Airborne Scanning Laser Altimetry Research Articles

Using LiDAR-DEM based rapid flood inundation modelling framework to map floodplain inundation extent and depth

Mapping floods is important for policy makers to make timely decisions in regards to emergency responses and future planning. It is therefore crucial to develop a rapid inundation modelling framework to map flood inundation. This study develops an airborne scanning laser altimetry (LiDAR) digital elevation model (DEM) based Rapid flood Inundation Modelling framework (LiDAR-RIM) for assessment of inundation extent, depth, volume and duration for flood events. The modelling framework has been applied to the mid-Murrumbidgee region in the southeast Murray-Darling Basin, Australia for two flood events occurred in December 2010 and March 2012. The inundation extents estimated using this methodology compared well to those obtained from two Landsat ETM+ images, demonstrating suitability and applicability of this method. For testing possibility of larger area application, the framework also uses 30-m resolution shuttle radar topography mission (SRTM)-DEM to replace LiDAR-DEM for the same modelling. The inundation extents obtained by using the SRTM-DEM are smaller than those obtained using the LiDAR-DEM, especially for large flood events. A possible reason is that the river cross sections obtained from the SRTM-DEM are not accurate enough for inundation modelling. The LiDAR-RIM has an advantage for process modelling and scenario modelling under future climatic conditions.

The effects of topographic depressions on multiscale overland flow connectivity: A high‐resolution spatiotemporal pattern analysis approach based on connectivity statistics

AbstractIn watershed modelling, the traditional practice of arbitrarily filling topographic depressions in digital elevation models has raised concerns. Advanced high‐resolution remote sensing techniques, including airborne scanning laser altimetry, can identify naturally occurring depressions that impact overland flow. In this study, we used an ensemble physical and statistical modelling approach, including a 2D hydraulic model and two‐point connectivity statistics, to quantify the effects of depressions on high‐resolution overland flow patterns across spatial scales and their temporal variations in single storm events. Computations for both models were implemented using graphic processing unit‐accelerated computing. The changes in connectivity statistics for overland flow patterns between airborne scanning laser altimetry‐derived digital elevation models with (original) and without (filled) depressions were used to represent the shifts of overland flow response to depressions. The results show that depressions can either decrease or increase (to a lesser degree and shorter duration) the probability that any two points (grid locations) are hydraulically connected by overland flow pathways. We used macro‐connectivity states (Φ) as a watershed‐specific indicator to describe the spatiotemporal thresholds of connectivity variability caused by depressions. Four states of Φ are identified in a studied watershed, and each state represents different magnitudes of connectivity and connectivity changes (caused by depressions). The magnitude of connectivity variability corresponds to the states of Φ, which depend on the topological relationship between depressions, the rising/recession limb, and the total rainfall amount in a storm event. In addition, spatial distributions of connectivity variability correlate with the density of depression locations and their physical structures, which cause changes in streamflow discharge magnitude. Therefore, this study suggests that depressions are “nontrivial” in watershed modelling, and their impacts on overland flow should not be neglected. Connectivity statistics at different spatial scales and time points within a watershed provide new insights for characterizing the distributed and accumulated effects of depressions on overland flow.

Using airborne scanning laser altimetry (LiDAR) to estimate surface connectivity of floodplain water bodies

ABSTRACTThis study tested the applicability of airborne scanning laser altimetry (LiDAR) for characterizing surface connectivity of floodplain water bodies by comparing it with in situ measurements of water levels, and examined whether LiDAR derived data accurately predicted the occurrence of globally imperilled unionid mussels. We intensively examined 10 isolated and 3 connected floodplain water bodies (IWBs and CWBs, respectively) located within a 15‐km lowland segment of the Kiso River in Japan. Using a digital elevation model (DEM) of floodplain ground surface, which is derived from LiDAR, and water surface DEM, which is obtained from records of water level fluctuations, the frequency of surface connectivity between IWBs and the main channel (inundation frequency) was calculated. Inundation frequency of IWBs was also measured in situ using water level and temperature probes. Also, the occurrence of mussels in CWBs and IWBs were examined. LiDAR derived data well predicted in situ derived one with a high level of accuracy (r2 > 0.77), validating the LiDAR‐based approach. Some errors existed in the predictive model, indicating that the applicability of LiDAR data is limited by its spatial resolution and snapshot nature. The occurrence of mussels was positively explained by an increasing level of inundation frequency, and the high accuracies of empirical models were validated using data for other 67 water bodies within the study segment. This study overall demonstrated a high potential of LiDAR data for efficiently monitoring hydrological and biological conditions of floodplain water bodies in the Kiso River and beyond. Copyright © 2010 John Wiley & Sons, Ltd.

