GISBased Assessment of Habitat Networks for Conservation Planning in KasKekova Protected Area (Turkey) The determination of protected area (PA) boundaries and the level of restrictions is very important for sustainable conservation, and such decisions must involve biodiversity data and assessment. In a PA, the consensus of the government and the locals is crucial to ensure protectionxe2x80x93use balance. The PA restrictions constrain legal human activities, and the boundary determination and the restrictions should be based on various scientific analyses to achieve consensus. In this study, a GISbased approach is proposed to utilize the biodiversity data for efficient conservation and land use planning in KasKekova PA, which is among the most important PAs in Turkey. Spatial analysis methods, i.e., kernel density estimation, natural breaks classification and integrated density index, were performed for the assessment of the habitat networks using georeferenced biodiversity datasets, and the results were evaluated with respect to the actual land use data and the land ownership pattern. The developed spatial analysis approach is efficient to produce the conservation base maps required for regional land use planning, for defining sustainable conservation strategies, and to provide a widely accepted base for land use planning and biodiversity monitoring in the PA; although careful investigations and expert opinions are still required for data deficient areas.

A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multitemporal landsat data Abstract   Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994xe2x80x932017xe2x80xafas shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p