Modelovanie neistoty vo výpočte sklonov z LIDAR-ových DMR: prípadová štúdia vybraného malého územia v ČR

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. In this case, although the slope error results (comparing random uncorrelated and empirical autocorrelated error fields) did not show any statistical significant difference, the input elevation error pattern was not normally distributed and therefore the random error generator realization is not a suitable interpretation of the true state of elevation errors. The normal distribution was rejected because of the high kurtosis and extreme values (outliners). On the other hand, it can show an important insight into the expected elevation and slope errors. Geology does not influence the slope error in the study area.Táto práca zhŕňa metódu a výsledky modelovania chýb a analýzu šírenia chýb vo výpočte sklonov z DMR získaných LIDAR-om v skúmanej lokalite okolia sútoku riek Olše a Stonávka. V terénnych analýzach výstupy uvedenej analýzy sú vždy funkciou vstupu. Na generovania pola výškových chýb boli použité dve rozdielne metódy podľa vstupných dát. Modelované chyby v nadmorských výškach následne vstupovali do analýzy šírenia chýb. Hlavným cieľom práce bolo tak ako aj poukázanie na význam kvality vstupných dát vo výpočte sklonov a odhad šírenej chyby z nadmorských výšok v sklonoch tak aj identifikácia chýb v DMR a ich dopad. Závislosti chýb boli vyhodnotené hlavne pre lepší odhad chyby v sklonoch. V simuláciách boli použité 4 vstupné DMR s rozlíšením 0.5, 1, 5 a 10 metrov s RMSE chybou do 0.317 metra (10 m DMR). Všetky DMR boli získané z mračna bodov získaných LIDAR metódou zberu dát. Šírenie chýb bolo modelované pomocou stochastickej simulácie Monte Carlo s 250 iteráciami. Článok sa zameriava na šírenie chýb z vysoko presných vstupných dát na malom území. RMSE chyba bola získaná v prvom prípade z dát získaných terénnym prieskumom (RTK GPS) a v druhom prípade z porovnania dvoch kvalitatívne rozdielnych DMR. V prvom prípade sa vypočítali chyby vo výškach pomocou náhodného generátora chýb bez autokorelácie chýb. V druhom prípade sa s pomocou semivariogramu namodelovalo autokorelované pole chýb vo výškach. Použitím vhodných štatistík boli odvodené výsledky simulácie a overené stanovené hypotézy. Tak ako sa očakávalo chyby v sklonoch sú vyššie s zvyšujúcou sa chybou v nadmorských výškach. Tiež závislosti chýb od vypočítaných sklonov boli preskúmané, kde sa potvrdila závislosť chýb na sklonoch. Na druhej strane geológia nemala žiaden vplyv na chybu v sklonoch. Chyby namodelované bez autokorelácie nevedú vo väčšine prípadov k štatisticky významnej odchýlke. Vzhľadom však k rozmiestneniu chýb v priestore (vysoká autokorelácia, zamietnutie normálneho rozdelenia pre vysokú špicatosť a extrémne hodnoty) nie je táto metóda vhodná. Napriek tomu dáva dobrú možnosť nahliadnutia do očakávanej chyby v sklonoch a nadmorských výškach

Developing tools and models for evaluating geospatial data integration of official and VGI data sources

PhD ThesisIn recent years, systems have been developed which enable users to produce, share and update information on the web effectively and freely as User Generated Content (UGC) data (including Volunteered Geographic Information (VGI)). Data quality assessment is a major concern for supporting the accurate and efficient spatial data integration required if VGI is to be used alongside official, formal, usually governmental datasets. This thesis aims to develop tools and models for the purpose of assessing such integration possibilities. Initially, in order to undertake this task, geometrical similarity of formal and informal data was examined. Geometrical analyses were performed by developing specific programme interfaces to assess the positional, linear and polygon shape similarity among reference field survey data (FS); official datasets such as data from Ordnance Survey (OS), UK and General Directorate for Survey (GDS), Iraq agencies; and VGI information such as OpenStreetMap (OSM) datasets. A discussion of the design and implementation of these tools and interfaces is presented. A methodology has been developed to assess such positional and shape similarity by applying different metrics and standard indices such as the National Standard for Spatial Data Accuracy (NSSDA) for positional quality; techniques such as buffering overlays for linear similarity; and application of moments invariant for polygon shape similarity evaluations. The results suggested that difficulties exist for any geometrical integration of OSM data with both bench mark FS and formal datasets, but that formal data is very close to reference datasets. An investigation was carried out into contributing factors such as data sources, feature types and number of data collectors that may affect the geometrical quality of OSM data and consequently affect the integration process of OSM datasets with FS, OS and GDS. Factorial designs were undertaken in this study in order to develop and implement an experiment to discover the effect of these factors individually and the interaction between each of them. The analysis found that data source is the most significant factor that affects the geometrical quality of OSM datasets, and that there are interactions among all these factors at different levels of interaction. This work also investigated the possibility of integrating feature classification of official datasets such as data from OS and GDS geospatial data agencies, and informal datasets such as OSM information. In this context, two different models were developed. The first set of analysis included the evaluation of semantic integration of corresponding feature classifications of compared datasets. The second model was concerned with assessing the ability of XML schema matching of feature classifications of tested datasets. This initially involved a tokenization process in order to split up into single words classifications that were composed of multiple words. Subsequently, encoding feature classifications as XML schema trees was undertaken. The semantic similarity, data type similarity and structural similarity were measured between the nodes of compared schema trees. Once these three similarities had been computed, a weighted combination technique has been adopted in order to obtain the overall similarity. The findings of both sets of analysis were not encouraging as far as the possibility of effectively integrating feature classifications of VGI datasets, such as OSM information, and formal datasets, such as OS and GDS datasets, is concerned.Ministry of Higher Education and Scientific Research, Republic of Iraq