How does co-registration affect geomorphic change estimates in multi-temporal surveys?

Multi-temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)-borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi-temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi-temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV-borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf-off conditions (e.g., cross-flight strategy) turned out to be sufficient for our geomorphic research purposes.

<p>LiDAR (Light Detection and Ranging) systems such as ALS (Airborne Laser Scanning) are increasingly being used in studies that analyse the forest structure and for the characterisation of their ecosystem processes. The main reason is their ability to provide an accurate three-dimensional description of the canopy structure, compared to other existing methods, such as passive sensors or photogrammetry. In addition, the high positional accuracy of ALS and their capacity of penetrating the canopy through small gaps in the forest canopy allow the estimation of parameters such as aboveground biomass, vegetation height, or leaf area index, among others. In forestry applications, the acquisition of these parameters usually requires a pre-processing analysis of the point clouds, which includes ground point filtering, Digital Terrain Model (DTM) and Canopy Height Model (CHM) derivation, tree detection, and segmentation, among other processes.  In the last decades, point cloud processing has benefited by the development of dedicated software packages such as LAStools, FUSION, or Terrascan, focused on obtaining DTM/CHM and LiDAR-derived metrics. However, the recent development of more sophisticated software packages, such as LidR or Pycrown, allow implementing novel and state-of-the-art algorithms as well as specific user-created functions.</p><p>The wide variety of licensed and open-source software packages for ALS data processing, together with the increasing diversity of existing algorithms and methodologies, has provoked a multitude of comparative analysis of the most widely used algorithms in the scientific literature.  However, given the recent development of the field, a robust and exhaustive review of the current use of these software and the related algorithms is still missing. In this contribution, we present a synthesis review of 613 papers on the use of software packages and algorithms for ALS processing used between 2016 and 2020. The review focuses in forest environments with a complex structure where the difference in elevation, slope, and the existence of multiple vegetation strata usually requires more complex and specialised algorithms. Therefore, three specific steps of LiDAR processing workflow were considered: ground point filtering, DTM interpolation and crown detection and segmentation. The results showed that ground point filtering (84% of the studies) is the most common step in ALS processing, compared to DTM interpolation (71%) and tree segmentation (36%). For the DTM interpolation step, TIN construction was the most used method (13%) compared to other methods such as ordinary kriging (3%). Conventional software packages that employ algorithms based on progressive TIN densification or hieratical robust interpolation approaches were the most commonly used in ground point filtering for DTM generation. Meanwhile, other user developed advanced algorithms were used more frequently in canopy segmentation processing, especially in those articles using datasets with high point densities (165.93 p/m<sup>2</sup> on average), compared to datasets processed with more general software solutions as FUSION (13.81 p/m2).</p>

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively.

Reconstructing DEM using TLS point cloud data and NURBS surface

High resolution topography, by involving Digital Terrain Models (DTMs) and further accurate techniques for a proper displacement identification, is a valuable tool for a good and reliable description of unstable slopes. By comparing multitemporal surveys, the geomorphology of a landslide may be analyzed as well as the changes over time, the volumes transportation and the boundaries evolution. Being aware that a single technique is not sufficient to perform a reliable and accurate survey, this paper discusses the use of multi-platform, multi-source and multi-scale observations (both in terms of spatial scale and time scale) for the study and monitoring of unstable slopes. The final purpose is to highlight and validate a methodology based on multiple sensors and data integration, useful to obtain a comprehensive GIS (Geographic Information System) which can successfully be used to manage natural disasters or to improve the knowledge of a specific phenomenon in order to prevent and mitigate the hydro-geological risk. The novelty of the present research lies in the spatial integration of multiple remote sensing techniques such as: integration of Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) to provide a comprehensive and accurate surface description (DTM) at a fixed epoch (spatial continuity); continuous monitoring by means of spatial integration of Automated Total Station (ATS) and GNSS (Global Navigation Satellite System) to provide accurate surface displacement identification (time continuity). Discussion makes reference to a rockslide located in the northern Apennines of Italy from 2010 to 2013. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

