High-resolution LiDAR Digital Elevation Model Research Articles

The influence of roads on depressional storage capacity estimates from high-resolution LiDAR DEMs in a Canadian Prairie agricultural basin

The Canadian Prairies are a post-glacial agricultural landscape, where millions of small depressions store surface water, form wetlands and control runoff contributing area. Their management is key to flood and drought hydrology, groundwater recharge, ecological integrity, migratory bird habitat and agricultural productivity. Depression drainage and infilling is common in the region, where it is often used to increase cropped area. The regularly spaced, rural ‘grid-road‘ network also impedes drainage, but associated culvert drainage can mitigate those effects. Management of depressions can be informed by hydrological modelling, but accurate surface water storage capacity estimates are needed to ensure accurate model results. Simple representation of road embankments in digital elevation models (DEMs) neglects the effects of culvert drainage. Here, a raster-based depression-filling algorithm was used to delineate depressions from three LiDAR-derived DEMs: a 10-m DEM with roads intact, a 2-m DEM with roads intact, and a 2-m DEM with roads breached at culvert locations. Road breaching was conducted manually in the 2-m DEM to remove artifact depressions that form alongside roads where culverts exist. Results indicated that increasing DEM resolution from 10-m to 2-m in a 393.5 km2 basin did not significantly change depression area or storage capacity estimates; however, breaching roads in the 2-m DEM decreased depression area by 29% (from 98.5 km2 to 69.8 km2) and estimated storage capacity by 48% (from 47.4 × 106 m3 to 23.8 × 106 m3), compared to leaving roads intact in the 2-m DEM. Depressions delineated from the 2-m roads-breached DEM also covered 48% more area and offered 53% more storage capacity than Canadian Wetland Inventory (CWI) aerial-photograph delineated wetlands, which occupied 47.1 km2 with an estimated storage capacity of 15.5 × 106 m3. The implications of these results for the ability of hydrological models to calculate runoff contributing areas and streamflow are discussed.

Inventory of glacial curvilineations (GCLs) at the southern periphery of the last Scandinavian Ice Sheet

Glacial curvilineations (GCLs) are enigmatic landforms recently discovered in the southern peripheral area of the last Scandinavian Ice Sheet (SIS). They consist of multiple parallel, winding ridges several metres high and up to several kilometers long, systematically arranged in groups of common geomorphic appearance. GCLs create spectacular landscapes whose origin generates much controversy. Expanding from the first GCLs finding in central Poland, we now present an inventory of other GCLs occurrences along the SIS margin in Poland, Germany and Denmark based on analysis of high-resolution LiDAR digital elevation models. We document a total of 59 glacial curvilineation fields and focus on the geomophic characteristics of the 43 most conspicuous ones. Each of these fields consists of smaller units called GCL swarms, 137 in total. The fields differ strongly in areal coverage (between 1 and 358 km2), number of swarms (between 1 and 21) and number of individual landforms (between 2 and 255). All GCL fields occur in tunnel valleys terminating at the ice margin, typically at various topographical levels. Our findings, interpreted in the context of glacial geological processes, support the origin of GCLs as erosional remnants of antecedent landscapes carved by channelized subglacial meltwater flows.

High-Resolution LiDAR Digital Elevation Model Referenced Landslide Slide Observation with Differential Interferometric Radar, GNSS, and Underground Measurements