Flood Detection in Urban Areas Using TerraSAR-X

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. An investigation of the ability of high-resolution TerraSAR-X synthetic aperture radar (SAR) data to detect flooded regions in urban areas is described. The study uses a TerraSAR-X image of a one-in-150-year flood near Tewkesbury, U.K., in 2007, for which contemporaneous aerial photography exists for validation. The German Aerospace Center (DLR) SAR end-to-end simulator (SETES) was used in conjunction with airborne scanning laser altimetry (LiDAR) data to estimate regions of the image in which water would not be visible due to shadow or layover caused by buildings and taller vegetation. A semiautomatic algorithm for the detection of floodwater in urban areas is described, together with its validation using aerial photographs. Of the urban water pixels that are visible to TerraSAR-X, 76% were correctly detected, with an associated false positive rate of 25%. If all the urban water pixels were considered, including those in shadow and layover regions, these figures fell to 58% and 19%, respectively. The algorithm is aimed at producing urban flood extents with which to calibrate and validate urban flood inundation models, and these findings indicate that TerraSAR-X is capable of providing useful data for this purpose.

Calibration of uncertain flood inundation models using remotely sensed water levels

Summary A traditional method of validating the performance of a flood model when remotely sensed data of the flood extent are available is to compare the predicted flood extent to that observed. The performance measure employed often uses areal pattern-matching to assess the degree to which the two extents overlap. Recently, remote sensing of flood extents using synthetic aperture radar (SAR) and airborne scanning laser altimetry (LiDAR) has made more straightforward the synoptic measurement of water surface elevations along flood boundaries (waterlines), and this has emphasised the possibility of using alternative performance measures based on height. This paper considers the advantages that can accrue from using a performance measure based on waterline elevations rather than one based on areal patterns of wet and dry pixels. The two measures were compared for their ability to estimate flood inundation uncertainty maps from a set of LISFLOOD-FP model runs carried out to span the acceptable model parameter range. A 1-in-5-year flood on the Thames in 1992, observed in an ERS-1 SAR image, was used as a test event. Waterlines were delineated in fused SAR and LiDAR data using an active contour model (snake). The performance measure based on height differences of corresponding points along the observed and modelled waterlines was found to be significantly more sensitive to the channel friction parameter than the measure based on areal patterns of flood extent. A result of this was that there was less uncertainty in the final flood hazard map. The height-based measure was found to be more sensitive when increased heighting accuracy was achieved by requiring that observed waterline heights varied slowly along the reach. The technique was shown to allow the decomposition of the reach into sections, with different effective channel friction parameters used in different sections. However, an evaluation of the modelled inundation uncertainty using the calibration event showed significant differences between the uncertainty map and the observed flood extent, especially for the height-based measure. This was probably due to the conceptually simple flood inundation model and the coarse application resolution employed in this case. The increased sensitivity of the height-based measure may lead to an increased onus being placed on the model developer in the production of a valid model.

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

AbstractFlood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, digital surface models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub‐metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. This paper describes the development of a LiDAR post‐processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post‐processing produces a digital terrain model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR‐derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ∼1 m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a two‐dimensional finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features (such as buildings and roads) and taller vegetation features (such as trees and hedges). This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features. Copyright © 2007 John Wiley & Sons, Ltd.

Extraction of tidal channel networks from airborne scanning laser altimetry

Tidal channel networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. This paper describes a semi-automatic technique developed to extract networks from high-resolution LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low-level algorithms first extract channel fragments based mainly on image properties then a high-level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism. The algorithm may be extended to extract networks from aerial photographs as well as LiDAR data. Its performance is illustrated using LiDAR data of two study sites, the River Ems, Germany and the Venice Lagoon. For the River Ems data, the error of omission for the automatic channel extractor is 26%, partly because numerous small channels are lost because they fall below the edge threshold, though these are less than 10 cm deep and unlikely to be hydraulically significant. The error of commission is lower, at 11%. For the Venice Lagoon data, the error of omission is 14%, but the error of commission is 42%, due partly to the difficulty of interpreting channels in these natural scenes. As a benchmark, previous work has shown that this type of algorithm specifically designed for extracting tidal networks from LiDAR data is able to achieve substantially improved results compared with those obtained using standard algorithms for drainage network extraction from Digital Terrain Models.