The objective of this study was to identify and quantify surface depressions on grass-covered land surfaces using a high-resolution terrestrial laser scanning (TLS) point cloud, and a triangulated irregular network (TIN). The entire grassy land surface in the study area was divided into five subwatersheds of different topographic attributes (i.e., depression depth and surface slope). Surface depressions were identified and quantified using a TIN generated from a high-resolution TLS point cloud. The results indicated that microtopography of the grassy land surface was well-characterized within each subwatershed in comparison with field observations. With the terrestrial light detection and ranging (LIDAR) point cloud of 15-mm point spacing and the TIN method, surface depression storage depths of the five subwatersheds ranged from 1.73 to 14.28 mm in the study area. The surface depression storage depth, as expected, increased with the maximum depth of surface depression. It was also found to increase when the land surface slope became milder. A sensitivity analysis indicated that a point cloud with a point spacing of 30 mm was sufficient to obtain an accurate representation of the terrain surface in the study area. This study also indicated the TIN method can represent the ground surface and the surface depression more realistically than the commonly used digital elevation model (DEM) method due to the TIN method’s higher capability of identifying and filtering out surface obstructions such as blades of grass. Moreover, by using the high-resolution TLS technology and the TIN method, our study provides an important and broad range of reference data on the surface depression storage depth commonly needed in application of the Storm Water Management Model (SWMM) and other watershed models.

Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50–100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.

ALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey conditions.This article proposes the use of a new estimation method in the filtering of point clouds obtained from airborne laser scanning (ALS), provisionally called Msplit-estimation. The application of Msplit-estimation in ALS data filtering requires the determination of the appropriate functional model for the surface, which will be used in the filtering of the set of points. A polynomial terrain surface model was selected for this purpose. Two methods of filtering using the Msplit method are presented. The first is based on the estimated parameters of the polynomial describing the surface (called the ‘quality’ approach in the article). The second method (provisionally called the ‘quantity’ method) is carried out in two stages. The first stage is point cloud filtering, which results in two subsets being created. One of these is the subset of points intended for DTM creation, while the other contains the remaining points. The second stage of the approach is the creation of a DTM from the first subset.Since the Msplit method has an analytical character, the ATIN method was selected to verify the correct operation of the method. The ATIN method is based on computational geometry and uses repeated Delaunay triangulation and statistical evaluation of the geometric parameters. Comparison of Msplit with a method based on different principles mitigates errors arising from similarly functioning methods belonging to the same group of filters. The choice of the ATIN method was also dictated by its established position among filtering algorithms. The method is well-known, documented, and verified and this ensures that filtering by this method provides a reliable result that can serve as a reference for comparison with the proposed new filtering method.The theoretical discussion presented in this article was verified with two practical examples. The results obtained from computation by the Msplit method with appropriate terrain models encourage more detailed theoretical and empirical tests of this method for the filtering and segmentation of ALS data-sets.

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs.

Light Detection and Ranging (LIDAR) has been used for years with a variety of applications, including the efficient creation of digital terrain models (DTMs) for large-scale, high- accuracy mapping. LIDAR technology offers fast, real-time collection of 3-D points so that allows the production of higher-accuracy orthophotos than traditional stereo-compilation methods. The technology is moving quickly toward offering more efficient collection techniques for applications such as city modelling and true orthophoto production. Traditional orthophoto production suffers from the fact that buildings (and any other objects above ground) are not correctly placed in the orthophoto. True orthophoto production overcomes these deficiencies by taking the digital surface model (DSM) into account. However, the resulting true orthophotos still suffer from occluded areas and unsharp edges of buildings and roads. The experimental investigations with different data sets show that it is essential to use high quality DSMs to produce high quality true orthophotos. In this paper, aspects of the improvement of true orthophoto production will be discussed: (a) in the first approach which has been widely used in photogrammetry, the DSM is used for generating true orthophotos and (b) in comparison to the DSM based method, semi-automatically collected vector data of buildings are superimposed on DTM and are used as input data for generating true orthophotos in the second approach. The processes and problems of both true orthophoto production procedures will be investigated in detail and results include the comparison of true orthophoto productions based on DSM (from LIDAR data) and DTM plus vector data. For the experimental investigation, a LIDAR data set for downtown Stuttgart is used.

Digital elevation models (DEMs) resulting from LIDAR (light detection and ranging) surveys are now more available in the hydrology and hydraulics (H&H) community, not only for hydraulic applications in small areas close to river networks but also for hydrologic applications in whole basins. Several questions arise when trying to combine LIDAR data and hydrologic models. Despite the long processing time, LIDAR-derived DEMs can provide more accurate information that is useful for basin hydrogeomorphic characterization, in comparison with DEMs at resolutions commonly used in hydrologic applications (cell size 20-30 m). Of particular focus here are river network properties and the instantaneous unit hydrograph (IUH) framework. Two case studies, one in Italy and the other in the USA, are presented in which three DEMs are analyzed with differing resolutions as follows: "standard," i.e., a resolution commonly used in hydrologic applications (cell size 20-30 m), LIDAR (cell size 1-2.5 m), and LIDAR-resampled at the same resolution as the "standard" DEM. Results suggest that the higher spatial resolution LIDAR-derived data are preferable and can introduce more detailed information about basin hydrogeomorphic behavior.

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.