Arctic High-Resolution Elevation Models: Accuracy in Sloped and Vegetated Terrain

Transcription

1 Technical Note Arctic High-Resolution Elevation Models: Accuracy in Sloped and Vegetated Terrain Craig Glennie, Ph.D., P.Eng. 1 Abstract: New high-resolution elevation models for Alaska have recently been released; they were created using interferometric synthetic aperture radar (IFSAR) and automated matching of high-resolution optical satellite stereo imagery (OSSI). These products promise to fill a void in available digital elevation models (DEMs) for the Arctic. However, the effective use of these models requires knowledge of their expected accuracy, and to date, a detailed analysis of these models in remote Arctic locations has not been undertaken. Expected accuracy is necessary to gauge the uncertainty of any scientific conclusions based upon analysis of these DEM sources. To that end, both aforementioned DEM techniques were compared to airborne LiDAR (light detection and ranging) in the area surrounding Sitka, Alaska. It was found that both the IFSAR and OSSI DEMs provide vertical accuracy at the 2 4-m level (1 s)in flat and open terrain but perform significantly worse in areas of vegetation cover with standard deviations increasing to 7 12 m. The DEM errors were found to have a strong positive correlation with vegetation height, and the overall error pattern suggests that neither OSSI nor IFSAR accurately model either the ground or top of the tree canopy, instead representing a surface between the canopy and topographic elevation. DOI: /(ASCE)SU American Society of Civil Engineers. Introduction The Arctic is particularly sensitive to changes in climate. Differences in the amount of precipitation, the intensity and duration of storms, and the rise in mean temperature in the Arctic have amplified changes in the Arctic climate (IPCC 2014). In fact, recent studies indicate that surface air temperatures in the Arctic may be rising at twice the global rate (Hinzman et al. 2005). This rapid change in climate has manifested itself in rapid and complex environmental responses in terrestrial and marine ecosystems, leading to impacts such as coastal zone erosion (Jones et al. 2009), surface warming (Steele et al. 2008), and decreased permafrost (Brown and Romanovsky 2008). Unfortunately, other than dense observations of temperature, there remains a paucity of geospatial data that can be used to accurately document and study these changes. There are existing global elevation models that cover the Arctic, for example the ASTER DEM (digital elevation model) (Slater et al. 2011), and TanDEM-X (Martone et al. 2013). However, their coarse resolution (12 m spacing for TanDEM-X and 30 m spacing for ASTER), and large elevation uncertainties (10 m at 90% confidence for TanDEM-X and 20 m at 95% confidence for ASTER), render them unsuitable for a majority of scientific studies related to climate change and its impact on the Arctic environment. Higher resolution terrain models are an essential dataset for understanding the rapid changes manifesting themselves in the Arctic. Airborne LiDAR (Light Detection and Ranging) observations have, in the past two decades, emerged as the de facto standard for high-resolution terrain models (Glennie et al. 2013). However, the high cost of acquisition and the difficult and remote environment of 1 Associate Professor, Dept. of Civil & Environmental Engineering, Univ. of Houston, Houston, TX ORCID: clglennie@uh.edu Note. This manuscript was submitted on June 7, 2017; approved on September 7, 2017; published online on October 25, Discussion period open until March 25, 2018; separate discussions must be submitted for individual papers. This technical note is part of the Journal of Surveying Engineering, ASCE, ISSN Arctic Alaska has hampered efforts to obtain high-resolution LiDAR models of the state. Therefore, in response to the need for geospatial data of Alaska, two separate initiatives have been undertaken to create high-resolution topographic models. The first, initiated in 2010, and coordinated by the United States Geological Survey (USGS) in cooperation with state and federal partners, is using Interferometric Synthetic Aperture Radar (IFSAR) to generate elevation models (Nelson et al. 2009) of Alaska. The second, more recent, initiative is a partnership between the National Science Foundation (NSF), the National