Landscape Grain Effect in Yancheng Coastal Wetland and Its Response to Landscape Changes

Peng Tian, Luodan Cao, Jialin Li, Ruiliang Pu, Xiaoli Shi, Lijia Wang, Ruiqing Liu, Hao Xu, Chen Tong, Zijing Zhou and Shuyao Shao
Additional contact information
Peng Tian: Donghai Institute, Ningbo University, Ningbo 315211, China
Luodan Cao: Donghai Institute, Ningbo University, Ningbo 315211, China
Jialin Li: Donghai Institute, Ningbo University, Ningbo 315211, China
Ruiliang Pu: School of Geosciences, University of South Florida, Tampa, FL 33620, USA
Xiaoli Shi: Editorial Department of Journal of Ningbo University, Ningbo 315211, China
Lijia Wang: Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China
Ruiqing Liu: Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China
Hao Xu: Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China
Chen Tong: Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China
Zijing Zhou: Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China
Shuyao Shao: Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China

IJERPH, 2019, vol. 16, issue 12, 1-20

Abstract: The landscape grain effect reflects the spatial heterogeneity of a landscape and it is used as a research core of landscape ecology. The landscape grain effect can be used to not only explore spatiotemporal variation characteristics of a landscape pattern, but also to disclose variation laws of ecological structures and functions of landscapes. In this study, the sensitivity of landscape pattern indexes to grain sizes 50–1000 m was studied based on landscape data in Yancheng Coastal Wetland acquired in 1991, 2000, 2008, and 2017. Response of the grain effect to landscape changes was analyzed and an optimal grain size for analysis in the study area was determined. Results indicated that: (1) among 27 indexes (12 in a class level and 15 in a landscape level), eight indexes were highly sensitive to grains, ten indexes presented moderate sensitivity, eight indexes presented low sensitivity, and one was unresponsive. It was shown that the area-margin index and the shape index were more sensitive to the different grain sizes. The aggregation index had some differences in the grain size change, and the diversity index had a low response degree to the grain size. (2) Landscape indexes showed six different responses to different grains, including slow reduced response, fast reduced and then slow reduced response, monotonically increased response, fluctuating reduced response, up-down responses, and stable response, which indicated that the landscape index was closely related to the spatial grain. (3) From 1991 to 2017, variation curves of the landscape grain size of different landscape types could be divided into four types: fluctuation rising type, fluctuation type, monotonous decreasing type, and monotonous rising type. Different grain size curves had different interpretations of landscape changes, but in general, Yancheng Coastal Wetland’s landscape tended to be fragmented and complicated, internal connectivity was weakened, and dominant landscape area was reduced. Natural wetlands were more sensitive to grain size effects than artificial wetlands. (4) The landscape index at the 50 m grain size had a strong response to different grain size changes, and the loss of landscape information was the smallest. Therefore, it was determined that the optimal landscape grain size in the study area was 50 m.

Keywords: grain effect; landscape pattern; landscape level; class level; coastal wetland; Yancheng (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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