Modeling Forest Productivity Using Envisat MERIS Data

The aim of this study was to derive land cover products with a 300-m pixel resolution of Envisat MERIS (Medium Resolution Imaging Spectrometer) to quantify net primary productivity (NPP) of conifer forests of Taurus Mountain range along the Eastern Mediterranean coast of Turkey. The Carnegie-Ames-Stanford approach (CASA) was used to predict annual and monthly regional NPP as modified by temperature, precipitation, solar radiation, soil texture, fractional tree cover, land cover type, and normalized difference vegetation index (NDVI). Fractional tree cover was estimated using continuous training data and multi-temporal metrics of 47 Envisat MERIS images of March 2003 to September 2005 and was derived by aggregating tree cover estimates made from high-resolution IKONOS imagery to coarser Landsat ETM imagery. A regression tree algorithm was 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 detail in the quantification of NPP over a topographically complex terrain at the regional scale than those used at the global scale such as AVHRR.