Geospatial Intelligence Agency (NGA), and DigitalGlobe to produce DEMs of the Arctic using high-resolution optical satellite stereo imagery (OSSI). While both of these new sources of elevation models will provide near-continuous coverage of the state of Alaska, it is important to quantify their expected accuracy to correctly interpret the scientific conclusions and spatial indices derived from the elevation models. An incomplete understanding of the limitationsof the DEMs, both in terms of accuracy and resolution, can lead to erroneous scientific conclusions. Previous studies of DEMs created using highresolution optical satellite stereo imagery have focused primarily on snow- and ice-covered areas that are devoid of vegetation. In these studies (Noh and Howat 2015; Poli et al. 2015), circular errors of approximately 3 10 m were reported. However, these could be reduced to a few decimeters in the vertical if the optical DEM was first registered with a three-dimensional translation to the reference DEM, such as the LiDAR DEM used in the study by Noh and Howat (Noh and Howat 2015). Of course, for most Arctic regions, high-accuracy LiDAR DEMs are not available, and therefore this referencing of the OSSI DEM may not be feasible. There are also large portions of the Arctic where surface slopes are significant and that are covered with vegetation of various types and heights; therefore, it is important to quantify accuracy in both highly sloped and vegetated areas. This manuscript endeavors to evaluate the accuracy of the IFSAR and stereo-image DEMs over Alaska in vegetated and mountainous areas by comparing them with high-accuracy and high-resolution elevation models generated from low-altitude highresolution airborne LiDAR surveys. The results of this technical note will enable a quantification of uncertainty for any Arctic ASCE J. Surv. Eng.

2 studies that use the IFSAR and/or OSSI DEMs as a basis for analysis or change detection. First, the study site and the various DEMs evaluated are described, followed by the methodology used to compare the DEMs to the airborne LiDAR reference DEMs. Next the results of the comparison are presented and the differences between the elevation models discussed. The final section gives conclusions regarding the accuracy of the DEM products. Study Site and Datasets Study Site The study site for the test was the area surrounding Sitka, AK, whose location is shown in Fig. 1 and which consists of an area of approximately 25 km 2 (3600 m by 7000 m). The site was chosen because of the variation in terrain (flat to mountainous), vegetation cover, and the presence of some manmade structures and landscapes. This variation within the site provided a variety of land classes and terrain slopes within which the accuracy of the terrain products could be assessed. Histograms of vegetation heights and terrain slopes at 2 m sampling intervals for the entire study area are given in Fig. 2. ArcticDEM ArcticDEM is a public-private initiative between the National Science Foundation, the National Geospatial-Intelligence Agency and DigitalGlobe to produce digital surface models (DSMs) of the Arctic (all areas above 60 N latitude) using high-resolution OSSI. The DSMs are generated using autocorrelation stereo imagery techniques on overlapping pairs of satellite images, which are predominately the 0.5-m resolution Worldview 1, 2, and 3 images from DigitalGlobe. The autocorrelation approach called SETSM (surface extraction with TIN-based search-space mnimization) was utilized to extract 2-m posting DSMs from satellite imagery, as detailed in (Noh and Howat 2015). It should be noted that the final product was not a bare earth product or a digital terrain model (DTM) because the source imagery does not penetrate vegetation or man-made structures. The data sets are also provided as is, with little or no manual validation of the final elevation product. It should also be noted that the DSMs were computed using all available imagery at all times of year and that therefore there are no guarantees that the DSM are free of artifacts from, for example, fog, snow cover, or clouds. In addition, there is currently no formal accuracy specifications or metadata for the ArcticDEM product (which is in part the motivation for this work), but in general, with external ground Fig. 1. (Color) Location of study site in Sitka, AK ASCE J. Surv. Eng.

