Using Lidar Data to Analyse Sinkhole Characteristics Relevant for Understory Vegetation under Forest Cover\u2014Case Study of a High Karst Area in the Dinaric Mountains

In this article, we investigate the potential for detection and characterization of sinkholes under dense forest cover by using airborne laser scanning data. Laser pulse returns from the ground provide important data for the estimation of digital elevation model (DEM), which can be used for further processing. The main objectives of this study were to map and determine the geomorphometric characteristics of a large number of sinkholes and to investigate the correlations between geomorphology and vegetation in areas with such characteristics. The selected study area has very low anthropogenic influences and is particularly suitable for studying undisturbed karst sinkholes. The information extracted from this study regarding the shapes and depths of sinkholes show significant directionality for both orientation of sinkholes and their distribution over the area. Furthermore, significant differences in vegetation diversity and composition occur inside and outside the sinkholes, which indicates their presence has important ecological impacts

Sub-canopy terrain modelling for archaeological prospecting in forested areas through multiple-echo discrete-pulse laser ranging: a case study from Chopwell Wood, Tyne & Wear

Airborne Light Detection and Ranging (LiDAR) technology is assessed for its effectiveness as a tool for measuring terrain under forest canopy. To evaluate the capability of multiple-return discrete-pulse airborne laser ranging for detecting and resolving sub-canopy archaeological features, LiDAR data were collected from a helicopter over a forest near Gateshead in July 2009. Coal mining and timber felling have characterised Chopwell Wood, a mixed coniferous and deciduous woodland of 360 hectares, since the Industrial Revolution. The state-of-the-art Optech ALTM 3100EA LiDAR system operated at 70,000 pulses per second and raw data were acquired over the study area at a point density of over 30 points per square metre. Reference terrain elevation data were acquired on-site to ‘train’ the progressive densification filtering algorithm of Axelsson (1999; 2000) to identify laser reflections from the terrain surface. A number of sites, offering a variety of tree species, variable terrain roughness & gradient and understorey vegetation cover of varying density, were identified in the wood to assess the accuracy of filtered LiDAR terrain data. Results showed that the laser scanner over-estimated the elevation of reference terrain data by 13±17 cm under deciduous canopy and 23±18 cm under coniferous canopy. Terrain point density was calculated as 4.1 and 2.4 points per square metre under deciduous and coniferous forest, respectively. Classified terrain points were modelled with the kriging interpolation technique and topographic archaeological features, such as coal tubways (transportation routes) and areas of subsidence over relic mine shafts, were identified in digital terrain models (DTMs) using advanced exaggeration and artificial illumination techniques. Airborne LiDAR is capable of recording high quality terrain data even under the most dense forest canopy, but the accuracy and density of terrain data are controlled by a combination of tree species, forest management practices and understorey vegetation

Airborne LiDAR and high resolution satellite data for rapid 3D feature extraction

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary?.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98% for tree feature extraction and 96% for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia

The effects of data reduction on LiDAR-based digital elevation models

LiDAR data enables highly accurate terrain representations, however, various applications are hampered by data handling efficiency; specifically lengthy processing times. To address this, both point density reductions and the use of various resolution grids are compared as data reduction methods to test their effects on the accuracy and handling efficiency of the derived Digital Elevation Model (DEM). A series of point densities of 1%, 10%, 25%, 50% and 75% were interpolated along a range of horizontal resolutions (1-, 2-, 3-, 4-, 5-, 10-, and 30- m). Results indicate that resolution reduction provides the most efficient DEMs in terms of their data handling. DEMs generated at a 3 m resolution using all of the data points deviated less than 6% from the 1mDEM100%, while significantly only taking 10% of the processing time. Resolution reduction provided sufficient accuracies for varying terrain complexities