Light Detection And Ranging Elevation Data Research Articles

Generalization of digital elevation models for military passability maps development

Abstract. This paper presents the methodology of the digital elevation model (DEM) generalization which aims to reduce the generation time of the military passability maps. The generalization is based on the local standard deviations of slopes on the analyzed area. It assumes that in areas, where the diversification of relief is low, the elevation points should be reduced, which may significantly increase the efficiency of the process of the military passability maps development. The generalization was performed on the LIDAR (Light Detection and Ranging) elevation data, which can be obtained with no charges from the Polish Head Office of Geodesy and Cartography. The data are in 1 m resolution and in an ASCII grid file, which contains all coordinates (X, Y and H) of elevation points. The conducted research shows that the modification of parameters used in the generalization process (shape and size of primary fields, number of classes and standard deviation threshold) influences the degree of generalization of obtained results. What is more, the generalized DEMs allowed to reduce the processing time of the passability maps by about 3.5 times compared to the non-generalized model. The parameters used for generalization can be adjusted by the user to individual needs which makes the tool universal.

Point cloud does matter. Selected issues of using airborne LiDAR elevation data in geomorphometric studies of rugged sandstone terrain under forest – Case study from Central Europe

Contemporary airborne Light Detection And Ranging (LiDAR) elevation data provide a high-resolution input to quantitative studies within various branches of geomorphological research. Availability of such data played an especially significant role in the recent developments of geomorphometric studies focused on extremely rugged terrains, such as dissected sandstone plateaus. These are the areas of very complex topography, featuring structurally-controlled landforms such as tabular hills, canyons, cleft-and-valley systems, numerous high and near-vertical rock walls, isolated tors and dense boulder slope covers. In this paper we focus on issues of LiDAR data handling for geomorphometric research in Central European sandstone areas in Czechia, Poland and Germany, where the extremely rugged terrains are usually located in the forest and, thus, usability of LiDAR DEM (Digital Elevation Model) products for morphometric studies can be constrained. The impact of forest density on local decrease of ground point density may be considerable, adversely affecting the quality of DEMs, which become unsuitable to represent properly terrain features. Therefore, the role of DEM pre-processing in geomorphometric workflow is emphasized. Using the original point cloud data one can provide a much more detailed terrain representation that includes numerous convex landforms (tors, boulders) which are erroneously filtered-out from ready-to-use DEMs. Results of such procedure are shown explicitly in the form of Elevation Difference maps, as well as on examples of selected morphometric indices such as Topographic Wetness Index, Terrain Ruggedness Index and Morphometric Protection Index. The awareness of qualities of particular LiDAR datasets should be considered crucial while assessing LiDAR data applicability for morphometric studies on rugged erosional landscapes, especially if the study areas are transboundary.

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

Coastal erosion and mass wasting along the Canadian Beaufort Sea based on annual airborne LiDAR elevation data

Erosion of permafrost coasts has received increasing scientific attention since 1990s because of rapid land loss and the mobilisation potential of old organic carbon. The majority of permafrost coastal erosion studies are limited to time periods from a few years to decades. Most of these studies emphasize the spatial variability of coastal erosion, but the intensity of inter-annual variations, including intermediate coastal aggradation, remains poorly documented. We used repeat airborne Light Detection And Ranging (LiDAR) elevation data from 2012 and 2013 with 1m horizontal resolution to study coastal erosion and accompanying mass-wasting processes in the hinterland. Study sites were selected to include different morphologies along the coast of the Yukon Coastal Plain and on Herschel Island. We studied elevation and volume changes and coastline movement and compared the results between geomorphic units. Results showed simple uniform coastal erosion from low coasts (up to 10m height) and a highly diverse erosion pattern along coasts with higher backshore elevation. This variability was particularly pronounced in the case of active retrogressive thaw slumps, which can decrease coastal erosion or even cause temporary progradation by sediment release. Most of the extremes were recorded in study sites with active slumping (e.g. 22m of coastline retreat and 42m of coastline progradation). Coastline progradation also resulted from the accumulation of slope collapse material. These occasional events can significantly affect the coastline position on a specific date and can affect coastal retreat rates as estimated in long term by coastline digitalisation from air photos and satellite imagery. These deficiencies can be overcome by short-term airborne LiDAR measurements, which provide detailed and high-resolution information about quickly changing elevations in coastal areas.

