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TitleDetecting and quantifying extended landscape structure with spatial co-occurrence surfaces
AuthorZhang, Y; Guindon, B
SourcePattern Analysis and Applications vol. 20, no. 2, 2015 p. 519-529,
Alt SeriesEarth Sciences Sector, Contribution Series 20150380
PublisherSpringer Nature
Mediapaper; on-line; digital
File formatpdf; html
Subjectsgeophysics; remote sensing; satellite imagery; Methodology; Land cover; Classification; Mathematics
Illustrationssatellite images; images; plots; schematic representations
ProgramRemote Sensing Science
Released2015 09 24
AbstractThe attribute adjacency matrix is a fundamental component of many metrics used to characterize landscape heterogeneity from land cover land use maps. Since it quantifies adjacent pixel class co-occurrence, it is unsuited to detect broader-scale structure in land cover maps. This paper proposes a generalization of the adjacency matrix concept by incorporating lag distances into class co-occurrence estimation. The spectrum of spatial structure is presented in the form of a spatial co-occurrence surface. These surfaces can provide a wealth of information on landscape structure including the size and spatial distribution of patches of a single class as well as inter-class spatial associations.
Summary(Plain Language Summary, not published)
Over the past few decades, satellite data from the Landsat and SPOT satellites has been used to generate regional and national land cover products. These products can capture complex landscape structures. To optimize the quality and consistency of such products, it is important to characterize the structure of different landscapes quantitatively and objectively. This study develops a new methodology to quantify landscape spatial features (e.g., size and distribution), which is particularly useful for geographic regions with high levels of human activity as occurs in urban areas or natural resource development sites. The developed methodology is well-suited to analysis of landscapes with repeated patterns, such as that of the oil and gas infrastructure landscape in Alberta, Canada, and is therefore useful for the management of natural resources lands. The methodology also has potential to improve automation for extraction of land cover information from Earth Observation imagery.

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