Bare-earth Digital Elevation Models Research Articles

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Focusing the view: Improved methods for assessing viewshed impacts of onshore wind turbines

Onshore wind turbine capacity continues to grow and will only accelerate, though siting can be challenging given community opposition. We apply the hedonic valuation method with residential property sales data to assess nearby residents’ willingness to pay to avoid having views of turbines from their property. In doing so, we aim to improve methods of assessing viewshed impacts for turbines and other amenities and disamenities that have a visual component. Our recommended viewshed approach uses a Digital Surface Model (DSM), which accounts for trees and buildings that obstruct views. For comparison, we also create viewsheds based on bare-earth Digital Elevation Model (DEM), which has been more typically used other studies. Using data from New England, USA, we use a difference-in-differences identification strategy with treatment defined by the visibility of a wind turbine, while also controlling for proximity-based treatment effects. The results suggest that property values decline by 2.2%–2.5% when a wind turbine is visible, with larger impacts in urban and coastal areas. DEM methods misclassify viewshed for about 75% of properties, when compared to the DSM-based viewshed, and the resulting DEM-based valuation estimates are attenuated.

Quantifying the Impact of Hurricane Harvey on Beach−Dune Systems of the Central Texas Coast and Monitoring Their Changes Using UAV Photogrammetry

Historically, the Texas Gulf Coast has been affected by many tropical storms and hurricanes. The most recent severe impact was caused by Hurricane Harvey, which made landfall in August 2017 on the central Texas coast. We evaluated the impact of Hurricane Harvey on the barrier islands of the central Texas coast, including San Jose Island, Mustang Island, and North Padre Island. We used public data sets, including 1 m resolution bare-earth digital elevation models (DEMs), derived from airborne lidar acquisitions before (2016) and after (2018) Hurricane Harvey, and sub-meter scale aerial imagery pre- and post-Harvey to evaluate changes at a regional scale. Shoreline proxies were extracted to quantify shoreline retreat and/or advance, and DEM differencing was performed to quantify net sediment erosion and accretion or deposition. Unmanned aerial vehicle surveys were conducted at each island to produce high-resolution (cm scale) imagery and topographic data used for morphological and change analyses of beaches and dunes at the local scale. The results show that Hurricane Harvey caused drastic local shoreline retreat, reaching 59 m, and significant erosion levels of beach−dune elements immediately after its landfall. Erosion and recovery processes and their levels were influenced by the local geomorphology of the beach−foredune complexes. It is also observed that local depositional events contributed to their post-storm rebuilding. This study aims to enhance the understanding of major storm impacts on coastal areas and help in future protection planning of the Texas coast. It also has broader implications for coastlines on Earth affected by major storms.

Consumer‐grade UAV solid‐state LiDAR accurately quantifies topography in a vegetated fluvial environment

AbstractUnoccupied aerial vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion (SfM) photogrammetry. Advances in UAV payloads and the advent of solid‐state LiDAR have enabled consumer‐grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV‐acquired images. We evaluate the application of a DJI Zenmuse L1 solid‐state LiDAR sensor on a Matrice 300 RTK UAV to generate digital elevation models (DEMs). To assess flying height (60–80 m) and speed parameters (5–10 ms−1) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter and classify the point clouds to produce a raster DEM product. A dense control network showed that there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multiscale Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of −0.015 m on unvegetated gravel bars. Multiscale Model to Model Cloud Comparison (M3C2) residuals compared UAV LiDAR and Terrestrial Laser Scanner point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid‐state LiDAR was effective at penetrating sparse canopy‐type vegetation but was less penetrable through dense ground‐hugging vegetation (e.g. heather, thick grass). Whilst UAV solid‐state LiDAR needs to be supplemented with bathymetric mapping to produce wet–dry DEMs, by itself, it offers advantages to comparable geomatics technologies for kilometre‐scale surveys. Ten best practice recommendations will assist users of UAV solid‐state LiDAR to produce bare earth DEMs.

