Lidar Elevation Research Articles

Ecohydrological and socioeconomic integration for the operational management of environmental flows

Investment in and operation of flow control infrastructure such as dams, weirs, and regulators can help increase both the health of regulated river ecosystems and the social values derived from them. This requires high-quality and high-resolution spatiotemporal ecohydrological and socioeconomic information. We developed such an information base for integrated environmental flow management in the River Murray in South Australia (SA). A hydrological model was used to identify spatiotemporal inundation dynamics. River ecosystems were classified and mapped as ecohydrological units. Ecological response models were developed to link three aspects of environmental flows (flood duration, timing, and inter-flood period) to the health responses of 16 ecological components at various life stages. Potential infrastructure investments (flow control regulators and irrigation pump relocation) were located by interpreting LiDAR elevation data, digital orthophotography, and wetland mapping information; and infrastructure costs were quantified using engineering cost models. Social values were quantified at a coarse scale as total economic value based on a national survey of willingness-to-pay for four key ecological assets; and at a local scale using mapped ecosystem service values. This information was integrated using a constrained, nonlinear, mixed-integer, compromise programming optimization model and solved using a stochastic Tabu search algorithm. We tested the model uncertainty and sensitivity using 390 Monte Carlo model runs at varying weights of ecological health vs. social values. Integrating ecohydrological and socioeconomic information identified environmental flow management regimes that efficiently achieved both ecological and social objectives. Using an ecologically weighted efficient and socially weighted efficient scenario, we illustrated model outputs including a suite of cost-effective infrastructure investments and an operational plan for new and existing flow control structures including dam releases, weir height manipulation, and regulator operation on a monthly time step. Both the investments and management regimes differed substantially between the two scenarios, suggesting that the choice of weightings on ecological and social objectives is important. This demonstrates the benefit of integrating high-quality and high-resolution spatiotemporal ecohydrological and socioeconomic information for guiding the investment in and operational management of environmental flows.

Groundwater responses to controlled water releases in the limitrophe region of the Colorado River: Implications for management and restoration

Flow regulation and water diversion for irrigation have considerably affected the exchange of surface water between the Colorado River and its floodplains. However, the way in which both have impacted groundwater–surface water interactions is not completely understood. The objective of this study was to conduct a hydrologic analysis of the 2009–2010 winter flows released into the limitrophe region of the Colorado River in order to characterize the surface flows along this dry reach and, for the first time, study the impact of winter flows on the groundwater conditions in the area. The study used existing data on groundwater levels that was collected from regional piezometers on both sides of the limitrophe every five years from 1980 to 2005. Regional flow direction from NE to SW was observed in all years. A groundwater depression cone in the southwest part of the limitrophe was identified from 1980 to 1995. A general rise of groundwater levels was observed from 2000 to 2005 on both sides of the limitrophe, but during the same time period, a depression cone formed along the border between Arizona and Sonora, in the Mesa Arenosa on the Mexican side of the border. In order to identify the water table evolution within the limitrophe riparian zone, nine sets of piezometers were constructed in an arrangement perpendicular to the main river channel. Water table levels were measured automatically every 30min and were also manually measured periodically. Nine geohydrological cross sections were constructed using the topographic relief from LIDAR elevation points and depth to groundwater measurements during water discharges in the Colorado River channel at Morelos dam. Groundwater seepage from irrigation canals, irrigation returns, and river discharge flows were identified and the depth to groundwater and its influence on riparian vegetation was analyzed. A strong correlation between flow discharge (up to 60.49hm3 from November 2009 to April 2010) and groundwater elevation (average elevation change of 1.62m on January 22, 2010) in time and space was found. The percentage of water retained in the main river channel decreased from 100% in the first discharge event (December 12–13, 2009) to 36% after the last discharge event (April 9–17, 2010), due to remaining moisture in the unsaturated soil. The total volume of water retained, infiltrated, and evaporated, was 60% (36.6×106m3) of the total water discharged. The delay time of the groundwater front during a discharge event was on average 6:30, 20:06, and 28:53h from section 1 to section 2, 1–3, and 1–4 respectively. This historical study provides insight into how floods affect the groundwater system, which is the foundation for aquatic and riparian biodiversity. This issue is of increasing relevance given growing international interest in rehabilitating the riparian and aquatic ecosystems of the Colorado River delta through intentional flood releases.

