Evaluating the performance of topobathymetric LiDAR to support multi-dimensional flow modelling in a gravel-bed mountain stream

Stream biophysical processes are commonly studied using multi-dimensional numerical modelling that quantifies flow hydraulics from which parameters such as habitat suitability, stream carrying capacity, and bed mobility are derived. These analyses would benefit from accurate high-resolution stream bathymetries spanning tens of kilometres of channel, especially in small streams or where navigation is difficult. Traditional ground-based survey methods are limited by survey time, dense vegetation and stream access, and are usually only feasible for short reaches. Conversely, airborne topobathymetric LiDAR surveys may overcome these limitations, although limited research is available on how errors in LiDAR-derived digital elevation models (DEMs) might propagate through flow models. This study investigated the performance of LiDAR-derived topobathymetry in support of multi-dimensional flow modelling and ecohydraulics calculations in two gravel-bedded reaches (approximately 200 m long), one morphologically complex and one morphologically simple, and at the segment scale (32 km-long stream segment) along a 15 m-wide river in central Idaho, USA. We compared metre and sub-metre-resolution DEMs generated from RTK-GPS ground and Experimental Advanced Airborne Research LiDAR-B (EAARL-B) surveys and water depths, velocities, shear stresses, habitat suitability, and bed mobility modelled with two-dimensional (2D) hydraulic models supported by LiDAR and ground-surveyed DEMs. Residual statistics, bias (B), and standard deviation (SD) of the residuals between depth and velocity predicted from the model supported by LiDAR and ground-survey topobathymetries were up to −0.04 (B) and 0.09 m (SD) for depth and −0.09 (B) and 0.20 m s⁻¹ (SD) for velocity. The accuracy (B = 0.05 m), precision (SD = 0.09 m), and point density (1 point m⁻²) of the LiDAR topobathymetric survey (regardless of reach complexity) were sufficient to support 2D hydrodynamic modelling and derivative stream habitat and process analyses, because these statistics were comparable to those of model calibration with B = 0 m and SD = 0.04 m for water surface elevation and B = 0.05 m s⁻¹ and SD = 0.22 m s⁻¹ for velocity in our investigation. © 2020 John Wiley & Sons, Ltd.     

Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry

This study developed and evaluated a hybrid approach to remote measurement of river morphology that combines LiDAR topography with spectrally based bathymetry. Comparison of filtered LiDAR point clouds with surveyed cross‐sections indicated that subtle features on low‐relief floodplains were accurately resolved by LiDAR but that submerged areas could not be detected due to strong absorption of near‐infrared laser pulses by water. The reduced number of returns made the active channel evident in a LiDAR point density map. A second dataset suggested that pulse intensity also could be used to discriminate land from water via a threshold‐based masking procedure. Fusion of LiDAR and optical data required accurate co‐registration of images to the LiDAR, and we developed an object‐oriented procedure for achieving this alignment. Information on flow depths was derived by correlating pixel values with field measurements of depth. Highly turbid conditions dictated a positive relation between green band radiance and flow depth and contributed to under‐prediction of pool depths. Water surface elevations extracted from the LiDAR along the water's edge were used to produce a continuous water surface that preserved along‐channel variations in slope. Subtracting local flow depths from this surface yielded estimates of the bed elevation that were then combined with LiDAR topography for exposed areas to create a composite representation of the riverine terrain. The accuracy of this terrain model was assessed via comparison with detailed field surveys. A map of elevation residuals showed that the greatest errors were associated with underestimation of pool depths and failure to capture cross‐stream differences in water surface elevation. Nevertheless, fusion of LiDAR and passive optical image data provided an efficient means of characterizing river morphology that would not have been possible if either dataset had been used in isolation. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process‐oriented investigations of flow hydraulics, sediment dynamics and in‐stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through‐water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. While the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM‐photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary‐winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50–100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM‐photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). Copyright © 2014 John Wiley & Sons, Ltd.     

