LiDAR DTM Research Articles

Effects of Spatial Resolution on Topographic Representations and Drainage Networks Derived from LiDAR Digital Terrain Model

Effects of Spatial Resolution on Topographic Representations and Drainage Networks Derived from LiDAR Digital Terrain Model

Integrating geodiversity in animal spatial ecology: microhabitat selection of Eurasian lynx (Lynx lynx) and European wildcat (Felis silvestris) in a karst landscape

Geodiversity, encompassing various geophysical elements, can have an important impact on species distribution and affect animal behaviour patterns. Although many wild felids are attracted to rugged terrain and conspicuous relief features, most previous research was limited to general topographical characteristics (e.g., slope or terrain ruggedness) and rarely considered the effects of specific microhabitat characteristics. This gap is primarily due to the limited availability of high-resolution digital terrain models (DTMs) and relief features data at larger scales. However, LiDAR DTMs can be used in combination with various automatic methods to detect relief features, enabling non-contact and accurate mapping of large, remote and densely-forested areas. Here, we investigated the selection patterns of various karstic relief features, as well as topographic, anthropogenic and vegetation characteristics, by two sympatric felids, the Eurasian lynx (Lynx lynx) and the European wildcat (Felis silvestris), in the Dinaric Mountains, Slovenia. We used LiDAR DTM to calculate topographic characteristics and detect karst relief features based on automatic methods. We compared the selection of these features between the GPS-collared lynx and wildcats under a use-availability approach. We also investigated the differences in the selection of these features by lynx based on their origin and experience (remnant vs. translocated and naive vs. experienced, respectively). We observed significant impact of relief features on space use by both felids and detected distinct selection patterns between the two species. Lynx selected rugged terrain and proximity of caves, cliffs, karst depressions, ridges, small rocky outcrops, and roads, but avoided human settlements and forest edges. Wildcats selected areas with lower surface slope, closer to main roads, forest edges, caves and ridges, but avoided cliffs, forest roads and human settlements. We observed stronger selection/avoidance patterns among the translocated compared to the remnant lynx, while the differences in experience levels were less important. Our study demonstrates the potential of integrating remote sensing techniques and information on geodiversity into the study of animal spatial ecology. Furthermore, our results indicate that specific relief features provide important abiotic microhabitats for felids and may influence habitat segregation between sympatric species. Our findings provide further evidence for the importance of geodiversity conservation and the need to incorporate abiotic microhabitat features in wildlife habitat selection studies.

Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds

ABSTRACT Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier’s results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation.

Assessing flood susceptibility with ALOS PALSAR and LiDAR digital terrain models using the height above nearest drainage (HAND) model

Assessing flood susceptibility with ALOS PALSAR and LiDAR digital terrain models using the height above nearest drainage (HAND) model

Reconstruction of ancient drainage in the contact karst of the Harmanecká dolina Valley, Western Carpathians, based on mineralogical data from the allochthonous sediments and isobase geomorphometry

AbstractCaves are unique sedimentary archives that may record the evolution of the surrounding landscape. In this study, we applied combination of mineralogical data on allochthonous sediments with digital modelling of isobase surfaces to reconstruct the multi‐phased geomorphological development of the karst of the Harmanecká dolina Valley. The allochthonous cave sediments contain quartz and translucent heavy minerals (HMs) such as tourmaline, garnet, rutile, apatite, zircon, pyroxene, amphibole, staurolite, spinel, monazite, corundum, epidote, xenotime and florencite. Rounded shape and surface microtextures preserved on quartz and most HM grains point to predominantly a fluvial transport of detritus and its multiple recycling history. Pyroxene and amphibole are exceptions that denoted a very close source and the first‐cycle sedimentation from the adjacent volcanic Kremnické vrchy Mts. corresponding to obtained SHRIMP U–Pb detrital zircon ages of ~13.8–12.8 Ma. Heavy‐mineral geochemistry (e.g., zoned grossular‐rich garnet) and wide range of the Palaeozoic ages (Ordovician to Permian) confirms that the studied detritus sourced dominantly in the Western‐Carpathian crystalline basement rocks of the Veporic/Tatric tectonic units initially distributed by the palaeo‐Hron River and its tributaries from the east. Rare cummingtonite, remarkable tourmaline with omphacite inclusions and florencite point to the mixture of recycled sediments of Neogene Danube Basin formations containing also Austroalpine pre‐Neogene basement detritus, and confirm the original widespread distribution of the Neogene sediments currently eroded. Geomorphometric analyses of a LiDAR digital terrain model and derived isobase surface elevation models indicate the topographic development of wider area and support the findings about the transport direction of detritus. Presented multi‐method approach is suitable not only for the understanding of development of the surroundings of Harmanecká dolina Valley but also for the palaeogeographic and geomorphological reconstructions of similar karst and non‐karst areas.