LiDAR-Derived DEM and Raw Height Comparisons along Profile Corridor Gradients within a Forest

We compared field based and airborne LiDAR-derived profile corridor measurements across forest canopy types and terrain ranging from 37% to 49% slope. Both LiDAR-derived DEM and raw LiDAR point elevations were compared to field data. Primary objectives included examining whether canopy type or terrain slope influenced LiDAR-derived profile measurements. A secondary objective included comparing cable logging payloads based on field measured profile elevations to payloads based on LiDAR-derived elevations. Average RMSE elevation errors were slightly lower for profile point to LiDAR DEM values (0.43 m) than profile point to nearest LiDAR elevation point (0.49 m) with differences being larger when sites within forest clearings were removed from analysis. No statistically significant relationship existed between field measured ground slopes and associated profile point and LiDAR DEM elevation differences but a mild correlation existed when LiDAR raw point elevation differences were compared. Our payload analysis determined the limiting payload distance and had consistent results across study sites. The DEM-based profile outperformed the nearest point profile by 5% on average. Results suggest that forest analysts should consider using the nearest LiDAR DEM value rather than the nearest LiDAR point elevation for terrain heights at discrete locations, particularly when forest canopy occludes locations of interest.

Combining airborne lidar and Landsat ETM+ data with photoclinometry to produce a digital elevation model for Langjökull, Iceland

An incomplete airborne lidar survey of Langjökull, Iceland's second largest ice cap (˜900 km2) and the surrounding area was undertaken in August 2007. Elevation data were interpolated between the lidar swaths using the technique of photoclinometry (PC), which relates Sun-parallel slope angles to image brightness. A Landsat Enhanced Thematic Mapper Plus (ETM+) image from March 2002 was used for this purpose. Different bands and band combinations were assessed and Band 4 (760–900 nm) was found to be the most appropriate. Parameters in the slope–brightness equation were derived empirically by comparing the image brightness with lidar elevation data in a 4 km × 4 km region in the centre of the ice cap. This relationship was then used to calculate the slopes, and, by integration between tie points of known lidar elevation, the elevations of the 30 m pixels that were not surveyed by lidar. The root-mean-square (RMS) precision (repeatability) of lidar elevations was 0.18 m and the accuracy was estimated to be 0.25 m. The 68.3% quantile of absolute difference relative to lidar (analogous to root-mean-square error (RMSE)) of all interpolated areas where PC assumptions are met was 5.44 m (4.66 m and 8.73 m for on- and off-ice areas, respectively). Where one or more PC assumptions were not met (e.g. self-shading, sensor saturation), the 68.3% quantile of absolute difference relative to lidar was 27.89 m (18.52 m on the ice cap and 32.91 m off-ice). These accuracies were applicable to 63%, 31%, and 6% of the ice cap and 59%, 28%, and 13% of the final digital elevation model (DEM), respectively. The area-weighted average 68.3% quantiles were 2.89 m for the ice cap and 6.75 m for the entire DEM. The PC technique applied to satellite imagery is a useful and appropriate method for interpolating a lidar survey of an ice cap.