The area of Taiwan is 70% hillsides. In addition, the topography fluctuates wildly, and it is active in earthquakes and young orogenic movements. Landslides are a widespread disaster in Taiwan. However, landslides are not a disaster until someone enters the mountain area for development. Therefore, landslide displacement monitoring is the primary task of this study. Potential landslide areas with mostly slate geological conditions were selected as candidate sites in this study. The slate bedding in this area is approximately 30 to 75 degrees toward the southeast, which means that creep may occur due to gravity deformation caused by high-angle rock formation strikes. In addition, because the research site is located in a densely vegetated area, the data noise is very high, and it is not easy to obtain good results. This study chose ESA Sentinel-1 data for analysis and 1-m LiDAR DEM as reference elevation. The 1-m LiDAR DEM with high accuracy can help to detect more complex deformation from DInSAR. The Sentinel-1 series of satellites have a regular revisit period. In addition, the farm areas of roads, bridges, and buildings in the study area provided enough reflections to produce good coherence. Sentinel-1 images from March 2017 to June 2021 were analyzed, obtaining slope deformation and converting it to the vertical direction. Deformation derived from SAR is compared with other measurements, including GNSS and underground slope inclinometer. The SBAS solution process provides more DInSAR pairs to overcome the problem of tremendous noise and has increased accuracy. Moreover, the SBAS method’s parameter modification derives more candidate points in the vegetated area. The vertical deformation comparison between the GNSS installation location and the ascending SBAS solution’s vertical deformation is consistent. Moreover, the reliable facing of the slope toward the SAR satellite is discussed. Due to the limitations of the GNSS stations, this study proposes a method to convert the observed deformation from the slope inclinometer and convert it to vertical deformation. The displacement of the slope indicator is originally a horizontal displacement. It is assumed that it is fixed at the farthest underground, and the bottom-to-top movement is integrated with depth. The results show that the proposed equation to convert horizontal to vertical displacement fits well in this condition. The activity of landslides within the LiDAR digital elevation model identified as scars is also mapped.

Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions

The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data quality, ARA studies to date have not achieved effective computer-based ARA components delineation, limiting the efficacy of the ARA framework in terms of informing riparian conservation and management. To determine the optimal input elevation data for future ARA studies, this study tested a novel digital elevation model (DEM) smoothing algorithm and assessed ARA outputs derived from a range of DEMs for accuracy and efficiency. It was found that the tested DEM smoothing algorithm allows the ARA framework to take advantage of high-resolution LiDAR DEM and considerably improves the accuracy of high-resolution LiDAR DEM derived ARA results; smoothed LiDAR DEM in 5-m spatial resolution best balanced ARA accuracy and data processing efficiency and is ultimately recommended for future ARA delineations across large regions. The scientific findings provided by this study further enhance the efficacy of the ARA framework, and ultimately the confidence in modelled ARA outputs for application in riparian conservation and management contexts across broad geographic regions.

Application of Geological Mapping Using Airborne-Based LiDAR DEM to Tunnel Engineering: Example of Dongao Tunnel in Northeastern Taiwan

The use of digital elevation models (DEMs) that use airborne-based light detection and the ranging technique (airborne-based LiDAR) to understand large-scale geological structures has become important in geological surveying and mapping. Taking the Dongao Tunnel area in northeastern Taiwan as the study area, this study used the airborne-based LiDAR DEM and related value-added maps to interpret the topographic and geomorphic features of the area and identify locations for geological investigation. The characteristics of the rock mass were observed on-site and revealed by excavation of the highway tunnel in the study area; they were compared with the interpreted topographic and geomorphic features to determine the potential of using 1 m-resolution LiDAR DEM in geological surveys and in the evaluation of engineering characteristics of underground rock masses. The results of this study demonstrated that the DEM accurately captured geomorphic features: the strata composed of slate and schist had distinct appearances in both the clinometric map and the hillshade map; the locations of faults, lineaments, and drainage were consistent with those observed on-site, and the positions of these features were captured more accurately than those on conventional maps. Evident microrelief features, including the distribution of scarps, erosion gullies, and mini-drainage systems provide an effective basis for interpreting a deep-seated gravitational deformation slope and for an on-site inspection for validation. The use of high-resolution LiDAR DEM to interpret geomorphic features along with geological surveys provides a more comprehensive understanding of the survey area, supporting surveys and geological mapping, revealing the locations of potential slope failures, and enabling the assessment of tunnel engineering risks.

Geometry and topology of tectonolineaments in the Gorce Mts. (Outer Carpathians) in Poland

The present study was focused on analysis of the geometrical and topological features of the lineament network in the Gorce Mountains, Polish Outer Carpathians. The source data was a 1m high-resolution LiDAR digital elevation model (DEM). Network topology was analysed using the NetworkGT toolset for ArcGIS software. The topological analysis represented the first attempt to apply research of this type in the Polish Carpathians. Five lithostratigraphic units occurring in the research area, characteristic of the Krynica Zone of the Magura Nappe, were selected for analysis. Topology was studied for each of the units separately as well as for the whole area. It was found that the units were differentiated in terms of some topological parameters, such as 2D intensity. Moreover, latitudinal trends were observed due to the presence of wide fault zones. The geometry of the network is consistent with the general Carpathian overview. Sets of conjugate strike slip-faults with splaying ends were identified. In the southern part of the area, the strike of the oldest faults was E-W. This shows that on the scale of the observation (hundreds to thousands of metres), the network was a product of general thrusting processes rather than derivative of a local structural plan. The applicability of both topological analyses and high-resolution DEM as supplementary to classical methods in a study of the Outer Carpathians was confirmed with analysis conducted, despite the identification of some limitations, such as GIS-operator-related bias. The scale of the analyses is linked, in a multi-scale approach, to structural network analyses.

Surficial geology of Pictured Rocks National Lakeshore, Michigan, USA

ABSTRACT Pictured Rocks National Lakeshore is located in the Upper Peninsula of Michigan along the southern shore of Lake Superior. The park spans approximately 300 km2 and is almost fully incorporated within the ten 7.5-minute quadrangles presented in this new, detailed surficial geology map. The western quadrangles are mainly characterized by thin accumulations of undifferentiated Quaternary sediment above shallow bedrock while the eastern quadrangles mostly show thick accumulations of various glacial outwash units. This map was produced by integrating field data with a high-resolution LiDAR digital elevation model and associated hillshade, aerial imagery, and passive seismic data. The high-resolution elevation information was essential to differentiate between outwash units, post-glacial sand accumulations, and interpreting subtle glacial features that would have otherwise remained unrecognized. This new map provides fundamental information to help decipher the glacial history of this region to enhance our understanding of how this popular landscape developed.

Bare Earth DEM Generation for Large Floodplains Using Image Classification in High-Resolution Single-Pass InSAR

The increasing frequency and severity of flood events demand improved accuracy of hydrodynamic models to better mitigate the societal and economic consequences of these disasters. Ideally, these models derive elevation data from high-accuracy LiDAR, which often captures the subtle variations in elevation and microtopography—elements that are critical to high-resolution hydrodynamic modeling. Due largely to the cost of acquisitions, these data, however, are not widely available at high spatial resolutions for large-scale areas, i.e. floodplains and coastal regions, especially outside of the U.S. and Western Europe, where flood-prone communities already lack the infrastructure and resources to manage these disasters. High-resolution interferometric synthetic aperture radar (InSAR) systems may offer a viable and cost-effective alternative to LiDAR, with comparable spatial resolutions and vertical accuracies. Like any swath mapping sensor, systematic errors in calibration knowledge increase as the lever-arm increases when viewing further off vertical. InSAR-derived digital elevation models (DEMs) are often corrected using LiDAR or ground control points, although this limits the application of InSAR to those areas where these data exist. In this paper, we present a novel approach for using near-range InSAR data to correct inherent systematic bias that propagates across adjacent, overlapping swaths for the same airborne acquisition. This eliminates the need for LiDAR in generating InSAR DEMs, while maintaining a vertical error of approximately 0.26 m relative to LiDAR, at a spatial resolution of 30 m. Data was acquired using the NASA/JPL airborne, single-pass, Ka-band GLISTIN-A (Glacier and Ice Surface Topography Interferometer) InSAR over the Red River Basin (RRB), ND. The accuracy of the final DEM is evaluated using National Geodetic Survey (NGS) GPS and a high-resolution LiDAR DEM.