Mining under the canopy: Unveiling the archaeo-mining record in the Colline Metallifere with LiDAR analysis and multidisciplinary studies

The study of ancient disused mining contexts in the Mediterranean region is particularly challenging due to the impact of modern reclamation works and the presence of dense vegetation, which impede the application of conventional archaeological research methods.Pivoting on the multidisciplinary approach driven by the ERC nEU-Med project to the study of historical landscapes, and thanks to a new LiDAR survey promoted by the project in 2019, new investigations were carried out in ancient and modern mining contexts in the territory of Colline Metallifere (southern Tuscany, Italy). The district is renowned for its important metal deposits characterized by copper, lead, zinc and silver vein bodies that have been mined since the Bronze Age, with a particularly intense phase of exploitation during the Medieval period. The primary objective of the LiDAR DTM analysis was to map features and delineate the characteristics of the archaeo-mining landscape in five densely vegetated contexts. The analysis revealed numerous novel evidence related to mining activities (574 within the five territorial samples analyzed in this contribution), of which 72% were also inspected through fieldwalking surveys. These features have been interpreted by means of archaeological surveys, documentary research and statistical and spatial analysis. A particular focus was placed on the study of the most prevalent type of feature, which is the mining shaft. The development of reliable parameters of interpretation, including the dimensional aspects of each individual mining shaft (area, diameter and depth of the basin), the distances between shafts, and presence or absence of dumps, played a crucial role in disentangling the complex topography of the mining sites. The favourable results achieved through this study will enable the application of the method on a larger scale to other nearby mining contexts, overcoming the constraints of accessibility and visibility that have so far significantly influenced the analysis of mining territories.

Surface-subsurface connectivity in the morphological evolution of sandstone-capped tabular hills – How much analogy to karst?

This paper presents geomorphic evidence for the significant role of surface-subsurface connectivity in the geomorphological evolution of tabular hills (mesas) capped by moderately thick sandstone beds. The study area is located in Central Europe, in the Sudetes mountain range (Poland, Czechia), in an area built of a clastic succession of Late Cretaceous age. Research involved analysis of high-resolution LiDAR DTM and its various derivatives, providing insights into characteristics of drainage routes and patterns of ruiniform relief, as well as fieldwork. The latter included landform recognition and mapping, surveying representative cleft and cave systems. Specific surface landforms indicative of drainage diversion underground and subterranean pathways of sand removal include boulder-filled troughs and hollows, open clefts and plazas, pseudo-sinkholes and tilted blocks. Further evidence is provided by abundant, joint-aligned roofed slots and boulder caves, whose origin is attributed to selective sandstone weathering, sand evacuation by groundwater, and gradual subsidence. Sandy cones and sheets below exits of slots and fractures are the complementary sedimentary evidence. The percentage of caprock area drained via subterranean outflow varies from 16 % to 89 %. Reasons for the high efficacy of subterranean water and sediment transport include both structural factors (regular jointing pattern, high porosity) and, very likely, pre-conditioning role of dissolution of cement in the quartz arenites (arenization), which facilitates detachment of individual grains even by low flows under low hydraulic gradient. Thus, the sandstone-capped mesas show numerous analogies to the karst proper, especially silicate karst. They are manifested in both similar landform inventories, main preparatory processes, and considerable surface-subsurface connectivity.

Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation

ABSTRACT To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs, including SRTM1, AW3D30, and COPDEM30. Taking LiDAR DTM as the ground truth, the accuracy of the GDEMs before and after VB correction is assessed, as well as two existing GDEMs including MERIT and FABDEM. Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types, with the largest biases of 21.5 m for SRTM1, 26.3 m for AW3D30, and 27.18 m for COPDEM30. Taking data randomly sampled from the corrected area as the training points, the proposed model reduces the mean errors (root mean square errors) of the three GDEMs by 98.8%−99.9% (55.1%−75.8%) in the three forests. When training data have the same forest type as the corrected GDEM but under different local situations, the proposed model lowers the GDEM errors by at least 76.9% (44.1%). Furthermore, our corrected GDEMs consistently outperform the existing GDEMs for the two cases.