3 Frequency (a) Frequency (b) Vegetation Height Histogram Vegetation Height (m) Terrain Slope Histogram Terrain Angle (deg) Fig. 2. Histograms of (a) vegetation height and (b) terrain slope for the study area; samples based on a 2-m grid of the entire study area control, the documentation states that 4-m vertical and horizontal accuracy should be expected as the data has been registered to ground control in the form of ICESat (Ice, Cloud, and land Elevation Satellite) laser altimetry data (Atwood et al. 2007). Additional details on the ArcticDEM product can be found in the documentation of the product on the University of Minnesota Polar Geospatial Center website ( For the Sitka study site, there were three separate DSMs available from the Arctic DEM project. They are summarized in Table 1, and a colored hillshade of each DEM is given in Fig. 3. Note that the coverage of the ArcticDEM products is not complete, with several holes and voids in the datasets. Per the ArcticDEM documentation, because the DEMs are derived from optical imagery, voids may be present due to clouds, fog, shadows or dust. Additional voids may have been caused by errors in SETSM autocorrelation by issues such as homogenous terrain (e.g., covered by snow), swaying trees or areas of open water. Table 1 also details the number of ICESat ground control points (GCPs) that were used to georeference the DEMs, and the mean vertical residual difference between the ICESat GCPs and the OSSI DEMs after adjustment. Note that the number of control points is quite varied, with the Worldview 3 DEM having significantly more control points. IFSAR Image Models The USGS, in collaboration with State and Federal partners, is currently undertaking a project to acquire 3D elevation data statewide for Alaska. The ongoing project started in 2010 and is obtaining IFSAR to generate DEM models for the entire state. The final elevation products are given in 5-m postings, with a specified accuracy of 3 m vertical (90% for 0 10 slope) and 5.7 m RMSE (root mean Table 1. Description of the Tested OSSI Datasets along with the Number of ICESat Ground Control Points Used to Georeference the Final DEM, and the Mean Vertical Residual on the GCPs after Adjustment Image platform Acquisition date Number of ICESat GCPs Mean vertical residual (m) Worldview 1 (WV01) August 19, Worldview 2 (WV02) March 29, Worldview 3 (WV03) February 26, Note: Names in parentheses are used to refer to each of the datasets. square error) for the horizontal. Two elevation products are provided with the IFSAR DEMs: a DSM and a DTM. The former is the top of the land cover surface, while the latter is an estimate of ground topography. The DSM products derived from IFSAR observations for the study area are given in Fig. 3. For the purposes of the reported comparisons, the DSM and DTM products from IFSAR were upsampled to 2-m postings using simple bilinear interpolation to have identical grid spacing to the OSSI DEMs. While upsampling may have caused interpolation errors, in most cases these errors were expected to be small (Shi et al. 2005). To verify that interpolation errors were not a significant source of error, the analysis presented herein was also performed using 5 m grid postings. No significant statistical differences were identified in the results. The IFSAR DEMs examined were based upon data collected in 2014 by Fugro, using the dual-band GeoSAR IFSAR platform, which collects both X and P band radar data simultaneously (Kampes et al. 2011; Williams et al. 2010). The P-band data is able to penetrate vegetation and therefore allows the generation of a DTM, while the X-band radar is reflected from the vegetation and therefore provides the DSM. The look angle of the GeoSAR system is between 25 and 60, which means that some occlusions may have occurred in areas of steep terrain. Airborne LiDAR Survey Reference airborne LiDAR data was collected from a helicopter (Robinson R44 Raven II) on May 2 and 3, 2016 using a Riegl VQ-480i laser scanner. Data acquisition was performed at an elevation of 500 m AGL with 50% flight line overlap, which resulted in a raw laser shot density of 25 pts/m 2.Anareaof approximately 65 km 2 was acquired, although for this analysis only a portion (25 km 2 ) of the dataset was examined. Final point clouds were generated in NAD83 (2011 adjustment) with NAVD88 heights derived using the NGS (National Geodetic Survey) Geoid12B model ( /GEOID12B/). For an independent verification of the airborne LiDAR DTM, independent DGPS (differential global positioning system) ground truths were collected using Trimble R10 GPS receivers with short differential baselines (<20 km) and post-processed carrier phase observables with fixed ambiguities. In all, 30 checkpoints were collected in both vegetated and nonvegetated areas to confirm the expected vertical accuracy of the final LiDAR DTM. The statistical comparisons between the final LiDAR DTM and the GPS checkpoints are given in Table 2.The statistics show that the vertical accuracy of the LiDAR DEMs should be better than the decimeter level (i.e., 6 cm at the 1- s level). This is an order of magnitude better than the several-meter accuracy expected from the IFSAR and OSSI DEMs, and therefore it provides an accurate reference surface for determining absolute elevation accuracy. ASCE J. Surv. Eng.