Evaluation of on-line DEMs for flood inundation modeling

Recent and highly accurate topographic data should be used for flood inundation modeling, but this is not always feasible given time and budget constraints so the utility of several on-line digital elevation models (DEMs) is examined with a set of steady and unsteady test problems. DEMs are used to parameterize a 2D hydrodynamic flood simulation algorithm and predictions are compared with published flood maps and observed flood conditions. DEMs based on airborne light detection and ranging (LiDAR) are preferred because of horizontal resolution, vertical accuracy (∼0.1 m) and the ability to separate bare-earth from built structures and vegetation. DEMs based on airborne interferometric synthetic aperture radar (IfSAR) have good horizontal resolution but gridded elevations reflect built structures and vegetation and therefore further processing may be required to permit flood modeling. IfSAR and shuttle radar topography mission (SRTM) DEMs suffer from radar speckle, or noise, so flood plains may appear with non-physical relief and predicted flood zones may include non-physical pools. DEMs based on national elevation data (NED) are remarkably smooth in comparison to IfSAR and SRTM but using NED, flood predictions overestimate flood extent in comparison to all other DEMs including LiDAR, the most accurate. This study highlights utility in SRTM as a global source of terrain data for flood modeling.     

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Remote sensing is a powerful tool for examining river morphology. This study used detailed field surveys to assess the capability of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR to measure bed elevations in rivers with disparate optical characteristics. Field measurements of water column optical properties in the clear Snake River, the more complex Blue and Colorado, and highly turbid Muddy Creek were used to calculate depth retrieval precision and dynamic range. Differences in depth of a few centimeters were detectable via passive optical techniques in the clearest stream, but precision was greatly reduced under turbid conditions. The bathymetric LiDAR evaluated in this study could not detect shallow depths or differences in depth smaller than 11 cm owing to the difficulty of distinguishing water surface and bottom returns in laser waveforms. In clear water and with high radiometric resolution, hyperspectral systems such as CASI could detect depths approaching 10 m, but semi‐empirical analysis of the Aquarius LiDAR indicated that maximum detectable depths were of the order of 2–3 m in the clear‐flowing Snake River, and closer to 1 m in the more turbid streams. Turbidity also constrained spectrally based depth retrieval, and depth estimates from the Blue/Colorado were far less reliable than on the Snake. Both sensors yielded positively biased (0.03 m for CASI, 0.08 m for Aquarius) bed elevations on the Snake, with precisions of 0.16–0.17 m. For the Blue/Colorado, mean errors were of the order of 0.2 m, biased shallow for optical data and biased deep for LiDAR, although no Aquarius laser returns were recorded from the deepest parts of these channels; precisions were reduced to 0.29–0.32 m. Both approaches have advantages and limitations, and prospective users must understand the capabilities and constraints associated with various types of remote sensing to ensure efficient use of these evolving technologies. Copyright © 2015 John Wiley & Sons, Ltd.     

融合星载LiDAR系统GEDI数据与Sentinel-2影像的长江中游洲滩典型禾本科植物高度动态研究

植物是大型河流生态系统的重要成分。但受气候变化和人类活动影响,洲滩禾本科植物高度不断发生调整,进而影响洲滩生境和河道防洪安全,故需长期监测。近年来,伴随着星载激光雷达(LiDAR)技术的发展,应用LiDAR卫星数据反演洲滩禾本科植物高度成为一种可能。本文融合新一代星载LiDAR系统GEDI数据与Sentinel-2影像,基于XGBoost算法构建了考虑物候、累积温度与光合有效辐射指标的洲滩典型禾本科植物高度外推模型,同时利用Attention-UNet算法搭建了洪淹区域识别模型。随后以长江中游洲滩为例,探明了星载LiDAR技术在获取洲滩植株高度方面的性能,分析了各指标对模型精度的影响,并初步得出了洲滩典型禾本科植物高度对不同淹没条件的响应模式。主要结论包括:(1)星载LiDAR系统GEDI具有准确探测洲滩植物高度的能力,与无人机航测数据相比RMSE=0.43 m;(2)运用GEDI数据构建禾本科植物高度外推模型时,考虑物候和累积温度等指标可有效提升模型精度,提升幅度为6.8%～10.7%;(3)利用无人机航测数据对模型外推植物高度进行评价,RMSE=0.80m。同时从模型外推结果中可知...     

Detecting multi-scale riverine topographic variability and its influence on Chinook salmon habitat selection

Quantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1-m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid-wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley-scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle-pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small-scale wavelet power, which is related to pool-riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel-unit identification, and provided geomorphic assessment without laborious field surveys.     