Flood depth estimation by means of high-resolution SAR images and lidar data

Abstract. When floods hit inhabited areas, great losses are usually registered in terms of both impacts on people (i.e., fatalities and injuries) and economic impacts on urban areas, commercial and productive sites, infrastructures, and agriculture. To properly assess these, several parameters are needed, among which flood depth is one of the most important as it governs the models used to compute damages in economic terms. This paper presents a simple yet effective semiautomatic approach for deriving very precise inundation depth. First, precise flood extent is derived employing a change detection approach based on the normalized difference flood index computed from high-resolution synthetic aperture radar imagery. Second, by means of a high-resolution lidar digital elevation model, water surface elevation is estimated through a statistical analysis of terrain elevation along the boundary lines of the identified flooded areas. Experimental results and quality assessment are given for the flood that occurred in the Veneto region, northeastern Italy, in 2010. In particular, the method proved fast and robust and, compared to hydrodynamic models, it requires sensibly less input information.

The influence of floodplain geomorphology and hydrologic connectivity on alligator gar (Atractosteus spatula) habitat along the embanked floodplain of the Lower Mississippi River

The floodplain geomorphology of large lowland rivers is intricately related to aquatic ecosystems dependent upon flood pulse dynamics. The alligator gar (Atractosteus spatula) is native to the Lower Mississippi River and dependent upon floodplain backwater areas for spawning. In this study we utilize a geospatial approach to develop a habitat suitability index for alligator gar that explicitly considers hydrologic connectivity and the floodplain geomorphology along a frequently inundated segment of the Lower Mississippi River. The data sets include Landsat imagery, a high-resolution LiDAR digital elevation model (DEM), National Hydrography Dataset (NHD), and hydrologic and geomorphic data. A habitat suitability index is created based on the extent and frequency of inundation, water depth, temperature, and vegetation. A comparison between the remote sensing approach and the NHD revealed substantial differences in the area and location of water bodies available for alligator gar spawning. The final habitat suitability index indicates that a modest proportion (19%) of the overall embanked floodplain is available for alligator gar spawning. Opportunities exist for management efforts to utilize engineered and natural geomorphic features to facilitate hydrologic connectivity at flow levels below flood stage that would expand the habitat of alligator gar across the floodplain. The study results have direct implications regarding environmental restoration of the Lower Mississippi, an iconic example of an embanked meandering river floodplain.

Snow depth from ICESat laser altimetry — A test study in southern Norway

Direct snow depth measurements are sparse, especially in remote areas. In this study, we assess the potential of ICESat laser altimetry for providing snow depths for its operational period 2003–2009 on the example of the Scandinavian Mountains in southern Norway. Snow cover during ICESat campaigns typically results in positive elevation differences (dh) between ICESat GLAH14 elevations and reference elevations from Digital Elevation Models (DEMs). Three DEMs are used: the Norwegian national DEM for the entire study area, and the SRTM DEM and a high-resolution airborne lidar DEM for a spatial subset on the Hardangervidda mountain plateau. To account for uncertainty in elevation data, ICESat samples are grouped into spatial subsets, elevation bands, and over time (e.g. all winter campaigns together). We find that ICESat has the potential to provide regional-scale snow depths for the years 2003–2009 for its winter (March) and late spring (June) campaigns. ICESat-derived snow depth time series for different elevation bands agree well with measured (RMSE 0.47m) and modelled (RMSE 0.61m) snow depths in the study area. Annual differences in snow amounts and the increase of snow depths with elevation and coastal proximity over the study area are correctly reproduced. Uncertainties in reference elevations exceed ICESat elevation uncertainty and good control over errors and biases in reference DEMs turn out essential. Spatially varying vertical offsets between ICESat and the reference DEMs make it necessary to bias-correct March/June snow depths with autumn dh per spatial unit or elevation band. Best results are achieved when samples are summarised per season over the entire observation period. After correction of local DEM biases, the spatial pattern of ICESat 2003–2009 March dh matches spatially distributed modelled snow depths in southern Norway with decimeter-scale accuracy. In the western part of Hardangervidda, ICESat-based March snow depths agree better with measurements (RMSE ≤0.15m for all DEMs) than modelled snow depths do (RMSE 0.61m). In eastern Hardangervidda, the coarse resolution SRTM DEM (RMSE 0.41m) performs better than the 10m Norwegian DEM (RMSE 0.64m) which is based on a less consistent mosaic of elevation data. Using the high-resolution lidar DEM, even single footprints show good agreement (R2 0.59, RMSE 0.94m) with measured snow depths from the same year. Snow depth estimates could be further improved by using full waveform ICESat data or elevation measurements from ICESat-2 once this satellite is operational. Good quality reference DEMs may still be acquired in the future even in areas where no such data exists today.

Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape

Abstract. Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to the atmosphere under warming climate scenarios. Ice-wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. This microtopography plays a critical role in regulating the fine-scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behavior under the current as well as changing climate. We present here an end-to-end effort for high-resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites near Barrow, Alaska, spanning across low to transitional to high-centered polygons, representing a broad polygonal tundra landscape. A multiphase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using a high-resolution lidar digital elevation model (DEM), microtopographic features of the landscape were characterized and represented in the high-resolution model mesh. The best available soil data from field observations and literature were utilized to represent the complex heterogeneous subsurface in the numerical model. Simulation results demonstrate the ability of the developed modeling approach to capture – without recourse to model calibration – several aspects of the complex thermal regimes across the sites, and provide insights into the critical role of polygonal tundra microtopography in regulating the thermal dynamics of the carbon-rich permafrost soils. Areas of significant disagreement between model results and observations highlight the importance of field-based observations of soil thermal and hydraulic properties for modeling-based studies of permafrost thermal dynamics, and provide motivation and guidance for future observations that will help address model and data gaps affecting our current understanding of the system.

ICESat laser altimetry over small mountain glaciers

Abstract. Using sparsely glaciated southern Norway as a case study, we assess the potential and limitations of ICESat laser altimetry for analysing regional glacier elevation change in rough mountain terrain. Differences between ICESat GLAS elevations and reference elevation data are plotted over time to derive a glacier surface elevation trend for the ICESat acquisition period 2003–2008. We find spatially varying biases between ICESat and three tested digital elevation models (DEMs): the Norwegian national DEM, SRTM DEM, and a high-resolution lidar DEM. For regional glacier elevation change, the spatial inconsistency of reference DEMs – a result of spatio-temporal merging – has the potential to significantly affect or dilute trends. Elevation uncertainties of all three tested DEMs exceed ICESat elevation uncertainty by an order of magnitude, and are thus limiting the accuracy of the method, rather than ICESat uncertainty. ICESat matches glacier size distribution of the study area well and measures small ice patches not commonly monitored in situ. The sample is large enough for spatial and thematic subsetting. Vertical offsets to ICESat elevations vary for different glaciers in southern Norway due to spatially inconsistent reference DEM age. We introduce a per-glacier correction that removes these spatially varying offsets, and considerably increases trend significance. Only after application of this correction do individual campaigns fit observed in situ glacier mass balance. Our correction also has the potential to improve glacier trend significance for other causes of spatially varying vertical offsets, for instance due to radar penetration into ice and snow for the SRTM DEM or as a consequence of mosaicking and merging that is common for national or global DEMs. After correction of reference elevation bias, we find that ICESat provides a robust and realistic estimate of a moderately negative glacier mass balance of around −0.36 ± 0.07 m ice per year. This regional estimate agrees well with the heterogeneous but overall negative in situ glacier mass balance observed in the area.

Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland

Abstract. In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average ∼ 3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

High-Accuracy Elevation Data at Large Scales from Airborne Single-Pass SAR Interferometry

Digital elevation models (DEMs) are essential data sets for disaster risk management and humanitarian relief services as well as many environmental process models. At present, on the hand, globally available DEMs only meet the basic requirements and for many services and modeling studies are not of high enough spatial resolution and lack accuracy in the vertical. On the other hand, LiDAR-DEMs are of very high spatial resolution and great vertical accuracy but acquisition operations can be very costly for spatial scales larger than a couple of hundred square km and also have severe limitations in wetland areas and under cloudy and rainy conditions. The ideal situation would thus be to have a DEM technology that allows larger spatial coverage than LiDAR but without compromising resolution and vertical accuracy and still performing under some adverse weather conditions and at a reasonable cost. In this paper, we present a novel single pass In-SAR technology for airborne vehicles that is cost-effective and can generate DEMs with a vertical error of around 0.3 m for an average spatial resolution of 3 m. To demonstrate this capability, we compare a sample single-pass In-SAR Ka-band DEM of the California Central Valley from the NASA/JPL airborne GLISTIN-A to a high-resolution LiDAR DEM. We also perform a simple sensitivity analysis to floodplain inundation. Based on the findings of our analysis, we argue that this type of technology can and should be used to replace large regions of globally available lower resolution DEMs, particularly in coastal, delta and floodplain areas where a high number of assets, habitats and lives are at risk from natural disasters. We conclude with a discussion on requirements, advantages and caveats in terms of instrument and data processing.

Tracing glacier changes in Austria from the Little Ice Age to the present using a lidar-based high-resolution glacier inventory in Austria

Abstract. Glacier inventories provide the basis for further studies on mass balance and volume change, relevant for local hydrological issues as well as for global calculation of sea level rise. In this study, a new Austrian glacier inventory has been compiled, updating data from 1969 (GI 1) and 1998 (GI 2) based on high-resolution lidar digital elevation models (DEMs) and orthophotos dating from 2004 to 2012 (GI 3). To expand the time series of digital glacier inventories in the past, the glacier outlines of the Little Ice Age maximum state (LIA) have been digitalized based on the lidar DEM and orthophotos. The resulting glacier area for GI 3 of 415.11 ± 11.18 km2 is 44% of the LIA area. The annual relative area losses are 0.3% yr−1 for the ~119-year period GI LIA to GI 1 with one period with major glacier advances in the 1920s. From GI 1 to GI 2 (29 years, one advance period of variable length in the 1980s) glacier area decreased by 0.6% yr−1 and from GI 2 to GI 3 (10 years, no advance period) by 1.2% yr−1. Regional variability of the annual relative area loss is highest in the latest period, ranging from 0.3 to 6.19% yr−1. The mean glacier size decreased from 0.69 km2 (GI 1) to 0.46 km2 (GI 3), with 47% of the glaciers being smaller than 0.1 km2 in GI 3 (22%).

Evaluation of DEM generation based on Interferometric SAR using TanDEM-X data in Tokyo

This study is focused on the evaluation of a Digital Elevation Model (DEM) for Tokyo, Japan from data collected by the recently launched TerraSAR add-on for Digital Elevation Measurements (TanDEM-X), satellite of the German Aerospace Center (DLR). The aim of the TanDEM-X mission is to use Interferometric SAR techniques to generate a consistent high resolution global DEM dataset. In order to generate an accurate global DEM using TanDEM-X data, it is important to evaluate the accuracy at different sites around the world. Here, we report our efforts to generate a high-resolution DEM of the Tokyo metropolitan region using TanDEM-X data. We also compare the TanDEM-X DEM with other existing DEMs for the Tokyo region. Statistical techniques were used to calculate the elevation differences between the TanDEM-X DEM and the reference data. Two high-resolution LiDAR DEMs are used as independent reference data. The vertical accuracy of the TanDEM-X DEM evaluated using the Root Mean Square Error (RMSE) is considerably higher than the existing global digital elevation models. However, the local area DEM generated by Geospatial Information Authority of Japan (GSI DEM) showed the highest accuracy among all non-LiDAR DEM’s. The vertical accuracy in terms of RMSE estimated using the 2m LiDAR as reference is 3.20m for TanDEM-X, 2.44m for the GSI, 7.00m for SRTM DEM and 10.24m for ASTER-GDEM. We also compared the accuracy of TanDEM-X with the other DEMs for different types of land cover classes. The results show that the absolute elevation error of TanDEM-X is higher for urban and vegetated areas, likewise to those observed for other global DEM’s. This is probably because the radar signals used by TanDEM-X tend to measure the first reflective surface that is encountered, which is often the top of the buildings or canopy. Hence, the TanDEM-X based DEM is more akin to a Digital Surface Model (DSM).

