Derivation of sky-view factors from lidar data

The use of lidar (light detection and ranging), an active light-emitting instrument, is becoming increasingly common for a range of potential applications. Its ability to provide fine-resolution spatial and vertical-resolution elevation data makes it ideal for a wide range of studies. This article demonstrates the ability of lidar data to measure sky-view factors (ψ_s). The lidar data are used to generate a spatial map of ψ_s, which are then compared against photographically derived ψ_s at selected locations. At each location, three near-surface elevation measurements were taken and compared with collocated lidar-derived estimates. Generally a good agreement was found between the two methodologies, although with decreasing ψ_s, the lidar technique tended to overestimate ψ_s. This can be attributed in part to the spatial resolution of the lidar sampling. Nevertheless, airborne lidar systems can easily map ψ_s over a large area, potentially improving the use of such data in atmospheric and meteorological models.     

Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level

The use of lidar remote sensing for mapping the spatial distribution of canopy characteristics has the potential to allow an accurate and efficient estimation of tree dimensions and canopy structural properties from local to regional and continental scales. The overall goal of this paper was to compare biomass estimates and height metrics obtained by processing GLAS waveform data and spatially coincident discrete-return airborne lidar data over forest conditions in east Texas. Since biomass estimates are derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. More specific objectives were to compare the following parameters derived from GLAS and airborne lidar: (1) ground elevations; (2) maximum canopy height; (3) average canopy height; (4) percentiles of canopy height; and (5) above ground biomass. We used the elliptical shape of GLAS footprints to extract canopy height metrics and biomass estimates derived from airborne lidar. Results indicated a very strong correlation for terrain elevations between GLAS and airborne lidar, with an r value of 0.98 and a root mean square error of 0.78 m. GLAS height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models comparing GLAS and airborne lidar height metrics had R-square values above 0.9.     

Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.     

Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: implications for canopy height retrieval from future ICESat-2 data

We develop and validate an automated approach to determine canopy height, an important metric for global biomass assessments, from micro-pulse photon-counting lidar data collected over forested ecosystems. Such a lidar system is planned to be launched aboard the National Aeronautics and Space Administration’s follow-on Ice, Cloud and land Elevation Satellite mission (ICESat-2) in 2017. For algorithm development purposes in preparation for the mission, the ICESat-2 project team produced simulated ICESat-2 data sets from airborne observations of a commercial micro-pulse lidar instrument (developed by Sigma Space Corporation) over two forests in the eastern USA. The technique derived in this article is based on a multi-step mathematical and statistical signal extraction process which is applied to the simulated ICESat-2 data set. First, ground and canopy surfaces are approximately extracted using the statistical information derived from the histogram of elevations for accumulated photons in 100 footprints. Second, a signal probability metric is generated to help identify the location of ground, canopy-top, and volume-scattered photons. According to the signal probability metric, the ground surface is recovered by locating the lowermost high-photon density clusters in each simulated ICESat-2 footprint. Thereafter, canopy surface is retrieved by finding the elevation at which the 95th percentile of the above-ground photons exists. The remaining noise is reduced by cubic spline interpolation in an iterative manner. We validate the results of the analysis against the full-resolution airborne photon-counting lidar data, digital terrain models (DTMs), and canopy height models (CHMs) for the study areas. With ground surface residuals ranging from 0.2 to 0.5 m and canopy height residuals ranging from 1.6 to 2.2 m, our results indicate that the algorithm performs very well over forested ecosystems of canopy closure of as much as 80%. Given the method’s success in the challenging case of canopy height determination, it is readily applicable to retrieval of land ice and sea ice surfaces from micro-pulse lidar altimeter data. These results will advance data processing and analysis methods to help maximize the ability of the ICESat-2 mission to meet its science objectives.     

