Modeling Forest Productivity Using Envisat MERIS Data

The aim of this study was to derive land cover products with a 300-m pixelresolution of Envisat MERIS (Medium Resolution Imaging Spectrometer) to quantify netprimary productivity (NPP) of conifer forests of Taurus Mountain range along the EasternMediterranean coast of Turkey. The Carnegie-Ames-Stanford approach (CASA) was usedto predict annual and monthly regional NPP as modified by temperature, precipitation,solar radiation, soil texture, fractional tree cover, land cover type, and normalizeddifference vegetation index (NDVI). Fractional tree cover was estimated using continuoustraining data and multi-temporal metrics of 47 Envisat MERIS images of March 2003 toSeptember 2005 and was derived by aggregating tree cover estimates made from high-resolution IKONOS imagery to coarser Landsat ETM imagery. A regression tree algorithmwas used to estimate response variables of fractional tree cover based on the multi-temporal metrics. This study showed that Envisat MERIS data yield a greater spatial detailin the quantification of NPP over a topographically complex terrain at the regional scalethan those used at the global scale such as AVHRR

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43), tree cover (R = 0.93) and temperature (R = 0.42) were strongly correlated with forest fire probability in the USB region

Quantifying coastal inundation vulnerability of Turkey to sea-level rise

WOS: 000252873100009PubMed ID: 17503205The vulnerability of low-lying coastal areas in Turkey to inundation was quantified based on the sea-level rise scenarios of 1, 2, and 3 m by 2205. Through digital elevation model (DEM) acquired by the shuttle radar topography mission (SRTM), the extent and distribution of the high to low-risk coastal plains were identified. The spatio-temporal analysis revealed the inundated coastal areas of 545, 1,286, and 2,125 km(2) at average rates of 5, 10, and 15 mm yr(-1) for 200 years, respectively. This is equivalent to minimum and maximum land losses by 2205 of 0.1-0.3% of the total area and of 1.3-5.2% of the coastal areas with elevations of less than 100 m in the country, respectively. This study provides an initial assessment of vulnerability to sea-level rise to help decision-makers, and other concerned stakeholders to develop appropriate public policies and land-use planning measures