Measurement of canopy geometry characteristics using LiDAR laser altimetry: a feasibility study

Airborne scanning laser altimetry offers the potential for extracting high-resolution vegetation structure characteristics for monitoring and modeling the land surface. A unique dataset is used to study the sensitivity of laser interception profiles and laser-derived leaf area index (LAI) to assumptions about the surface structure and the measurement process. To simulate laser interception, one- and three-dimensional (3-D) vegetation structure models have been developed for maize and sunflower crops. Over sunflowers, a simple regression technique has been developed to extract laser-derived LAI, which accounts for measurement and model biases. Over maize, a 3-D structure/interception model that accounts for the effects of the laser inclination angle and detection threshold has enabled the fraction of radiation reaching the ground surface to be modelled to within 0.5% of the observed fraction. Good agreement was found between modelled and measured profiles of laser interception with a vertical resolution of 10 cm.

Using topographic lidar to map flood risk from storm-surge events for Charlottetown, Prince Edward Island, Canada

As part of a recent project to determine coastal impacts of climate change and sea-level rise on Prince Edward Island (PEI), airborne scanning laser altimetry (lidar) was employed to acquire high-resolution digital elevation models (DEMs) and other landscape information. The study area included both the Charlottetown urban area and an extensive portion of the rural North Shore of PEI. Problems with the lidar data included data gaps and incorrect classification of "ground" and "non-ground" laser hits along the waterfront. Accurate representation of wharves and other waterfront features in the DEM was achieved by combining "ground" and "non-ground" data. The importance of calibration and validation in lidar data acquisition and interpretation was demonstrated by three independent validation exercises that uncovered and adjusted for a vertical offset attributed to calibration problems. The ground DEM was adjusted to hydrographic chart datum and used to model flood extent at three storm-surge water levels, one observed in the record storm of 21 January 2000 and two higher levels representing flood scenarios under rising sea level. Flood modelling was executed in a geographic information system (GIS) on the gridded ground DEM. The resulting binary grids were vectorized along the flooding limit. Low-lying areas isolated from free exchange with the harbour were excluded from the flood area. Vectors depicting the storm-surge water lines for the three flood scenarios were implemented on the geographic information system (GIS) in the city planning department and overlain on property boundary and assessment layers. This study demonstrated that validated DEMs derived from airborne lidar data are efficient and adequate tools for mapping flood risk hazard zones in coastal communities.

Assessment of airborne scanning laser altimetry (lidar) in a deltaic wetland environment

The objective of the study was to evaluate the airborne scanning light detection and ranging (lidar) technology for hydrological applications in wetlands, deltas, or other similar areas. A comparison of lidar data with in situ survey data revealed a negative elevation bias of 0.21 m, which was corrected by block adjustment. The evaluation demonstrated that the lidar pulses had difficulties penetrating thick willow cover and dense thatch layers beneath the grasses and sedges (graminoids). After block adjustment, the lidar data achieved a root mean squared error (RMSE) of 0.15 and 0.26 m in graminoid and willow vegetation, respectively. The bare ground points produced an RMSE of 0.07 m. To be useful in hydrologic modelling, elevation data need to be interpolated into an even grid, or a digital elevation model (DEM). Four interpolation algorithms were evaluated for accuracy. The input elevations were best honoured when interpolated into a 0.25 m grid using a kriging algorithm and, thereafter, averaged to a 4 m resolution DEM. The RMSE of the DEM was 0.22 m. Despite some problems in dense vegetation, the lidar DEM provided useful topographic detail in a resolution and accuracy that is acceptable for many hydrologic purposes.

Floodplain friction parameterization in two‐dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry

AbstractTwo‐dimensional (2‐D) hydraulic models are currently at the forefront of research into river flood inundation prediction. Airborne scanning laser altimetry is an important new data source that can provide such models with spatially distributed floodplain topography together with vegetation heights for parameterization of model friction. The paper investigates how vegetation height data can be used to realize the currently unexploited potential of 2‐D flood models to specify a friction factor at each node of the finite element model mesh. The only vegetation attribute required in the estimation of floodplain node friction factors is vegetation height. Different sets of flow resistance equations are used to model channel sediment, short vegetation, and tall and intermediate vegetation. The scheme was tested in a modelling study of a flood event that occurred on the River Severn, UK, in October 1998. A synthetic aperture radar image acquired during the flood provided an observed flood extent against which to validate the predicted extent. The modelled flood extent using variable friction was found to agree with the observed extent almost everywhere within the model domain. The variable‐friction model has the considerable advantage that it makes unnecessary the unphysical fitting of floodplain and channel friction factors required in the traditional approach to model calibration. Copyright © 2003 John Wiley & Sons, Ltd.