Lidar-based mapping of flood control levees in South Louisiana

Flood protection in south Louisiana is largely dependent on earthen levees, and in the aftermath of Hurricane Katrina the state’s levee system has received intense scrutiny. Accurate elevation data along the levees are critical to local levee district managers responsible for monitoring and maintaining the extensive system of non-federal levees in coastal Louisiana. In 2012, high resolution airborne lidar data were acquired over levees in Lafourche Parish, Louisiana, and a mobile terrestrial lidar survey was conducted for selected levee segments using a terrestrial lidar scanner mounted on a truck. The mobile terrestrial lidar data were collected to test the feasibility of using this relatively new technology to map flood control levees and to compare the accuracy of the terrestrial and airborne lidar. Metrics assessing levee geometry derived from the two lidar surveys are also presented as an efficient, comprehensive method to quantify levee height and stability. The vertical root mean square error values of the terrestrial lidar and airborne lidar digital-derived digital terrain models were 0.038 m and 0.055 m, respectively. The comparison of levee metrics derived from the airborne and terrestrial lidar-based digital terrain models showed that both types of lidar yielded similar results, indicating that either or both surveying techniques could be used to monitor geomorphic change over time. Because airborne lidar is costly, many parts of the USA and other countries have never been mapped with airborne lidar, and repeat surveys are often not available for change detection studies. Terrestrial lidar provides a practical option for conducting repeat surveys of levees and other terrain features that cover a relatively small area, such as eroding cliffs or stream banks, and dunes.     

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.     

Landscape modelling using integrated airborne multi-spectral and laser scanning data

By integrating multi-spectral and elevation data from airborne sensors (CASI and ALTM) and adopting a parcel-based approach, a progression is achieved from land-cover classification to landscape modelling. This work involved data integration, per-parcel classification, knowledge-based correction and the derivation of landscape objects. For a 1 km 2 study area, a 14 land-cover class vector dataset was generated in which the parcels relate to landscape objects and contain information on their structure and 'terrain' context. At a 1 m spatial resolution, the correspondence between land-cover mapped using the airborne sensor data and identified by Countryside Survey 2000 field surveyors was 88%.     

Small‐footprint,waveform‐resolving lidar estimation of submerged and sub‐canopy topography in coastal environments

The experimental advanced airborne research lidar (EAARL) is an airborne lidar instrument designed to map near‐shore submerged topography and adjacent land elevations simultaneously. This study evaluated data acquired by the EAARL system in February 2003 and March 2004 along the margins of Tampa Bay, Florida, USA, to map bare‐earth elevations under a variety of vegetation types and submerged topography in shallow, turbid water conditions. A spatial filtering algorithm, known as the iterative random consensus filter (IRCF), was used to extract ground elevations from a point cloud of processed last‐surface EAARL returns. Filtered data were compared with acoustic and field measurements acquired in shallow submerged (0–2.5 m water depth) and sub‐canopy environments. Root mean square elevation errors (RMSEs) ranged from 10–14 cm for submerged topography to 16–20 cm for sub‐canopy topography under a variety of vegetation communities. The effect of lidar sampling angles and global positioning system (GPS) satellite configuration on accuracy was investigated. Results show high RMSEs for data acquired during periods of poor satellite configuration and at large sampling angles along the edges of the lidar scan. The results presented in this study confirm the cross‐environment capability of a green‐wavelength, waveform‐resolving lidar system, making it an ideal tool for mapping coastal environments.     

The infrared spectral signature of volcanic ash determined from high-spectral resolution satellite measurements

High-spectral resolution infrared spectra of the earth's atmosphere and surface are routinely available from satellite sensors, such as the Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI). We exploit the spectral content of AIRS data to demonstrate that airborne volcanic ash has a unique signature in the infrared (8-12 μm) that can be used to infer particle size, infrared opacity and composition. The spectral signature is interpreted with the aid of a radiative transfer model utilizing the optical properties of andesite, rhyolite and quartz. Based on the infrared spectral signature, a new volcanic ash detection algorithm is proposed that can discriminate volcanic ash from other airborne substances and we show that the algorithm depends on particle size, optical depth and composition. The new algorithm has an improved sensitivity to optically thin ash clouds, and hence can detect them for longer (~ 4 days) and at greater distances from the source(~ 5000 km).     

