Accessible light detection and ranging: estimating large tree density for habitat identification

Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868 , 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Detection and delineation of individual trees is important for the estimation of forest information using Light Detection and Ranging (LiDAR) point clouds. However, over-segmentation of canopy height models (CHMs) is frequently caused by subcrowns and branches, whereas under-segmentation is caused by adjacent crowns, especially in deciduous forests. To eliminate such problems, an approach for delineating individual trees based on curvature attributes derived from LiDAR data is proposed. To detect accurate treetops for reducing over-segmentation, the dominant sizes of tree crowns are first determined and used to fit the dome-shaped tree crowns in CHMs, and then the minimum curvature values of fitted CHMs at different scales are calculated for treetop detection. For delineation of individual trees, tree features such as height variation and curvature are chosen to form a feature vector as a pixel-level logistic regression classifier. Then an ensemble classifier combining multiple logistic regression classifiers is trained to analyze the similarity between neighboring points to reduce under-segmentation. Finally, segment maps at different scales are integrated to generate the final segment map. Our experimental results on 2 test areas demonstrate that the proposed method has promising applications for eliminating over-segmentation and under-segmentation in deciduous forests.

Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

A second-generation Gamma Remote Sensing Portable RAdio Detection and Ranging (RADAR) Interferometer (GPRI-II) and a Lieca C-10 LIght Detection and Ranging (LIDAR) were employed to monitor the volume change behavior of two thin compacted clay test sections at the University of Arkansas Cato Springs Research Center (UACRSC). The viability of using remote sensing methods to accurately identify and quantify deformation associated with expansive clay behavior was evaluated. Two 464.52 m2 (5,000 ft2) sections consisting of 20.3 cm (8 inches) thick compacted clay layer underlain by a 5.1 cm (2 inches) thick sand drainage blanket were constructed. One test section was amended with three (Phase 1) and six (Phase 2) percent sodium bentonite. Four LIDAR and 355 RADAR observations were captured from the roof of the UACSRC building between January and July, 2012. Multiple RADAR images were captured on a weekly basis and after significant precipitation events. RADAR measurements, obtained by calculating the interferometric phase difference between coregistered temporally separated RADAR images, were used to determine topography and deformation. LIDAR measurements, as obtained from LIDAR point clouds, were also used to determine topography and deformation. To obtain accurate topography and deformation measurements the vegetation effects within the LIDAR data was removed. Deformation maps generated using both the RADAR and LIDAR methods are presented and compared. The results indicate both technologies are capable of detecting surface movements associated with expansive clay behavior. Moreover, the surface movements associated with expansive clay behavior can be accurately quantified using the LIDAR device.

Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

The Light detection and ranging (LiDAR) sensor is used for perceiving the environment of an autonomous vehicle. LiDAR data or point cloud is processed to get the obstacles and their speed around an autonomous vehicle. Based on the information retrieved from LiDAR data and the data from other sensors, real-time decisions are taken for the proper navigation. Hence, the time taken in LiDAR data processing should be minimized. One of the important steps for LiDAR data processing is the segmentation of the obstacles. In this paper, we present a quantitative comparison between two different approaches for point cloud segmentation, Euclidean distance-based Cluster Extraction and Cylindrical range image-based method. Based on the simulation performed on ROS (Robot Operating System) platform, we found that the second method is much faster as compared to the first method. In addition to that, the second method can perform proper segmentation at a larger distance from the sensor.

NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement

Light Detection and Ranging (LiDAR) systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality control (QC) of LiDAR data has not followed the same trend. In other words, a lack in reliable, practical, cost-effective, and commonly acceptable QC procedures is evident. A frequently adopted procedure for QC is comparing the LiDAR data to ground control points. Aside from being expensive, this approach is not accurate enough for the verification of horizontal accuracy, unless specifically designed LiDAR targets are used. This paper is dedicated to providing accurate, economical, and convenient internal QC procedures for the evaluation of LiDAR data, which is captured from parallel flight lines. The underlying concept of the proposed methodologies is that, in the absence of systematic and random errors in system parameters and measurements, conjugate surface elements in overlapping strips should perfectly match each other. Consistent incompatibilities and the quality of fit between conjugate surface elements in overlapping strips can be used to detect systematic errors in the system parameters/measurements and to evaluate the noise level in the LiDAR point cloud, respectively. Experimental results from real data demonstrate that all the proposed methods, with one exception, produce compatible estimates of systematic discrepancies between the involved data sets, as well as good quantification of inherent noise.

High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical & digital imagery information for various applications.

Abstract. Trees in green areas offer numerous benefits for the environment and human health. Up-to-date, information about trees in urban green areas is crucial for sustainable urban planning. Traditionally, the detection and inventory of urban trees have been conducted through field surveys and terrestrial measurements. However, this labour-intensive approach can be replaced with the more efficient LiDAR (Light Detection and Ranging) systems, an active remote sensing technology. Urban trees can be quickly and automatically determined using 3-dimensional (3D) LiDAR point cloud data. The objective of this study is to acquire trees in densely populated areas of large cities using raw LiDAR data. The urban study area was chosen in the Fatih district of Istanbul, which includes Sultanahmet Square, a site registered on the UNESCO World Heritage List. To detect urban trees, initially, eight classes representing the ground surface were obtained from LiDAR data with a point-based classification approach which is called hierarchical rule-based classification, and the high vegetation class was separated from the other classes. Noise points, which did not correspond to urban trees within the high vegetation class, were removed using the machine learning-based Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. The remaining high vegetation points were subsequently segmented using the machine learning-based Mean Shift clustering algorithm to obtain individual tree crowns. An accuracy assessment was conducted through completeness and correctness analyses, demonstrating the effectiveness of the proposed point-based approach for the automatic detection of urban trees from LiDAR data. According to the proposed Mean Shift clustering approach, the completeness was 60% and the correctness was 77.42% in test area A, while in test area B, the completeness was 62.30% and the correctness was 80.85%. The much higher completeness (78.26%) and correctness (100%) values were obtained for street trees with regular structure in test area B in comparison with the proposed Mean Shift clustering approaches.

Single tree detection is important for monitoring tree plantations in order to estimate crop yield, which can be used for assessing food and biofuels. In the field of remote sensing, there have been studies regarding single tree extraction using Light Detection and Ranging (LiDAR) point cloud data. Canopy Height Model (CHM) can be simply derived from these LiDAR data where trees can be analysed using different physical characteristics such as tree crown width, and height. However, using these physical characteristics can’t be very useful for identifying which trees are within a plantation, a forest, or a single tree. This is the motivation for developing algorithms of automated differentiation of random trees from plantation trees. Object Based Image Analysis (OBIA) was used to acquire the locations of individual trees. Trees were separated from other land cover in the image using a CHM derived from LiDAR point cloud data. Using watershed algorithm in eCognition, trees with good canopy spacing was easily distinguished. A spatial point pattern analysis was done from the extracted locations of trees to differentiate regularly patterned points from randomly distributed points. Nearest neighbour statistics was done to provide measurement of the distribution of the extracted points. The Z-Score parameter from nearest neighbor was then added to the layers in eCognition as supplement to the available height derivatives such as mean, standard deviation, and different textural parameters. Supervised type of classification was used in order to classify the trees on the image. Training samples of each tree from the image were entered in Support Vector Machine (SVM) to classify the different trees. Two sets of validation were done to compare the results of using the object parameters only, and the object parameters and Z-Score. Addition of the Z-Score from the object parameters increased the obtained accuracy on both validation sets. Classification accuracy based on validation set 1 and set 2 increased by about 20% and 6%, respectively. Additional spatial pattern analysis methods and validation can further prove the use of such layers in improving classification of trees.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.