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ŠUMARSKI LIST 7-8/2015 str. 69     <-- 69 -->        PDF

stock capacity in the forested area of North-eastern China between 1982 and 1999 were identified by satellite. To do this, forest inventory and NOAA / AVHRR normalized difference vegetation index for three different periods 1984–1988, 1989–1993 and 1994–1998 was used. Different types of optical sensor data, such as Landsat, SPOT, ASTER, CBERS, QuickBird, MODIS, and AVHRR can be used for biomass estimation (Lu et al. 2014). Landsat TM7, TM4, SR, RVI, and SAVI were significantly and positively correlated with AGB in Savannakhet Province (Vicharnakorn et al. 2014; Dube and Mutanga 2015). The study conducted by Comez (2012) determined that there are significant differences in carbon stocks stored in among different stand types, tree mass, dead and live trees. Carbon stocks in trees, ground vegetation, and forest layers were significantly different between stand types due to forest growth and past silvicultural treatments. Annual carbon sequestration of standing trees was estimated between 0.520–3.076 tC/ha/year in relation to stand types. Bulut (2012) obtained a biomass equation by biomass measurements of Beech wood species. The resulting equation and the other equations in the literature are used to calculate the total biomass amount of the field and the related stored carbon content. This calculated value is used to estimate the carbon stock capacity of the whole area with controlled classification of three different satellite images (SPOT-5, Quickbird-2, Landsat). The highest accuracy was achieved by the Landsat image.
Although these are a number of recently published papers focusing on biomass estimation, carbon stock and the use of satellite imagery the abundant array of possibilities forced us to device this study, in which the above ground carbon stock capacity of sample plots were calculated, using RapidEye satellite imagery. To do this, above ground carbon stock capacity from 344 sample plots taken within Camyazi Forest Directorate were calculated using “BEF and Carbon coefficient” from Turkey’s National Greenhouse Gas Inventory Report (NIR) for UNFCCC. In this report, BEF coefficient generated by “Asan 2006” for Turkish forests. The stand parameters such as diameter, height, etc. obtained from field survey were using in the equations for calculating the aboveground carbon stock capacity at sample areas. The purpose of this particular study is to formalize a regression equation between the above ground carbon stock capacity calculated through extensive field surveying of 344 sample point areas taken in Camyazi Forest Directorate, Turkey and the reflectance values corresponding to each sample point from RapidEye imagery.
Materials and Methods
MATERIJALI I METODE
In this study area, located in Kars province, Turkey where there are dense stands of Scotch pine (Pinus sylvestris L.), 9 917 ha of Camyazi Forest Directorate within the total acreage of 61 646 ha of Erzurum Regional Forest Directorate was selected as the study area (Figure 1).
1/25 000 scaled standard topographic maps of the study area, data acquired from field surveying done by a licensed forestry subcontractor, Buse Inc., to make a Forest Management Plan 2009 and a coinciding RapidEye satellite imagery dated 2009 were used. Figure 2 shows the land use in the study area map where the dominant tree species is Scotch pine.
Biomass estimation using remote sensing technology is a comprehensive procedure with many steps: field survey data collection, biomass calculation at plot level, remote sensing data selection, variable extraction, proper algorithm selection, and error evaluation.