An airborne lidar sampling strategy to model forest canopy height from Quickbird imagery and GEOBIA

High-resolution digital canopy models derived from airborne lidar data have the ability to provide detailed information on the vertical structure of forests. However, compared to satellite data of similar spatial resolution and extent, the small footprint airborne lidar data required to produce such models remain expensive. In an effort to reduce these costs, the primary objective of this paper is to develop an airborne lidar sampling strategy to model full-scene forest canopy height from optical imagery, lidar transects and Geographic Object-Based Image Analysis (GEOBIA). To achieve this goal, this research focuses on (i) determining appropriate lidar transect features (i.e., location, direction and extent) from an optical scene, (ii) developing a mechanism to model forest canopy height for the full-scene based on a minimum number of lidar transects, and (iii) defining an optimal mean object size (MOS) to accurately model the canopy composition and height distribution. Results show that (i) the transect locations derived from our optimal lidar transect selection algorithm accurately capture the canopy height variability of the entire study area; (ii) our canopy height estimation models have similar performance in two lidar transect directions (i.e., north-south and west-east); (iii) a small lidar extent (17.6% of total size) can achieve similar canopy height estimation accuracies as those modeled from the full lidar scene; and (iv) different MOS can lead to distinctly different canopy height results. By comparing the best canopy height estimate with the full lidar canopy height data, we obtained average estimation errors of 6.0 m and 6.8 m for conifer and deciduous forests at the individual tree crown/small tree cluster level, and an area weighted combined error of 6.2 m, which is lower than the provincial forest inventory height class interval (i.e., ≈ 9.0 m).     

Remote monitoring of Indonesian volcanoes using satellite data from the Internet

The Internet now harbours vast amounts of cheap and potentially useful remote sensing data. Advanced Very High Resolution Radiometer (AVHRR) data are being increasingly used for volcano surveillance, and the provision of AVHRR Global Area Coverage (GAC) imagery at no cost over the Internet offers the possibility of cheap volcano monitoring on a global scale. Herein we use an extensive, 690-scene AVHRR GAC dataset to observe volcanic activity in the Indonesian island arc between January 1996 and November 1997. Indonesia contains over 70 active volcanoes, with styles of activity during the observation period including active lava domes, lava flows, pyroclastic flows and hot crater lakes, many in close proximity to major centres of population. The detection potential of these and other phenomena in GAC data is assessed. Thermal anomalies were identified at ～18 volcanoes during the observation period, including lava flows at Anak Krakatau, persistent open-vent activity at Semeru and a previously unreported eruption at Sangeang Api volcano. Using these results, a classification scheme for night-time Indonesian GAC data is presented. Routine use of freely available high temporal resolution data such as AVHRR GAC could help elucidate cyclic activity at active volcanoes, which would contribute significantly to hazard mitigation in affected areas. Browse images of higher resolution data (e.g. SPOT) from the daily updated archives of the Centre for Remote Imaging, Sensing and Processing (CRISP) in Singapore also show potential as an aid to volcano monitoring in the region.     

Accuracy assessment of a mobile terrestrial lidar survey at Padre Island National Seashore

The higher point density and mobility of terrestrial laser scanning (light detection and ranging (lidar)) is desired when extremely detailed elevation data are needed for mapping vertically orientated complex features such as levees, dunes, and cliffs, or when highly accurate data are needed for monitoring geomorphic changes. Mobile terrestrial lidar scanners have the capability for rapid data collection on a larger spatial scale compared with tripod-based terrestrial lidar, but few studies have examined the accuracy of this relatively new mapping technology. For this reason, we conducted a field test at Padre Island National Seashore of a mobile lidar scanner mounted on a sport utility vehicle and integrated with a position and orientation system. The purpose of the study was to assess the vertical and horizontal accuracy of data collected by the mobile terrestrial lidar system, which is georeferenced to the Universal Transverse Mercator coordinate system and the North American Vertical Datum of 1988. To accomplish the study objectives, independent elevation data were collected by conducting a high-accuracy global positioning system survey to establish the coordinates and elevations of 12 targets spaced throughout the 12 km transect. These independent ground control data were compared to the lidar scanner-derived elevations to quantify the accuracy of the mobile lidar system. The performance of the mobile lidar system was also tested at various vehicle speeds and scan density settings (e.g. field of view and linear point spacing) to estimate the optimal parameters for desired point density. After adjustment of the lever arm parameters, the final point cloud accuracy was 0.060 m (east), 0.095 m (north), and 0.053 m (height). The very high density of the resulting point cloud was sufficient to map fine-scale topographic features, such as the complex shape of the sand dunes.     

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.     

