Dem Local Accuracy Patterns in Land-Use/Land-Cover Classification
Abstract: Global and nation-wide DEM do not preserve the same height accuracy throughout the area of study. Instead of assuming a single RMSE value for the whole area, this study proposes a vario-model that divides the area into sub-regions depending on the land-use/landcover (LULC) classification, and assigns a local accuracy per each zone, as these areas share similar terrain formation and roughness, and tend to have similar DEM accuracies. A pilot study over Lebanon using the SRTM and ASTER DEMs, combined with a set of 1,105 randomly distributed ground control points (GCPs) showed that even though the inputDEMs have different spatial and temporal resolution, and were collected using difierent techniques, their accuracy varied similarly when changing over difierent LULC classes. Furthermore, validating the generated vario-models proved that they provide a closer representation of the accuracy to the validating GCPs than the conventional RMSE, by 94% and 86% for the SRTMand ASTER respectively. Geostatistical analysis of the input and output datasets showed that the results have a normal distribution, which support the generalization of the proven hypothesis, making this finding applicable to other input datasets anywhere around the world.
- Location
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                Deutsche Nationalbibliothek Frankfurt am Main
 
- Extent
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                Online-Ressource
 
- Language
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                Englisch
 
- Bibliographic citation
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                Dem Local Accuracy Patterns in Land-Use/Land-Cover Classification ; volume:8 ; number:1 ; year:2016 ; pages:760-770 ; extent:11
Open Geosciences ; 8, Heft 1 (2016), 760-770 (gesamt 11)
 
- Creator
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                Katerji, Wassim
Farjas Abadia, Mercedes
Morillo Balsera, Maria del Carmen
 
- DOI
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                        10.1515/geo-2016-0052
 
- URN
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                        urn:nbn:de:101:1-2501051445040.200180422630
 
- Rights
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                        Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
 
- Last update
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                        15.08.2025, 7:22 AM CEST
 
Data provider
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Associated
- Katerji, Wassim
 - Farjas Abadia, Mercedes
 - Morillo Balsera, Maria del Carmen