Volume IV-4/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 153-159, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-153-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 153-159, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-153-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Nov 2017

13 Nov 2017

SPATIAL PREDICTION OF AIR TEMPERATURE IN EAST CENTRAL ANATOLIA OF TURKEY

B. C. Bilgili1, S. Erşahin2, and M. Özyavuz3 B. C. Bilgili et al.
  • 1Çankırı Karatekin University, Faculty of Forestry, Department of Landscape Architecture, 18200, Çankırı, Turkey
  • 2Çankırı Karatekin University, Faculty of Forestry, Department of Forest Engineering, 18200, Çankırı, Turkey
  • 3Namık Kemal University, Department of Landscape Architecture,59030, Tekirdağ, Turkey

Keywords: Ordinary Kriging, Ordinary Cokriging, Malatya, Apricot, Model Efficiency

Abstract. 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.