Exploring the spatial patterns of vegetation fragmentation using local spatial autocorrelation indices

Abstract. The spatial heterogeneity of urban vegetation obtained from discrete land cover classes is sensitive to classification errors and can result in a substantial loss of information, due to the degradation of continuous quantitative information. Although there is an increasing ecological need to use continuous methods to understand spatial heterogeneity and vegetation fragmentation, they remain unexplored. Since local indicators of spatial association (LISA) can capture important spatial patterns of clustering and dispersion at a local scale, it can capture important ecological patterns and process of vegetation fragmentation. This work examines the utility of LISA which allows exploration of local patterns in spatial data in identifying high (hot spots) and low (cold spots) spatial clusters of vegetation patches and fragmentation patterns in Harare metropolitan city in Zimbabwe. The LISA indices of Getis-Ord Gi* and local Moran’s I are computed both on continuous normalized difference vegetation index and discrete land cover data of vegetation and nonvegetation of Sentinel 2016 and 2018. Local spatial clustering patterns are identified with Z-score values that indicate the significance of each statistic. High positive Z-scores are located in the large core, undisturbed, and homogeneous vegetation. Negative Z-scores are located in more dispersed and highly fragmented vegetation. The results suggest that there is a strong tendency for large core, undisturbed, and homogeneous vegetation patches to be spatially clustered and for small, isolated and sparse vegetation patches to be dispersed. The highly fragmented vegetation patches are located in the heavily urbanized part of the city. Overall, findings of this study underscore the relevance of the spatially explicit method of LISA as a valuable source of spatial information for the assessment of local spatial clustering and dispersion of urban vegetation patches. This can be used to develop policies that support effective conservation and restoration strategies.

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