DTM generation with Radarsat-2 Data without GCP

Abstract. Digital terain models (DTMs) were extracted from high-resolution Radarsat-2 stereo high-resolution images. The metadata supplied by MacDonald, Dettwiler and Associates Ltd. were used to replace the ground control data used to compute for the deterministic geometric model. The DTMs are then evaluated with 0.2-m accurate lidar elevation data. Because DTMs included the height of land covers, elevation linear errors with 68 and 90 percent confidence level (LE68 and LE90) were both computed over the full study site and the bare surfaces. Comparisons were also performed with the same rigorous model using accurate differential GPS as ground control points (GCPs). Using the radargrammetric model using eight dGPS GCPs achieved the best results (3.3 m LE68) than using only the metadata (3.9 m LE68). However, this accuracy is compensated by the fact that the user does not have to collect any ground data, which offers a strong advantage in remote and harsh environments.

Remote detection of small wetlands in the Atlantic coastal plain of North America: Local relief models, ground validation, and high-throughput computing

Isolated wetlands are ecologically important freshwater ecosystems that occur frequently throughout the Atlantic coastal plain ecoregions of North America. Known to support 86 species recognized by the US Fish and Wildlife Service as threatened or endangered, isolated wetlands are a conservation priority in the United States and elsewhere. They are often obscure and methods to detect them at the spatial scales necessary for systematic conservation planning and forest management have been time consuming, cost ineffective, or too coarse-filter. To fill existing information gaps and develop a repeatable, high-resolution methodology, we subjected LiDAR elevation data to custom relief models designed to elucidate fine-scale geomorphology, specifically small, localized changes in concavity, as a location predictor. Because fine grain size and large spatial extent can impose processing limits in landscape-level analysis, we executed our workflow in a high-throughput computing (HTC) environment, which achieved a 91× time-savings over our 55,000ha study area. We conducted field validation at 114 randomly selected sites to measure model commission (14.9%), approximate omission (5.3%) error rates and estimate wetland boundaries. Depressional wetlands predicted in this study (n=4610) were mostly small (x¯=0.37±0.69ha) and previously unmapped sites. The mapping accuracy of this effort (85.1%) suggests that local relief models captured slight geomorphologic changes that successfully predict wetland boundaries in low-relief ecosystems. Many small wetlands are centers of biodiversity in forested landscapes and such analyses will provide information and improved methods for landscape-scale management and conservation.

3D PHOTOGRAMMETRIC PROCESSING OF WORLDVIEW-2 DATA WITHOUT GCP

Abstract. A hybrid model based on 3D deterministic Toutin's model developed at the Canada Centre for Remote Sensing and taking full advantages of the image metadata was used for the 3D photogrammetric processing of WorldView-1 stereo images without in situ ground control points (GCP) collection. The accuracy of the modelling results in monoscopy and stereoscopy was around 0.7-1.8 m in planimetry and 2.7 m in altimetry computed over 40 dGPs checked points. Elevations were thus extracted and compared to 0.2 m accurate lidar elevation data. Elevations linear error with 68 percent confidence level (LE68) computed over bare surfaces was 2.6 m, with small biases. These results were compared with the solution using 8 dGPS GCPs and demonstrated there is no significant difference in auccuracy. Consequently, the new hybrid model will offer a strong advantage of no-control collection in operational conditions, mainly in remote and harsh environments or when cartographic or ground control data do not exist.

Impact of no GCP on elevation extraction from WorldView stereo data

A hybrid model based on 3D deterministic Toutin’s model developed at the Canada Centre for Remote Sensing and taking full advantages of the image metadata was used to geometrically process WorldView-1 and -2 stereo images without in situ ground control points (GCPs) collection. Elevations were thus extracted and compared to 0.2-m accurate lidar elevation data to see the impact of no-GCP. Elevations linear errors with 68% confidence level (LE68) computed over bare surfaces were 2.6m and 2.1m for WorldView-1 and -2, respectively with small biases. The not-significant difference in LE68 (around 10–20cm), when compared to the solution using accurate GCPs, will offer strong advantages of no-control collection in operational conditions, mainly in remote and harsh environments or when cartographic or ground control data do not exist.

ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

Radarsat-2 DSM Generation With New Hybrid, Deterministic, and Empirical Geometric Modeling Without GCP

Digital surface models (DSMs) extracted from high-resolution Radarsat-2 stereo-images using different geometric modeling (deterministic, new hybrid, and empirical) are evaluated. The 3-D deterministic models are Toutin's and hybrid Toutin's models (TM and HTM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). TM is computed with one and eight ground control points (GCPs), HTM without GCP and RFM supplied by MacDonald, Dettwiler and Associates Ltd. is postprocessed with 3-9 GCPs depending of degrees of 2-D polynomial functions. The DSMs are then generated and compared to 0.2-m accurate lidar elevation data. Because DSMs included the height of land covers, elevation linear errors with 68% and 90% confidence level (LE68 and LE90) are computed and compared over bare surfaces only. LE90 results are: TM with eight GCPs achieves the best results (6.3 m), then HTM with no GCP (7 m), TM with one GCP (8.6 m), and finally RFM the worst (9.7 m) whatever the polynomial degree and GCP number. HTM is the only modeling not using any GCP, which offers a strong advantage in operational environments.

Comparing Habitat Models Using Ground-Based and Remote Sensing Data: Saltmarsh Sparrow Presence Versus Nesting

Remote sensing data can represent various habitat characteristics, and thus can substitute for detailed ground sampling when constructing habitat models. To predict saltmarsh sparrow (Ammodramus caudacutus) distribution and nesting activity, we compared Bayesian hierarchical models in which variables were generated from field or remote sensing data, at a scale of 1-ha plots and at the landscape scale. Field data consisted of plant structure and plant composition variables. Data derived from remote sensing included high and low marsh classifications, LiDAR elevation data, and a classification derived from spectral characteristics specifically associated with saltmarsh sparrow habitat use. The best sparrow presence model used a variable derived from spectral reflectance values associated with plots where sparrows did not occur, indicating that the remote sensing data included additional information about conditions associated with saltmarsh sparrow occurrence than was provided by plant composition, structure, or community classes. In contrast, nest presence was modeled best using vegetation structure variables that required data collection on the ground, although the best remote-sensing model was almost as good. These results reinforce the value of remote-sensing data in habitat modeling, and highlight the need to distinguish between sites that contribute to reproduction and sites where a species is merely present.

Development of the Holocene foredune plain in the Narva-Joesuu area, eastern Gulf of Finland.

Morphogenesis and inner structure of the Holocene foredune plain in the Narva -Joesuu area, eastern Gulf of Finland were studied using the optically stimulated luminescence dating, ground penetrating radar study and interpretation of airborne LIDAR elevation data . The results show that the Narva-Joesuu foredune plain consists of c. 100 parallel coastal foredune ridges built of well - sorted fine sand underlain by gently (~7o) sea ward-dipping sandy beach deposits. The distal part of the plain which consists of at least 15 ridges, was formed during the regressive phase of the Ancylus Lake/early Litorina Sea , serving as a barrier for the lagoon behind it. A larger portion of ridges, with an average progradation rate 0.26 m a –1 , was formed in the conditions of falling relative sea level during the Litorina regression and has been separated from the older foredune succession by a hiatus related to the Litorina transgression at 8.5-7.3 cal. ka BP. In the highest central part of the plain the foredune growth was interrupted by foredune instability and a re-blowing episode dated to 5.4±0.9 ka BP which may correlate with a larger regional cooling at 5.8-5.1 cal. ka BP in the North Atlantic and central Europe . During the last 3000 years, the foredune progradation rate decreased to 0.17 m a –1 , most probably because of decelerated land-uplift and increased human impact due to coastal protection.

