Light Detection And Ranging Digital Surface Model Research Articles

Windthrow damage detection in Nordic forests by 3D reconstruction of very high-resolution stereo optical satellite imagery

ABSTRACT We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.

LIDAR ASSESSMENTS AND MAPPING FOR KLANG VALLEY: A CASE STUDY AT JINJANG DISTRICT, SELANGOR

Abstract. Light Detection and Ranging (LiDAR) technology has become a significant factor in producing up-to-date and accurate topographic data in the current world. LiDAR technology has been used for years for many applications, including the efficient creation of digital model for large scale, high accuracy mapping. This technology offers fast, accurate, expedient and cost-effective ways of capturing wide area elevation information to produce highly detailed digital model of the earth. LiDAR is based on airborne laser scanners enables to acquire dense and accurate 3D data of the surveyed area, i.e., the Digital Surface Model (DSM). This paper presents an exploratory study to assess the accuracy of constructed DTM (Digital Terrain Model) and evaluating ground height without surface features using LiDAR Digital Surface Model (DSM). The study area comprised of an undulated area situated at Jinjang in the Klang Valley region, Malaysia covering an area of one kilometre square. LiDAR DSM and DTM constructed and derived from LiDAR were critically assessed with reference to the USGS Map Accuracy Standards. The accuracy of derived DSM and DTM were evaluated using ground control points derived from conventional surveying technique. The constructed models were accessed quantitatively and qualitatively.

Efficient Convolutional Neural Architecture Search for LiDAR DSM Classification

Light detection and ranging (LiDAR) data provides rich elevation information, so it plays an irreplaceable role in ground objects classification. Recently, convolutional neural networks (CNNs) have shown excellent performance in LiDAR Digital Surface Models (DSM) classification. However, the architecture of CNN model relies heavily on manual design, so it has great limitations. In addition, different sensors capture LiDAR datasets with different properties, so the model should be designed to suit for different datasets, which further increases the workload of architecture design. Therefore, this paper proposes a method of automatic design of LiDAR DSM classification model. First, attention mechanism is introduced into search space to improve the feature extraction capability of the model. Then a gradient-based search strategy is used to obtain the optimal architecture from this search space. Second, a learning rate adjustment strategy is proposed to reduce the time spent in the search stage and evaluation stage to improve the classification accuracy of the model. Finally, a regularization scheme is introduced to enhance the robustness of the model and avoid overfitting. Experimental results on three public LiDAR datasets (Bayview Park, Recology and Houston) obtained from different sensors show that the proposed neural architecture search method achieves the impressive classification performance compared to several state-of-the-art classification methods and improves the classification accuracy under the condition of limited training samples.

Comparing LiDAR and SfM digital surface models for three land cover types

Abstract Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle structure from motion (UAV-SfM) are two major methods used to produce digital surface models (DSMs) for geomorphological studies. Previous studies have used both types of DSM datasets interchangeably and ignored their differences, whereas others have attempted to locally compare these differences. However, few studies have quantified these differences for different land cover types. Therefore, we simultaneously compared the two DSMs using airborne LiDAR and UAV-SfM for three land cover types (i.e. forest, wasteland, and bare land) in northeast China. Our results showed that the differences between the DSMs were the greatest for forest areas. Further, the average elevation of the UAV-SfM DSM was 0.4 m lower than that of the LiDAR DSM, with a 95th percentile difference of 3.62 m for the forest areas. Additionally, the average elevations of the SfM DSM for wasteland and bare land were 0.16 and 0.43 m lower, respectively, than those of the airborne LiDAR DSM; the 95th percentile differences were 0.67 and 0.64 m, respectively. The differences between the two DSMs were generally minor over areas with sparse vegetation and more significant for areas covered by tall dense trees. The findings of this research can guide the joint use of different types of DSMs in certain applications, such as land management and soil erosion studies. A comparison of the DSM types in complex terrains should be explored in the future.

Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and $F$ -score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision >0.9 and favorable Recall and $F$ -score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.

Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR

The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.

