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| Titel |
The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data |
| VerfasserIn |
R. Bou Kheir, P. K. Bøcher, M. B. Greve, M. H. Greve |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 14, no. 6 ; Nr. 14, no. 6 (2010-06-01), S.847-857 |
| Datensatznummer |
250012327
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| Publikation (Nr.) |
 copernicus.org/hess-14-847-2010.pdf |
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| Zusammenfassung |
| Accurate information about organic/mineral soil occurrence is a prerequisite
for many land resources management applications (including climate change
mitigation). This paper aims at investigating the potential of using
geomorphometrical analysis and decision tree modeling to predict the
geographic distribution of hydromorphic organic landscapes in unsampled area
in Denmark. Nine primary (elevation, slope angle, slope aspect, plan
curvature, profile curvature, tangent curvature, flow direction, flow
accumulation, and specific catchment area) and one secondary (steady-state
topographic wetness index) topographic parameters were generated from
Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light
Detection and Ranging) systems. They were used along with existing digital
data collected from other sources (soil type, geological substrate and
landscape type) to explain organic/mineral field measurements in
hydromorphic landscapes of the Danish area chosen. A large number of
tree-based classification models (186) were developed using (1) all of the
parameters, (2) the primary DEM-derived topographic
(morphological/hydrological) parameters only, (3) selected pairs of
parameters and (4) excluding each parameter one at a time from the potential
pool of predictor parameters. The best classification tree model (with the
lowest misclassification error and the smallest number of terminal nodes and
predictor parameters) combined the steady-state topographic wetness index
and soil type, and explained 68% of the variability in organic/mineral
field measurements. The overall accuracy of the predictive organic/inorganic
landscapes' map produced (at 1:50 000 cartographic scale) using the best
tree was estimated to be ca. 75%. The proposed classification-tree model
is relatively simple, quick, realistic and practical, and it can be applied
to other areas, thereby providing a tool to facilitate the implementation of
pedological/hydrological plans for conservation and sustainable management.
It is particularly useful when information about soil properties from
conventional field surveys is limited. |
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