INTEGRATING GEOSPATIAL TECHNIQUES AND UAS TECHNOLOGY TO UPDATE LIDAR DTM FOR FLOOD MODELING IN LAS NIEVES, AGUSAN DEL NORTE, PHILIPPINES

Abstract. The Digital Terrain Model (DTM) is essential in generating the topographic structure of an area by eliminating its external features. Conventional survey techniques are still employed to obtain accurate geographic data on the earth's surface. However, accurate land surveys are now achievable because of the development of Unmanned Aerial Vehicles (UAVs). This study aims to update the LiDAR DTM for Flood Modeling in Las Nieves, Agusan del Norte, using GNSS and UAS integration. The Static GNSS survey was carried out to collect precise points for the direct georeferencing of the DJI Phantom 4 GNSS-RTK UAV. There is a continuous investigation of the influence of flight parameters in creating DTM. Hence, this study also evaluates the effects of overlap percentage and flight altitude on the quality of the generated DTM. There were 16 flight plans prepared using various combinations of flight parameters. The UAS data collected was processed using the Structure from Motion. The quality of the DTM was assessed based on its accuracy and level of completeness to identify the optimal parameters for generating the data model. Based on the results of the accuracy and completeness of the DTMs, the optimal parameters for generating are 90% and 120 meters. Subsequently, the UAS-based DTM generated using this combination of flight parameters was utilized in updating the existing DTM of Las Nieves to create the flood model of the area. The flood model was generated using the hydrologic and hydraulic modeling of the HEC RAS Mapper.

Bare-earth DEM generation from ArcticDEM and its use in flood simulation

Abstract. In urban areas, topography data without above-ground objects are typically preferred in wide-area flood simulation but are not yet available for many locations globally. High-resolution satellite photogrammetric DEMs, like ArcticDEM, are now emerging and could prove extremely useful for global urban flood modelling; however, approaches to generate bare-earth DEMs from them have not yet been fully investigated. In this paper, we test the use of two morphological filters (simple morphological filter – SMRF – and progressive morphological filter – PMF) to remove surface artefacts from ArcticDEM using the city of Helsinki (192 km2) as a case study. The optimal filter is selected and used to generate a bare-earth version of ArcticDEM. Using a lidar digital terrain model (DTM) as a benchmark, the elevation error and flooding simulation performance for a pluvial scenario were then evaluated at 2 and 10 m spatial resolution, respectively. The SMRF was found to be more effective at removing artefacts than PMF over a broad parameter range. For the optimal ArcticDEM-SMRF the elevation RMSE was reduced by up to 70 % over the uncorrected DEM, achieving a final value of 1.02 m. The simulated water depth error was reduced to 0.3 m, which is comparable to typical model errors using lidar DTM data. This paper indicates that the SMRF can be directly applied to generate a bare-earth version of ArcticDEM in urban environments, although caution should be exercised for areas with densely packed buildings or vegetation. The results imply that where lidar DTMs do not exist, widely available high-resolution satellite photogrammetric DEMs could be used instead.

Különböző felbontású domborzatmodellek befolyása vulkáni salakkúpok morfometriai paramétereire – összehasonlító tanulmány

Since the early 2000s, rapidly improving resolution digital terrain models (DTMs) have been used increasingly as input data in various geomorphometric studies. They are excellent for studying large areas, clusters, and geomorphological forms, and with their help, it is not difficult to analyse hundreds of forms. A common problem in the statistical evaluation of DTMs is that the results may depend on the resolution of the input data. My aim in this study was to examine one of the simplest volcanic forms, the cinder cones, from a geomorphometric point of view and to show the extent to which the result was affected by the different resolutions of the DTMs. The study areas were the San Francisco Volcanic Field (SRTM DTM resolutions 30 and 10 m) in Arizona (USA) and ChaĒne des Puys (LiDAR DTM resolutions 90 and 0.5 m) in Auvergne (France). Polar coordinate transformation (PCT) was used, descriptive parameters (cone height and average slope) were calculated, and a comparison was carried out (with the help of the Mann-Whitney test) to show statistically significant differences for age groups and values derived from different resolutions. The slope values are the most sensitive to the resolution of the input data: comparing the age groups with themselves, but with values calculated from DTM with different resolutions, the slope shows a statistically significant difference in almost all cases. Based on the tests, an ideal resolution for scoria cone morphometric examination should be at least 10 meters, but if it is an area that contains older, smaller cones, a better resolution is a better choice. On the other hand, the 0.5 meters seems unnecessary for symmetry/parameter tests.