Wavelet‐based regularization of the extracted topographic index from high‐resolution topography for hydro‐geomorphic applications

High‐resolution topography, e.g. 1‐m digital elevation model (DEM) from light detection and ranging (LiDAR), offers opportunity for accurate identification of topographic features of relevance for hydrologic and geomorphologic modelling. Yet, the computation of some derived topographic properties, such as the topographic index (TI), is characterized by daunting challenges that hamper the full exploration of topography‐based models. Particular problems, for example, arise when a distributed (or semi‐distributed) rainfall–runoff model is applied to high‐resolution DEMs. Indeed, the characteristic dependency between landscape resolution and the computed TI distribution results in the formation of un‐physical, unconnected saturated zones, which in turn cause unrealistic representations of rainfall–runoff dynamics. In this study, we present a methodology based on a multi‐resolution wavelet transformation that, by means of a soft‐thresholding scheme on the wavelet coefficients, filters the noise of high‐resolution topography to construct regularized sets of locally smoother topography on which the TI is computed. While the methodology needs a somewhat arbitrary definition of the wavelet coefficients threshold, our study shows that when the information content (entropy) of the TI distribution is used as a filtering efficiency metric, a critical threshold automatically emerges in the landscape reconstruction. The methodology is demonstrated using 1‐m LiDAR data for the Elder Creek River basin in California. While the proposed case study uses a TOPMODEL approach, the methodology can be extended to different topography‐based models and is not limited to hydrological applications. Copyright © 2012 John Wiley & Sons, Ltd.     

The topographic data source of digital terrain models as a key element in the accuracy of hydraulic flood modelling

The effects of the topographic data source and resolution on the hydraulic modelling of floods were analysed. Seven digital terrain models (DTMs) were generated from three different altimetric sources: a global positioning system (GPS) survey and bathymetry; high‐resolution laser altimetry data LiDAR (light detection and ranging); and vectorial cartography (1:5000). Hydraulic results were obtained, using the HEC‐RAS one‐dimensional model, for all seven DTMs. The importance of the DTM's accuracy on the hydraulic modelling results was analysed within three different hydraulic contexts: (1) the discharge and water surface elevation results from the hydraulic model; (2) the delineation of the flooded area; and (3) the relative sensitivity of the hydraulic model to changes in the Manning's n roughness coefficient. The contour‐based DTM was the least accurate with a root mean square error (RMSE) of 4·5 m in the determination of the water level and a variation of up to 50 per cent in the estimation of the inundated area of the floodplain. The GPS‐based DTM produced more realistic water surface elevation results and variations of up to 8 per cent in terms of the flooded area. The laser‐based model's RMSE for water level was 0·3 m, with the flooded area varying by less than 1 per cent. The LiDAR data also showed the greatest sensitivity to changes in the Manning's roughness coefficient. An analysis of the effect of mesh resolution indicated an influence on the delineation of the flooded area with variations of up to 7·3 per cent. In addition to determining the accuracy of the hydraulic modelling results produced from each DTM, an analysis of the time–cost ratio of each topographic data source illustrates that airborne laser scanning is a cost‐effective means of developing a DTM of sufficient accuracy, especially over large areas. Copyright © 2006 John Wiley & Sons, Ltd.     

Length scales and statistical characteristics of outer bank roughness for large elongate meander bends: The influence of bank material properties,floodplain vegetation and flow inundation

This article explores the length scales and statistical characteristics of form roughness along the outer banks of two elongate bends on a large meandering river through investigation of topographic variability of the bank face. The analysis also examines how roughness varies over the vertical height of the banks and when the banks are exposed subaerially and inundated during flood stage. Detailed data on the topography of the outer banks were obtained subaerially using terrestrial LiDAR during low flow conditions and subaqueously using multibeam echo sounding (MBES) during near‐bankfull conditions. The contributions of various length scales of topographic irregularity to roughness for subaerial conditions were evaluated for different elevation contours on the bank faces using Hilbert–Huang Transform (HHT) spectral analysis. Statistical characteristics for discrete areas on the bank faces were determined by calculating the root‐mean‐square of normal distances from a triangulated irregular network (TIN) surface. Results of the HHT analysis show that the characteristics of roughness along bank faces composed primarily of non‐cohesive sediment, and eroding into cropland, vary with bank elevation and exhibit a dominant range of roughness length scales (~15–50 m). However, bank faces composed predominantly of cohesive material and carved into a forested floodplain have relatively uniform topographic roughness characteristics over the vertical extent of the bank face and do not exhibit a dominant roughness length scale or range of length scales. Additionally, comparison between local surface roughness for subaerial versus subaqueous conditions shows that roughness decreases considerably when the banks are submerged, most likely because of the removal of vegetation and eradication of small‐scale erosional features in non‐cohesive bank materials by flow along the bank face. Thus, roughness appears to be linked to the hydraulic conditions affecting the bank, at least relative to conditions that develop when banks are exposed subaerially. Copyright © 2017 John Wiley & Sons, Ltd.     

