Abstract
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.
Highlights
Vegetation plays an important role in the energy, matter and heat exchange between the land surface and atmosphere
This study showed that Envisat MERIS data successfully captured the heterogeneity of the Mediterranean land covers for the quantification of monthly net primary productivity (NPP) patterns over a complex terrain
NPP maps revealed that mean monthly NPP differed significantly from one another and ranged from 0.65 to 125 g C m-2 month-1
Summary
Vegetation plays an important role in the energy, matter and heat exchange between the land surface and atmosphere. NPP is an important component of the global carbon budget and a key indicator of ecosystem function [1]. Improved methodologies and remote sensing technologies to account for carbon sources and sinks in different land uses and land covers (e.g. agriculture, forest, grassland, mining lands, and urban areas) are essential to a better understanding and quantification of the global carbon cycle and devising mitigative and preventive measures towards the stabilization of atmospheric CO2 concentration. Forest ecosystems as major terrestrial carbon sinks are important to the global carbon cycle and are being destructed increasingly by local human activities [2]. This necessitates bottom-up approach for monitoring and quantification of rate, extent, and magnitude of local human-induced disturbances inflicted on forests (e.g. deforestation, over-harvesting, biomass burning, and outbreaks). Interpolations from the global and continental scales and extrapolations from the site scales may result in inadequate representation and information loss about changes in ecosystem structure and function, over complex terrains, and need to be bridged by meso-scale models [29,30,31]
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