Realising the potential of airborne LiDAR data in high quality DEM generation : tests in applications to a catchment management region

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Accurate delineation of drainage networks is critical for many hydrologically related applications. The commonly used methods for drainage network extraction from digital elevation models (DEMs) have limitations in low-relief terrain areas. High-quality DEMs are required for effectively applying these methods in extracting drainage networks in low-relief terrains. Airborne light detection and ranging (LiDAR) offers high-accuracy terrain data. With LiDAR data, high-accuracy and high-resolution DEMs can be generated. The results of drainage network extraction for two sub-catchments on the western Victorian Volcanic Plains (VVP) are reported. Drainage networks and some parameters describing drainage network composition, including the stream orders, the numbers of streams and the stream lengths, were derived from both the LiDAR DEM and the Vicmap DEM. The LiDAR-derived DEM is shown to offer significantly more detail, especially for delineating low-order stream (headwater) segments in sub-catchments of low-relief terrain.

Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.

In general, low density airborne LiDAR (Light Detection and Ranging) data are typically used to obtain the average height of forest trees. If the data could be used to obtain the tree height at the single tree level, it would greatly extend the usage of the data. Since the tree top position is often missed by the low density LiDAR pulse point, the estimated forest tree height at the single tree level is generally lower than the actual tree height when low density LiDAR data are used for the estimation. To resolve this problem, in this paper, a modified approach based on three-dimensional (3D) parameter tree model was adopted to reconstruct the tree height at the single tree level by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. The approach was applied to two coniferous forest plots in the subtropical forest region, Fujian Province, China. The following conclusions were reached after analyzing the results: The marker-controlled watershed segmentation method is able to effectively extract the crown profile from sub meter-level resolution images without the aid of the height information of LiDAR data. The adaptive local maximum method satisfies the need for detecting the vertex of a single tree crown. The improved following-valley approach is available for estimating the tree crown diameter. The 3D parameter tree model, which can take advantage of low-density airborne LiDAR data and high resolution images, is feasible for improving the estimation accuracy of the tree height. Compared to the tree height results from only using the low density LiDAR data, this approach can achieve higher estimation accuracy. The accuracy of the tree height estimation at the single tree level for two test areas was more than 80%, and the average estimation error of the tree height was 0.7 m. The modified approach based on the three-dimensional parameter tree model can effectively increase the estimation accuracy of individual tree height by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data.

An airborne laser scanning (ALS) system with LiDAR (Light Detection and Ranging) technology is a highly precise and accurate 3D point data acquisition technique. LiDAR technology has been extensively used in digital surface/terrain modelling (DSM/DTM), and related applications such as 3D city modelling and building extraction. The capability of LiDAR systems to record the intensity of the return laser pulse backscattered energy in addition to the range data has motivated researchers to investigate the use of LiDAR intensity data for extracting land cover information. The main goal of this research is to maximize the benefits of the use of LiDAR data independently of any external source of data for automatically extracting accurate land cover information. Several new approaches are introduced in this research: a) classifying and filling the LiDAR intensity point cloud to produce a land cover image, b) combing multiple classified data of multiple LiDAR data-strips, c) statistical analysis segmentation technique that uses the concept of the kurtosis change curve algorithm for automatic classification of LiDAR data, and d) accelerating the classification process of large datasets by partitioning the large datasets into small, manageable datasets. Applying the traditional image classification techniques on LiDAR elevation and intensity data exclusively is included. Pixel-based, object-based, and point-based classification logics are conducted, and their results are compared to reference data. The results indicated that LiDAR data (range and intensity) can independently be used in land cover classification. By applying traditional pixel-based, supervised image classification techniques, the classification results show that auxiliary layers, which are extracted from range and intensity data, can be used for land cover classification. However, applying the supervised classification techniques on the LiDAR point cloud data without converting the data into images (Point-based logic) produced more accurate land cover classification results. The experiments on the proposed classification approach using the statistical analysis segmentation technique (based on the concept of the kurtosis change curve algorithm) show that it can be used to classify LiDAR data for land cover mapping.