Modelling rating curves using remotely sensed LiDAR data

Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage‐discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics‐based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m‐wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90‐m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a ‘hybrid model’ rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see ‘below’ the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics‐based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations. Copyright © 2012 John Wiley & Sons, Ltd.     

Kinematics of active earthflows revealed by digital image correlation and DEM subtraction techniques applied to multi‐temporal LiDAR data

Earthflow‐type landslides are persistent natural hazards having deep socio‐economic and environmental consequences. They have significantly contributed to the geomorphic evolution of mountainous slopes in Europe since the Late Glacial. An understanding of their complex kinematics is crucial to better constrain the processes governing their occurrence and mobility. In this work we explored the possibility to quantify displacement vectors on a spatially distributed basis and to quantify volumetric transfer at the slope scale with regard to a large flow‐type landslide located in the northern Apennines of Italy. For this purpose we applied digital image correlation (DIC) and digital elevation model differencing (DEMoD) techniques to multi‐temporal airborne LiDAR surveys of 2006, 2007 and 2009. The DIC was applied to greyscale slope gradient maps retrieved after precise co‐registration of LiDAR surveys. Thereby, movement patterns over various sectors of the landslide were reconstructed and quantified, most notably up to 20 m in the head zone, up to 51 m in the lower main track, and up to about 27 m at the landslide toe. The DEMoD analysis revealed significant mass transfer from the source to the tracks and toe zone, with the upper flow tracks acting as temporal storage of large amounts of material. The mass balance indicated that significant amounts of advancing landslide debris were eroded by a local stream. An integrated analysis of DEMoD and DIC results allowed for a discussion of governing processes, such as the transition from slide to flow, the influence of underlying topography on earthflow mobility, and the role of undrained loading as a mechanism of toe zone reactivation. In conclusion, the successful application of DIC and DEMoD to the case study underlines the added value of high‐resolution DEMs in the analysis of earthflow kinematics toward a better understanding of their role in the geomorphic evolution of slopes. Copyright © 2012 John Wiley & Sons, Ltd.     

APPLICATION OF UAVLS TO RAPID GEOLOGICAL SURVEYS

Three-dimensional scanning with LiDAR has been widely used in geological surveys. The LiDAR with high accuracy is promoting geoscience quantification. And it will be much more convenient, efficient and useful when combining it with the Unmanned Aerial Vehicle (UAV). This study focuses on UAV-based Laser Scanning (UAVLS)geological field mapping, taking two examples to present advantages of the UAVLS in contrast with other mapping methods. For its usage in active fault mapping, we scanned the Nanpo village site on the Zhangxian segment of the West Qinling north-edge fault. It effectively removed the effects of buildings and vegetation, and uncovered the fault trace. We measured vertical offset of 1.3m on the terrace T₁ at the Zhang river. Moreover, we also scanned landslide features at the geological hazard observatory of Lanzhou University in the loess area. The scanning data can help understand how micro-topography affects activation of loess landslides. The UAVLS is time saving in the field, only spending about half an hour to scan each site. The amount of average points per meter is about 600, which can offer topography data with resolution of centimeter. The results of this study show that the UAVLS is expected to become a common, efficient and economic mapping tool.     

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

Flood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, digital surface models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub‐metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. This paper describes the development of a LiDAR post‐processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post‐processing produces a digital terrain model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR‐derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ～1 m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a two‐dimensional finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features (such as buildings and roads) and taller vegetation features (such as trees and hedges). This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features. Copyright © 2007 John Wiley & Sons, Ltd.     

Extracting drainage networks and their connectivity using LiDAR data

Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north‐eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.     

