Progressive Morphological Filter Research Articles

An automated system for 2D building detection from UAV-based geospatial datasets

The focus of this manuscript is on integrating optical images and laser point clouds carried on low-cost UAVs to create an automated system capable of generating urban city models. After pre-processing both datasets, we co-registered both datasets using the DLT transformation model. We estimated structure heights from the LiDAR dataset through a progressive morphological filter followed by removing bare ground. Unsupervised and supervised image classification techniques were applied to a six-band image created from the optical and LiDAR datasets. After finding building footprints, we traced their edges, outlined their borderlines, and identified their geometric boundaries through several image processing and rule-based feature identification algorithms. Comparison between manually digitized and automatically extracted buildings showed a detection rate of about 92.3 % with an average of 7.4 % falsely identified areas with the six-band image in contrast to classifying only the RGB image that detected about 63.2 % of the building pixels with 25.3 % pixels incorrectly identified. Moreover, our building detection rate with the 6-band image was superior to that attained by performing traditional image segmentation for only the LiDAR DEM. Shifts in the horizontal coordinates between corner points identified by a human operator and those detected by the proposed system were in the range of 10–15 cm. This is an improvement over traditional satellite and manned-aerial large mapping systems that have lower accuracies due to sensor limitations and platform altitude. These findings demonstrate the benefits of fusing multiple UAV remote sensing datasets over utilizing a single dataset for urban area mapping and 3D city modeling.

Bare-earth DEM generation from ArcticDEM and its use in flood simulation

Abstract. In urban areas, topography data without above-ground objects are typically preferred in wide-area flood simulation but are not yet available for many locations globally. High-resolution satellite photogrammetric DEMs, like ArcticDEM, are now emerging and could prove extremely useful for global urban flood modelling; however, approaches to generate bare-earth DEMs from them have not yet been fully investigated. In this paper, we test the use of two morphological filters (simple morphological filter – SMRF – and progressive morphological filter – PMF) to remove surface artefacts from ArcticDEM using the city of Helsinki (192 km2) as a case study. The optimal filter is selected and used to generate a bare-earth version of ArcticDEM. Using a lidar digital terrain model (DTM) as a benchmark, the elevation error and flooding simulation performance for a pluvial scenario were then evaluated at 2 and 10 m spatial resolution, respectively. The SMRF was found to be more effective at removing artefacts than PMF over a broad parameter range. For the optimal ArcticDEM-SMRF the elevation RMSE was reduced by up to 70 % over the uncorrected DEM, achieving a final value of 1.02 m. The simulated water depth error was reduced to 0.3 m, which is comparable to typical model errors using lidar DTM data. This paper indicates that the SMRF can be directly applied to generate a bare-earth version of ArcticDEM in urban environments, although caution should be exercised for areas with densely packed buildings or vegetation. The results imply that where lidar DTMs do not exist, widely available high-resolution satellite photogrammetric DEMs could be used instead.

Analysis of UAS-LiDAR Ground Points Classification in Agricultural Fields Using Traditional Algorithms and PointCNN

Classifying bare earth (ground) points from Light Detection and Ranging (LiDAR) point clouds is well-established research in the forestry, topography, and urban domains using point clouds acquired by Airborne LiDAR System (ALS) at average point densities (≈2 points per meter-square (pts/m2)). The paradigm of point cloud collection has shifted with the advent of unmanned aerial systems (UAS) onboard affordable laser scanners with commercial utility (e.g., DJI Zenmuse L1 sensor) and unprecedented repeatability of UAS-LiDAR surveys. Therefore, there is an immediate need to investigate the existing methods, and to develop new ground classification methods, using UAS-LiDAR. In this paper, for the first time, traditional ground classification algorithms and modern machine learning methods were investigated to filter ground from point clouds of high-density UAS-LiDAR data (≈900 pts/m2) over five agricultural fields in North Dakota, USA. To this end, we tested frequently used ground classification algorithms: Cloth Simulation Function (CSF), Progressive Morphological Filter (PMF), Multiscale Curvature Classification (MCC), and ArcGIS ground classification algorithms along with the PointCNN deep learning model were trained. We investigated two aspects of ground classification algorithms and PointCNN: (a) Classification accuracy of optimized ground classification algorithms (i.e., fine adjustment is user-defined parameters) and PointCNN over training site, and (b) transferability potential over four yet diverse test agricultural fields. The well-established evaluation metrics of omission error, commission error, and total error, along with kappa coefficients showed that deep learning outperforms the traditional ground classification algorithms in both aspects: (a) overall classification accuracy, and (b) transferability over diverse agricultural fields.

An improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data

Airborne lidar is a technology for mapping surface spatial information and has been widely used in many areas of geospatial information disciplines. The filtering process of removing non-ground points has always been the focus of research. PTD (Progressive Triangular Irregular Network Densification) filtering algorithm is a widely used filtering algorithm for airborne lidar data. However, this algorithm has shortcomings in retaining ground points in steep areas, leading to large type Ⅰ errors. Therefore, this paper proposes an improved PTD algorithm. The improvement is the addition of the seed points filtering. Specifically, after the potential seed points are obtained by the progressive morphological filter, the seed points filtering is performed on it to remove the non-ground points, so that the obtained seed points are more accurate. The benchmark dataset of ISPRS (International Society for Photogrammetry and Remote Sensing) Working Group III is used to assess the proposed method. Results show that the method is effective in decreasing type Ⅰ error in steep areas. Comparing with the classic PTD algorithm, the type Ⅰ error and total error are decreased by 8.46% and 5.06% respectively. In addition, the proposed method shows a great advantage in computational efficiency, that is eight times more efficient than the classic PTD algorithm.

Progressive Morphological Filtering Algorithm Combined with Thin-Plate Spline Interpolation for Airborne LiDAR

Progressive Morphological Filtering Algorithm Combined with Thin-Plate Spline Interpolation for Airborne LiDAR

High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.

Filtering airborne LiDAR point clouds based on a scale-irrelevant and terrain-adaptive approach

Separating ground from non-ground points is a challenging and essential task before the applications of airborne Light Detection And Ranging (LiDAR) data. The classical filters generally show good results on simple landscapes, but suffer from large filtering errors on complex landscapes with steep slopes and terrain discontinuities. To produce more satisfactory results over these landscapes, a scale-irrelevant and terrain-adaptive interpolation-based filter is presented in this paper. The contributions of the proposed method include a 1D spline-based algorithm for the collection of evenly distributed ground seeds as many as possible, a scale-irrelevant interpolation for estimating the heights of unclassified points and a terrain-adaptive elevation threshold to adapt to various terrain characteristics. The performance of the proposed method was first evaluated on the International Society Photogrammetry and Remote Sensing (ISPRS) benchmark dataset. Results show that the proposed method with the average total error of 2.70% and kappa coefficient of 90.84% outperforms the existing filtering algorithms developed in recent decade (2010–2020). The performance of the proposed method was further assessed on four high-density airborne LiDAR point clouds located in urban and forested sites and compared with four state-of-the-art filters including progressive morphological filter (PMF), cloth simulation filter (CSF), progressive TIN densification (PTD) and multiresolution hierarchical filter (MHF). Results demonstrate that the proposed method is averagely more accurate than the well-known filters in terms of total error and kappa coefficient, and much faster than PTD and MHF. Moreover, the proposed method produces more satisfactory DEMs than the classical methods.