Two‐dimensional hydraulic flood modelling using a finite‐element mesh decomposed according to vegetation and topographic features derived from airborne scanning laser altimetry

AbstractAirborne scanning laser altimetry (LiDAR) is an important new data source that can provide two‐dimensional river flood models with spatially distributed floodplain topography for model bathymetry, together with vegetation heights for parameterization of model friction. Methods are described for improving such models by decomposing the model's finite‐element mesh to reflect floodplain vegetation features such as hedges and trees having different frictional properties to their surroundings, and significant floodplain topographic features having high height curvatures. The decomposition is achieved using an image segmentation system that converts the LiDAR height image into separate images of surface topography and vegetation height at each point. The vegetation height map is used to estimate a friction factor at each mesh node. The spatially distributed friction model has the advantage that it is physically based, and removes the need for a model calibration exercise in which free parameters specifying friction in the channel and floodplain are adjusted to achieve best fit between modelled and observed flood extents. The scheme was tested in a modelling study of a flood that occurred on the River Severn, UK, in 1998. A satellite synthetic aperture radar image of flood extent was used to validate the model predictions. The simulated hydraulics using the decomposed mesh gave a better representation of the observed flood extent than the more simplistic but computationally efficient approach of sampling topography and vegetation friction factors on to larger floodplain elements in an undecomposed mesh, as well as the traditional approach using no LiDAR‐derived data but simply using a constant floodplain friction factor. Use of the decomposed mesh also allowed velocity variations to be predicted in the neighbourhood of vegetation features such as hedges. These variations could be of use in predicting localized erosion and deposition patterns that might result in the event of a flood. Copyright © 2003 John Wiley & Sons, Ltd.

Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Robust predictive models of the effects of habitat change on species abundance over large geographical areas are a fundamental gap in our understanding of population distributions, yet are urgently required by conservation practitioners. Predictive models based on underpinning relationships between environmental predictors and the individual organism are likely to require measurement of spatially fine-grained predictor variables. Further, models must show spatial generality if they are to be used to predict the consequences of habitat change over large geographical areas. Remote sensing techniques using airborne scanning laser altimetry (LiDAR) and high resolution multi-spectral imagery allow spatially fine-grained predictor variables to be measured over large geographical areas and thus facilitate testing of the spatial generality of organism-habitat models. These techniques are considered using the skylark as an example species. A range image segmentation system for LiDAR data is described which allows measurement of skylark habitat predictor variables such as within-field vegetation height, boundary height and shape for individual fields within the LiDAR image. Additional variables such as field vegetation type and fractional vegetation ground cover may be obtained from co-registered multi-spectral data. These techniques could have wide application in testing the generality of relationships between populations and habitats, and in ecological monitoring of change in habitat structures and the associated effects on wildlife, over large geographical areas.

Image processing of airborne scanning laser altimetry data for improved river flood modelling

Airborne scanning laser altimetry (LiDAR) is an important new data source for environmental applications, being able to map topographic height, and the height of surface objects, to high vertical and horizontal accuracy over large areas. This paper describes a range image segmentation system for data from a LiDAR measuring either time of last significant return, or measuring time of both first and last returns. We focus on the application of the segmenter to improving the data required by 2D hydraulic flood models, i.e. maps of topographic height which provide model bathymetry, and vegetation height, which could be converted to distributed floodplain friction coefficients. In addition, the location of river channels and a suitable height contour are used to define the extent of the model domain. An advantage of segmentation is that it allows different topographic and vegetation height extraction algorithms to be used in regions of different cover type. LiDAR data for a reach of the River Severn, UK, is presented. Short vegetation heights (grass and cereal crops) are predicted with a rms error of 14 cm. The topography underlying such cover differs from manually measured spot heights by 17 cm (rms error). The topographic accuracy decreases in the presence of a densely wooded slope. Errors in the vegetation height map, apparent at the overlap regions of adjacent swaths, are reduced by the removal of heights measured at large scan angles.

Application of airborne scanning laser altimetry to the study of tidal channel geomorphology

Tidal channels play a fundamental role in the hydrodynamic and morphological processes operating within a tidal basin. Conventional means of mapping intertidal zones are either too cumbersome or do not provide sufficient information for accurate quantitative measurements of the channels. This paper describes the use of airborne scanning altimetry (light detection and ranging—LiDAR) for studying tidal channel geomorphology. The main emphasis is on developing a technique for extracting tidal channels from LiDAR data. Some common existing techniques of fluvial channel extraction are evaluated with LiDAR data of tidal basins and found moderate in performance. A semiautomatic approach is proposed which has a performance better than that of existing techniques. This approach is realised using an adaptive height thresholding technique to locate channel fragments. A channel joining mechanism then connects the channel fragments using a weighted distance transform. The paper also describes briefly an automatic procedure to carry out measurements on the basin image and derive geomorphological parameters such as drainage density. The results obtained illustrate differences in the geomorphology of tidal and terrestrial basins.

