Land cover classification and modelling the productivitiy of some forest stands at the Upper Seyhan River Basin using remote sensing

In this study, land cover classification was performed in the Upper Seyhan River Basin using IKONOS and LANDSAT ETM+ data and the Net Primary Productivity (NPP) was modelled within a Geographical Information Systems environment using laboratory analysis, field surveys conducted at the representative forest stands and remotely sensed satellite data.The field works were implemented at Katran Çukuru area as it highly represents the region. This research includes three phases: (i) collecting ground data (biotic dataset) (ii) image classification and percent tree cover, (iii) data integration and modelling productivity. Biotic data set comprises tree height, diameter, age, litter, dry matter data measured from five test sites set up within Crimean pine, Lebanese cedar, Turkish pine, juniper and mixed conifer forest stands. LANDSAT ETM+ images recorded over the study area were classified using supervised training to produce current land cover pattern. At the final stage of this study, spatial distribution and the quantity of productivity was modelled with NASA-CASA (Carnegie-Ames-Stanford-Approach) by integrating remotely sensed and ground data within a GIS environment.