A Generic Toolkit for the Visualization of Archaeological Features on Airborne LiDAR Elevation Data

ABSTRACTA range of techniques have become established for the visualization and analysis of airborne LiDAR elevation data within the field of archaeology. In this paper we discuss the visualization of test data representing archaeological features in a variety of terrains using a suite of techniques, all available through generic geographical information system or image processing software. These comprise elevation shading using constrained colour ramps, slope analysis, hill‐shading, principal component analysis of multi‐azimuth hill‐shading, local relief models and solar insolation modelling. The strengths and weaknesses of each technique are discussed and a generic toolkit, suited to the visualization of airborne LiDAR data for archaeological purposes, is presented. Copyright © 2011 John Wiley & Sons, Ltd.

A comparison of GPS and lidar salt marsh DEMs

ABSTRACTDigital elevation models (DEMs) were compared to characterize how well airborne lidar (light detection and ranging) data depict the microtopography of a salt marsh. 72,000 GPS points and 700,000 lidar points from a 1 km2 salt marsh island were linearly interpolated using identical DEM configurations. Overall, 78% of lidar elevations were within ±0.15 m of the high precision GPS elevations. Spatial arrangement of difference values reveal that lidar performed best on the marsh platform, and poorly along tidal creeks and creek heads. Also, the overall shape of the salt marsh was poorly defined, even where lidar data were within the reported range of accuracy. These observations indicate that lidar appears to be a robust tool for mapping intertidal landscapes. However, lidar DEMs may not adequately resolve the microtopographic variations of a salt marsh, and for research questions that require accurate depiction of small‐scale tidal creek networks and subtle terrain features lidar data should be augmented with other information. Copyright © 2011 John Wiley & Sons, Ltd.

Managing for change: wetland transitions under sea-level rise and outcomes for threatened species

Aim Global sea-level rise (SLR) could be as much as 1.8metres by 2100, which will impact coastal wetland communities and threatened species. We evaluated the likely outcomes of SLR for wetland communities using a process-based simulation model and coupled this with a metapopulation model for a threatened native rodent (Xeromys myoides). Furthermore, we tested the amplified impacts of SLR, urban growth and introduced predators on X. myoides persistence. Location South-east Queensland, Australia. Methods We adapted the Sea Level Affects Marshes Model to subtropical Australia. We used LiDAR elevation data, field data to parameterize surface accretion and shallow subsidence, and local knowledge to configure wetland transitions. SLR was simulated based on the IPCC B1 and A1FI scenarios, as well as the maximal limit of 1.8m by 2100. Further, we coupled our demographic model to projected shifts in wetland habitat, and estimates of future wetland loss to urban expansion and feral cat (Felis catus) predation. Results Our models project a general decline in wetland communities under SLR, with a noted exception of mangroves. Under the A1FI scenario, SLR allows mangroves to migrate inland, with urban development acting as an obstruction in some areas. Mangrove expansion provides an unexpected benefit for dependent X. myoides populations, although the inclusion of predation and habitat loss due to urban development still suggests extirpation in c. 50years. Main conclusions Through this case study, we illustrate the usefulness of process-based SLR models in understanding outcomes for wetland communities and dependent species. Our models will underscore decision-making in a dynamic system, with global applications for urban planning, conservation prioritization and wildlife management. © 2011 Blackwell Publishing Ltd.

Characterizing the error distribution of lidar elevation data for North Carolina

Spatial data quality is a paramount concern in all geographical information systems (GIS) applications. Existing standards and guidelines for spatial data commonly assume the positional error is normally distributed. While non-normal behaviour of the error in digital elevation data has been observed in previous research, current guidelines for digital elevation data still assume that the errors for observations in open terrain are normally distributed. This research employed an accuracy assessment dataset from a substantial lidar data collection effort, the North Carolina Floodplain Mapping Program. Strong evidence was found that the vertical error of lidar elevation data is not normally distributed and that both major and minor outliers are very common. Of the five land cover types considered, only the distribution for urban areas approximated a normal distribution, even though these observations were generally much less accurate than those for open terrain. No influence of slope on the occurrence of non-normal behaviour in the distributions was found. The RMSEz (root mean square error) statistic used to characterize the fundamental accuracy of digital elevation data was found to be very sensitive to the occurrence of outliers, questioning its use in current guidelines.