4 Fig. 3. (Color) DSMs from WV01, WV02, WV03, and IFSAR along with the reference LiDAR DTM and vegetation height map derived by subtracting the LiDAR DTM from LIDAR DSM; black areas in the OSSI DSMs indicate voids where no elevation value was reported Table 2. LiDAR Vertical DTM Accuracy Statistics: Comparison to DGPS Ground Control Parameter Value Minimum (m) Maximum (m) Mean (m) SD (m) Methodology The airborne LiDAR observations were processed to a final point cloud by the NSF supported National Center for Airborne Laser Mapping (NCALM, and the accuracy of the final point cloud was then verified by both internal consistency checks (examining overlap areas between adjacent flightlines), and by comparison with external DGPS checkpoints (Table 2). The final point cloud was classified into ground and aboveground features using the automated classification tools in the software package Terrascan ( The automated classification products were then also manually validated by NCALM researchers. The final classified point cloud was then used to derive 2-m gridded DTM and DSM models that were interpolated from a TIN (triangulated irregular network) model of the ground, and ground and vegetation point clouds respectively. The difference between the LiDAR DTM and DSM was used to derive vegetation height (Fig. 3), and terrain slope was calculated from the DTM. The three OSSI DEMs were given at identical 2-m postings, and the LiDAR and IFSAR DEMs were therefore sampled to the same grid postings as the OSSI DEMs for comparison. A common area of 25 km 2 was selected for analysis and was completely covered by all DEMs, except for the WV03 DEM, which has only 75% coverage. To examine elevation errors the OSSI and IFSAR DEMs were then directly differenced from the LIDAR DEMs. These differences were then used to determine standard statistical metrics (i.e. minimum, maximum, mean, and standard deviation of the differences). The differences were also analyzed through histograms. Finally, the DEM errors were also ASCE J. Surv. Eng.

5 analyzed for any correlation to terrain slope and/or vegetation height. The DEM differencing described above would have been adversely affected, especially in areas of significant topographic relief, by any horizontal biases that exist between the LiDAR and the compared DEM. Therefore, to investigate whether any horizontal offsets may have existed between the LIDAR, OSSI, and IFSAR DEMs, a 3D technique, called iterative closest point (ICP) (Besl and McKay 1992; Chen and Medioni 1992), was applied to determine whether there were any significant horizontal offsets. The entire project area was broken into cells of 500 m by 500 m, and a moving window approach, similar to that described in (Zhang et al. 2015), was applied to estimate a 3D rigid transformation at 100-m grid spacing. The 100-m grid of estimated 3D offsets was then examined for any systematic trends symptomatic of a horizontal and/or vertical offset between the DEMs and the reference LiDAR surfaces. Table 3. Statistics of DEM Errors versus LiDAR DSM and DTM Reference Surfaces Reference surface LIDAR DTM LIDAR DSM DEM analyzed Mean SD Maximum Minimum Outliers Total IFSAR DSM IFSAR DTM WV01 WV02 WV03 IFSAR DSM IFSAR DTM WV01 WV02 WV ,009 52, ,094 18,569 3,223 80,446 50, ,314 50,378 62,354 5,236,387 5,190,049 4,912,844 3,766,238 3,281,751 5,161,954 5,192,184 4,910,675 3,734,441 3,222,639 Note: Outliers were considered to be >3s (three standard deviations) from the mean and have been excluded from the calculation of the statistics. All measurements are in meters. Fig. 4. (Color) Elevation difference map; data voids are shown as white, and areas outside the color bar are shown as dark purple (< 25 m) and dark red (>25 m) ASCE J. Surv. Eng.

