Raster Cell Research Articles

Modeling Water Accessibility of Natural River Networks Using the Fine-Grained Physical Watershed Characteristics at the Grid Scale

Traditional assessments of water resources use water availability as an evaluating metric. Water availability is a statistical average and cannot reflect the spatial differences of water resources in a region or basin. This assessment usually engenders a paradox, that is, water resources are abundant in a region but it is difficult to obtain water for the people on some spatial points in this region. In this study we define a new term, water accessibility, to eliminate this paradox and develop a model of water accessibility (the SHRD model) to meet the need of spatial details in water resources assessment at the grid scale. In the case study the water accessibility of the Hanjiang River Basin is mapped and assessed by the SHRD model. The comprehensive index of water accessibility is 0 to 4.5 and tends to increase from northwest to southeast in the study area. The results of the SHRD model can show the spatial pattern of water accessibility in a region, and can server a more finely-tuned water resources management approach. This model and method push the macroscopic (regional) water resources assessment forward from the microcosmic (raster cell) analysis.

GIS MODULE FOR THE ESTIMATION OF THE HILLSLOPE TORRENTIAL PEAK FLOW

In rural regions, characterized by a large number of small basins, some of them torrential and others without permanent flow, high intensity convective rainfall generate overland flow, not always on the same path. Overland hillslope flow, followed by a dangerous peak discharge, can happen almost anywhere at any time. The purpose of this paper is to present a GIS module for computation of possible peak flow in any point of a hillslope. The workflow is based on the rational method, but it is applied rather differently by avoiding the previous delineation of watersheds and working on a raster cell level. The module that we present is more than just an automation of known GIS procedures because the result of this model also includes the path of the maximum flow and the peak flows, which would otherwise remain unknown. There is a possibility of another path for the peak flow under different rainfall and antecedent moisture conditions. In this case, the module needs to be rerun for the new conditions when a new torrential rainfall event occurs. The module provides an easy-to-understand graphical representation of peak flow paths, maximum flows and associated maps that can be useful for further decisions and prevention measures.

Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia

The main objective of this study is to develop a reliable model for the identification of locations for the installation of wind farms, which will provide significant support to planners in the strategy for the development and management of wind energy. The proposed model is based on the combined application of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) using the multi-criteria technique of Decision Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP) and Multi-Attributive Border Approximation area Comparison (MABAC). Application of the model is presented by means of a case study on the province of Vojvodina, Serbia. The model considers 11 constraints and 11 evaluation criteria which are grouped into economic, social and environmental clusters. The DEMATEL-ANP (DANP) method is used to determine the weight coefficients of the evaluation criteria, and the MABAC method is used to rank the selected viable locations. The final map of benefits is presented using raster cells (alternatives) which are evaluated in the range of 1 (least suitable) to 7 (most suitable). The results show that an area of 321 km2 in Vojvodina is very suitable for the installation of wind farms. Ranking of viable locations using the MABAC method shows that a location in the vicinity of the village of Laudonovac (L8) is most suitable for the installation of wind farms in the province of Vojvodina. A sensitivity analysis, carried out by changing the input weights of the clusters, indicates that the model is useful for identifying suitable locations for the development of wind farm projects, as well as for assessing the suitability of already licensed projects for the construction of wind farms. The proposed method and the results of this study can be used for spatial development policy at all levels of public administration related to renewable energy resources. The model could also help to successfully identify suitable locations for the installation of wind farms in other areas with similar geographical conditions.

Statistical Models for Tornado Climatology: Long and Short-Term Views.

This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.

Database extension for digital soil mapping using artificial neural networks

Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.

An easy approach to assess the susceptibility of a landslide by utilizing simple raster overlay methods: A case study on Huangtupo landslide (P.R. China)

Semi qualitative index based methods using rankings and ratings are commonly used in susceptibility estimations over a wide area. However, generalized ranking and ratings are not applicable for one single landslide. This paper gives an easy and transferable approach to a susceptibility assessment of Huangtupo landslide (P.R. China), using raster addition without taking account for ranking and ratings. Slope, aspect, curvature, location and drainage buffer distance raster data sets have been obtained out of open source digital elevation models using ESRI's ArcGIS. These conditioning factor raster data sets have been translated into raster data sets including simple yes or no criteria, referring to triggering or not. Subsequently they have been added by raster math to acquire a simple raster overlay map. After that this map is compared to initial displacement measurements, obtained by using a ground based synthetic aperture radar device. Acquired data is recalculated to a raster data set using the same spatial extent, to provide the possibility of comparison of the two raster data sets. The results reveal, that 76.35% of all measured movements occur in areas where raster cells include three or more conditioning factors, indicating that easy raster math operations can lead to satisfying results in local scale.