Fault structure and segmentation of the Punta Montalva Fault in southwestern Puerto Rico using high-resolution digital elevation models

The Punta Montalva Fault is an active fault that slipped during the 2019–2023 Puerto Rico seismic sequence. It lies in southwestern Puerto Rico within the ∼250-km-wide deformation zone of the North American-Caribbean plate boundary. The fault cuts Miocene limestone and contains surface features that suggest left-lateral displacement. Previous work suggested that the fault connects with the North Boquerón Bay Fault for a length of 33 km and poses a large seismic hazard. However, not much is known about the structure, segmentation, and spatial extent of the fault as it is obscured under thick vegetation. This study used high-resolution topographic data to map the structure, segmentation, and spatial extent of the fault. The results would allow us to better assess the seismic potential of the Punta Montalva Fault by constraining its spatial extent and how strain has been accommodated. A 0.5 m resolution bare-earth digital elevation model (DEM) derived from a 2018 airborne LiDAR survey and another 1 m resolution 2018 DEM showing shallow bathymetry were used to map the Punta Montalva Fault. High-resolution mapping revealed that the Punta Montalva Fault is comprised of three main fault segments on land, a possible additional scarp, and three fault scarps on the seafloor to the SE. No evidence suggests the Punta Montalva Fault extends further NW of Punta Montalva into La Parguera in Lajas. The DEM revealed that the fault cuts a shallowly dipping sedimentary sequence of the Ponce Limestone that has karst topography in places. On land, the fault is conspicuous where it forms scarps, drags and cuts sedimentary layers, and forms a duplex structure with a predominantly left-lateral offset. A left-lateral offset of an intermittent stream has been refined to 133.6 ± 7.3 m. The fault scarps along a marine shelf strike E-W and show a maximum normal offset of 4.8 m. Another sub-parallel fault scarp in the seafloor strikes NW-SE and displays a left-lateral offset of 98.3 m. The total length of the fault segments is ∼2.67 km long. The surface segmentation suggests that the fault would have a lower seismic hazard than if it were continuous and connected to the North Boquerón Bay Fault. However, if the Punta Montalva Fault is continuous beneath the surface, its total fault length would be approximately 7.97 km long and would pose a larger seismic hazard than that suggested by the surface segmentation. Strike-slip, folding, and normal offset suggest that the area is being deformed by transtentional deformation, which supports seismic analyses and tectonic models.

Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) generates digital elevation models (DEMs) that range from the local to global scale. Collectively, these DEMs are essential to determining the timing and extent of coastal inundation and improving community preparedness, event forecasting, and warning systems. We initiated a comprehensive framework at NCEI, the Continuously Updated DEM (CUDEM) Program, with seamless bare-earth, topographic-bathymetric and bathymetric DEMs for the entire United States (U.S.) Atlantic and Gulf of Mexico Coasts, Hawaii, American Territories, and portions of the U.S. Pacific Coast. The CUDEMs are currently the highest-resolution, seamless depiction of the entire U.S. Atlantic and Gulf Coasts in the public domain; coastal topographic-bathymetric DEMs have a spatial resolution of 1/9th arc-second (~3 m) and offshore bathymetric DEMs coarsen to 1/3rd arc-second (~10 m). We independently validate the land portions of the CUDEMs with NASA’s Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observatory and calculate a corresponding vertical mean bias error of 0.12 m ± 0.75 m at one standard deviation, with an overall RMSE of 0.76 m. We generate the CUDEMs through a standardized process using free and open-source software (FOSS) and provide open-access to our code repository. The CUDEM framework consists of systematic tiled geographic extents, spatial resolutions, and horizontal and vertical datums to facilitate rapid updates of targeted areas with new data collections, especially post-storm and tsunami events. The CUDEM framework also enables the rapid incorporation of high-resolution data collections ingested into local-scale DEMs into NOAA NCEI’s suite of regional and global DEMs. Future research efforts will focus on the generation of additional data products, such as spatially explicit vertical error estimations and morphologic change calculations, to enhance the utility and scientific benefits of the CUDEM Program.

Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis

Abstract. Water ponding and pluvial flash flooding (PFF) on roadways can pose a significant risk to drivers. Furthermore, climate change, growing urbanization, increasing imperviousness, and aging stormwater infrastructure have increased the frequency of these events. Using physics-based models to predict pluvial flooding at the road segment scale requires notable terrain simplifications and detailed information that is often not available at fine scales (e.g., blockage of stormwater inlets). This brings uncertainty into the results, especially in highly urbanized areas where micro-topographic features typically govern the actual flow dynamics. This study evaluates the potential for flood observations collected from Waze – a community-based navigation app – to estimate the likelihood of PFF at the road segment scale. We investigated the correlation of the Waze flood reports with well-known flood observations and maps, including the National Flood Hazard Layer (NFHL), high watermarks, and low water crossings data inventories. In addition, highly localized surface depressions and their catchments are derived from a 1 m resolution bare-earth digital elevation model (BE-DEM) to investigate the spatial association of Waze flood reports. This analysis showed that the highest correlation of Waze flood reports exists with local surface depressions rather than river flooding, indicating that they are potentially useful indicators of PFF. Accordingly, two data-driven models, empirical Bayes (EB) and random forest (RF) regression, were developed to predict the frequency of flooding, a proxy for flood susceptibility, for three classes of historical storm events (light, moderate, and severe) in every road segment with surface depressions. Applying the models to Waze data from 150 storms in the city of Dallas showed that depression catchment drainage area and imperviousness are the most important predictive features. The EB model performed with reasonable precision in estimating the number of PFF events out of 92 light, 41 moderate, and 17 severe storms with 0.84, 0.85, and 1.09 mean absolute errors, respectively. This study shows that Waze data provide useful information for highly localized PFF prediction. The superior performance of EB compared to the RF model shows that the historical observations included in the EB approach are important for more accurate PFF prediction.

A multi-proxy assessment of terrace formation in the lower Trinity River valley, Texas

Abstract. A proposed null hypothesis for fluvial terrace formation is that internally generated or autogenic processes, such as lateral migration and river-bend cutoff, produce variabilities in channel incision that lead to the abandonment of floodplain segments as terraces. Alternatively, fluvial terraces have the potential to record past environmental changes from external forcings that include temporal changes in sea level and hydroclimate. Terraces in the Trinity River valley have been previously characterized as Deweyville groups and interpreted to record episodic cut and fill during late Pleistocene sea level variations. Our study uses high-resolution topography of a bare-earth digital elevation model derived from airborne lidar surveys along ∼ 88 linear kilometers of the modern river valley. We measure both differences in terrace elevations and widths of paleo-channels preserved on these terraces in order to have two independent constraints on terrace formation mechanisms. For 52 distinct terraces, we quantify whether terrace elevations fit distinct planes – expected for allogenic terrace formation tied to punctuated sea level and/or hydroclimate change – by comparing variability in a grouped set of Deweyville terrace elevations against variability associated with randomly selected terrace sets. Results show Deweyville groups record an initial valley floor abandoning driven by allogenic forcing, which transitions into autogenic forcing for the formation of younger terraces. For these different terrace sets, the slope amongst different terraces stays constant. For 79 paleo-channel segments preserved on these terraces, we connected observed changes in paleo-channel widths to estimates for river paleo-hydrology over time. Our measurements suggest the discharge of the Trinity River increased systematically by a factor of ∼ 2 during the late Pleistocene. Despite this evidence of increased discharge, the similar down-valley slopes between terrace sets indicate that there were likely no increases in sediment-to-water discharge ratios that could be linked to allogenic terrace formation. This is consistent with our elevation clustering analysis that suggests younger terraces are indistinguishable in their elevation variance from autogenic terrace formation mechanisms, even if the changing paleo-channel dimensions might, viewed in isolation, provide a mechanism for allogenic terrace formation. Methods introduced here combine river-reach-scale observations of terrace sets and paleo-hydrology with local observations of terraces and paleo-channels to show how interpretations of allogenic versus autogenic terrace formation can be evaluated within a single river system.

Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique)

Flood hazard and risk analysis in developing countries is a difficult task due to the absence or scarce availability of flow data and digital elevation models (DEMs) with the necessary quality. Up to eight DEMs (ALOS Palsar, Aster GDEM, Bare Earth DEM, SRTM DEM, Merit DEM, TanDEM-X DEM, NASA DEM, and Copernicus DEM) of different data acquisition, spatial resolution, and data processing were used to reconstruct the January 2015 flood event. The systematic flow rate record from the Mocuba city gauge station as well as international aid organisms and field data were used to define both the return period peak flows in years for different flood frequencies (Tyear) and the January 2015 flooding event peak flow. Both visual and statistical analysis of flow depth values at control point locations give us a measure of the different hydraulic modelling performance. The results related to the Copernicus DEM, both in visual and statistical approach, show a clear improvement over the results of the other free global DEMs. Under the assumption that Copernicus DEM provides the best results, a flood hazard analysis was carried out, its results being in agreement with previous data of the effects of the January 2015 flooding event in the Mocuba District. All these results highlight the step forward that Copernicus DEM represents for flood hazard analysis in developing countries, along with the use of so-called “citizen science” in the form of flooding evidence field data acquisition.