6 Results and Discussion Initial differences at 2-m postings were generated for each of the IFSAR (DSM and DTM) and OSSI DEMs (WV01, WV02, WV03) in comparison with the LiDAR DTM and DSM models, for a total of 10 differences. These differences are statistically summarized in Table 3. To mitigate the effect of outliers, points further than three standard deviations from the initial mean value were removed and the statistics recomputed. Table 3 lists the number of outlier points removed in each of the 10 comparisons and gives the number of points considered. The statistical summary in Table 3 shows significant mean differences between the evaluated DEMs and the reference models. In comparison with the LiDAR DTM, there is a significant positive mean bias for all of the evaluated DEMs. This positive value means that the tested DEMs all have average elevations above the LiDAR DTM. This is to be expected for the OSSI models and for the IFSAR DSM, because they are models that more closely approximate the top of the surface, i.e., the top of the vegetation canopy. The IFSAR DTM model had a significantly lower mean bias and standard deviation than the LiDAR DTM, which again is to be expected, because it is the only DEM that estimates terrain height only. The large mean difference values for the IFSAR DSM, and OSSI DEMs with respect to the LiDAR DSM disappear, and again this is to be expected. The IFSAR DTM model has a positive mean value when compared to the LIDAR DSM, but a negative mean value when compared to the LIDAR DTM. The IFSAR observations were expected to penetrate the vegetation canopy (Medeiros et al. 2015; Ni et al. 2014), but these results suggest that it only partially penetrated the tree canopy and defined a surface that is somewhere between the top of canopy and the true ground layer. Finally, it should be noted that the statistics for the WV01 DEM are significantly worse than that of the other DEM models; it also has significantly more outlier points, as can be visually verified by examining Relative Frequency Fig. 4. Unfortunately, because the source imagery is not available for ArcticDEM products, it is difficult to deduce the source of the outliers in the WV01 DEM, although fog, clouds, or snow cover would be probable sources of error. Overall, the mean and standard deviation comparisons between IFSAR, WV02 and WV03 show a similar level of accuracy. However, these values are significantly higher than the accuracy suggested by the documentation for these DEM sources. As discussed in the methodology section, the vertical differences given in Table 3 could be contaminated by horizontal biases between the tested and reference DEMs. Therefore, to investigate this possibility ICP was utilized to estimate a 100-m grid of 3D offsets between each of the tested DEMs and its respective LiDAR reference. The gridded estimated offsets showed no discernable pattern for any of the tested DEMs, with mean values of of 3 5 m to the east and the north and 1 m in elevation, with standard deviations on the order of 5 10 m. These mean offsets were applied but did not significantly change the statistics given in Table 3, and given the large standard deviations, the mean horizontal biases are likely not significant. Therefore it appears that the datasets do not have any significant horizontal offsets from the LIDAR surface models. To further examine the distribution of errors in these elevation models, histograms of the elevation differences are shown in Fig. 5. The histograms for the comparisons with the LiDAR DTM (solid lines in Fig. 5) show an obvious bimodal error distribution: one peak for the terrain surface (nearer to zero mean), and a second peak at m, which represents the dominant tree canopy height. The histograms from comparison with the LiDAR DSM do not exhibit a bimodal distribution and are more normally distributed about the mean bias. This supports the conclusion that the WV01, WV02, WV03, and IFSAR DSM estimates are better approximations of the top of the canopy than of the ground surface, which is to be expected. Elevation Error versus LiDAR DTM (Solid) and LiDAR DSM (Dotted) Elevation Difference (m) IFSAR DSM IFSAR DTM WV01 WV02 WV03 IFSAR DSM IFSAR DTM WV01 WV02 WV03 Fig. 5. (Color) Histogram of elevation differences between IFSAR and OSSI DEMs and LiDAR reference DEMs ASCE J. Surv. Eng.

