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Estimating Net Primary Production of Swedish Forest Landscapes by Combining Mechanistic Modeling and Remote Sensing

Tagesson, Torbern LU ; Smith, Benjamin LU ; Lofgren, Anders ; Rammig, Anja ; Eklundh, Lars LU and Lindroth, Anders LU (2009) In Ambio: a Journal of Human Environment 38(6). p.316-324
Abstract
The aim of this study was to investigate a combination of satellite images of leaf area index (LAI) with process-based vegetation modeling for the accurate assessment of the carbon balances of Swedish forest ecosystems at the scale of a landscape. Monthly climatologic data were used as inputs in a dynamic vegetation model, the Lund Potsdam Jena-General Ecosystem Simulator. Model estimates of net primary production (NPP) and the fraction of absorbed photosynthetic active radiation were constrained by combining them with satellite-based LAI images using a general light use efficiency (LUE) model and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable.... (More)
The aim of this study was to investigate a combination of satellite images of leaf area index (LAI) with process-based vegetation modeling for the accurate assessment of the carbon balances of Swedish forest ecosystems at the scale of a landscape. Monthly climatologic data were used as inputs in a dynamic vegetation model, the Lund Potsdam Jena-General Ecosystem Simulator. Model estimates of net primary production (NPP) and the fraction of absorbed photosynthetic active radiation were constrained by combining them with satellite-based LAI images using a general light use efficiency (LUE) model and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average, well simulated but too homogeneous among vegetation types when compared with field estimates using forest inventory data. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Ambio: a Journal of Human Environment
volume
38
issue
6
pages
316 - 324
publisher
Springer
external identifiers
  • wos:000270193300005
  • scopus:77649260612
ISSN
0044-7447
language
English
LU publication?
yes
id
40796a48-2dde-42bb-97ac-724acdc7f44e (old id 1490171)
date added to LUP
2016-04-01 13:49:11
date last changed
2020-01-12 13:48:13
@article{40796a48-2dde-42bb-97ac-724acdc7f44e,
  abstract     = {The aim of this study was to investigate a combination of satellite images of leaf area index (LAI) with process-based vegetation modeling for the accurate assessment of the carbon balances of Swedish forest ecosystems at the scale of a landscape. Monthly climatologic data were used as inputs in a dynamic vegetation model, the Lund Potsdam Jena-General Ecosystem Simulator. Model estimates of net primary production (NPP) and the fraction of absorbed photosynthetic active radiation were constrained by combining them with satellite-based LAI images using a general light use efficiency (LUE) model and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average, well simulated but too homogeneous among vegetation types when compared with field estimates using forest inventory data.},
  author       = {Tagesson, Torbern and Smith, Benjamin and Lofgren, Anders and Rammig, Anja and Eklundh, Lars and Lindroth, Anders},
  issn         = {0044-7447},
  language     = {eng},
  number       = {6},
  pages        = {316--324},
  publisher    = {Springer},
  series       = {Ambio: a Journal of Human Environment},
  title        = {Estimating Net Primary Production of Swedish Forest Landscapes by Combining Mechanistic Modeling and Remote Sensing},
  volume       = {38},
  year         = {2009},
}