Effect of Digital Elevation Model Resolution on Shallow Landslide Modeling Using TRIGRS

AbstractIn Korea, landslide damage areas have increased significantly from the 1990s to the 2000s owing to increases in rainfall intensity and the number of rainy days in addition to indiscriminate land development. This study was conducted to predict shallow landslide-prone areas by using the transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS). A landslide event that occurred on July 27, 2011, in Mt. Umyeon, Seoul, was modeled, and the stability results were compared with 140 observed landslide points in both time and location to evaluate the performance of TRIGRS. Simulated outcomes from five different raster cells of 5, 10, 15, 20, and 25 m with the same background information were compared to identify the ultimate grid scale. The results revealed that in location estimation, smaller grid size resulted in more accurate results. However, the 10 and 15 m grid sizes gave better results in the timing assessment When the best grid size was considered TRIGRS overestimated t...

Temporal stability and patterns of runoff and runon with different cover crops in an olive orchard (SW Andalusia, Spain)

Conventional tillage (CT) and cover crops (CC) trigger different runoff (Q) and runon (Qin) magnitudes and patterns in woody crops. The spatial and temporal stability of these patterns is not well known yet. In this study, we run the uncalibrated DR2-2013© SAGA v1.1 model (0.5×0.5m of cell size) to simulate time to ponding (Tp), runoff duration (TQ), initial runoff per raster cell (q0), Qsim and Qin in six olive plots (480m2 per plot) during two years (108 rainfall events and 648 simulations). Two plots were managed with a mixture of plant species (CC-I), two with one single plant species (CC-II) and two with CT. Runoff yield from each plot was collected (Qobs) in gauging-stations during 27 time-integrated samples and used for modelling validation (162 control points). On average, Qobs was 9% higher under CT than under CC-I, and 8% higher than under CC-II. Topsoil saturation was simulated for the entire plots during 29 events (test-period), and Qsim appeared in another 51 and 52 events in the plots with CC and CT. Tp with CT was 2.3 times higher (59s) than the average duration with CC and the topsoil became saturated 3.3 times faster in the inter-rows than below the trees. Values of q0 with CC were 2.3% lower than with CT and total Qsim with CC was 2% higher than with CT. However, the differences of Qsim between the different treatments were not statistically significant. The mean observed and simulated runoff coefficients were of 11 and 14%, with median values of 7 and 10%. Qsim correlated well with Qobs (Pearson ca. 0.861), and Qsim was overestimated ca. 10%. The model performed better when rainfall depth and intensity were high, and the range of variability of both Qsim and Qobs was similar. The average, best and worst Nash–Sutcliffe coefficients were 0.665, 0.791 (P6) and 0.512 (P3) and thus model simulations were satisfactory. The four plots with CC presented on average a worse performance (Kling–Gupta coefficient=0.607) than the two plots with CT (KGE=0.769). The lowest spatial variability of q0, Qobs, Qsim and actual available water (Waa, the sum of Qin and stored water in the soil surface) were found in the plots with CC. CT triggered higher spatial variability of runoff and higher temporal variability of runon than CC.