7 Fig. 5 clearly suggests that the DEM error is correlated to vegetation height. It is also possible that the error is correlated with terrain slope, as highly sloped terrain does not provide ideal viewing geometry for either IFSAR or satellite-based stereo photography. Therefore, to further investigate the error distributions in Fig. 5, plots of DEM error versus terrain slope and vegetation height were analyzed, as both of these characteristics were expected to have a correlation with DEM error. These results are plotted in Fig. 6 (DEM error with respect to terrain angle), and Fig. 7 (DEM error with respect to vegetation height). Fig. 6(a) shows that there was not a strong correlation between terrain slope and mean elevation error for all DEMs. The comparisons with the LiDAR DSM (dotted lines in figure) are relatively flat across the entire range of terrain slopes. For the comparisons with the LiDAR DTM, all of the elevation models show poorer performance for lower angles of terrain slope (10 40 ). This is likely due to the increased presence of vegetation in areas of less extreme terrain slope. However, the graph in Fig. 6(b) clearly shows that as terrain slope increases, there is a resultant increase in the standard deviation of the elevation differences. Both the IFSAR and OSSI DEMs clearly get noisier with increasing terrain slope. This behavior is expected, and similar conclusions for radar and optical image DEMs were reported in Bolkas et al. (2016). Fig. 6. (Color) (a) Mean and (b) standard deviation of elevation error for IFSAR and OSSI DEMs with respect to terrain slope ASCE J. Surv. Eng.

8 40 Elevation Error by Vegetation Height versus LiDAR DTM (Solid) and LiDAR DSM (Dotted) 30 Mean Elevation Difference (m) Vegetation Height (m) IFSAR DSM IFSAR DTM WV01 WV02 WV03 IFSAR DSM IFSAR DTM WV01 WV02 WV03 Fig. 7. (Color) Mean elevation error for IFSAR and OSSI DEMs versus vegetation height The results in Fig. 7 show a very strong correlation between vegetation height and mean DEM error. All models are consistently higher than the LiDAR DTM (solid lines) and their error grows almost linearly with respect to vegetation height. The IFSAR DTM has the lowest correlation with vegetation height and shows the best overall performance when comparing to the LiDAR DTM, however the effect of vegetation is still clearly visible in this DTM as well. This is to be expected, because in thick vegetation the P band of the GeoSAR may not be able to penetrate through the canopy to the ground (Sexton et al. 2009). Comparisons of the DEMs with the LiDAR DSM showed that all DEM models underestimated the height of the top of canopy, and the estimation error grew as a function of vegetation height. However, the slope of the increase in error was significantly less than the slope of the error with respect to the LiDAR DTM. This is to be expected because the majority of the DEMs were actually representations of the first return or the top of vegetation canopy surface. Fig. 7 also clearly shows that all DEM sources perform significantly better in areas with little or no vegetation. In addition, the accuracy specifications for the IFSAR DEM products are quoted for terrain slopes of less than 10. Therefore, as a final evaluation, DEM performance was examined in areas of no vegetation and minimal terrain slope. This may be useful for examining things like snow accumulation (Deems et al. 2013), changes in vegetation biomass, and glacier movement from temporally spaced DEMs (Telling et al. 2017). Therefore, from the analyzed DEMs, a subset of points was selected where vegetation height was less than 1 m and where the terrain slope was less than 10. The statistics of these comparisons are given in Table 4. Note that WV01 was not included in this comparison: the results in Table 3 clearly showed that there were significant outliers in this model, and its performance was significantly worse that WV02 and WV03 and therefore not representative of DEM accuracy from OSSI. By comparison with the statistics given in Table 3,Table4 clearly shows that the DEM performance is significantly better in flat and open areas. Both mean errors and standard deviations are significantly reduced; however, the statistics in Table 4 are still worse than the Table 4. Statistics for Non-Vegetated and Flat Areas (Vegetation Height <1 m and Terrain Slope <10 ) DEMs compared Mean (m) Minimum (m) Maximum (m) SD (m) Number of Points LIDAR DSM v. WV ,607 LIDAR DSM v. WV ,488 WV03 v. WV ,345 LIDAR DSM v. IFSAR DSM ,131 LIDAR DTM v. IFSAR DTM ,316 expected accuracy of both the IFSAR and OSSI models. It is also interesting to note that the number of ICESat GCPs used to georeference the OSSI DEMs does not appear to significantly improve the results. The WV03 DEM was constrained with 18 times more GCPs than WV02 (72 versus 4, Table 1), yet the performance of the WV03 DEM was not significantly better, with the exception of a smaller mean bias in comparison to the LiDAR DSM (Table 3). As a final observation, it should be noted that the datasets for IFSAR, OSSI, and LiDAR were all collected at different dates and times. Therefore, it would be expected that a change may have occurred between each of these acquisitions. However, the selected study area is very remote, and any changes would likely be predominantly due to vegetation growth, which would be negligible over the time period of the study. The LiDAR and IFSAR observations were collected in the summer, where there was little or no snow accumulation; however, WV02 and WV03 were both collected in the late winter/early spring where significant snow accumulation could be present, especially at higher elevations. Unfortunately, ArcticDEM products do not provide access to the raw imagery and therefore snow cover could not be verified in the source imagery. An analysis of snow cover from Landsat imagery collected near the acquisition times for the WV02 and WV03 datasets indicated snow cover predominantly at higher elevations (above 1000 m), which represents < 2% of the study site. In addition, historical weather shows that Sitka ASCE J. Surv. Eng.

9 averages only 33 inches of snow per year, with 2014 and 2015 receiving considerably less than average ( Given these observations, it is unlikely that snow cover is the cause of the large deviations between the OSSI DEMs and the LiDAR elevation models, despite the differences in acquisition dates. Conclusions The OSSI and IFAR elevation models were analyzed for the sloped and forested terrain surrounding Sitka, AK by comparing them with a high-accuracy DSM and DTM extracted from airborne LiDAR observations. The site offered a variety of vegetation height covers and significant variation in terrain slope that allowed an assessment of the DEM sources in varying conditions. In flat and open terrain, both elevation models performed with a standard deviation of 4 5 m.this is slightly better than the quoted accuracy of the IFSAR elevation models: the OSSI DEM products do not currently have formal accuracy specifications. The performance of both DEM sources degraded significantly in the presence of vegetation, with standard deviations increasing to 7 12 m, with a similar increase in magnitude of the mean bias of the DEM when compared to either a LiDAR DTM (in the case of the IFSAR DTM) or LiDAR DSM (for all other comparisons). The DEM error (for both DSM and DTM estimates) was found to have a strong positive correlation with vegetation height. The error pattern overall suggests that the DSM sources do not accurately model the top of the tree canopy but instead represent a surface between the canopy and the DTM elevation. Likewise, the errors in the IFSAR DTM were also found to be highly correlated with vegetation height. For the OSSI DEMs, the number of ground control points from ICESat does not seem to reduce the standard deviation of the DSM, as thevalueforbothwv02(4icesatgcps)andwv03(72icesat GCPs) are similar. However, the increased number of control points does seem to reduce the magnitude of the mean bias when comparing to the LiDAR DSM. Finally, one of the major drawbacks of the OSSI DEMs is that they are produced from all available imagery, including winter acquisitions; however, the original source imagery is not currently available. Therefore, it is difficult to determine whether the presence of snow had a significant effect on the resultant elevation estimates of the DSM. For the OSSI DEMs studied, historical snowfall observations and Landsat imagery would suggest that snow cover was not a major limiting factor, but either the source imagery, or an estimate of snow-covered areas derived from it, would be valuable metadata for both estimating DEM quality and uncertainty. Acknowledgments This work was partially supported by the U.S. Army Engineer Research and Development Center Cold Regions Research and the Engineering Laboratory Remote Sensing/GIS Center of Expertise. 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