Updating geomorphic features of watersheds and their boundaries in hazardous areas using satellite synthetic aperture radar

Volcanic disasters can cause severe loss of human life and damage to property. The main damage is caused during an eruption and from subsequent erosion of deposited materials. Heavy rainfall in volcanic areas erodes volcanic deposits, mainly pyroclastic flows and ash fall deposits, which flow as lahar to the foothill of the mountain and cause drastic damage to economically important areas. This post-eruption disaster becomes complex due to the occurrence of stream captures and watershed breakouts that lead to devastating lahars. Continuous monitoring of such geomorphic and hydrologic changes is necessary to cope with changing hazard conditions. Therefore it is important to update the watershed boundaries in order to study current hazard conditions and develop mitigation plans for future disasters. Changes of geomorphic and watershed boundary have occurred in the Mayon Volcano in the Philippines mainly as a result of a major volcanic eruption of 1993, due to which mitigation structures were constructed and modified in the low lying areas. In this study interferometry was used to develop DEM from SAR data to delineate watershed boundaries. New lava flows, pyroclastic flows and lahar deposits in the watersheds were mapped using elevation changes, coherence and intensity derived from the SAR images. Updating geomorphic features of the watersheds and their boundaries using SAR provides a new weather independent alternative technique for monitoring the effect of volcanic activity.     

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape

Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r²=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.     

Volcano detection and monitoring using AVHRR data: the Krafla eruption, 1984

Abstract. Many volcanic eruptions go essentially unmonitored. Potentially the Advanced Very High Resolution Radiometer (AVHRR), with its global coverage, frequent return period, and sensitivity in the thermal infrared, represents a data source capable of monitoring surface volcanic activity unrecorded by ground observations or other satellite sensors. In this study an attempt is made to demonstrate this potential by extracting information for the 1984 eruption at Krafla, Iceland. Seven cloud-free AVHRR images were available for the 14 day period of eruptive activity. The surface activity was detectable as a major thermal anomaly in all three of the longer wavelength channels and was vigorous enough during one night-time pass to be detectable in the near-infrared channel (0.725-1.1μm). Channel 2 and 4 radiance data were used to calculate the size and temperature of sub-pixel heat sources within the lava flow field, and a heat source at 1050° C was estimated as occupying an area of approximately 240000 m², which was distributed across 20 pixels. Detection and measurement of volcanic heat sources at such short wavelengths using low spatial resolution data has rarely been reported before. Field reports and maps were used to guide and confirm the analysis. Digital number variations within the anomaly could be related to various known features of the eruption. To monitor the eruption a weighted average method was derived and used to sharpen up the images, and the density sliced sharpened images enabled the development of the eruption to be mapped. Results compared well with field reports, suggesting that AVHRR and similar systems could be a useful source of data for monitoring eruptions where contemporaneous field observations are unavailable or incomplete.     

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

We present a new algorithm for digital terrain model (DTM) generation from an airborne laser scanning point cloud, called repetitive interpolation (REIN). It is especially applicable in steep, forested areas where other filtering algorithms typically have problems distinguishing between ground returns and off-ground points reflected in the vegetation. REIN can produce a DTM either in a vector grid or in a TIN data structure. REIN is applied after an initial filtering, which involves removal of all negative outliers and removal of many, but not necessarily all, off-ground points by some existing filtering algorithm. REIN makes use of the redundancy in the initially filtered point cloud (FPC) in order to mitigate the effect of the residual off-ground points. Multiple independent random samples are taken from the initial FPC. From each sample, ground elevation estimates are interpolated at individual DTM locations. Because the lower bounds of the distributions of the elevation estimates at each DTM location are almost insensitive to positive outliers, the true ground elevations can be approximated by adding the global mean offset to the lower bounds, which is estimated from the data. The random sampling makes REIN unique among the methods of filtering airborne laser data. While other filters behave deterministically, always generating a filter error in special situations, in REIN, because of its random aspects, these errors do not occur in each sample, and typically cancel out in the final computation of DTM elevations. Reduction of processing time by parallelization of REIN is possible. REIN was tested in a test area of 2 hectares, encompassing steep relief covered by mixed forest. An Optech ALTM 1020 lidar was used, with a flying height of 260-300 m above the ground, the beam divergence was 0.3 mrad, and the obtained point cloud density for the last returns was 8.5 m^− 2. A DTM grid was generated with 1 m horizontal resolution. The root mean square elevation error of the DTM ranged between ± 0.16 m and ± 0.37 m, depending on REIN sampling rate and number of samples taken, the lowest value achieved with 4 samples and using a 23% sampling rate. The paper also gives a short overview on existing filtering algorithms.     

