A sequential iterative dual-filter for Lidar terrain modeling optimized for complex forested environments

Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain

<p>Gully erosion is regarded as one of the worst land degradation processes in the world. Rapid identification of the location of gully features is urgently required, to aid in recognizing regions where gully erosion is prominent. Manual digitizing of gully features is both time consuming and prone to bias. Generating semi-automated or automated workflows to detect gully erosion allows quick and unbiased mapping of gully features over large extents.</p><p>In the Sandspruit catchment, South Africa, contour banks with a combined length of approximately 25000km have been constructed to mitigate soil erosion. Gullies are now mostly confined to narrow slivers in the natural vegetation, fynbos and Renosterveld, between agricultural fields. The morphological similarity and proximity of contour banks and gullies in this region provides a good test site to evaluate whether a semi-automated detection workflow could map gullies in complex, rough agricultural terrain.</p><p>Here, a Digital Surface Model (DSM) with a spatial resolution of 2m was used to test a semi-automated detection workflow in a Geographical Information System (GIS) environment. Two main building blocks were generated from the DSM: 1) a normalized DSM, created by subtracting a convolved mean DSM with a designated filter size from the original DSM, and 2) local slope generated from the normalized DSM. Subsequently, using expert knowledge, mapped gully polygons were refined and smoothed, by threshold determination, masking features not related to drainage, and pixel-based growing and shrinking. The semi-automated workflow was completed for two different spatial resolutions: 1) the native 2m-resolution and 2) a 0.5m-resolution DSM, upsampled without producing artificial values from interpolation methods. A GeoEye-1 image with a spatial resolution of 0.5m was included at the backend of the workflow as an additional step, to test whether gully mapping from using terrain attributes only, could be improved upon.     </p><p>Gully detection from terrain attributes only, achieved an overall accuracy of 0.68 (0.5m DSM) and 0.74 (2m DSM) with kappa values ranging from 0.36 (0.5m DSM) and 0.35 (2m DSM). The upsampled 0.5m DSM performed worse than the native 2m DSM due to increased noise detection. Although reasonable performance was obtained from the 2m DSM, issues encountered include: 1) vegetation that caused some inaccuracies in gully boundary delineation and discontinuities along gully channels and 2) false positive detection of contour banks. The addition of the GeoEye-1 image increased overall accuracy to 0.79 and kappa value to 0.5, mostly because of the elimination of false positives in agricultural fields.</p><p>The accuracy statistics indicate that the semi-automated detection workflow developed here shows promise as a tool to detect gully erosion on a catchment scale. Furthermore, due to the workflow being built upon the distinct morphology of gully features, it could be transferable to other regions that are dissimilar to the Sandspruit catchment. The transferability of the workflow should be tested in future, in addition to how accuracy would be affected if the DSM were substituted with a Digital Terrain Model (DTM) of similar spatial resolution.</p>

When Earth-locating (geolocating) modern moderate resolution instrument data, such as from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) Terra and Aqua satellites, corrections for Earth terrain effects are applied to enable accurate retrieval of global geophysical parameters. The current approach to terrain correction calculates the pierce point of the center of each observation with a digital terrain model. With the recent Shuttle Radar Terrain Mapping Mission (SRTM) terrain model data, which has a higher spatial resolution and better accuracy than previous global terrain models, there is an opportunity to improve geolocation accuracy in rough terrain by using more advanced techniques to compute geolocations that are more representative of the centroid of each observation. The authors evaluate whether calculating geolocation using an observation weighting approach is significantly better than calculating the pierce point geolocation and if so under what conditions. The relative additional computational cost of an approximate technique is weighed against the possible increase in geolocation accuracy.

ALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey conditions.This article proposes the use of a new estimation method in the filtering of point clouds obtained from airborne laser scanning (ALS), provisionally called Msplit-estimation. The application of Msplit-estimation in ALS data filtering requires the determination of the appropriate functional model for the surface, which will be used in the filtering of the set of points. A polynomial terrain surface model was selected for this purpose. Two methods of filtering using the Msplit method are presented. The first is based on the estimated parameters of the polynomial describing the surface (called the ‘quality’ approach in the article). The second method (provisionally called the ‘quantity’ method) is carried out in two stages. The first stage is point cloud filtering, which results in two subsets being created. One of these is the subset of points intended for DTM creation, while the other contains the remaining points. The second stage of the approach is the creation of a DTM from the first subset.Since the Msplit method has an analytical character, the ATIN method was selected to verify the correct operation of the method. The ATIN method is based on computational geometry and uses repeated Delaunay triangulation and statistical evaluation of the geometric parameters. Comparison of Msplit with a method based on different principles mitigates errors arising from similarly functioning methods belonging to the same group of filters. The choice of the ATIN method was also dictated by its established position among filtering algorithms. The method is well-known, documented, and verified and this ensures that filtering by this method provides a reliable result that can serve as a reference for comparison with the proposed new filtering method.The theoretical discussion presented in this article was verified with two practical examples. The results obtained from computation by the Msplit method with appropriate terrain models encourage more detailed theoretical and empirical tests of this method for the filtering and segmentation of ALS data-sets.

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m3 to 135,902 m3 in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m3. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

Multi-temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)-borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi-temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi-temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV-borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf-off conditions (e.g., cross-flight strategy) turned out to be sufficient for our geomorphic research purposes.

