MODELLING THE UNCERTAINTY OF SLOPE ESTIMATION FROM A LIDAR-DERIVED DEM: A CASE STUDY FROM A LARGE-SCALE AREA IN THE CZECH REPUBLIC

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F14%3A86092012" target="_blank" >RIV/61989100:27350/14:86092012 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092012

Výsledek na webu

<a href="http://gse.vsb.cz/2013/LIX-2013-2-25-39.pdf" target="_blank" >http://gse.vsb.cz/2013/LIX-2013-2-25-39.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

MODELLING THE UNCERTAINTY OF SLOPE ESTIMATION FROM A LIDAR-DERIVED DEM: A CASE STUDY FROM A LARGE-SCALE AREA IN THE CZECH REPUBLIC

Popis výsledku v původním jazyce

This paper summarizes the methods and results of error modelling and propagation analyses in the Olše and Stonávka confluence area. In terrain analyses, the outputs of the aforementioned analysis are always a function of input. Two approaches according to the input data were used to generate field elevation errors which subsequently entered the error propagation analysis. The main goal solved in this research was to show the importance of input data in slope estimation and to estimate the elevation error propagation as well as to identify DEM errors and their consequences. Dependencies were investigated as well to achieve a better prediction of slope errors. Four different digital elevation model (DEM) resolutions (0.5, 1, 5 and 10 meters) were examined with the Root Mean Square Error (RMSE) rating up to 0.317 meters (10 m DEM). They all originated from a LIDAR survey. In the analyses, a stochastic Monte Carlo simulation was performed with 250 iterations. The article focuses on the error propagation in a large-scale area using high quality input DEM and Monte Carlo methods. The DEM uncertainty (RMSE) was obtained by sampling and ground research (RTK GPS) and from subtraction of two DEMs. According to empirical error distribution a semivariogram was used to model spatially autocorrelated uncertainty in elevation. The second procedure modelled the uncertainty without autocorrelation using a random N(0,RMSE) error generator. Statistical summaries were drawn to investigate the expected hypothesis. As expected, the error in slopes increases with the increasing vertical error in the input DEM. According to similar studies the use of different DEM input data, high quality LIDAR input data decreases the output uncertainty. Errors modelled without spatial autocorrelation do not result in a greater variance in the resulting slope error.

Název v anglickém jazyce

MODELLING THE UNCERTAINTY OF SLOPE ESTIMATION FROM A LIDAR-DERIVED DEM: A CASE STUDY FROM A LARGE-SCALE AREA IN THE CZECH REPUBLIC

Popis výsledku anglicky

This paper summarizes the methods and results of error modelling and propagation analyses in the Olše and Stonávka confluence area. In terrain analyses, the outputs of the aforementioned analysis are always a function of input. Two approaches according to the input data were used to generate field elevation errors which subsequently entered the error propagation analysis. The main goal solved in this research was to show the importance of input data in slope estimation and to estimate the elevation error propagation as well as to identify DEM errors and their consequences. Dependencies were investigated as well to achieve a better prediction of slope errors. Four different digital elevation model (DEM) resolutions (0.5, 1, 5 and 10 meters) were examined with the Root Mean Square Error (RMSE) rating up to 0.317 meters (10 m DEM). They all originated from a LIDAR survey. In the analyses, a stochastic Monte Carlo simulation was performed with 250 iterations. The article focuses on the error propagation in a large-scale area using high quality input DEM and Monte Carlo methods. The DEM uncertainty (RMSE) was obtained by sampling and ground research (RTK GPS) and from subtraction of two DEMs. According to empirical error distribution a semivariogram was used to model spatially autocorrelated uncertainty in elevation. The second procedure modelled the uncertainty without autocorrelation using a random N(0,RMSE) error generator. Statistical summaries were drawn to investigate the expected hypothesis. As expected, the error in slopes increases with the increasing vertical error in the input DEM. According to similar studies the use of different DEM input data, high quality LIDAR input data decreases the output uncertainty. Errors modelled without spatial autocorrelation do not result in a greater variance in the resulting slope error.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DA - Hydrologie a limnologie

OECD FORD obor

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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)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

GeoScience Engineering

ISSN

1802-5420

e-ISSN

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Svazek periodika

LIX

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

15

Strana od-do

25-39

Kód UT WoS článku

000392378700014

EID výsledku v databázi Scopus

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