A change vector analysis technique to monitor land-use/land-cover in the yildiz mountains, Turkey

This paper presents the method to detect the intensity of change and the dimension of change using Change Vector Analysis (CVA) method. The aim of this study is to confirm the changes in the Yildiz Mountains using proof from natural ecosystems (natural deciduous forest, wetland and sand dune ecosystems) and other various ecosystems ecologically significant all over Europe and Turkey by using the CVA technique. Two Landsat TM scenes recorded in 1990 and 2009 were used to minimize change detection error introduced by seasonal differences. Images were geometrically and atmospherically corrected. As a result of the ratings, it-is observed that agricultural areas decreased by 17.5 and Crimean pine forests by 16.4% whereas oak forests increased by 4.1%. Ash trees, sand dunes and bare ground reflected insignificant changes, too. © by PSP

SPATIAL PREDICTION OF AIR TEMPERATURE IN EAST CENTRAL ANATOLIA OF TURKEY

Air temperature is an essential component of the factors used in landscape planning. At similar topographic conditions, vegetation may show considerable differences depending on air temperature and precipitation. In large areas, measuring temperature is a cost and time-consuming work. Therefore, prediction of climate variables at unmeasured sites at an acceptable accuracy is very important in regional resource planning. In addition, use a more proper prediction method is crucial since many different prediction techniques yield different performance in different landscape and geographical conditions. We compared inverse distance weighted (IDW), ordinary kriging (OK), and ordinary cokriging (OCK) to predict air temperature at unmeasured sites in Malatya region (East Central Anatolia) of Turkey. Malatya region is the most important apricot production area of Turkey and air temperature is the most important factor determining the apricot growing zones in this region. We used mean monthly temperatures from 1975 to 2010 measured at 28 sites in the study area and predicted temperature with IDW, OC, and OCK techniques, mapped temperature in the region, and tested the reliability of these maps. The OCK with elevation as an auxiliary variable occurred the best procedure to predict temperature against the criteria of model efficiency and relative root mean squared error