Improving knowledge of urban vegetation by applying GIS technology to existing databases.

Abstract

Question: Can we improve the knowledge of urban vegetation using data from ongoing floristic and management projects with a data mining approach? We have two questions: 1. How strong is the relationship between land cover pattern and the species composition of vegetation? 2. What is the relationship between land cover pattern and species richness? Location: Trieste, northeastern Italy. Methods: Using land cover maps and GIS we characterized the cells of a floristic project grid by percentage cover of land cover types. We applied Canonical Correlation Analysis to test the correlation between floristic composition of the cells and land cover. We classified the cells by clustering methods, based on land cover description. With these clusters, we analysed the variation of species composition of urban vegetation along a gradient of urban density. We used Jaccard\u2bcs similarity index to compare floristic composition of the clusters with the floristic composition of the homogeneous cells with respect to the land cover types. To answer question 2, we calculated land cover heterogeneity with the Shannon index and correlated the number of species in clusters with land cover heterogeneity and urban density. Results: Each land cover type contributes to species richness and species composition of the clusters. Species richness decreases significantly and linearly as urban density increases and land cover heterogeneity decreases in the clusters. Conclusions: A data mining approach can combine different existing projects to improve knowledge of the urban vegetation system. The methods we have applied offer tools to answer the specific questions mentioned above