Beach topography mapping—a comparsion of techniques

A comparison of current techniques for measuring elevations in the beach and near-shore zones is presented. Techniques considered include traditional methods such as ground survey along transects and airborne stereophotogrammetry, and also newer methods based on remote sensing such as airborne scanning laser altimetry (LiDAR). The approach taken was to identify a representative group of users of beach elevation data, elicit their requirements regarding these data, then assess how well the different methods met these requirements on both technical and financial grounds.

An airborne scanning laser altimetry survey of Long Valley, California

Between 28 September and 7 October 1995, we conducted an airborne laser altimetry experiment over the Long Valley caldera, California, in which each of two scanning laser altimeters (dubbed SLICER and RASCAL) were flown in a NASA T-39 jet aircraft. Operating concurrently were a Global Positioning System (GPS) guidance system and dual frequency receivers for precise navigation and post-flight calculation of the airplane trajectory relative to a ground station, and an inertial navigation system (INS) for attitude determination. Reduction of raw laser ranges requires merging the differential kinematic GPS aircraft trajectory and the INS data with the laser data, and determination of the atmospheric delay. Data geolocation consists of obtaining the centre location and the mean elevation within each footprint in a geodetic coordinate system. The elevation of Crowley Lake is recovered to an accuracy of ∼3 cm or better from 3 km above ground level and crossover analysis indicates that the elevation estimates are consistent from pass to pass. We test our geolocation procedures by comparing laser-derived elevations with those determined in situ for recognizable ground features. A comparison of laser and GPS-derived positions shows that the horizontal accuracy is better than the diameter of the footprint and vertical accuracy is within the error inherent in the range measurement. A comparison of SLICER elevation data with digital elevation models (DEMs) of the region shows that the DEM data provides surface topography to within stated accuracy limits. Although research continues to utilize the full potential of laser altimetry data, our results constitute a successful demonstration that the technique may be used to perform geodetic monitoring of surface topographic changes.

Airborne measurement of glacier surface elevation by scanning laser altimeter

Airborne scanning laser altimetry is a relatively new technique for remote sensing of ground elevation. A laser ranger is scanned across a swath beneath the aircraft, producing a two-dimensional distribution of elevations when combined with data on aircraft position and orientation. Smooth snow-covered glaciers are ideal surfaces for laser scanning since they are highly reflective. A new prototype laser system is described together with results from Hardangerjökulen, Norway. An analysis of the data shows that noise levels are very low at around 2 cm, and that repeatability between overlapping swaths is approximately ± 10 cm. This is consistent with an absolute accuracy of 15 cm or better from manufacturer’s and other measurements. Swath widths of over 1 km are attainable, at lowing complete coverage of small to medium-sized glaciers using parallel flight tracks. The high accuracy and dense, even coverage (about 20 000 points per km2) gives good-quality derived products such as DEMs and enables reliable measurement of glacier volume change. Scanning laser altimetry has many advantages over photogrammetry, which was previously the only remote-sensing method of measuring elevations over large areas but which performs poorly over snow-covered glaciers.

Airborne measurement of glacier surface elevation by scanning laser altimeter

Airborne scanning laser altimetry is a relatively new technique for remote sensing of ground elevation. A laser ranger is scanned across a swath beneath the aircraft, producing a two-dimensional distribution of elevations when combined with data on aircraft position and orientation. Smooth snow-covered glaciers are ideal surfaces for laser scanning since they are highly reflective. A new prototype laser system is described together with results from Hardangerjökulen, Norway. An analysis of the data shows that noise levels are very low at around 2 cm, and that repeatability between overlapping swaths is approximately ± 10 cm. This is consistent with an absolute accuracy of 15 cm or better from manufacturer’s and other measurements. Swath widths of over 1 km are attainable, at lowing complete coverage of small to medium-sized glaciers using parallel flight tracks. The high accuracy and dense, even coverage (about 20 000 points per km2) gives good-quality derived products such as DEMs and enables reliable measurement of glacier volume change. Scanning laser altimetry has many advantages over photogrammetry, which was previously the only remote-sensing method of measuring elevations over large areas but which performs poorly over snow-covered glaciers.