Developing bare-earth digital elevation models from structure-from-motion data on barrier islands

Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy. Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.

Automated landslide detection model to delineate the extent of existing landslides

Landslides are one of the most common natural hazards and cause major socioeconomic impacts worldwide. Identifying the locations of the active or inactive landslides before development may play a major role in identifying areas of high risk. Traditional methods for inventorying landslides involve field surveying and interpretation of photogrammetric data. The advent of recent remote sensing technologies has expedited this process, and as a result, several computer-based algorithms used to identify the locations of past landslides have been proposed. Computer-based analyses provide significant advantages over traditional methods; however, a majority of these computer-based analyses require the user to define the properties of the landslide prior to the search and require supervision and quality assurance. The purpose of this study is to present a simple, new methodology that can be implemented with readily available tools and datasets without the need to supervise the analysis after the parameters regarding landslide morphology are defined for that region. This methodology is referred to as automated landslide detection model (ALDM). Three areas with LiDAR bare earth digital elevation models (DEMs) have been used to test the ALDM, each consisting of a varying range of mapped landslide features. The ALDM results were compared against data obtained from the Pennsylvania Department of Conservation and Natural Resources and landslides that were determined visually from the hillshade map of the study area. The results demonstrate that the ALDM method was able to accurately capture both the landslides and non-landslides in all of the areas evaluated with accuracies of 70% and 92%, respectively. Additionally, the study showed that the proposed ALDM method could be implemented in different regions where landslides of different shapes and sizes could be detected.

Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: an application to the Gold Basin landslide complex, northern Washington

The landslide complex at Gold Basin, Washington, has been drawing considerable attention after a catastrophic runout of the nearby landslide at Oso, Washington, in 2014. To evaluate potential threats of the Gold Basin landslide to the campground down the slope, remote sensing and numerical modeling were integrated to monitor recent landslide activity and simulate hypothetical runout scenarios. Bare-earth LiDAR DEM (digital elevation model) differencing, InSAR (Interferometric Synthetic Aperture Radar), and offset tracking of SAR images reveal that localized collapses at the headscarps have been the primary type of landslide activity at Gold Basin from 2005 to 2019, and currently no signs indicative of movement of a large centralized block or a deep-seated main body were detected. The maximum horizontal deformation rate is 5 m/year occurring primarily from headscarp recession of the middle lobe, and the annual landsliding volume of the whole landslide complex averages 1.03 × 105 m3. From three-dimensional limit equilibrium analysis of generalized terrace structures, the maximum landslide volume is estimated as 2.0 × 106 m3. Simulations of hypothetical runout scenarios were carried out using the depth-averaged two-phase model D-claw with above-obtained landslide geometry constraints. The simulation results demonstrate that debris flows with volume less than 105 m3 only pose limited threats to the campground, while volumes over 106 m3 could cause severe damages. Consequently, the estimated maximum landslide volume of 2.0 × 106 m3 suggests a potential risk to the campground nearby. Adaption of our methodology could prove useful for evaluating other similar landslides globally for hazards prevention and mitigation.

Estimates of three-dimensional rupture surface geometry of deep-seated landslides using landslide inventories and high-resolution topographic data