Landslide susceptibility assessment on a large scale in the Podsljeme area, City of Zagreb (Croatia)

ABSTRACT The study presents a landslide susceptibility assessment on a large scale in the City of Zagreb (Croatia). The susceptibility analysis was performed using the Weight of Evidence model in the pilot area (21 km2) and applying the obtained weight values for each class of conditioning factors in the study area (130 km2). The input data were LiDAR-based landslide inventory and six conditioning factors derived from 5 m LiDAR DTM, 5 m SfM DEM, and geological and land-use maps. The validation of the susceptibility assessment for the study area was evaluated with a ROC curve, which showed a high prediction rate (AUC = 84.4%), similar to the prediction rate for the pilot area (AUC = 86.9%). Based on the results, it can be concluded that the proposed method for large-scale landslide susceptibility assessment, where susceptibility conditions are defined in smaller pilot areas, can be applied to larger research areas with similar geomorphological and geological conditions.

LiDAR DTM Application for Flood Inundation Modeling and Visualization (Case Study: Batu City, East Java)

LiDAR DTM has been widely used for simulating flood inundation from river overflows. This flood inundation simulation needs to be done as an alternative step in the flood control system and flood disaster mitigation to determine the estimated area affected by the flood. Flood simulation is a form of unsteady flow that is simulated with the HEC-RAS program that allows the user to perform one-dimensional steady flow, one and two-dimensional unsteady flow calculations, sediment transport/mobile bed computations, and water temperature/water quality modeling, and visualizes flooded areas and flood-affected areas using a geographic information system approach. Hydrological data is obtained from daily rainfall processing at the time of station rain. Hydrograph analysis was performed using the SCS CN model on the HEC-HMS software to estimate the flow rate and peak discharge. Watershed area, curve number, Impervious value, Initial ion, and time lag are parameters that affect hydrograph analysis. From the results of the hydrograph analysis, the peak discharge of the model is 119.992 m2/s. Meanwhile, hydraulic analysis using HEC-RAS resulted in an inundation of 364 m2 which inundated three villages, namely Bulukerto, Bumiaji and Sidomulyo villages. The depth of the inundated area ranged from 0-15 m with an average depth of 4.534 m. The Sidomulyo area affected by the flood inundation scenario is 4,924 meters with an area of 0,02 ha. Bumiaji village was flooded with an area of 0,007 ha with a scenario of inundation depth of 4,049 m. Bulukerto village which was affected by the flood has an area of 20,972,832 m2 with a scenario of inundation depth reaching 4,343 m. Then some land cover such as residential land that is inundated by this flood simulation is 0,002 ha. Buildings from residential areas that were flooded reached 209 buildings. Then the area of agricultural land affected is 0,02 ha. In addition, there are also green open spaces affected by this simulation with an inundation area of 0,003 ha.

Illuminating geology in areas of limited exposure using texture shading of lidar digital terrain models

Abstract Regions of sparse exposure challenge geologic mappers because of limited information available on the underlying structure and continuity of the map units. We introduce here a little-known technique for post-processing bare earth digital terrain models (DTMs) that can dramatically improve knowledge of the underlying structure in covered areas. Texture shading enhances changes in slope and does not suffer from limitations introduced by artificial illumination required in hillshade or shaded relief images. When this technique is applied to lidar DTMs, layers of rock units with variable resistance to erosion can be clearly imaged, even in areas with limited outcrop. This technique enables one to collect comprehensive orientation data in areas of deformed sedimentary strata, assess the continuity of metamorphic and igneous rock units, and depict basement fracture sets. We demonstrate the use of texture shading in the Valley and Ridge of northern Pennsylvania, metamorphic rocks in the Berkshire Hills of western Massachusetts and Green Mountains of Vermont, and glacial deposits in the Finger Lakes region of upstate New York (northeastern United States).