The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale

The need to allocate the existing water in a sustainable manner, even with the projected population growth, has made to assess the consumptive use or evapotranspiration (ET), which determines the irrigation demand. As underscored in the literature, Penman-Monteith method which is a combination of aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the application of Penman-Monteith relies on many climate parameters such as relative humidity, solar radiation, temperature, and wind speed. Therefore, there exists a need to determine the parameters that are most sensitive and correlated with dependent variable (i.e., ET), to strengthen the knowledge base. However, the sensitivity of ET using Penman-Monteith is oftentimes estimated using meteorological data from climate stations. Such estimation of sensitivity may vary spatially and thus there exists a need to estimate sensitivity of ET spatially. Thus, in this paper, based on One-AT-A-Time (OAT) method, a spatial sensitivity tool that can geographically encompass all the best available climate datasets to produce ET and its sensitivity at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010. To summarize the outcome of the sensitivity analysis using OAT method, sensitivity indices are developed for each raster cell. The demonstration of the tool shows that, among the considered parameters, the computed ET using Penman-Monteith is highly sensitive to solar radiation followed by temperature for the state of Nebraska, as depicted by the sensitivity index. The computed sensitivity index of wind speed and the relative humidity are not that significant compared to the sensitivity index of solar radiation and temperature.

Multi-resolution, pattern-based segmentation of very large raster datasets

GIScience 2016 Short Paper Proceedings Multi-resolution, pattern-based segmentation of very large raster datasets J. Jasiewicz 1,2 , J. Niesterowicz 1 , T. F. Stepinski 1 Space Infromatics Lab, University of Cincinnati, 401 Braunstein Hall, Cincinnati, OH 45221-0131, US Email: {niestejk; stepintz}@mail.uc.edu Instititute of Geoecology and Geoinformation, Adam Mickiewicz University, Dziegielowa 27, Poznan, Poland Email: jarekj@amu.edu.pl Abstract We present an algorithm which efficiently segments very large categorical rasters based on patterns of their categories. It operates on a grid of motifels – square blocks of raster cells representing a local pattern. Our algorithm is based on the seeded region growing principle but it uses a novel grid topology and seeds stack with individual thresholds. It has a single free parameter – the spatial scale of a pattern. Algorithm was proven to be robust on land cover data, topographic landforms data, and high resolution color-quantized RGB images. We present a multi-scaled segmentation of NLCD2011 as an example. Potential applications of the new algorithm include ecology, geomorphology, pedology, forestry, agriculture, and urban studies. 1. Introduction Segmentation is the process of partitioning a raster dataset into multiple homogeneous segments. The goal of segmentation is to spatially generalize a raster so it provides more insight and is easier to analyze. The bulk of the existing work (Zhang et al. 2008) has focused on segmentation of images of relatively small scenes. However, segmentation of datasets that originated from remote sensing and cover large, continental or even global-scale areas, are also important, but existing segmentation algorithms are ineffective for such large datasets and the custom algorithms are lacking. Examples of such datasets are the National Land Cover Dataset (NLCD), which covers the conterminous US (CONUS) with the resolution of 30 m, or the SRTM-based DEM, which has a world-wide extent at 90 m resolution. Segmentation of NLCD could yield landscape types – precursors to ecoregions, and segmentation of world-wide DEM could delineate physiographic regions. In this paper we describe a segmentation algorithm especially designed for very large rasters. Specificities of such datastes are as follows. (1) They are the mosaics of multiple datasets, thus it is better to segment a secondary product of uniform quality (for example, a land cover) rather than a montage of primary data of variable quality (for example, a montage of Landsat scenes). (2) They are large; for example, the NLCD consists of ~8 billion cells and has the size of ~16 GB. (3) The goal of the segmentation is to identify regions characterized by patterns which are homogeneous on the scale that is large in comparison to the resolution of the raster, since the need for pattern-based segmentation. To deal with a large size of the input the proposed algorithm is based on the concept of Complex Object-Based Image Analysis (COBIA) (Vatsavai 2013, Stepinski et al. 2015). In COBIA the

Knowledge-Based Raster Mapping Approach to Wetland Assessment: a Case Study in Suzhou, China

We present a knowledge-based raster mapping (KBRM) approach to assessing wetlands based on the wetland-environment model, which is built by local experts using their knowledge and experience. The Delphi method and the Analytic Hierarchy Process (AHP) were employed to improve the consistency and the quality of the elicited expert knowledge. The environmental factors selected by local experts include edge density, aquatic connectivity, water-body density, road density, urban area density, vegetation screening index, and slope gradient. The experts assigned weights to those environmental factors and created optimality function curves to characterize the relationships between individual factors and the wetland condition. At each raster cell, an optimality value for each environmental factor was calculated based on the local value of that factor, and an overall optimality score was then calculated as the weighted average of the optimality values for individual factors. As a case study, we implemented this approach in assessing the wetlands in Suzhou, China. While our approach does not directly require field information, its result is comparable with the result of the rapid field method that is directly based on field data. In addition, our approach is advantageous in representing interior details of a wetland.

