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| Titel |
Comparison of LAI estimation based vegetation index and MODIS LAI for gross primary productivity estimation, deciduous broadleaf forest in Japan |
| VerfasserIn |
Supannika Potithep, Kenlo Nishida Nasahara, Rikie Suzuki |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250034849
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| Zusammenfassung |
| Leaf area index (LAI) is the main input parameter in the ecological models, which defined as
one-sided green leaf area per unit ground area (Myneni et al., 1988). It is considered as key
variable for scaling up forest productivity from leaf to large scale. In several ecological
models, LAI is used to determine the solar radiation absorption for gross primary productivity
(GPP) estimation. GPP is the assimilation of carbon absorption by plants. It can be
indicated the carbon status on the biosphere which is to understand global carbon cycle
process.
Among ecological models, Spatial 3-PG (Physiological Principles Prediction Growth in
Spatial version) is selected to apply for GPP estimation. It is extended version of 3-PG
(Landsberg and Waring, 1997), which is the original model, from one dimension to two
dimensions by coupled with satellite images. The main inputs are meteorological data, site
factors, and species-specific parameters with the additional satellite images. The use of the
satellite image allows estimating LAI. The LAI data from the satellite image are up to date
and close to real conditions on the ground. Subsequently, GPP is calculated by
converting absorbed photosynthetically active radiation (APAR) to GPP using canopy
quantum efficiency (αC). Canopy quantum efficiency is constrained by environmental
conditions.
In this study, the satellite images are used to examine the relationship between LAI and
vegetation index (VI) (LAI–VI relationship), which is added to the model. Two VIs are
commonly used to estimate LAI as Normalised Difference Vegetation Index (NDVI)
and Enhanced Vegetation Index (EVI). MODIS 8 days composite data are used to
calculate in terms of each VI. The primary study site is located in central Japan,
Takayama. The main vegetation type is deciduous broadleaf forest. In situ LAI data at
Takayama collected from 2005 to 2006 were compared with the VIs data, which are
extracted from the one pixel corresponding to the location of study site on the satellite
image. The relationship of LAI-VI is separated into 2 periods as growing season and
falling season. Among VIs, EVI shows the highest correlation with LAI, with R2
equal to 0.88. Then, LAI-EVI is chosen for GPP estimation in the Spatial 3-PG
model.
Moreover, MODIS LAI 8 days composite are used as another input parameter instead of
LAI estimation based EVI in the model. The result of GPP estimation from LAI-EVI based
are compared with MODIS LAI based. As well, in situ GPP are used to validate with both
results as well as MODIS GPP. These comparisons can perform the errors of MODIS
products, when it is used to apply to the ecological model. The results showed that GPP based
MODIS LAI and MODIS GPP give the overestimated value more than GPP based
LAI-EVI relationship. Since the algorithm of MODIS LAI is used a lookup table
method, based on the six canopy types, which cannot cover in more details in the
local scale. As well, MODIS LAI gives high LAI value because it includes LAI
from the vegetation floor. However, the satellite images still have the potential to
support GPP estimation at certain levels of accuracy for quantitative measurement. |
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