A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy

In recent years, many airborne point clouds filtering methods have been developed. However, it is still challenging for distinguishing ground and non-ground points in forested areas due to the rugged terrains, dense vegetation canopy and low-level penetration of laser pulses. To derive satisfactory filtering results, this paper proposed a mean shift segmentation morphological filter. In this method, the mean shift segmentation is used for acquiring object primitives to determine filtering window sizes automatically. The point clouds detrending is proposed for improving the adaptability towards sloped terrains. A point cloud shifting in x and y directions technique is developed to acquire more ground seeds for generating a more accurate trending surface. Finally, the filtered ground points by the progressive morphological filter are recovered by adopting the surface-based filtering strategy. The proposed method is tested and validated using 14 samples with different forested environments. Experimental results show that the proposed method can achieve the average total error of 1.11%. The kappa coefficients of all these 14 samples are larger than 90% and the average kappa coefficient is 96.43%. The average root mean square error (RMSE) of the proposed method is 0.63. All these indicators are the best when compared to some other famous filtering methods.

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.

COMPARISON OF ALGORITHMS FOR CONSTRUCTION DETECTION USING AIRBORNE LASER SCANNING AND NDSM CLASSIFICATION

Traditional approach to classify the point cloud of airborne laser scanning is based on the processing of a normalized digital surface model (nDSM), when ground facilities are detected and classified. The main feature to detect a ground facility is height difference between adjacent points. The simplest method to extract a ground facility is region-growing algorithm, which applies threshold to identify the connection between two points. Region growing algorithm is working with the constant value of height difference. Therefore, it is not applicable due to diverse conditions of earth surface, when height difference must be defined for each region separately. As result, researchers propose hierarchical, statistical and cluster methods to solve this problem. The study goal is to compare four algorithms to generate nDSM: region growing, progressive morphological filter, adaptive TIN surfaces and graph-cut. The experiment is divided into two stages: 1) to calculate the number of detected and lost buildings in nDSM; 2) to measure the classification accuracy of extracted shapes. The experiment results have showed that progressive morphological filter and graph-cut provides the minimal loss of buildings (only 1%). The most effective algorithm for ground facility detection is the graph-cut (total accuracy 0.95, Cohen’s Kappa 0.89, F1 score 0.93).

REVISED PROGRESSIVE MORPHOLOGICAL METHOD FOR GROUND POINT CLASSIFICATION OF AIRBORNE LIDAR DATA

Airborne Light Detection and Ranging (LiDAR) has been very effectively used in collecting terrain information over different scales of area. Inevitably, filtering the non-ground returns is the major step of digital terrain model (DTM) generation and this step poses the greatest challenge especially for tropical forest environment which consists of steep undulating terrain and mostly covered by a relatively thick canopy density. The aim of this research is to assess the performance of the Progressive Morphological (PM) algorithm after the implementation of local slope value in the ground filtering process. The improvement on the PM filtering method was done by employing local slope values obtained either using initial filtering of airborne LiDAR data or ground survey data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The revised PM filtering method has decreasing pattern of DTM error with increasing filtering iterations with minimum ±0.520 m of RMSE value. The results also suggest that spatially distributed slope value applied in PM filtering algorithm either from LiDAR ground points or ground survey data is capable in preserving discontinuities of terrain and correctly remove non-terrain points especially in steep area.

Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding

Digital elevation models (DEMs) derived from remote sensing data provide a valuable and consistent data source for mapping coastal flooding at local and global scales. Mapping of flood risk requires quantification of the error in DEM elevations and its effect on delineation of flood zones. The ASTER, SRTM, ALOS, and TanDEM-X (TDX) DEMs for the island of Hispaniola were examined by comparing them with GPS and LiDAR measurements. The comparisons were based on a series of error measures including root mean square error (RMSE) and absolute error at 90% quantile (LE90). When compared with >2000 GPS measurements with elevations below 7 m, RMSE and LE90 values for ASTER, SRTM, ALOS, TDX DEMs were 8.44 and 14.29, 3.82 and 5.85, 2.08 and 3.64, and 1.74 and 3.20 m, respectively. In contrast, RMSE and LE90 values for the same DEMs were 4.24 and 6.70, 4.81 and 7.16, 4.91 and 6.82, and 2.27 and 3.66 m when compared to DEMs from 150 km2 LiDAR data, which included elevations as high as 20 m. The expanded area with LiDAR coverage included additional types of land surface, resulting in differences in error measures. Comparison of RMSEs indicated that the filtering of TDX DEMs using four methods improved the accuracy of the estimates of ground elevation by 20–43%. DTMs generated by interpolating the ground pixels from a progressive morphological filter, using an empirical Bayesian kriging method, produced an RMSE of 1.06 m and LE90 of 1.73 m when compared to GPS measurements, and an RMSE of 1.30 m and LE90 of 2.02 m when compared to LiDAR data. Differences in inundation areas based on TDX and LiDAR DTMs were between −13% and −4% for scenarios of 3, 5, 10, and 15 m water level rise, a much narrower range than inundation differences between ASTER, SRTM, ALOS and LiDAR. The TDX DEMs deliver high resolution global DEMs with unprecedented elevation accuracy, hence, it is recommended for mapping coastal flood risk zones on a global scale, as well as at a local scale in developing countries where data with higher accuracy are unavailable.