Regional zoogeographical zoning using species distribution modelling by the example of small mammals of South-Eastern Transbaikalia

Zoogeographical studies of regional scale always deal with incompleteness of faunal information. Such information is usually available as a set of localities, covering the studied area as an irregular network. At the same time, full coverage of data is needed for any spatial analysis. In this study, we attempted to perform faunal zoning at a regional level, formalising the procedure to the greatest extent possible. We used 47 small mammal species distribution models (SDM) as initial data for faunal zoning. SDMs were previously constructed based on localities determined using museum labels and environmental data with the maximum entropy method. SDMs were converted to binary values using fixed threshold. We calculated 1-Jaccard similarity coefficients between unique sets of predicted species compositions in each raster cell. The resulting dissimilarity matrix was analysed using hierarchical cluster analysis with the Ward and unweighted average methods. We distinguished three large clusters with nine subclusters based the similarity of the fauna composition. Patterns of the spatial distribution of species numbers and species composition homogeneity were obtained. The relationships between the distribution of species richness and the spatial heterogeneity of the fauna with latitude, longitude, altitude and environmental factors were studied using regression and discriminant analysis. Finally, two faunas were found in South-Eastern Transbaikalia, and a large territory in this region is occupied by a zone of their interpenetration. Analysis of stacked SDMs proposed as important tool for investigation of regional zoogeographical heterogeneity. It is especially useful for extrapolation of faunal data to a larger unstudied territory.

PARAMETERIC ANALYSIS FOR AUTOMATED EXTRACTION OF ROAD EDGES FROM MOBILE LASER SCANNING DATA

Abstract. The applicability of Mobile Laser Scanning (MLS) systems continue to prove their worth in route corridor mapping due to the rapid, continuous and cost effective 3D data acquisition capability. LiDAR data provides a number of attributes which can be useful for extracting various road features. Road edge is a fundamental feature and its accurate knowledge increases the reliability and precision of extracting other road features. We developed an automated algorithm for extracting left and right edges from MLS data. The algorithm involved several input parameters which are required to be analysed in order to find their optimal values. In this paper, we present a detailed analysis of the dimension parameters of input data and raster cell in our algorithm. These parameters were analysed based on temporal, completeness and accuracy performance of our algorithm for their different sets of values. This analysis provided the estimation of an optimal values of parameters which were used to automate the process of extracting road edges from MLS data.

Validation of a spatial model used to locate fish spawning reef construction sites in the St. Clair–Detroit River system

Lake sturgeon (Acipenser fulvescens) populations have suffered precipitous declines in the St. Clair–Detroit River system, following the removal of gravel spawning substrates and overfishing in the late 1800s to mid-1900s. To assist the remediation of lake sturgeon spawning habitat, three hydrodynamic models were integrated into a spatial model to identify areas in two large rivers, where water velocities were appropriate for the restoration of lake sturgeon spawning habitat. Here we use water velocity data collected with an acoustic Doppler current profiler (ADCP) to assess the ability of the spatial model and its sub-models to correctly identify areas where water velocities were deemed suitable for restoration of fish spawning habitat. ArcMap 10.1 was used to create raster grids of water velocity data from model estimates and ADCP measurements which were compared to determine the percentage of cells similarly classified as unsuitable, suitable, or ideal for fish spawning habitat remediation. The spatial model categorized 65% of the raster cells the same as depth-averaged water velocity measurements from the ADCP and 72% of the raster cells the same as surface water velocity measurements from the ADCP. Sub-models focused on depth-averaged velocities categorized the greatest percentage of cells similar to ADCP measurements where 74% and 76% of cells were the same as depth-averaged water velocity measurements. Our results indicate that integrating depth-averaged and surface water velocity hydrodynamic models may have biased the spatial model and overestimated suitable spawning habitat. A model solely integrating depth-averaged velocity models could improve identification of areas suitable for restoration of fish spawning habitat.