Volcanic ash cloud detection from remote sensing images using principal component analysis

Thermal infrared remote sensing can quickly and accurately detect the volcanic ash cloud. However, remote sensing data have pretty strong inter-band correlation and data redundancy, both of which have decreased to a certain degree the detecting accuracy of volcanic ash cloud. Principal component analysis (PCA) can compress a large number of complex information into a few principal components and overcome the correlation and redundancy. Taking the Eyjafjallajokull volcanic ash cloud formed on April 19, 2010 for example, in this paper, the PCA is used to detect the volcanic ash cloud based on moderate resolution imaging spectroradiometer (MODIS) remote sensing image. The results show that: the PCA can successfully acquire the volcanic ash cloud from MODIS image; the detected volcanic ash cloud has a good consistency with the spatial distribution, SO₂ concentration and volcanic absorbing aerosol index (AAI).     

Evaluating error associated with lidar-derived DEM interpolation

Light detection and ranging (lidar) technology is capable of precisely measuring a variety of vegetation metrics, the estimates of which are usually based on relative heights above a digital elevation model (DEM). As a result, the development of these elevation models is a critical step when processing lidar observations. A number of different algorithms exist to interpolate lidar ground hits into a terrain surface. We tested seven interpolation routines, using small footprint lidar data, collected over a range of vegetation classes on Vancouver Island, British Columbia, Canada. The lidar data were randomly subsetted into a prediction dataset and a validation dataset. A suite of DEMs were then generated using linear, quintic, natural neighbour, regularized spline, spline with tension, a finite difference approach (ANUDEM), and inverse distance weighted interpolation routines, at spatial resolutions of 0.5, 1.0 and 1.5 m. In order to examine the effects of terrain and ground cover on interpolation accuracies, the study area was stratified by terrain slope, vegetation structural class, lidar ground return density, and normalized difference vegetation indices (NDVI) derived from Quickbird and Landsat7 ETM+ imagery. The root mean square (RMS) and mean absolute errors of the residuals between the surfaces and the validation points indicated that the 0.5 m DEMs were the most accurate. Of the tested approaches, the regularized spline and IDW algorithms produced the most extreme outliers, sometimes in excess of ±6 m in sloping terrain. Overall, the natural neighbour algorithm provided the best results with a minimum of effort. Finally, a method to create prediction uncertainty maps using classification and regression tree (CART) analysis is proposed.     

Application of high-resolution, interferometric DEMs to geomorphic studies of fault scarps, Fish Lake Valley, Nevada-California, USA

Recent developments in interferometric radar remote sensing provide a method for deriving detailed topographic and slope information for fault-scarp detection. In October 1996, the airborne TOPSAR instrument [IEEE Trans. Geosci. Remote Sens., 30 (1992) 933] was flown over southwest Nevada and east-central California. Topography calculated from TOPSAR data are in the form of a high-resolution (5-m spatial grid) digital elevation model (DEM). In this study, we focus upon the large, steep fault scarps that cut alluvium and alluvial fans in Fish Lake Valley, east of the White Mountains of Nevada-California. A series of topographic profiles extracted from the DEM reveal that the larger fault scarps are greater than 40 m in height and that the average midsection slope angle for all measured scarps is approximately 23°. These large scarps are the product of multiple offsets rather than a single event. Other relevant geomorphic features present in the digital topography include splays and benches along the main fault, levees, cutbanks, gullies incising fault scarp slopes, shutterridges, offset drainage, and small normal faults with scarp heights of only 4-6 m. Field work corroborated general geomorphologic landforms, confirmed fault-scarp morphometry, and aided the evaluation of the accuracy of the DEM. We are also able to assess fault segmentation models that divide the Fish Lake Valley fault zone (FLVFZ) into discrete segments based upon surface-rupture characteristics.