Upscaling of methane exchange in a boreal forest using soil chamber measurements and high-resolution LiDAR elevation data

Forest soils are generally considered to be net sinks of methane (CH4), but CH4 fluxes vary spatially depending on soil conditions. Measuring CH4 exchange with chambers, which are commonly used for this purpose, might not result in representative fluxes at site scale. Appropriate methods for upscaling CH4 fluxes from point measurements to site scale are therefore needed. At the boreal forest research site, Norunda, chamber measurements of soils and vegetation indicate that the site is a net sink of CH4, while tower gradient measurements indicate that the site is a net source of CH4. We investigated the discrepancy between chamber and tower gradient measurements by upscaling soil CH4 exchange to a 100ha area based on an empirical model derived from chamber measurements of CH4 exchange and measurements of soil moisture, soil temperature and water table depth. A digital elevation model (DEM) derived from high-resolution airborne Light Detection and Ranging (LiDAR) data was used to generate gridded water table depth and soil moisture data of the study area as input data for the upscaling. Despite the simplistic approach, modeled fluxes were significantly correlated to four out of five chambers with R>0.68. The upscaling resulted in a net soil sink of CH4 of −10μmolm−2h−1, averaged over the entire study area and time period (June–September, 2010). Our findings suggest that additional contributions from CH4 soil sources outside the upscaling study area and possibly CH4 emissions from vegetation could explain the net emissions measured by tower gradient measurements.

Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama

Landsat and Shuttle Radar Topography Mission (SRTM) imagery have recently been used to identify broad-scale floristic units in Neotropical rain forests, corresponding to geological formations and their edaphic properties. Little is known about the structural and functional variation between these floristic units, however, and Landsat and SRTM data lack the spectral and spatial resolution needed to provide this information. Imaging spectroscopy and LiDAR (Light Detection and Ranging) have been used to measure canopy structure and function in a variety of ecosystems, but the ability of these technologies to measure differences between compositionally-distinct but otherwise uniform tropical forest types remains unknown. We combined 16 tree inventories from central Panama with imaging spectroscopy and LiDAR elevation data from the Carnegie Airborne Observatory to test our ability to identify patterns in plant species composition, and to measure the spectral and structural differences between adjacent closed-canopy tropical forest types. We found that variations in spectroscopic imagery and LiDAR data were strong predictors of spatial turnover in plant species composition. We also found that these compositional, chemical, and structural patterns corresponded to underlying geological formations and their geomorphological properties. We conclude that imaging spectroscopy and LiDAR data can be used to interpret patterns identified in lower resolution sensors, to provide new information on forest function and structure, and to identify underlying determinants of these patterns.

A NEW FEATURE DESCRIPTOR FOR LIDAR IMAGE MATCHING

Abstract. LIght Detection And Ranging (LiDAR) data is available in a point cloud corresponding to long overlapping strips on the ground. The percentage of overlap in these LiDAR strips varies between 10 and 30. The strips are unregistered with respect to each other. Any further interpretation or study of the whole area requires registration of these strips with respect to each other. This process is called strip-adjustment. Traditionally, LiDAR point clouds are matched and strip-adjusted using techniques based on iterative closest point (ICP) or modifications of the same, which as the name suggests, runs over multiple iterations. Iterative algorithms, however, are time consuming and this paper offers a solution to the problem. In this paper, point correspondences are found on overlapping strips so that they can be registered with each other. We introduce a new method for point matching on LiDAR data. Our algorithm combines the power of LiDAR elevation data with a keypoint detector and descriptor that is based on the Scale Invariant Feature Transform (SIFT) method. The keypoint detector finds interesting keypoints in the LiDAR intensity image from the SIFT keypoints; a unique signature of each keypoint is then obtained by examining a patch surrounding that point in the elevation image. Histograms of subdivisions of the patch are set as the keypoint descriptor. Once all the keypoints and descriptors are obtained for two overlapping patches, correspondences are found using the nearest neighbor distance ratio method.

