Elevation error modeling and propagation in slope estimation: A case study from Olse and Stonavka confluence area
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F12%3A86083331" target="_blank" >RIV/61989100:27350/12:86083331 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27740/12:86083331
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Elevation error modeling and propagation in slope estimation: A case study from Olse and Stonavka confluence area
Popis výsledku v původním jazyce
This paper presents the practice and results of error modelling and propagation analyses for large-scaled area using high quality input DEM. Five different digital elevation models were examined. Four DEMs originated in LIDAR survey and one in photogrammetry. Root Mean Square Error was rating up to 0.317 in case of 10 m LIDAR DEM (respectively 1.128 for photogram-metric DEM). In the analyses was performed a stochastic Monte Carlo simula-tion. According to empirical error distribution it has been used semivariogram to model spatially autocorrelated error pattern in elevation and later propagated in slope estimation. Notable error appeared in result, even despite the fact that high precision input data has been used. As expected; the error in slopes is in-creased with the vertical error in elevation and with decreasing slope. Using LIDAR input for 10 meter DEM was the average slope error decreased to 78.36% of photogrammetric input.
Název v anglickém jazyce
Elevation error modeling and propagation in slope estimation: A case study from Olse and Stonavka confluence area
Popis výsledku anglicky
This paper presents the practice and results of error modelling and propagation analyses for large-scaled area using high quality input DEM. Five different digital elevation models were examined. Four DEMs originated in LIDAR survey and one in photogrammetry. Root Mean Square Error was rating up to 0.317 in case of 10 m LIDAR DEM (respectively 1.128 for photogram-metric DEM). In the analyses was performed a stochastic Monte Carlo simula-tion. According to empirical error distribution it has been used semivariogram to model spatially autocorrelated error pattern in elevation and later propagated in slope estimation. Notable error appeared in result, even despite the fact that high precision input data has been used. As expected; the error in slopes is in-creased with the vertical error in elevation and with decreasing slope. Using LIDAR input for 10 meter DEM was the average slope error decreased to 78.36% of photogrammetric input.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
DE - Zemský magnetismus, geodesie, geografie
OECD FORD obor
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Návaznosti výsledku
Projekt
<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2012
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů