appropriate extent and grain using multi–temporal Landsat TM/OLI images acquired in the years 2000, 2004, 2008, 2013 and 2017, and then we calculated landscape ecological risk indices. Based on this model, the entire process of the spatiotemporal evolution of ecological risk patterns of the open coastal wetlands in Jiangsu, China, was determined. The principal findings are as follows: (1) The main landscape types in the study area are tidal flats and farmland, and the main features of the landscape evolution are a significant increase in aquafarming and a substantial decrease in the tidal flat area, while the landscape heterogeneity increased; (2) In the past 20 years, the areas of low and relatively low ecological risk in the study region were greatly reduced, while the areas of medium, relatively high, and high ecological risk greatly increased; the areas of high-grade ecological risk areas are mainly around Dongtai and Dafeng; (3) The area of ecological risk from low-grade to high-grade occupied 71.75% of the study area during 2000–2017. During the previous periods (2000–2004 and 2004–2008), the areas of low-grade ecological risk were transformed to areas of middle-grade ecological risk area, while during the later periods (2008–2013 and 2013–2017) there was a substantial increase in the proportion of areas of high-grade ecological risk. Our results complement the official database of coastal landscape planning, and provide important information for assessing the potential effects of MMLL processes on coastal environments. View Full-Text
Keywords: landscape patterns; spatial heterogeneity; landscape ecological risk assessment; remote sensing and GIS; open coastal wetlands landscape patterns; spatial heterogeneity; landscape ecological risk assessment; remote sensing and GIS; open coastal wetlands
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Liu, Y.; Liu, Y.; Li, J.; Lu, W.; Wei, X.; Sun, C. Evolution of Landscape Ecological Risk at the Optimal Scale: A Case Study of the Open Coastal Wetlands in Jiangsu, China. Int. J. Environ. Res. Public Health 2018, 15, 1691.

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