The use of medium point density LiDAR elevation data to determine plant community types in Baltic coastal wetlands

This study investigates the use of medium point density LiDAR (light detection and ranging) elevation data for identifying and locating plant community types in Baltic coastal wetlands. Ground-surveyed micro-topography data obtained using real-time kinematic differential GPS were used to analyse and correct the accuracy of LiDAR sourced elevation values in Matlab R2010a. Following the application of elevation corrections, 3 interpolation techniques were applied: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and the Delaunay Triangulated Irregular Network (TIN) interpolation. Analysis showed that the TIN method produced a more robust model that adequately represented the micro-topographical variation across the wetland. The TIN elevation interpolation was then used to characterise the extent and distribution of the plant communities using known elevation preferences in one coastal wetland site, Tahu. The plant community model developed from the TIN elevation interpolation was then further ground-truthed in two separate Baltic coastal wetland sites, Matsalu and Kudani, in order to test model validity.The results showed that the accuracy of the LiDAR data varied dependent upon plant community type. In 5 of the 6 studied community types, the LiDAR data gave a mean elevation difference of 0.177m (0.016SD) above the dGPS elevations. With dGPS elevation corrections, the LiDAR data produced an accurate model of the location of the different plant community types, Kappa coefficient (κ) 0.63, at the initial site, Tahu. The model was found to be very accurate in one of the subsequent ground-truthed sites, κ 0.81, with a similar management history, but less accurate, κ 0.53, in a wetland more exposed to wave action and with a longer history of vegetation management.The inclusion of dGPS ground survey data was shown to considerably improve the accuracy of the LiDAR elevation data to facilitate the identification of plant community types on the basis of elevation. The approach developed in this study of Baltic coastal wetlands may provide a transferable method for rapidly and accurately mapping plant communities in other open environments.

ASTER GDEM validation using LiDAR data over coastal regions of Greenland

Elevation data from airborne Light Detection and Ranging (LiDAR) campaigns are used in an attempt to evaluate the accuracy of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) in Greenland. The LiDAR elevation data set is characterized by a high spatial resolution of about 1 m and elevation accuracy of 20–30 cm root mean square error (RMSE). The LiDAR data sets used were acquired during ice-monitoring campaigns carried out from 2003 to 2008. The study areas include ice-free regions, local ice caps and the ice sheet margin. A linear error of 15–65 m was derived, which is far greater than the 20-m product specification. This estimation is biased by both the seasonal and the climatic changes in local ice caps because the ASTER GDEM was computed from imagery acquired in the period 2000–2009. High sloping areas along the coastal regions of Greenland and the effect of the number of scenes used to generate the ASTER GDEM as well as relief are associated with the GDEM accuracy.

Identifying critical agricultural areas with three-meter LiDAR elevation data for precision conservation

Determining which portions of agricultural landscapes are major sources of pollution within a watershed is time consuming and labor intensive. Small critical areas of the landscape contribute disproportionate amounts of sediment and phosphorus to nearby waterways. Critical areas are defined here as areas of accumulated overland runoff that are hydrologically connected to surface waters. With advancements in light detection and ranging (LiDAR) technologies, landscape topography can be represented with highly accurate terrain data. The objective of this study is to determine the effectiveness of using LiDAR–based terrain attributes to identify fine-scale critical areas in selected Minnesota watersheds and to analyze cost efficiency of this type of analysis. The LiDAR digital elevation model data were acquired for two south central Minnesota watersheds, and the terrain attributes slope, flow accumulation, and stream power index were calculated with a 3 m (9.8 ft) spatial resolution. Field surveys were conducted in these watersheds along the riparian corridor to identify side inlets and active gullies that contribute to surface water quality degradation. Terrain attributes were able to identify 80% of field-verified gullies in the study watersheds. Furthermore, an even higher percentage of gullies with a high sediment delivery potential were identified using terrain attributes. Gully size was ranked during field surveys, and 31 of the 32 largest gullies ranked in the field were successfully identified with LiDAR–based terrain attributes. In contrast, only 7 of these gullies could be identified using 30 m (98 ft) digital elevation model terrain attributes. The LiDAR approach for identifying critical source areas using terrain attributes has a large potential for cost savings relative to time-consuming field surveys. With an ever-increasing availability of LiDAR data, terrain analysis may prove very useful in the future for targeting best management practices to critical areas for reductions in nonpoint source pollution.

Integrating building footprints and LiDAR elevation data to classify roof structures and visualise buildings

Three-dimensional urban models are increasingly needed for applications as varied as urban planning and design, microclimate investigation and tourism. Light Detection And Ranging (LiDAR) data are considered to be highly suitable for the three-dimensional reconstruction of urban features such as buildings. Ongoing research is determining how best to integrate LiDAR elevation data with already available vector-based data. This paper reports research on combining building footprints and LiDAR to visualise an urban area (Portbury near Bristol, England) with an emphasis on representing buildings in a GIS environment. The main emphasis here is on retaining a vector model that is suitable for representing regular man-made structures. A major difference between this and earlier work is that before visualisation, this work classifies roof types of buildings as either flat or pitched. We compared LiDAR data at three point densities in terms of successful building type detection and visualisation: 1 (low), 16 (medium) and 40 (high) points per m 2. There are important data acquisition cost issues at each of these resolutions. High density LiDAR yielded the highest overall accuracy of building type detection and proved useful for identifying roof features, yet lower densities proved more useful for revealing overall roof morphology.

Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area

Separating ground from nonground laser returns from airborne light detection and ranging (LiDAR) data is a key step in creating digital terrain models (DTMs). In this letter, bare-earth and forested surfaces are classified from LiDAR intensity data in a data set from central Idaho, U.S. Next, a Gaussian fitting (GF) method is applied to determine ground elevations from LiDAR elevation data according to the land-cover information. In comparison to ground-based reference data, the GF method generated an accurate DTM in this study area. Overall, the DTM underestimated the ground observations by approximately 31 cm. A combination of LiDAR intensity and elevation data may be effectively used to develop DTMs in similar terrain of relatively simple land-cover classes.

Integrating LIDAR elevation data, multi‐spectral imagery and neural network modelling for marsh characterization

Vertical elevation relative to mean sea level is a critical variable for the productivity and stability of salt marshes. This research classified a high spatial resolution Airborne Data Acquisition and Registration (ADAR) digital camera image of a salt marsh landscape at North Inlet, South Carolina, USA using an artificial neural network. The remote sensing‐derived thematic map was cross‐referenced with Light Detection and Ranging (LIDAR) elevation data to compute the frequency distribution of marsh elevation relative to tidal elevations. At North Inlet, the median elevation of the salt marsh dominated by Spartina alterniflora was 0.349 m relative to the North American Vertical Datum 1988 (NAVD88), while the mean high water level was 0.618 m (2001 to May, 2003) with a mean tidal range of 1.39 m. The distribution of elevations of Spartina habitat within its vertical range was normal, and 80% of the salt marsh was situated between a narrow range of 0.22 m and 0.481 m. Areas classified as Juncus marsh, dominated by Juncus roemerianus, had a broader, skewed distribution, with 80% of the distribution between 0.296 m and 0.981 m and a median elevation of 0.519 m. The Juncus marsh occurs within the intertidal region of brackish marshes and along the upper fringe of salt marshes. The relative elevation of the Spartina marsh at North Inlet is consistent with recent work that predicts a decrease in equilibrium elevation with an increasing rate of sea‐level rise and suggests that the marshes here have not kept up with an increase in the rate of sea‐level rise during the last two decades.

Application of Light Detection and Ranging Technology to Highway Safety

An application of light detection and ranging (LIDAR) technology to highway intersection safety is presented. LIDAR can be used to collect information about a surface by reflecting thousands of light beams per second off the surface and measuring the return time of the beams. The surface profile is collected as a digital signature that can be used in a variety of applications. Collection of information on the surface profile of the earth in the form of elevation data is one of several LIDAR applications that have been used for mapping and contouring. The focus of the described application is use of LIDAR elevation data to obtain information on intersection geometry that can lead to the discovery of potential obstructions in driver sight lines. After appropriate transformations, LIDAR elevation data were used in line-of-sight analysis to obtain information on sight-line obstructions at six intersections on the IA-1 corridor in Iowa. Intersection crash frequency and data availability were considerations in the selection of the six intersections. Results from the line-of-sight analysis were validated by visits to the intersections in the field and verification of the existence of obstructions detected during the analysis. Sixty-six lines of sight were blocked during the line-of-sight analysis, of which 62 (89.8%) were confirmed during the validation process. Four (5.8%) sight-line obstructions were not confirmed during the validation. At least three (4.4%) potential sight-line obstructions discovered during validation were not detected during the line-of-sight analysis. The intersection with the highest crash frequency was correctly found to have obstructions located within the intersection sight triangles. It can be concluded that LIDAR elevation data can be used successfully for identifying potential sight-distance problems at intersections. Identified potential problems can be verified and rectified in the field. LIDAR is a relatively costly data source, and a single application, such as this one, cannot justify the high cost of LIDAR data acquisition. Other potential highway safety enhancing applications of LIDAR must be investigated to offset the high data-acquisition cost. Suggestions for other highway safety applications are provided.