Zonal lacunarity analysis: a new spatial analysis tool for geographic information systems

• Published in: Landscape ecology Vol. 34; no. 10; pp. 2245 - 2249
• Main Authors: Dong, Pinliang, Sadeghinaeenifard, Fariba, Xia, Jisheng, Tan, Shucheng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2019; Springer Nature B.V
• Subjects: Algorithms; Balances (scales); Biomedical and Life Sciences; cities; computer software; data collection; Ecological monitoring; Ecology; Environmental Management; Geographic information systems; Gliding; Grasslands; Heterogeneity; Lakes; Land cover; Landscape; Landscape Ecology; Landscape/Regional and Urban Planning; Life Sciences; Nature Conservation; Programming languages; Python; Raster; raster data; Remote sensing; Short Communication; Spatial analysis; Spatial distribution; Spatial heterogeneity; spatial variation; Spreadsheets; Sustainable Development; Texas; Texas; GIS; Spatial analysis; Lacunarity; Raster data; Zonal analysis
• ISSN: 0921-2973; 1572-9761
• DOI: 10.1007/s10980-019-00886-9

Context 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. Objectives 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). Methods 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. Results 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. Conclusions 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.