An improved progressive morphological filter for UAV-based photogrammetric point clouds in river bank monitoring

With the advent of unmanned aerial vehicle (UAV)-based photogrammetry and structure from motion (SFM) software, it is possible to obtain high-density point clouds of which the accuracy can meet the requirements of river bank monitoring. Ground filtering, i.e., removing the points belonging to above-ground objects, is an important process of digital terrain model (DTM) generation which is essential to river bank monitoring. Progressive morphological filter (PM) is a widely-adopted ground filtering algorithm and performs well with LiDAR data. However, it may incorrectly classify vegetation points as ground points when used to filter UAV-based photogrammetric point clouds because ground points beneath vegetation cannot be captured with the digital camera on-board UAV. In this study, we propose the improved progressive morphological filter (IPM) algorithm to improve the accuracy of ground filtering on UAV-based photogrammetric point clouds by introducing visible-band difference vegetation index (VDVI) to PM. The proposed IPM is subsequently evaluated along with the original PM algorithm and four other widely-used ground filtering algorithms in four test sites along the Yangtze River. The results show that IPM improves the overall accuracy from PM in all the four test sites, and produces the best results among the six ground filtering algorithms in three out of the four sites. IPM proves to be an effective ground filtering algorithm for UAV-based photogrammetric point clouds in river bank monitoring.

Joining the dots: reconstructing 3D environments and movement paths using animal-borne devices

BackgroundAnimal-attached sensors are increasingly used to provide insights on behaviour and physiology. However, such tags usually lack information on the structure of the surrounding environment from the perspective of a study animal and thus may be unable to identify potentially important drivers of behaviour. Recent advances in robotics and computer vision have led to the availability of integrated depth-sensing and motion-tracking mobile devices. These enable the construction of detailed 3D models of an environment within which motion can be tracked without reliance on GPS. The potential of such techniques has yet to be explored in the field of animal biotelemetry. This report trials an animal-attached structured light depth-sensing and visual–inertial odometry motion-tracking device in an outdoor environment (coniferous forest) using the domestic dog (Canis familiaris) as a compliant test species.ResultsA 3D model of the forest environment surrounding the subject animal was successfully constructed using point clouds. The forest floor was labelled using a progressive morphological filter. Trees trunks were modelled as cylinders and identified by random sample consensus. The predicted and actual presence of trees matched closely, with an object-level accuracy of 93.3%. Individual points were labelled as belonging to tree trunks with a precision, recall, and F_{beta } score of 1.00, 0.88, and 0.93, respectively. In addition, ground-truth tree trunk radius measurements were not significantly different from random sample consensus model coefficient-derived values. A first-person view of the 3D model was created, illustrating the coupling of both animal movement and environment reconstruction.ConclusionsUsing data collected from an animal-borne device, the present study demonstrates how terrain and objects (in this case, tree trunks) surrounding a subject can be identified by model segmentation. The device pose (position and orientation) also enabled recreation of the animal’s movement path within the 3D model. Although some challenges such as device form factor, validation in a wider range of environments, and direct sunlight interference remain before routine field deployment can take place, animal-borne depth sensing and visual–inertial odometry have great potential as visual biologging techniques to provide new insights on how terrestrial animals interact with their environments.