다변량 환경 공간변수 주성분 분석을 통한 남·북 생태계 차이

The objectives of this study are to analyze the quantitative ecological principal components of Korean Peninsula using the multivariate spatial environmental datasets and to compare the ecological difference between South and North Korea. Ecological maps with GIS(Geographical Information System) are constructed by PCA(Principal Component Analysis) based on seventeen raster(cell based) variables at 1km resolution. Ecological differences between South and North Korea are extracted by Factor Analysis using ecosystem maps masked from Korean ones. Spatial data include SRTM(Shuttle Radar Topography Mission), Temperature, Precipitation, SWC(Soil Water Content), fPAR(Fraction of Photosynthetically Active Radiation) representing for a productivity, and SR(Solar Radiation), which all cover Korean peninsula. When it performed PCA, the first three scores were assigned to red, green, and blue color. This color triplet indicates the relative mixture of the seventeen environmental conditions inside each ecological region. The first red one represents for 'physiographic conditions' worked by high elevation and solar radiation and low temperature. The second green one stands for 'seasonality' caused by seasonal variations of temperature, precipitation, and productivity. The third blue one means 'wetness condition' worked by high value such as precipitation and soil water contents. FA extraction shows that South Korea has relatively warm and humid ecosystem affected by high temperature, precipitation, and soil water contents whereas North Korea has relatively cold and dry ecosystem due to the high elevation, low temperature and precipitation. Results would be useful at environmental planning on inaccessible land of North Korea.

Integrating field measurements, a geomorphological map and stochastic modelling to estimate the spatially distributed rockfall sediment budget of the Upper Kaunertal, Austrian Central Alps

The estimation of catchment-scale rockfall rates relies on the regionalisation of local measurements. Here, we propose a new framework for such a regionalisation by the example of a case study in the Upper Kaunertal, Austrian Central Alps (62.5km2). Measurements of rockfall deposition during 12months onto six collector nets within the study area were combined with published mean annual rates from the literature, and a probability density function was fitted to these data. A numerical model involving a random walk routing scheme and a one-parameter friction model was used to simulate rockfall trajectories, starting from potential rockfall source areas that were delineated from a digital elevation model. Rockfall rates sampled from the fitted probability density function were assigned to these trajectories in order to model the spatial distribution and to estimate the amount of rockfall deposition. By recording all trajectories as edges of a network of raster cells, and by aggregating the latter to landforms (or landform types) as delineated in a geomorphological map of the study area, rockfall sediment flux from sources to different landforms could be quantified. Specifically, the geomorphic coupling of rockfall sources to storage landforms and the glacial and fluvial sediment cascade was investigated using this network model. The total rockfall contribution to the sediment budget of the Upper Kaunertal is estimated at c. 8000Mgyr−1, 16.5% of which is delivered to the glaciers, and hence to the proglacial zone.The network approach is favourable, for example because multiple scenarios (involving different probability density functions) can be calculated on the basis of the same set of trajectories, and because deposits can be back-linked to their respective sources. While the methodological framework constitutes the main aim of our paper, we also discuss how the estimation of the budget can be improved on the basis of spatially distributed production rates.

Predicting the extent of lakeshore development using GIS datasets

Land use along lakeshores impacts littoral habitat. We used raster-based land use and land cover datasets as well as a statewide dock polygon dataset to assess the development status of lake shoreland on 150 Minnesota lakes. A dataset containing dock polygons identified using a semiautomated process performed best statewide. An older raster dataset that classified rural development based on the presence of buildings performed better than a recent dataset based on dominant land cover. We classified points along the shore as developed or undeveloped using proximity to a development indicator: either a raster cell with a developed land use or a dock point. We compared classifications derived from GIS data to actual development, which was defined by the presence of a manually identified dock on aerial photos, and used total operating characteristic (TOC) analysis to evaluate the performance of each dataset. All 3 datasets classified development better than random chance. The dock dataset performed best, and its results were consistent statewide, while the raster datasets' performance varied among ecoregions. Researchers should be aware that the prevalence of the condition being classified has a large impact on some commonly used metrics, such as accuracy and positive predictive value. The costs and tradeoffs of different types of error (false alarms or missed detections) will vary in different situations and should be explicitly considered when deciding how and when to use classification systems like these.