Application of arsenic surveying for determining the position of former mining and metallurgical constructions: an example from the Radzimowice area (Lower Silesia, SW Poland)

Soil geochemical prospecting is becoming an increasingly important part of archaeological research. Therefore, it is possible to determine the location of various archaeological facilities that no longer exist in the study region. In this study, a morphological analysis of the “Stara Góra” deposit in Radzimowice (Lower Silesia, Poland) was performed using LiDAR DTM (light detection and ranging digital terrain model) images and historical data that describe mining in the vicinity of Radzimowice. This method identified numerous remains of centuries-old mining and metallurgical activity. The data collected were used to create a map of arsenic soil concentrations in this area. The map helped point to the exact locations of the old ore-processing facilities. Geochemical mapping was performed on a 20 × 20-m grid at a sampling depth of approximately 0.2 m. The samples were analysed by X-ray fluorescence spectrometry with energy-dispersive X-ray spectroscopy (XRF-EDS). The highest concentrations of this element occurred near the arsenic calciner and related dumps, as well as near dumps created during extraction from the Arnold shaft. In summary, in this article, we present the possibility of using a geochemical map of arsenic concentrations in soils supported by LiDAR data for archaeological purposes.

Multicriteria Accuracy Assessment of Digital Elevation Models (DEMs) Produced by Airborne P-Band Polarimetric SAR Tomography in Tropical Rainforests

The penetration capability of P-band radar waves through dense vegetation, along with the ability of tomography to separate the contributions of different layers in a vertical reflectivity profile, make P-band radar tomography a promising tool for digital terrain modeling in forested areas, specifically in dense tropical forests under which terrain topography remains poorly known. This paper aims to assess the overall quality of a digital terrain model (DTM) produced using tomographic processing of airborne P-band SAR imagery acquired during the TropiSAR campaign in French Guiana. Many quality descriptors are used to evaluate the quality of this DTM. Position and slope accuracies are computed based on a lidar DTM considered as the reference, and the impact of several parameters on these accuracies is studied, namely, slope, slope orientation, off-nadir angle and local incidence angle. The realism of the landforms is also studied according to geomorphological criteria. The results of this multicriteria accuracy assessment show the high potential of P-band SAR tomography in depicting the topography under forests, despite the intrinsic limitations related to the slant range geometry: the absolute elevation error is around 2 m; the slope is overestimated with an error of about 16°, mainly due to a processing artifact for which easy and direct solutions exist. Indeed, this error is equal to about 3° in flat artifact-free areas. These errors vary depending on the acquisition parameters and the local topography. The shapes are globally well preserved. These results are also discussed in the frame of the upcoming BIOMASS mission developed by the European Space Agency (ESA) and expected to be launched in 2024.

The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

The ATL08 as a height reference for the global digital elevation models

ABSTRACT High-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to <20 m. The evaluations of ASTER, SRTM, and TDX DEMs by ATL08 strong and weak beams are close to those assessed by LiDAR DTM points within 0.6 m for the strong beams. These findings indicate that ATL08 strong beams can be used as a height reference over vegetated mountainous regions.

GENERATION OF REFERENCE VEHICLE TRAJECTORIES IN REAL-WORLD SITUATIONS USING AERIAL IMAGERY FROM A HELICOPTER

Abstract. Highly accurate reference vehicle trajectories are required in the automotive domain e. g. for testing mobile GNSS devices. Common methods used to determine reference trajectories are based on the same working principles as the device under test and suffer from the same underlying error problems. In this paper, a new method to generate reference vehicle trajectories in real-world situations using simultaneously acquired aerial imagery from a helicopter is presented. This method requires independent height information which is coming from a LIDAR DTM and the relative height of the GNSS device. The reference trajectory is then derived by forward intersection of the vehicle position in each image with the DTM. In this context, the influence of all relevant error sources were analysed, like the error from the LIDAR DTM, from the sensor latency, from the semi-automatic matching of the vehicle marking, and from the image orientation. Results show that the presented method provides a tool for creating reference trajectories that is independent of the GNSS reception at the vehicle. Moreover, it can be demonstrated that the proposed method reaches an accuracy level of 10 cm, which is defined as necessary for certification and validation of automotive GNSS devices.

Improvement of VHR Satellite Image Geometry with High Resolution Elevation Models

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery.