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| Titel |
A hybrid Land Cover Dataset for Russia: a new methodology for merging statistics, remote sensing and in-situ information |
| VerfasserIn |
D. Schepaschenko, I. McCallum, A. Shvidenko, F. Kraxner, S. Fritz |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250025252
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| Zusammenfassung |
| There is a critical need for accurate land cover information for resource assessment,
biophysical modeling, greenhouse gas studies, and for estimating possible terrestrial
responses and feedbacks to climate change. However, practically all existing land cover
datasets have quite a high level of uncertainty and suffer from a lack of important details that
does not allow for relevant parameterization, e.g., data derived from different forest
inventories.
The objective of this study is to develop a methodology in order to create a hybrid land
cover dataset at the level which would satisfy requirements of the verified terrestrial biota full
greenhouse gas account (Shvidenko et al., 2008) for large regions i.e. Russia. Such
requirements necessitate a detailed quantification of land classes (e.g., for forests – dominant
species, age, growing stock, net primary production, etc.) with additional information on
uncertainties of the major biometric and ecological parameters in the range of 10-20% and a
confidence interval of around 0.9. The approach taken here allows the integration of different
datasets to explore synergies and in particular the merging and harmonization of
land and forest inventories, ecological monitoring, remote sensing data and in-situ
information.
The following datasets have been integrated: Remote sensing: Global Land Cover 2000
(Fritz et al., 2003), Vegetation Continuous Fields (Hansen et al., 2002), Vegetation
Fire (Sukhinin, 2007), Regional land cover (Schmullius et al., 2005); GIS: Soil
1:2.5 Mio (Dokuchaev Soil Science Institute, 1996), Administrative Regions 1:2.5
Mio, Vegetation 1:4 Mio, Bioclimatic Zones 1:4 Mio (Stolbovoi & McCallum,
2002), Forest Enterprises 1:2.5 Mio, Rivers/Lakes and Roads/Railways 1:1 Mio
(IIASA’s data base); Inventories and statistics: State Land Account (FARSC RF,
2006), State Forest Account – SFA (FFS RF, 2003), Disturbances in forests (FFS RF,
2006).
The resulting hybrid land cover dataset at 1-km resolution comprises the following
classes: Forest (each grid links to the SFA database, which contains 86,613 records);
Agriculture (5 classes, parameterized by 89 administrative units); Wetlands (8 classes,
parameterized by 83 zone/region units); Open Woodland, Burnt area; Shrub/grassland (50
classes, parameterized by 300 zone/region units); Water; Unproductive area. This study has
demonstrated the ability to produce a highly detailed (both spatially and thematically) land
cover dataset over Russia. Future efforts include further validation of the hybrid land cover
dataset for Russia, and its use for assessment of the terrestrial biota full greenhouse gas
budget across Russia.
The methodology proposed in this study could be applied at the global level. Results of
such an undertaking would however be highly dependent upon the quality of the available
ground data. The implementation of the hybrid land cover dataset was undertaken in a way
that it can be regularly updated based on new ground data and remote sensing products (ie.
MODIS). |
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