Although photogrammetry has long been used as the primary method to produce digital elevation models (DEMs), there are still some locations that are not covered by multiangle images because of various limitations. Moreover, traditional interpolation methods for producing denser DEMs often cause oversmoothing, particularly over rough terrain. To formulate a robust procedure that is capable of reconstructing the terrain surface using sparse ground control points in heterogeneous areas, this letter describes an improved spatial enhancement method combining perspective shape from shading (SFS) using a single optical image. This method is called SFS-based densification using multispectral information (SDMI). First, the image irradiance equation based on the perspective Lambertian model is formulated as a static Hamilton–Jacobi equation and is solved using a fast sweeping strategy. We then reconstruct the relative surface shape and apply an edge-preserved iterative interpolation method to generate a higher resolution DEM grid. Multispectral information is used to reveal the actual surface reflection properties, and the land surface is classified into several types to better estimate surface reflectance. Experiments indicate that SDMI is effective for the interpolation of a sparse grid DEM over heterogeneous terrain.

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Calculation of flow accumulation (also known as upstream area) matrices from digital elevation models (DEMs) is a very common procedure in hydrological studies, and also has been used in other disciplines within physical geography, such as glaciology. A problem with such calculations has always been the presence of closed depressions in DEMs; flow is directed towards such areas, but then cannot ‘escape’. In many implementations of flow accumulation algorithms such depressions have been removed from the DEM with some form of pre-processing algorithm which typically transform depressions into flat areas, across which area can then be routed. This approach effectively assumes that all depressions in a DEM are therefore artifacts, and not true features within the landscape. The proliferation of very high quality, high precision, and fine spatial resolution DEMs in recent years means that such an assumption is increasingly difficult to support. In this paper, some of the main flow accumulation algorithms and some existing techniques for dealing with closed depressions in DEMs are reviewed. A new algorithm is presented which assumes that such depressions are real features in the landscape, and which allows them to ‘fill’ and then ‘overflow’ into downstream areas within the DEM. Examples with a synthetic and two real DEMs suggest that, at least in these cases, the assumption that depressions are real is justified. These results also suggest that determining the size distribution for depressions within a DEM could form the basis for identifying whether artifact depressions are a problem in individual DEMs.

Adaptive algorithm for large scale dtm interpolation from lidar data for forestry applications in steep forested terrain

To design more and more dynamically stable vehicles to be able to use in exploration tasks in rough terrain environment of solar system planets is extremely important because of the difficulties in manipulating the vehicle from a long distance. In this study the application of innovative multi-stage bogie suspension system to a rough terrain vehicle and effects of changing the parameters of this suspension system on vehicle's stability has been Investigated. Firstly mathematical model of rough terrain has been created. In this model rough terrain is thought as an interpolated spline in which the obstacles are assumed as control points. Then an off-road vehicle with a new type of a bogie suspension system has been represented. It has been shown that, increasing the number of the stages of bogie mechanism results in a more stable vehicle when moving on a rough terrain. To eliminate the effects of inertia forces, vehicle is assumed as quasi-static. Solution of this analysis has been given as computer generated simulation and graphics.

We present a new fractal based method of generating realistic models of natural landscapes. In this paper, we describe a three-steps method that produces a terrain mesh using ridge lines and rivers drainage network. As opposed to previous methods that compute water erosion for a given terrain model --- Digital Elevation Map ---, our terrain mesh is generated constrained by a precomputed set of ridge lines and rivers network. A skeleton of a ridges and rivers network is computed and stored in a Digital Elevation Map (D.E.M.) as initial values. Then, the elevations data set is enriched using a novel interpolation method based on a Midpoint Displacement's Inverse process. Finally, a midpoint displacement subdivision is applied to interpolate the remaining elevation coordinates. The resulting terrain meshes lead to naturalistic landscape models and our method seems to be very promising. Images produced by our model are presented.

Estimation of mean dominant height using NAIP digital aerial photogrammetry and lidar over mixed deciduous forest in the southeastern USA

For extreme events like flash floods, infiltration is considered to be negligible, and the morphology of the watershed is considered to be the most significant factor. Thus, digital elevation models (DEMs) are the most efficient data source to define the catchment surface. To determine if the DEM is adapted, it is essential to analyze both the intrinsic accuracy and the required accuracy. In this paper we propose two complementary methodologies to analyze and evaluate these two kinds of DEM accuracy that relate to the hydrological applications to flash floods. The first methodology relates to the intrinsic accuracy: a diagnostic method analyzes the accuracy and stability of the extracted hydrographic network at catchment scale. To obtain correct positioning of the channels, results show the strong influence of the topographic context and the need for associating extra data on rivers, especially in flat areas. The impact of accuracy is evaluated through a scheme based on DEM grid rotation. A step by step and iterative process gives a fuzzy evaluation of the hydrographic network. These techniques also highlight the strong scale dependence of the extracted network. The second methodology relates to the required accuracy. To analyze the sensitivity to the DEM accuracy, we propose an approach by comparing the results of hydraulic models obtained from a "real" description of the river topography and from this description distorted by a numerical noise characterizing the lack of accuracy of the DEM. The impact of this noise on the overflows on the major bed is analyzed. The results show that the accuracy required by the thematicians is often greater than that strictly required by modeling, which opens interesting prospects to reduce the phases of data acquisition.