Remote Sensing of Carbon Balance across Scandinavian Forests

University dissertation from Dept. of Physical Geography and Ecosystems Analysis

Abstract: A methodology for estimating the carbon balance across Scandinavian forests using remote sensing is presented. By estimating the gross primary production (GPP) and the ecosystem respiration (ER), and thereby, the net ecosystem exchange (NEE), using parameters that are routinely retrieved from satellite, it is possible to implement an operational model for obtaining large scale NEE. GPP is successfully modeled using a vegetation index (enhanced vegetation index, EVI) and the amount of photosynthetically absorbed radiation (PAR) absorbed by vegetation (APAR), where APAR is given as the product of the amount of PAR incident on the canopy and the ractional absorption (FAPAR). The former is obtained through implementation of a simple radiative transfer model where the amount of PAR is a function of the solar constant, solar zenith angle and the atmospheric transmittance, which in turn is calculated using daily atmospheric data from the MODIS sensor onboard the NASA platforms Terra and Aqua. FAPAR is given by linear transformation of the normalized vegetation index (NDVI), also from the MODIS sensor, at 250 m resolution and composited every 16 days. The NDVI data is seasonally adjusted before transformed. With ER being highly correlated to temperature, and land surface temperature being routinely obtained from satellite, it is possible to obtain ER from space. However, in order to describe the variation in ER between sites, an annual site-specific value of the respiration rate at 10 deg. C (R10) needs to be included. The parameter is currently derived from measurements but relationships between respiratory processes and leaf area index (LAI) have been observed on an annual basis, suggesting that R10 can be included in an fully satellite-driven operational carbon balance model. The model explains more than 90% of the variation in measured GPP and ER on a monthly basis. The corresponding figure for the final NEE is 75% when evaluated in five coniferous forest stands in Northern Europe. The results prove the potential for remote sensing of the forest carbon balance.

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