Donghai Institute, Ningbo University, Ningbo 315211, China.
Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China.
Int J Environ Res Public Health. 2019 Jun 24;16(12):2225. doi: 10.3390/ijerph16122225.
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.
景观粒度效应反映了景观的空间异质性，是景观生态学的研究核心。景观粒度效应不仅可以用来探索景观格局的时空变化特征，还可以揭示景观生态结构和功能的变化规律。本研究基于 1991 年、2000 年、2008 年和 2017 年获取的盐城滨海湿地景观数据，研究了景观格局指数对 50-1000m 粒度的敏感性。分析了粒度效应对景观变化的响应，并确定了研究区分析的最佳粒度。结果表明：（1）在 27 个指数（12 个类水平和 15 个景观水平）中，8 个指数对粒度高度敏感，10 个指数中度敏感，8 个指数低度敏感，1 个无响应。表明面积-边缘指数和形状指数对不同粒度更为敏感。聚集指数在粒度变化上有一些差异，多样性指数对粒度的响应程度较低。（2）景观指数对不同粒度表现出六种不同的响应，包括缓慢减少响应、快速减少然后缓慢减少响应、单调增加响应、波动减少响应、上下响应和稳定响应，表明景观指数与空间粒度密切相关。（3）从 1991 年到 2017 年，不同景观类型的景观粒度变化曲线可分为波动上升型、波动型、单调下降型和单调上升型四种类型。不同的粒度曲线对景观变化有不同的解释，但总的来说，盐城滨海湿地的景观趋于破碎化和复杂化，内部连通性减弱，主导景观面积减少。自然湿地对粒度效应比人工湿地更敏感。（4）50m 粒度下的景观指数对不同粒度变化的响应较强，景观信息损失最小。因此，确定研究区的最优景观粒度为 50m。
