Minireview: Quantifying Landscape Spatial Pattern: What Is the State of the Art?

范畴变量 空间生态学 背景(考古学) 空间语境意识 生态学 地理 计算机科学 时间尺度 共同空间格局 空间变异性 景观规划 空间分析 景观流行病学 数据挖掘 自然地理学 生态环境 景观评价 人工智能 机器学习 数学 遥感 统计 生物 考古
作者
Eric J. Gustafson
出处
期刊:Ecosystems [Springer Science+Business Media]
卷期号:1 (2): 143-156 被引量:1582
标识
DOI:10.1007/s100219900011
摘要

Landscape ecology is based on the premise that there are strong links between ecological pattern and ecological function and process. Ecological systems are spatially heterogeneous, exhibiting considerable complexity and variability in time and space. This variability is typically represented by categorical maps or by a collection of samples taken at specific spatial locations (point data). Categorical maps quantize variability by identifying patches that are relatively homogeneous and that exhibit a relatively abrupt transition to adjacent areas. Alternatively, point-data analysis (geostatistics) assumes that the system property is spatially continuous, making fewer assumptions about the nature of spatial structure. Each data model provides capabilities that the other does not, and they should be considered complementary. Although the concept of patches is intuitive and consistent with much of ecological theory, point-data analysis can answer two of the most critical questions in spatial pattern analysis: what is the appropriate scale to conduct the analysis, and what is the nature of the spatial structure? I review the techniques to evaluate categorical maps and spatial point data, and make observations about the interpretation of spatial pattern indices and the appropriate application of the techniques. Pattern analysis techniques are most useful when applied and interpreted in the context of the organism(s) and ecological processes of interest, and at appropriate scales, although some may be useful as coarse-filter indicators of ecosystem function. I suggest several important needs for future research, including continued investigation of scaling issues, development of indices that measure specific components of spatial pattern, and efforts to make point-data analysis more compatible with ecological theory.

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