Deep-seated landslides cause significant damage to the built environment. Characterizing the shear surface and volume of these features is often limited to intensive site-specific monitoring, or at a larger scale, generalized empirical relationships developed from change analysis. However, there is limited means of generalizing regional-scale rupture surface information for deep-seated landslides where deposits still overlay much of the failure surface. Herein, a method is proposed that uses high-resolution bare earth digital elevation models with an inventory of mapped landslide extents to gather first order estimates of three-dimensional rupture surface geometries and volumes for rotational and compound landslide features at a regional scale. This approach is first calibrated to a suite of well-characterized landslides and then applied to over eight hundred deep-seated landslides in three different geologic regimes. Estimates of landslide area-volume relationships are proposed that are consistent with previous empirical assessments. The aforementioned relationships describe mean landslide thickness; added relationships relating landslide area to maximum landslide thickness are proposed. The presented results suggest limits on magnitudes of depth with landslide classification between geologic settings. Consistent with prior research, it is observed that landslide area exhibits a first-order control on maximum and mean landslide thickness, but further insight is provided towards the influence of a limited set of landslide classification, which exhibit controls on the range of magnitudes observed for maximum landslide depth associated with failure. Lastly, the implications and limitations of the developed three-dimensional mapping approach is discussed.

Review of geological and seismotectonic investigations related to 1998 Mw5.6 and 2004 Mw5.2 earthquakes in Krn Mountains

A review of geological and seismotectonic investigations conducted in the two decades after the 12 April 1998 earthquake in Krn Mountains, according to its magnitude the strongest earthquake in Slovenia in the 20th century, is given. Many of these studies have wider scientific meaning than expected from the size of the earthquake. This was the first case in Slovenia that a strong earthquake was undoubtedly related to a particular fault. Seismotectonic studies of seismogenic Ravne fault revealed that it is an actively propagating strike-slip fault growing by interaction of individual right stepping fault segments and breaching of local transtensional step-over zones. Airborne laser scanning (LiDAR) of Idrija and Ravne faults, which resulted in high resolution bare earth digital elevation model, was in 2005 for the first time used to study surface expression of an active fault in Europe. Among the primary characteristics of the 1998 earthquake were extensive environmental effects expressed mainly as massive rockfalls. They were systematically documented and evaluated for intensity assessment using European Macroseismic Scale (EMS-98) and Environmental Seismic Intensity (ESI) scale introduced in 2007, because application of the data on damage to buildings was limited in sparsely populated high mountains epicentral area. These studies were pioneering due to novelty of both intensity scales, indicating their strong points and weaknesses. Large variations in damage to buildings in the upper Soča valley at similar epicentral distances pointed to strong site effects due to very heterogeneous glacial and fluvial deposits in sedimentary basins and valleys. Therefore, different seismic microzonation maps were prepared to evaluate the influence of soft sediments on seismic ground motion. Conducted studies fostered development of several earthquake geology research methods in Slovenia as tectonic geomorphology, evaluation of environmental seismic effects and seismotectonics. They had positive impact also on the university education in the fields of geophysics, seismology and structural geology.

Removal of Positive Elevation Bias of Digital Elevation Models for Sea-Level Rise Planning

Digital elevation models (DEMs) based on LiDAR surveys provide critical information for predicting the vulnerability of coastal areas to sea-level rises. Due to the poor penetration of LiDAR pulses in marsh vegetation, bare-earth DEMs for coastal wetlands are often subject to positive elevation bias, and thus underestimate vulnerability. This data publication includes comprehensive elevation surveys from seven coastal wetlands in coastal New Jersey, and an evaluation of the accuracy and positive elevation bias of each publically available DEM. Resampling the DEMs at a coarser resolution, replacing cell values using the minimum value in a wider search window (4 m), removed this positive elevation bias with no loss of accuracy.

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

Long-term hydraulic mining sediment budgets: Connectivity as a management tool

Approximately 1.1 × 109 m3 of sediment were produced by late nineteenth century hydraulic gold mining in the Sierra Nevada of northern California. Modern geospatial methods combined with 2014 airborne LiDAR 1 × 1-m data are used to map and model a distributed sediment budget for upper Steephollow Creek, one of the most severely impacted catchments in the region. Digital elevation models (DEMs) were developed for three times using geomorphometry to construct sediment budgets. The 2014 surface is from the LiDAR bare-earth DEM, the pre-mining surface before 1853 was interpolated from contours extended along ridges and pre-mining valley bottoms, and the 1884 maximum aggradation surface was interpolated from contours extended across high terraces. Mine pit volumes indicate that ~23.5 × 106 m3 of hydraulic mining sediment (HMS) was produced in the 54.6 km2 study catchment. Volumes of HMS stored in the catchment were computed for 2014 (3.75 × 106 m3) and for ca. 1884 at the time of maximum aggradation (7.15 × 106 m3). The 2014 storage is 16% of the sediment produced in the catchment, indicating a sediment delivery ratio (SDR) of 84% of the HMS from the basin, which is higher than most agricultural basins and indicates a strong longitudinal sediment connectivity in this region. Storage in 1884 represents 30% of production indicating a SDR of 70% during the period of mining. Dynamics and strong scale dependencies of sediment connectivity are documented with regard to space and time. Over the past 130 years, 3.57 × 106 m3—approximately half of the storage in upper Steephollow Creek—was eroded and carried out of the catchment.