Algorithmic Foundation and Software Tools for Extracting Shoreline Features from Remote Sensing Imagery and LiDAR Data

This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.

Accuracy Assessment of Lidar Elevation Data Using Survey Marks

Airborne LiDAR has become the preferred technology for digital elevation data acquisition in a wide range of applications. The vertical accuracy with respect to a specified vertical datum is the principal criterion in specifying the quality of LiDAR elevation data. The quantitative assessment of LiDAR elevation data is usually conducted by comparing high-accuracy checkpoints with elevations estimated from the LiDAR ground data. However, the collection of a sufficient number of checkpoints by field surveying is a time-consuming task. This study used survey marks to assess the vertical accuracy of LiDAR data for different land covers in a rural area and explored the performance of different methods for deriving elevations from LiDAR data corresponding to the locations of checkpoints. Normality tests using both frequency histograms and quantile-quantile plots were performed for vertical differences between the LiDAR data and the checkpoints, so the appropriate methods were used for the vertical accuracy assessment of LiDAR data for different land cover categories. The results demonstrated the suitability of using survey marks as checkpoints for the assessment of the vertical accuracy of LiDAR data.

Impact of Radarsat-2 SAR Ultrafine-Mode Parameters on Stereo-Radargrammetric DEMs

The new high-resolution mode of Radarsat-2 was evaluated for digital elevation model (DEM) generation using stereo-radargrammetry with Toutin's 3-D physical model as part of the Canadian Space Agency's programs. In addition, the impact of radar parameters on stereo-extracted DEMs was evaluated. Three stereo pairs using different radar parameters (resolutions; HH and VV polarizations; slant and ground range; image spacing) from ultrafine-mode Radarsat-2 data acquired over a test site north of Québec City, Canada, were formed to generate DEMs, which were thus compared to 15-cm-accurate lidar elevation data. Results showed a good accuracy for the three stereo-extracted DEMs: less than 0.8 m for the horizontal positioning and around 3 m (1) for the elevation on bare soils, with small 3-D biases (2-3 m). The HH stereo pair in slant-range format slightly but consistently achieved the best results.

Relationships of aeolian erosion and deposition with LiDAR-derived landscape surface roughness following wildfire

The reduction of vegetation by wildfire can subject stable soil surfaces to increased aeolian transport. Vegetation and associated microtopography function as surface roughness elements that influence the entrainment, transport, and deposition of sediment by wind in burned and unburned semiarid shrublands. We examined whether surface roughness derived from LiDAR can explain variability in aeolian surface change following wildfire in loess soils of a cold desert shrub steppe in SE Idaho, USA. Erosion bridges were installed in Fall 2007, following a late summer wildfire to monitor soil surface change at sites in burned and downwind unburned areas. Surface elevation measurements were made when the erosion bridges were installed and again in Fall 2008. Surface change was determined from the difference in relative elevation between the two dates. Airborne LiDAR data were acquired in Fall 2007 following erosion bridge installation. Surface roughness was calculated at 2-m raster resolution using the standard deviation of all LiDAR elevations within the 2-m cells, after elevations were detrended to remove the effects of topographic slope. Surface change varied as a function of surface roughness among burned and unburned surfaces, with net erosion occurring on the relatively smooth, burned surfaces and net deposition occurring on the rough, unburned surfaces. Site mean surface change decreased linearly as a function of the inverse of site mean surface roughness ( r 2 = 0.77, p < 0.00). Quantile regression analysis indicated that changes in surface roughness were related to proportionally greater changes in erosion compared to deposition. Analysis of surface change at finer spatial scales suggested that aeolian processes occurred with strong spatial patterns on burned, but not unburned surfaces. Future research to examine relationships between aeolian transport and fine spatial resolution topographic variability, for example from ground-based LiDAR systems, is recommended.