Critical elevation levels for flooding due to sea-level rise in Hawai‘i

Coastal strand and wetland habitats in the Hawaiian Islands are often intensively managed to restore and maintain biodiversity. Due to the low gradient of most coastal plain environments, the rate and aerial extent of sea-level rise (SLR) impact will rapidly accelerate once the height of the sea surface exceeds a critical elevation. Here, we develop this concept by calculating a SLR critical elevation and joint uncertainty that distinguishes between slow and rapid phases of flooding. We apply the methodology to three coastal wetlands on the Hawaiian Islands of Maui and O‘ahu to exemplify the applicability of this methodology for wetlands in the Pacific island region. Using high-resolution LiDAR digital elevation models, flooded areas are mapped and ranked from high (80 %) to low (2.5 %) risk based upon the percent probability of flooding under the B1, A2, and A1Fl emissions scenarios. As the rate of flooding transitioned from the slow to rapid phase, the area (expressed as a percentage of the total) at a high risk of flooding under the A1Fl scenario increased from 21.0 to 53.3 % (south Maui), 0.3 to 18.2 % (north Maui), and 1.7 to 15.9 % (north O‘ahu). At the same time, low risk areas increased from 34.1 to 80.2, 17.7 to 46.9, and 15.4 to 46.3 %. The critical elevation of SLR may have already passed (2003) on south Maui, and decision makers on North Maui and O‘ahu may have approximately 37 years (2050) to develop, and implement adaptation strategies that meet the challenges of SLR in advance of the largest impacts.

Using LiDAR Digital Elevation Model data to map Lake Agassiz beaches, measure their isostatically-induced gradients, and estimate their ages

High-resolution LiDAR Digital Elevation Model (DEM) data were used to create DEM mosaics and hillshade maps of a large region in North Dakota for better defining and measuring the strandlines of glacial Lake Agassiz. Although many of the strandlines in this region had been identified and named more than a century ago by Warren Upham, LiDAR images clearly reveal their complexity, and identify additional small and low relief ridges not previously mapped. The elevations of the crests of six well-developed strandlines – the Campbell, McCauleyville, lower Blanchard, Emerado, upper Ojata, and lower Ojata beaches – were measured. For each of these strandlines, north–south gradients were calculated, which were produced by differential isostatic rebound. The steepest gradient is on the uppermost (Campbell) beach in the sequence and the gradient on each beach below is progressively lower, confirming that these beaches were deposited in sequence from oldest to youngest as the level of Lake Agassiz declined. Because there were no anomalous beach gradients in this sequence, the Ojata beach formed after 9400 14C (10,630 cal) BP, which is when the higher-elevation Campbell beach was deposited, rather than at ∼10,000 14C (11,480 cal) years ago as some have suggested because of the age of organics below the Ojata beach. Those organics and the subaerial surface on which they lie survived the Moorhead transgression to the Campbell beach, and the Ojata beach must have been deposited over the older organic unit later, as lake level regressed. A simple extrapolation model was used to estimate the ages of beaches in the sequence.

Estimation of ground deformation caused by the earthquake (M7.2) in Japan, 2008, from the geomorphic image analysis of high resolution LiDAR DEMs

In this study, a new method for quantitative and efficient measurement for the ground surface movement was developed. The feature of this technique is to identify geomorphic characteristics by image matching analysis, using the intelligent images made from high resolution DEM(Digital Elevation Model). This method is useful to extract the small ground displacement where the surface shape was not intensely deformed.