Fine scale mapping of fractional tree canopy cover to support river basin management

Management of water, regionally, nationally and globally will continue to be a priority and complex undertaking. In riverine systems, biotic components like flora and fauna play critical roles in filtering water so it is available for human use and consumption. Preservation of ecosystems and associated ecosystem functions is therefore vital. In highly regulated large river basins, natural ecosystems are often supported through provision of environmental flows. Flow delivery, however, should be underpinned by rigorous monitoring to identify and prioritise biotic water requirements. Currently, large-scale monitoring solutions are scaled from remote sensing data via measurement of field evapotranspiration for woody tree vegetation species. However, as there is generally a mismatch between field data collection area and remote sensing pixel size, new methods are required to proportion tree evapotranspiration based on tree fractional canopy area per pixel. We present a novel method to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi-arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). We used Sentinel-1 and Sentinel-2 (radar and multispectral imagery) in a Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin. A predictor model combining the results of both regions was able to explain between 71%–85% of FTCC variation when compared to LiDAR FTCC when output in 10% increments. Development of this method underpins the advancement of woody vegetation monitoring to inform environmental flow management in the Murray–Darling Basin. The method and fine scale outputs will also be of value to other catchment management concerns such as altered catchment water yields related to bushfires and as such has application to water management worldwide.     

高空间分辨率数字高程模型测量技术及其在活断层研究中的应用

首先分析了获取数字高程模型(DEM)的高分遥感立体测量、合成孔径雷达干涉测量技术、激光雷达测距技术和运动重建技术等技术手段,以及现阶段高空间分辨率数字高程模型(VHR DEM)提取技术的主要特点,实际应用中需根据研究区地形地貌特点选择合适的VHR DEM获取技术;然后,结合最新研究成果着重阐述了VHR DEM在活断层识别及几何结构分析、同震位移与累积位移获取和古地震研究等领域的最新应用;最后指出,VHR DEM由于其高精度、高空间分辨率的特点,正逐步改变传统活断层的研究方法,使得对活断层的研究进入到了前所未有的精细化水平.      

Assessing the ability of airborne LiDAR to map river bathymetry

Airborne bathymetric LiDAR was collected for 220 river kilometres in the Yakima and Trinity River Basins in the USA. Concomitant with the aerial data collection, ground surveys of the river bed were performed in both basins. We assess the quality of the bathymetric LiDAR survey from the perspective of its application toward creating accurate, precise and complete streambed topography for numerical modelling and geomorphological assessment. Measurement error is evaluated with respect to ground surveys for magnitude and spatial variation. Analysis of variance statistics indicate that residuals from two independent ground surveys in similar locations do not come from the same population and that mean errors at different study locations also come from different populations. Systematic error indicates a consistent bias in the data and random error falls within values of expected precision. Published in 2007 by John Wiley & Sons, Ltd.     

Slicer Laser Altimetry In The Eastern Caribbean

Data acquired by the airborne Scanning Lidar Imager of Canopies by EchoRecovery (SLICER) laser altimeter provided high-resolution digital topographicdata over Puerto Rico, the Dominican Republic and several of the Lesser AntillesIslands. The instrument was developed by the NASA-Goddard Space Flight Center.It has the capability of multibeam resolution of ground elevations beneath densecanopy areas. Data, therefore, can be used to generate a more accurate representation of the ground surface by removing the vegetation cover. Although internal precision is high (10 cm to 1 m), absolute accuracy is difficult to evaluate and depends on several factors, including the post-processed kinematic GPS (KGPS) flight path for the aircraft platform and clear identification of ground returns in the SLICER waveform. We compared topographic profiles from USGS 30 m and 1:250K DEMs for Puerto Rico with those generated by SLICER and with spot elevations derived from static and continuous GPS surveys. SLICER and KGPS surveys cross at six points in western Puerto Rico. Agreement between both elevation data sets is excellent and well fit (r = 0.921) by a linear model with a final residual bias of -0.501 m for SLICER ground returns relative to KGPS elevations. The agreement between SLICER and USGS 30 m DEMs is also very good with the largest errors associated with steep slopes and high vegetation cover. Residuals between KGPS and USGS 30 m DEMs are +1 ± 25 m, assuming a fixed uniform offset of +43.23 m between WGS84 and mean sea level.