Zonal lacunarity analysis: a new spatial analysis tool for geographic information systems
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Lacunarity as a scale-dependent measure of spatial heterogeneity has received great attention in landscape ecology. Most lacunarity measures have been obtained from greyscale or binary (0 and 1) data for an entire study area or fixed rectangular windows, and a zonal lacunarity tool for discrete raster data is still lacking in current geographic information systems.
This short communication presents the development of a free zonal lacunarity analysis tool for ArcGIS to support applications involving scale-dependent analysis of spatial heterogeneity, including landscape ecology. The application of the tool is also demonstrated using 2001 and 2011 land cover data from the National Land Cover Database (NLCD).
Based on the gliding-box algorithm for lacunarity estimation, a tool for zonal lacunarity analysis of discrete raster data is developed using ArcPy and the Python programming language. The tool uses discrete raster data as input, an optional zone feature class as zone data to partition the input raster data into different zones, and a spreadsheet with zonal lacunarity values as output.
As a demonstration, lacunarity measurements of grasslands in Corinth and Lake Dallas, Texas were calculated from the 2001 and 2011 NLCD data using box sizes (scales) of 2, 3, 4, 5, 6, 7, 8, 9, and 10. The results show that measures of grassland lacunarity in Lake Dallas were higher than Corinth at all scales, and the measures of grassland lacunarity in 2011 were higher than 2001 for both cities because of the increasing gap sizes in grasslands. The increasing gap sizes in grasslands were caused by converting the grasslands into developed areas.
The results suggest that the zonal lacunarity analysis tool can provide important information on the spatial distribution of gaps in the input discrete raster data at different scales. It is hoped that the zonal lacunarity analysis tool can be further evaluated using different datasets in landscape ecology.
KeywordsGIS Lacunarity Raster data Spatial analysis Zonal analysis
The first author is supported by summer research grants from Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, a joint project between the Department of Science and Technology of Yunnan Province and Yunnan University (C176240210019), and the Plateau Mountain Ecology and Earth’s Environment project of Yunnan University (C176240107). The authors would like to thank Reza Nikfal for his technical support and anonymous reviewers for the helpful comments.
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