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

High-resolution DEM generated from LiDAR data for water resource management

Terrain patterns play an important role in determining the nature of water resources and related hydrological modelling. Digital Elevation Models (DEMs), offering an efficient way to represent ground surface, allow automated direct extraction of hydrological features (Garbrecht and Martz, 1999), thus bringing advantages in terms of processing efficiency, cost effectiveness, and accuracy assessment, compared with traditional methods based on topographic maps, field surveys, or photographic interpretations. However, researchers have found that DEM quality and resolution affect the accuracy of any extracted hydrological features (Kenward et al., 2000). Therefore, DEM quality and resolution must be specified according to the nature and application of the hydrological features. The most commonly used DEM in Victoria, Australia is Vicmap Elevation delivered by the Land Victoria, Department of Sustainability and Environment. It was produced by using elevation data mainly derived from existing contour map at a scale of 1:25,000 and digital stereo capture, providing a state-wide terrain surface representation with a horizontal resolution of 20 metres. The claimed standard deviations, vertical and horizontal, are 5 metres and 10 metres respectively (Land- Victoria, 2002). In worst case, horizontal errors could be up to ±30m. Although high resolution stereo aerial photos provide a potential way to generate high resolution DEMs, under the limitations of currently used technologies by prevalent commercial photogrammetry software, only DSMs (Digital Surface Models) other than DEMs can be directly generated. Manual removal of the nonground data so that the DSM is transformed into a DEM is time consuming. Therefore, using stereo aerial photos to produce DEM with currently available techniques is not an accurate and costeffective method. Light Detection and Ranging (LiDAR) data covering 6900 km² of the Corangamite Catchment area of Victoria were collected over the period 19 July 2003 to 10 August 2003. It will be used to support a series of salinity and water management projects for the Corangamite Catchment Management Authority (CCMA). The DEM derived from the LiDAR data has a vertical accuracy of 0.5m and a horizontal accuracy of 1.5m. The high quality DEM leads to derive much detailed terrain and hydrological attributes with high accuracy. Available data sources of DEMs in a catchment management area were evaluated in this study, including the Vicmap DEM, a DEM generated from stereo aerial photos, and LiDAR-derived DEM. LiDAR technology and LiDAR derived DEM were described. In order to assess the capability of LiDAR-derived DEM for improving the quality of extracted hydrological features, sub-catchment boundaries and drainage networks were generated from the Vicmap DEM and the LiDAR-derived DEM. Results were compared and analysed in terms of accuracy and resolution of DEMs. Elevation differences between Vicmap and LiDAR-derived DEMs are significant, up to 65m in some areas. Subcatchment boundaries derived from these two DEMs are also quite different. In spite of using same resolution for the Vicmap DEM and the LiDARderived DEM, high accuracy LiDAR-derived DEM gave a detailed delineation of sub-catchment. Compared with results derived from LiDAR DEM, the drainage networks derived from Vicmap DEM do not give a detailed description, and even lead to discrepancies in some areas. It is demonstrated that a LiDAR-derived DEM with high accuracy and high resolution offers the capability of improving the quality of hydrological features extracted from DEMs

Canopy height estimation from lidar data using open source software compared with commercial software

The goal of this study is to analyze the performance of Open Source Software (OSS) towards the generation of Digital Terrain Model (DTM) and Digital Surface Model (DSM), further on estimates the canopy height by using Light Detection and Ranging (LIDAR) data. Generation of DTM and DSM are very important in this research to ensure that better canopy height can be modeled. DTM and DSM commonly known as a digital representation of earth surface topography where DTM only represent the ground surface while DSM represent all the features including buildings and trees. Many software that have a function to generate DTM and DSM were developed recently. However, most software has been commercialized; therefore it requires a high expenditure to own the software. Advanced technology has lead to the emergence of the growing OSS. OSS is software that can be downloaded for free via the internet. By taking the forestry area of Pekan, Pahang for this research, LIDAR data for that particular area is processed by using the OSS Geographic Resources Analysis Support System (GRASS). To determine the effectiveness and capability of GRASS in the DTM and DSM generation, the same data were processed using commercial software which is TerraScan so that the result can be compared, further on better canopy height can be modele

Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study

A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified

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