GEOINFORMATION DATABASE FOR EVALUATION OF POTENTIAL NITRATE POLLUTION OF SURFACE WATER AND GROUNDWATER

Presented by the rationale for the structure, methods and results of creation of the geoinformation database for assessing the potential nitrate-made pollution of surface and groundwater in Odessa region. The database includes basic derivatives and digital layers of spatial information characterizing relief, hydrographic network, soil cover, land-use and spatial distribution parameters of the mathematical model of evaluation, which is selected as a regression model, developed at the Aristotle University of Thessaloniki (Greece). The database is used raster and vector spatial data models, but the basic model is a raster with the size of the raster 3710 x 3632 and with the raster cell size 92.6 m.

THE PROBLEMS OF QUANTITATIVE ESTIMATION OF EROSIVE SOIL LOSSES

Purpose. Today, there are several dozen models that allow us to calculate and predict water erosion soil loss. A considerable part of them is quite weighty comprehensive study, the implementation of media-art software packages and used in different regions of the world with a practical purpose. If spatial changeability of all factors of water erosion of soil is even taken into account, there are some problems of the practical use of such models. In particular, it is the verification of models, and the spatial generalization of the got results. A question appears here, which a size of elementary area of space – raster cell – must be, from what it depends and on what it influences. Methodology. To study the impact of the raster cell size of the final result of modeling and forecasting used the GIS-realized spatial model implemented flush-accumulation of soil that was developed at the Department of Physical Geography and Nature Use of Odessa Mechnikov National University. Spatial GIS-realized a model is approved on a few test areas of steppe and forest-steppe areas of Ukraine and Russian Federation. Finding. As a result of quantitative experiments it appeared that on the electronic maps with cell size 5 and 10 meters at the calculations of a coefficient of form of hillside catchments are expressly traced the structure of hillside rill network. Use of digital model of relief with the cell size more than 10 meters for calculations the coefficient of form of hillside catchments results in a volume, that a concentration or dispersion of temporal rills is not almost traced. It is got, that the spatial differentiation of soil washing off is identified at the cell size to 25 meters, at the increase of cell size the spatial distributing is smoothed out, and the variation of values of washing off for areas diminishes considerably. The nearest to the actual soil washing off from of experimental area Ploska (0.271 t/ha/year) is a average value at the cell size 10 meters – 0.233 t/ha/year (a difference makes -13.6 %). Results. The research of influence of raster cell size on the results of calculations of norm of the thundershower soil washing off with the use of spatial GIS-realized models showed that raster cell, at the first, had a considerable influence on end-point – distributing and size of soil washing off, at the second, in dependence there is authentication of structure of the hillside flowing down on the cell size, at the third, for small hillside area with the purpose of optimization of the land use expediently to use the cell size not more than 10 meters.

VALIDATION OF BIM COMPONENTS BY PHOTOGRAMMETRIC POINT CLOUDS FOR CONSTRUCTION SITE MONITORING

Abstract. Construction progress monitoring is a primarily manual and time consuming process which is usually based on 2D plans and therefore has a need for an increased automation. In this paper an approach is introduced for comparing a planned state of a building (as-planned) derived from a Building Information Model (BIM) to a photogrammetric point cloud (as-built). In order to accomplish the comparison a triangle-based representation of the building model is used. The approach has two main processing steps. First, visibility checks are performed to determine whether or not elements of the building are potentially built. The remaining parts can be either categorized as free areas, which are definitely not built, or as unknown areas, which are not visible. In the second step it is determined if the potentially built parts can be confirmed by the surrounding points. This process begins by splitting each triangle into small raster cells. For each raster cell a measure is calculated using three criteria: the mean distance of the points, their standard deviation and the deviation from a local plane fit. A triangle is confirmed if a sufficient number of raster cells yield a high rating by the measure. The approach is tested based on a real case inner city scenario. Only triangles showing unambiguous results are labeled with their statuses, because it is intended to use these results to infer additional statements based on dependencies modeled in the BIM. It is shown that the label built is reliable and can be used for further analysis. As a drawback this comes with a high percentage of ambiguously classified elements, for which the acquired data is not sufficient (in terms of coverage and/or accuracy) for validation.