Hakan Mete Dogan

Applications of remote sensing and Geographic Information Systems to assess ferrous minerals and iron oxide of Tokat province in Turkey

Tokat province is one of the least studied areas in Turkey. Updated spatial information about ferrous minerals (FM) and iron oxide (IO) is often required for studies focusing on environmental modelling, agriculture, and mining. In this study, FM and IO attributes of the province were assessed and mapped by evaluating Landsat‐ETM+ satellite images. To avoid incorrect interpretations, dense plant or closed forest areas were determined using the normalized difference vegetative index, and excluded from the evaluation by masking. FM and IO index maps were produced using related algorithms and remote sensing tools. Classification and spatial analysis operations were conducted under the framework of Geographic Information Systems. A natural breaks method was employed for classification, and both indices were summarized in nine classes. The relationship between two index maps was investigated using a bi‐variety correlation analysis. The correlation between two index maps (0.549) was found to be significant at the 1% level. Developed index maps were tested using ancillary data from previous studies in the area. The results were summarized at the administrative district level.

Spatiotemporal change and ecological modelling of malaria in Turkey by means of geographic information systems.

We described the spatiotemporal change of malaria (Plasmodium vivax) in Turkey over 34 years (1975-2008), and assessed the role of environmental variables in this change. We developed seven 5-year-period raster maps by using geo-referenced malaria case data from the centres of 81 provinces and the kriging method with a spherical variogram model in a geographic information systems (GIS) model. We also modelled malaria incidence in GIS by using our average malaria incidence raster map, and complementary spatial database including the raster map layers of 14 environmental variables. We chose linear regression analysis with backward method to investigate relationships among variables and develop a model. The model was run in GIS to obtain a model incidence raster map. We tested the reliability of the model map by residual statistics, and found the model map dependable. Five-year-period maps revealed that the distribution of malaria cases moved from the East Mediterranean region to the Southeast Anatolia region due to changing human activities. The latitude, minimum temperature, distance to seas and elevation variables were found to have significant impacts on malaria. Consequently, the model incidence map established a good background for early warning systems to predict epidemics of malaria following environmental changes.

Spatial modelling of rheumatoid arthritis in Turkey by geographic information systems (GIS)

We described the recent spatial distribution of rheumatoid arthritis in Turkey and assessed the role of environmental variables in this distribution. We developed an observed rheumatoid arthritis (RA) incidence grid map by using georeferenced rheumatoid arthritis case data (2011) from the centres of 81 provinces and the kriging method with a spherical variogram model in geographic information systems (GIS). We also modelled rheumatoid arthritis incidence in GIS by using complementary spatial database including the grid map layers of 14 environmental variables of Turkey. We conducted principle component analysis and multiple regression to investigate the relationships among variables and develop a model, respectively. The produced model was run in GIS to obtain a predicted (model) RA map. We tested the reliability of the model map by residual statistics and found the model map dependable. Observed and model incidence maps revealed the geographic distribution of rheumatoid arthritis cases in Turkey. The mean temperature, minimum temperature, maximum temperature, water vapour pressure, elevation, potential evapotranspiration, latitude, distance to seas, sunshine fraction, precipitation, longitude and aspect variables were found to have significant impacts on rheumatoid arthritis. Consequently, the model incidence map established a good background to predict rheumatoid arthritis cases following environmental changes.

Evaluation of the NDVI in plant community composition mapping: a case study of Tersakan Valley, Amasya County, Turkey.

Mapping the composition of plant community types requires reliable spatial data obtained from field surveys and satellite‐derived indices. The normalized difference vegetation index (NDVI) is the simplest and most frequently used index in plant applications. If relationships between the NDVI and plant cover abundance are determined, this information can be used in the mapping process. In this study, we investigated these possible connections for mapping the plant community composition of Tersakan Valley in Turkey. We collected georeferenced cover‐abundance (Braun‐Blanquet, BB) data from 1077 quadrats in the field, and derived an NDVI raster map from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Then we classified this NDVI map by using various methods and class numbers. Using bivariate correlation analysis, we explored the relationships between 26 classified NDVI maps and BB, and chose the classified NDVI map with the strongest correlation. We further examined this relationship by using scatter plots, histograms and paired samples t‐tests. According to the results, the NDVI (equal‐interval 15) classes between 4 and 8 corresponded with the BB classes between 1 and 5, respectively. Using this relationship, the spatial distribution of 43 different plant community compositions were determined in geographic information systems (GIS). The results indicate that the NDVI has the potential to map plant community composition reliably.