An airborne laser scanning (ALS) system with LiDAR (Light Detection and Ranging) technology is a highly precise and accurate 3D point data acquisition technique. LiDAR technology has been extensively used in digital surface/terrain modelling (DSM/DTM), and related applications such as 3D city modelling and building extraction. The capability of LiDAR systems to record the intensity of the return laser pulse backscattered energy in addition to the range data has motivated researchers to investigate the use of LiDAR intensity data for extracting land cover information. The main goal of this research is to maximize the benefits of the use of LiDAR data independently of any external source of data for automatically extracting accurate land cover information. Several new approaches are introduced in this research: a) classifying and filling the LiDAR intensity point cloud to produce a land cover image, b) combing multiple classified data of multiple LiDAR data-strips, c) statistical analysis segmentation technique that uses the concept of the kurtosis change curve algorithm for automatic classification of LiDAR data, and d) accelerating the classification process of large datasets by partitioning the large datasets into small, manageable datasets. Applying the traditional image classification techniques on LiDAR elevation and intensity data exclusively is included. Pixel-based, object-based, and point-based classification logics are conducted, and their results are compared to reference data. The results indicated that LiDAR data (range and intensity) can independently be used in land cover classification. By applying traditional pixel-based, supervised image classification techniques, the classification results show that auxiliary layers, which are extracted from range and intensity data, can be used for land cover classification. However, applying the supervised classification techniques on the LiDAR point cloud data without converting the data into images (Point-based logic) produced more accurate land cover classification results. The experiments on the proposed classification approach using the statistical analysis segmentation technique (based on the concept of the kurtosis change curve algorithm) show that it can be used to classify LiDAR data for land cover mapping.

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Using airborne LiDAR and USGS DEM data for assessing rock glaciers and glaciers

Abstract. 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.

Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area

고해상도 위성영상을 성공적으로 활용하기 위해서는 지상기준점 등을 활용한 좌표등록 및 보정 과정이 필수적이다. 작업자의 수작업을 통한 기준점 획득의 경우 작업 시간이 오래 걸리므로, 자동화된 좌표 등록 방법에 대한 요구가 증대하고 있다. 보정하고자 하는 위성 영상을 정확한 좌표를 가진 참조 데이터에 영상 매칭을 수행하는 기법이 많이 소개 되었는데, 참조 데이터 중 라이다 데이터의 경우 공간 해상도 및 정확도가 높고 무엇보다 3차원 데이터이기 때문에 기복 변위 등을 내포하고 있지 않는 등의 장점을 보인다. 최근 라이다 데이터와 고해상도 위성영상간의 매칭을 위한 기법이 연구, 발표되었으나, 라이다 데이터의 특성상 대용량이기 때문에 처리에 많은 시간이 소요되는 등의 단점이 있었다. 따라서 본 논문에서는 일부의 공간만을 라이다 칩으로 추출 및 저장하여 위성영상의 좌표 등록에 활용하는 연구를 수행하였다. 이를 위해, 전체 라이다 포인트 데이터를 반사강도 정사영상 및 수치표고모델의 두 가지 형태로 변환하고 에지 추출을 통해 의미 있는 양의 에지 정보만을 포함하는 지역을 영상형태의 라이다 칩으로 추출, 저장하였으며, 용량이 현저히 줄어든 것을 확인할 수 있었다. 마지막으로 라이다 칩을 아리랑2호 및 아리랑3호 영상의 자동 좌표등록에 활용 해본 결과 평균 한 픽셀가량의 정확도 또한 확보할 수 있었다. The accurate geo-referencing processes that apply ground control points is prerequisite for effective end use of HRSI (High-resolution satellite imagery). Since the conventional control point acquisition by human operator takes long time, demands for the automated matching to existing reference data has been increasing its popularity. Among many options of reference data, the airborne LiDAR (Light Detection And Ranging) data shows high potential due to its high spatial resolution and vertical accuracy. Additionally, it is in the form of 3-dimensional point cloud free from the relief displacement. Recently, a new matching method between LiDAR data and HRSI was proposed that is based on the image projection of whole LiDAR data into HRSI domain, however, importing and processing the large amount of LiDAR data considered as time-consuming. Therefore, we wmotivated to ere propose a local LiDAR chip generation for the HRSI geo-referencing. In the procedure, a LiDAR point cloud was rasterized into an ortho image with the digital elevation model. After then, we selected local areas, which of containing meaningful amount of edge information to create LiDAR chips of small data size. We tested the LiDAR chips for fully-automated geo-referencing with Kompsat-2 and Kompsat-3 data. Finally, the experimental results showed one-pixel level of mean accuracy.

Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis

Recognition of geomorphic features, such as landslide scarps, is the first key step for landslide risk assessment and mitigation. Geomorphic features can be identified from high-resolution digital elevation model (DEM). Light Detection and Ranging (LiDAR) is a useful tool to collect high-density point elevation data from ground surfaces. LiDAR ground points are used to generate high-resolution DEMs. However, LiDAR sample sizes and interpolation methods are critical parameters for DEM estimation under various land cover types. To discuss the effect of the parameters, this study used a series of cases to estimate the DEMs and identify the landslide scarps, especially potential landslide scarps hidden in the forest.Results show that LiDAR sample size affects the visual identification rate of the landslide scarps. The point density of LiDAR data controls the level of detail that can be resolved in the LiDAR-derived DEM. Given low-density LiDAR ground points, the DEM accuracy is the worst, especially in dense forest. Particularly in sparse samples, the identification rate of the landslide scarp is sensitive to the interpolation method. In sparse samples, landslide scarp identification based on Kriging-estimated DEM showed the best results among the three interpolation methods. Hence, this study provides information for the assessment of the effects of sample sizes under land cover for further geomorphic monitoring, assessment and management.

The Solvay quarry displays karstified and heavily fractured strata of peritidal platform carbonates of late Barremian age, that can serve as an analog to subsurface fractured reservoirs. In addition of being a potential analog, this study also aims to improve the methodology used in building of DOM (Digital Outcrop Model). The originality of the applied methodology is the integration of conventional outcrop analysis, LIDAR (Light Detection and Ranging) and GPR (Ground Penetrating Radar) data. The goal is to produce an accurate and efficient DOM that resolves the three-dimensional sub-seismic heterogeneity of the fracture distribution in the strata. Stratigraphic and fracture analysis with conventional methods was performed on about 2 km of exposed cliff faces that were subsequently scanned with the LIDAR equipment. Transversal and longitudinal 2D GPR lines and 6 GPR cubes were acquired on the quarry floor to correlate the quarry walls. The 2D GPR data were statically corrected using the GPS horizontal coordinates of the transects, high-resolution topography provided from LIDAR data, and a replacement velocity of 0.098m/ns. GPR and LIDAR data were loaded into 3D CAD software to interpret each horizon and to reconstruct the structural framework. To characterize the fracture distribution; scanline measures were performed along the quarry walls, 3D migrated GPR data was interpreted by delineating high amplitude zones originating from focused diffractions that define fracture surfaces (Grasmueck et al. 2005) and LIDAR point clouds were processed to reveal the main planes families that form the rough wall surface. Two of GPR cubes show the coexistence of four sub-vertical fracture families trending N-S, E-W, NW-SE and NE-SW. The NE-SW fracture family is not detected in the outcrop using the scanline method because the fracture is parallel to the direction of the quarry wall, however the LIDAR algorithm found two families planes oriented near this fracture family. This planes are related to the morphological features of NE-SW joints like twist hackles. The 3D fractures constructed with GPR data allow to filter and understand the planes computed with LIDAR data and to determine the sampling bias due to scanline orientation. Subsequently, the LIDAR data and the scanline measures allow to obtain a continuous distribution of the families fractures along the quarry allowing to characterize dip and azimuth variations.