Extraction and Analysis of Feature Layers from Airborne LiDAR Point Clouds in Downtown Urban Landscapes

Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the ground surface and non-ground features such as vegetations, trees, roads and buildings etc. Extraction of non-ground features from airborne LiDAR measurements has been a main objective for researches in the past twenty years. This study aimed at exploring the different methods for extraction of non-ground features from airborne LiDAR point clouds for creation of reliable feature layers that can be employed in a wide range of engineering and environmental applications. Also, undertaking comparative study for the application of Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter on airborne LiDAR Digital Surface Models (DSMs) of varying window sizes in extraction of reliable feature layers have been a main objective of this study. Airborne LiDAR dataset captured over the downtown of the City of Vaihingen in Germany has been exploited in the study. Visual analysis of the extracted feature layers has shown that Gaussian low pass filter of 3x3 window size has removed a small range of non-ground features heights while detailed and structured feature layer has been obtained using window size of 21x21. Additionally, the focal analysis mean filter has achieved better removal of non-ground features compared to Gaussian low pass filter at similar window sizes where better representation of the downtown landscape has been obtained using window size of 21x21 and larger. On the other hand, visual analysis has not shown clear differences between the feature layers extracted using DTM slopebased filter due to changing the filter window sizes. Statistical analysis has indicated that the ranges of elevations in the feature layers from the different examined filters have increased with increasing the filter window size till 15x15. Also, the standard deviations of the feature layers have increased due to increasing the window sizes of Gaussian low pass and focal analysis mean filters, however increasing the window size of the DTM slope-based filter has produced slight increases in the standard deviation. Additionally, increasing the window size of Gaussian low pass and the focal analysis mean filters has produced feature layers of skewness approaching to zero with window size of 21x21. This has referred to feature layers of symmetrical Gaussian normal distribution curves. Moreover, dramatic decreases have occurred in the feature layer kurtosis due to increases in the filter window sizes till window sizes of 21x21 in the cases of Gaussian low pass and focal analysis mean filters which referred to more consistent and outlier free feature layers.

Pixel-Based Geometric Assessment of Channel Networks/Orders Derived from Global Spaceborne Digital Elevation Models

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.

Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images.

In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such as exceeding resolution, bad correction and terrain influence. Data collected by multiple sensors, as light detection and ranging (LIDAR), optical sensor etc., are used to improve the extraction. Using digital surface model (DSM) obtained from LIDAR data and optical images, traditional method can improve the extraction effect to a certain extent, but there are some defects in feature extraction. Since stacked sparse autoencoder (SSAE) neural network can learn the essential characteristics of the data in depth, SSAE was employed to extract buildings from the combined DSM data and optical image. A better setting strategy of SSAE network structure is given, and an idea of setting the number and proportion of training samples for better training of SSAE was presented. The optical data and DSM were combined as input of the optimized SSAE, and after training by an optimized samples, the appropriate network structure can extract buildings with great accuracy and has good robustness.

LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine

This letter proposes a novel framework for the classification of light detection and ranging (LiDAR)-derived features. In this context, several features are extracted directly from the LiDAR point cloud data using aggregated local point neighborhoods, including laser echo ratio, variance of point elevation, plane fitting residuals, and echo intensity. Additionally, the LiDAR digital surface model (DSM) is input to our classification. Thus, both the LiDAR raster DSM and also rich geometric and also backscatter 3-D point cloud information aggregated to images are considered in our workflow. These extracted features are characterized as base images to be fed to extinction profiles to model spatial and contextual information. Then, a composite kernel support vector machine is investigated to efficiently integrate the elevation and spatial information suitable for the LiDAR data. Results indicate that the proposed method can obtain high classification accuracy using LiDAR data alone (e.g., more than 86% overall accuracy on the benchmark Houston LiDAR data using the standard set of training and test samples on all 15 classes) in a short CPU processing time.

Assessment of solar radiation reduction from urban forests on buildings along highway corridors in Sydney

Urban forests play a critical role in mitigating heat island effects and in modifying local microclimates by intercepting solar radiation and shading heat-absorbing structural materials. Solar radiation is the prime determinant of how much energy urban surfaces absorb and of temperature distribution patterns in urban areas. Understanding the impacts of urban trees on solar radiation received by buildings is an important way to assess tree shading and potential energy conservation. In this study two remote sensing technologies have been integrated, to estimate solar radiation on building roofs along two major infrastructure routes in Sydney. In particular, the relationship between the radiation received by the roofs and the surrounding tree features has been investigated. The two chosen sources of remotely sensed data are light detection and ranging (LiDAR), and airborne hyperspectral imaging. Integrating these data sets provides a means to build digital surface models (DSMs) which only include trees and permits a distinction between evergreen and deciduous species. Global solar radiation profiles of both corridors have been estimated. Then, the direct and diffuse radiation received by building roofs are modelled by incorporating the LiDAR DSM, at hourly intervals on spring/autumn equinox and summer/winter solstice dates. The very high summer radiation in comparison with winter levels is demonstrated and, by correlating reductions in solar radiation with different tree and building dimensions, this paper shows that tree canopy height is most strongly correlated to solar radiation across the whole study area. The results also demonstrate benefits of more extensive use of deciduous species in industrialised or commercial areas. For landscape planners in Sydney, the importance of having and conserving taller trees for solar radiation reduction is clear.

Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models

This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable.

An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data

Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.

Integration Potential of INFOMAR Airborne LIDAR Bathymetry with External Onshore LIDAR Data Sets

Light detection and ranging (LIDAR) data are used for a wide array of purposes in the coastal zone. This can result in LIDAR data being collected multiple times in order to meet the specific needs of different agencies. This paper assesses the potential for airborne LIDAR bathymetry (ALB) and topographic LIDAR to be integrated for use in coastal research. Two topographic LIDAR data sets and an ALB data set are examined in three coastal test areas. Consideration of the potential for data integration focuses upon external validation of each data set using global positioning system (GPS) points, comparison of subareas and onshore-offshore cross-sections, horizontal feature matching onshore, and data set datum conversion. Data accuracy and datum integration potential confirm that all three data sets can be integrated onshore to facilitate extended LIDAR coverage and possibly also to minimise survey duplication in the coastal zone. Integration potential offshore is assessed by comparing the littoral component of an onshore topographic LIDAR digital surface model (DSM) data set with ALB data. Water-surface returns in the topographic LIDAR data collected during times of high water are found to constitute a barrier to data integration offshore, but topographic LIDAR data captured at low tide in one of the three coastal test areas suggest an opportunity to minimise duplicate surveying in the coastal zone.

Information fusion of aerial images and LIDAR data in urban areas: vector-stacking, re-classification and post-processing approaches

This research investigates information fusion approaches of high-resolution aerial images and elevation data from Light Detection and Ranging (LIDAR) for urban-environment mapping. Three feature fusion methods are proposed and compared: (1) the vector-stacking approach that combines spectral and LIDAR features in one classifier; (2) the re-classification approach that firstly processes spectral signals in a classifier and then integrates its output with LIDAR features to obtain the final result and (3) the post-processing approach that uses the LIDAR data to refine the results of spectral classification. The height features used in the above three algorithms are extracted from the LIDAR digital surface model (DSM) image; these include elevation difference, maximum and minimum values, variance and the grey-level co-occurrence matrix (GLCM) textures. In addition, the average height from object-based segmentation is also computed. In the experiments, support vector machines (SVMs) are used as classifiers for all fusion schemes due to their capability and robustness for many classification problems. The three algorithms are evaluated using a 40-cm spatial resolution digital orthophoto and the corresponding LIDAR data of Odense, Denmark. In the experiments, the vector-stacking method with the Maximum–Minimum (Max–Min) feature, the re-classification method with the Max–Min feature and the post-processing approach obtain promising results (94.7%, 95.0% and 94.6%, respectively), which are significantly higher than the spectral-only classification (82.5%).

Texture-based landform segmentation of LiDAR imagery

In this study, we implement and apply a region growing segmentation procedure based on texture to extract spatial landform objects from a light detection and ranging (LiDAR) digital surface model (DSM). The local binary pattern (LBP) operator, modeling texture, is integrated into a region growing segmentation algorithm to identify landform objects. We apply a multi-scale LBP operator to describe texture at different scales. The paper is illustrated with a case study that involves segmentation of coastal landform objects using a LiDAR DSM of a coastal area in the UK. Landform objects can be identified with the combination of a multi-scale texture measure and a region growing segmentation. We show that meaningful coastal landform objects can be extracted with this algorithm. Uncertainty values provide useful information on transition zones or fuzzy boundaries between objects.

Extracting urban features from LiDAR digital surface models

The use of airborne Light Detection And Ranging (LiDAR) technology offers rapid high resolution capture of surface elevation data suitable for a large range of applications. The representation of both the ground surface and the features on that surface necessitates the removal of these surface features if a ground surface Digital Elevation Model (DEM) product is to be produced. This paper examines methods for extracting surface features from a Digital Surface Model (DSM) produced by LiDAR. It is argued that for some applications the extracted surface feature layer can be of almost equal importance to the DEM. The example of flood inundation modelling is used to illustrate how a DEM and a surface roughness layer could be extracted from the original DSM. The potential for refining surface roughness estimates by classifying extracted surface features using both topographic and spectral characteristics is considered using an Artificial Neural Network to discriminate between buildings and trees.