On the use of global DEMs in ecological modelling and the accuracy of new bare-earth DEMs

It is now widely acknowledged that the increasing availability of remotely sensed data facilitates ecological modelling. Digital elevation models (DEMs) are arguably one of the most common remote sensing products used in this context. Topographic indices (e.g. slope, orientation, rugosity) derived from DEMs are widely used as surrogates for field-measured environmental variables. Available global DEMs, such as those from the shuttle radar topography mission (SRTM), however, do not provide information on bare-earth elevation as they measure elevation of the highest objects above the ground (e.g. canopy). This affects the derived topographic indices and limits the use of global DEMs in ecological modelling. Unfortunately, most ecological studies ignore this limitation despite the fact that methods to remove the vegetation offset have been developed. We used high resolution LiDAR DTM to assess the accuracy of two newly available global bare-earth DEMs where such methods were applied and to compare them with the SRTM DEM. Furthermore, we assessed the effect of DEMs’ vertical error on species distribution models (SDMs) by calculating slope and topographic wetness index (TWI) from these different models and evaluating their suitability for SDMs by adopting a virtual species approach. We simulated virtual species based on slope and TWI derived from accurate LiDAR DTM at three resolutions (30 m, 90 m and 900 m) and developed univariate generalized models to assess the performance of the bare-earth and SRTM DEMs. Our results show that the vertical error in both newly available, vegetation-corrected global DEMs is indeed successfully reduced. The overall vertical root mean squared error (RMSE) was 10.52 m for SRTM, while it was 6.80 m and 6.25 m for the two global bare-earth DEMs. The effect of the vertical error on SDMs was most significant at finer spatial resolutions. Using SRTM DEM, as opposed to a more accurate bare-earth DEM, led to a decline in area under curve (AUC) values from 0.94 to 0.77. SDMs fitted with slope and TWI derived from new global bare-earth DTMs performed slightly better than SRTM. Since methods for vegetation-offset removal in DEMs exist and corrected DEMs are freely available, we argue that the vertical accuracy of DEMs should be more consistently considered. Local, high-accuracy DEMs should be used where available; in remaining instances, however, global DEMs where vertical bias was minimized should be used in ecological modelling. Further improvement of global DEMs at 30 m and better resolutions are needed to enhance accuracy of derived indices and ecological models.

LiDAR—A Technology to Assist with Smart Cities and Climate Change Resilience: A Case Study in an Urban Metropolis

In this paper, we demonstrate three unique use cases of LiDAR data and processing, which can be implemented in an urban metropolis to determine the challenges that are associated with climate change. LiDAR data for the City of Toronto were collected in April 2015 with a density of 10 points/m2. We utilized both a digital terrain model and a bare earth digital elevation model in this work. The first case study estimated storm water, in which we compared flow accumulation values and catchment areas generated with a 20-m DEM and a 1-m LiDAR DEM. The finer resolution DEM demonstrated that the urban street features play a significant role in flow accumulation by directing flows. Urban catchment areas were found to occur on spatial scales that were smaller than the 20-m DEM cell size. For the second case study, the solar potential in the City of Toronto was calculated based on the slope and aspect of each land parcel. According to area, 56% of the city was found to have high solar potential, with 33% and 11% having medium and low solar potential. For the third case study, we calculated the building heights for 16,715 high-rise buildings in Toronto, which were combined with ambulance and fire emergency response times required to reach the base of the building. All buildings that had more than 17 stories were within a 5-min response time for both fire and ambulance services. Only 79% and 88% of these buildings were within a 3-min response time for ambulance and fire emergencies, respectively. LiDAR data provides a highly detailed record of the built urban environment and can provide support in the planning and assessment of climate change resilience activities.

Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM.