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.