A simple terrain relief index for tuning slope-related parameters of LiDAR ground filtering algorithms

Ground filtering is an essential procedure in almost all LiDAR applications. However, most existing ground filtering algorithms require different amounts of user input to manually set up initial parameters, such as terrain relief amplitude and average slope, which is subjective, time consuming, and prone to errors. Here, we propose a simple terrain relief index derived from raw airborne LiDAR data to automatically tune the slope-related parameters of ground filtering algorithms. The terrain relief index is a ratio between the height difference of the entire point cloud and the maximum above ground level of non-ground points. The latter variable can be estimated with the maximum local height difference of raw LiDAR data through gridding. We validated our method using the benchmark airborne LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing. The results showed a high correlation (r = 0.876) between the terrain relief index and the referential terrain relief amplitude. The degree of correlation was greater across larger areas (r = 0.926) than small areas (r = 0.861) regardless of the type of land cover (e.g., city or forest). The terrain relief index was introduced into two existing filtering algorithms: Cloth Simulation Filtering (CSF) and Progressive Morphological (PM) Filter, by relating the terrain relief index to the cloth rigidness of the CSF and the slope threshold of the PM filter. To compare the results, the two algorithms were implemented both with manually tuned parameters and with the parameters derived from the terrain relief index. The results showed that there was only a slight discrepancy in average Total Error (0.1%) between them in the CSF, which means that the terrain relief index can automatically determine the cloth rigidness without noticeable loss of accuracy. The average difference between the slope threshold provided by the terrain relief index and the manually tuned optimal slope threshold was 0.142 rad (8.136°) for the PM filter, which is acceptable relative to manually parameter setting without any prior knowledge. The terrain relief index can estimate the terrain relief amplitude from raw airborne LiDAR data, and the parameter settings suggested in this paper for the filtering algorithm can improve automation.

Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data

ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.

Improved progressive morphological filter for digital terrain model generation from airborne lidar data.

Obtaining high-precision filtering results from airborne lidar point clouds in complex environments has always been a hot topic. Mathematical morphology was widely used for filtering, owing to its simplicity and high efficiency. However, the morphology-based algorithms are deficient in preserving terrain details. In order to obtain a better filtering effect, this paper proposed an improved progressive morphological filter based on hierarchical radial basis function interpolation (PMHR) to refine the classical progressive morphological filter. PMHR involved two main improvements, namely, automatic setting of self-adaptive thresholds and terrain details preservation, respectively. The performance of PMHR was evaluated using datasets provided by the International Society for Photogrammetry and Remote Sensing. Experimental results show that PMHR achieved good performance under variant terrain features with an average total error of 4.27% and average Kappa coefficient of 84.57%.

A Two-Step Semiglobal Filtering Approach to Extract DTM From Middle Resolution DSM

Many filtering algorithms have been developed to extract the digital terrain model (DTM) from dense urban light detection and ranging data or the high-resolution digital surface model (DSM), assuming a smooth variation of topographic relief. However, this assumption breaks for a middle-resolution DSM because of the diminished distinction between steep terrains and nonground points. This letter introduces a two-step semiglobal filtering (TSGF) workflow to separate those two components. The first SGF step uses the digital elevation model of the Shuttle Radar Topography Mission to obtain a flat-terrain mask for the input DSM; then, a segmentation-constrained SGF is used to remove the nonground points within the flat-terrain mask while maintaining the shape of the terrain. Experiments are conducted using DSMs generated from Chinese ZY3 satellite imageries, verified the effectiveness of the proposed method. Compared with the conventional progressive morphological filter method, the usage of flat-terrain mask reduced the average root-mean-square error of DTM from 9.76 to 4.03 m, which is further reduced to 2.42 m by the proposed TSGF method.

An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment

Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions.