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Remote Sensing of Carbon Balance across Scandinavian Forests

Olofsson, Pontus LU (2007)
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Associate Professor Turner, David, Department of Forest Science, Oregon State University, Oregon 97331, USA.
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Forests, Natural science, Naturvetenskap, Carbon balance, Remote sensing
publisher
Department of Physical Geography and Ecosystem Science, Lund University
defense location
Sal 111 (Världen), Geocentrum I, Sölvegatan 12, Lund.
defense date
2007-05-04 10:15
external identifiers
  • scopus:34247372297
ISSN
0346-6787
ISBN
978-91-85793-01-3
language
English
LU publication?
yes
id
ecf15b76-1eca-45ed-beb7-46efaf30d6a3 (old id 548430)
date added to LUP
2007-09-06 11:03:42
date last changed
2017-01-01 07:20:11
@phdthesis{ecf15b76-1eca-45ed-beb7-46efaf30d6a3,
  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.},
  author       = {Olofsson, Pontus},
  isbn         = {978-91-85793-01-3},
  issn         = {0346-6787},
  keyword      = {Forests,Natural science,Naturvetenskap,Carbon balance,Remote sensing},
  language     = {eng},
  publisher    = {Department of Physical Geography and Ecosystem Science, Lund University},
  school       = {Lund University},
  title        = {Remote Sensing of Carbon Balance across Scandinavian Forests},
  year         = {2007},
}