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Modeling and mapping possibilities of Shannon-Wiener, Simpson, and number of species (NS) indices were researched using geographic information systems (GIS) and remote sensing (RS) tools in Nallihan forest ecosystem of Turkey. The relationships between the indices and a number of independent variables such as topography, geology, soil, climate, normalized difference vegetation index (NDVI), and land cover were investigated to understand relationships between plant diversity and ecosystem. Georeferenced field data from the established 56 quadrats (50 · 20 m) were used to calculate the indices. Principle component analysis (PCA) and multiple regression were employed for data reduction and model development, respectively. Three diversity maps were produced using the developed models. Residual maps and logical interpretations in ecological point of view were used to test the validity of the models. Elevation and climatic factors formed the most important components that are effective determinants of plant species diversity, but geological formations, soil, land cover and land-use characteristics also influenced plant diversity. Considering the different responses of the models, Shannon-Wiener (SWI) and NS models were found suitable for rare cover types, while Simpson (SIMP) model might be appropriate for single dominant land covers in the study area.

Utilizing remote sensing (RS) and geographic information systems (GIS) tools, mineral composite characteristics (ferrous minerals (FM), iron oxide (IO), and clay minerals (CM)) of the Kelkit River Basin (15913.07 km 2 ) in Turkey were investigated and mapped. Mineral composite (MC) index maps were produced from three LANDSAT-ETM+ satellite images taken in 2000. Resulting MC index maps were summarized in nine classes by using 'natural breaks' classification method in GIS. Employing bi-variety correlation analysis, relationships among index maps were investigated. According to the results, FM and IO index maps showed positive correlation, while CM index map is negatively correlated with FM and IO index maps. Negative correlations between iron and clay variables suggested that the dominant clay minerals of the study area might be smectite, illite, kaolinite, and chlorite, which have little or no iron content. Using field data for which their geographic coordinates had been determined by global positioning system (GPS), developed MC maps were verified, and found dependable for environmental and ecological modeling studies.