Multitemporal LiDAR improves estimates of fire severity in forested landscapes

2018 ◽  
Vol 27 (9) ◽  
pp. 581 ◽  
Author(s):  
Michael S. Hoe ◽  
Christopher J. Dunn ◽  
Hailemariam Temesgen
Keyword(s):  
Spatial Information ◽  
Mean Squared Error ◽  
Fire Severity ◽  
Three Dimensional ◽  
Basal Area ◽  
Point Clouds ◽  
Fire Effects ◽  
Maximum Height ◽  
Ecological Resources ◽  
Change In Mean

Landsat-based fire severity maps have limited ecological resolution, which can hinder assessments of change to specific resources. Therefore, we evaluated the use of pre- and post-fire LiDAR, and combined LiDAR with Landsat-based relative differenced Normalized Burn Ratio (RdNBR) estimates, to increase the accuracy and resolution of basal area mortality estimation. We vertically segmented point clouds and performed model selection on spectral and spatial pre- and post-fire LiDAR metrics and their absolute differences. Our best multitemporal LiDAR model included change in mean intensity values 2–10 m above ground, the sum of proportion of canopy reflection above 10 m, and differences in maximum height. This model significantly reduced root-mean-squared error (RMSE), root-mean-squared prediction error (RMSPE), and bias when compared with models using only RdNBR. Our top combined model integrated RdNBR with LiDAR return proportions <2 m above ground, pre-fire 95% heights and pre-fire return proportions <2 m above ground. This model also significantly reduced RMSE, RMSPE, and bias relative to RdNBR. Our results confirm that three-dimensional spectral and spatial information from multitemporal LiDAR can isolate disturbance effects on specific ecological resources with higher accuracy and ecological resolution than Landsat-based estimates, offering a new frontier in landscape-scale estimates of fire effects.

scholarly journals 3D Object Shape Reconstruction from Underwater Multibeam Data and Over Ground Lidar Scanning

2018 ◽  
Vol 25 (2) ◽  
pp. 47-56 ◽  
Author(s):  
Marek Kulawiak ◽  
Zbigniew Łubniewski
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Measurement Data ◽  
Point Clouds ◽  
Shape Reconstruction ◽  
Reconstruction Algorithms ◽  
Processing Scheme ◽  
3D Shape Reconstruction

Abstract The technologies of sonar and laser scanning are an efficient and widely used source of spatial information with regards to underwater and over ground environment respectively. The measurement data are usually available in the form of groups of separate points located irregularly in three-dimensional space, known as point clouds. This data model has known disadvantages, therefore in many applications a different form of representation, i.e. 3D surfaces composed of edges and facets, is preferred with respect to the terrain or seabed surface relief as well as various objects shape. In the paper, the authors propose a new approach to 3D shape reconstruction from both multibeam and LiDAR measurements. It is based on a multiple-step and to some extent adaptive process, in which the chosen set and sequence of particular stages may depend on a current type and characteristic features of the processed data. The processing scheme includes: 1) pre-processing which may include noise reduction, rasterization and pre-classification, 2) detection and separation of objects for dedicated processing (e.g. steep walls, masts), and 3) surface reconstruction in 3D by point cloud triangulation and with the aid of several dedicated procedures. The benefits of using the proposed methods, including algorithms for detecting various features and improving the regularity of the data structure, are presented and discussed. Several different shape reconstruction algorithms were tested in combination with the proposed data processing methods and the strengths and weaknesses of each algorithm were highlighted.

scholarly journals Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco

2020 ◽  
Vol 12 (23) ◽  
pp. 4005
Author(s):  
Beatriz Gobbi ◽  
Anton Van Rompaey ◽  
Dante Loto ◽  
Ignacio Gasparri ◽  
Veerle Vanacker
Keyword(s):  
Forest Structure ◽  
Forest Inventory ◽  
Three Dimensional ◽  
Point Clouds ◽  
Tree Canopy ◽  
Dry Forest ◽  
Anthropogenic Activity ◽  
Maximum Height ◽  
Functional Changes ◽  
Forest Inventories

Anthropogenic activity leading to forest structural and functional changes needs specific ecological indicators and monitoring techniques. Since decades, forest structure, composition, biomass, and functioning have been studied with ground-based forest inventories. Nowadays, satellites survey the earth, producing imagery at different spatial and temporal resolutions. However, measuring the ecological state of large extensions of forest is still challenging. To reconstruct the three-dimensional forest structure, the structure from motion (SfM) algorithm was applied to imagery taken by an unmanned aerial vehicle (UAV). Structural indicators from UAV-SfM products are then compared to forest inventory indicators of 64 circular plots of 1000 m2 in a subtropical dry forest. Our data indicate that the UAV-SfM indicators provide a valuable alternative for ground-based forest inventory’ indicators of the upper canopy structure. Based on the correlation between ground-based measures and UAV-SfM derived indicators, we can state that the UAV-SfM technique provides reliable estimates of the mean and maximum height of the upper canopy. The performance of UAV-SfM techniques to characterize the undergrowth forest structure is low, as UAV-SfM indicators derived from the point cloud in the lower forest strata are not suited to provide correct estimates of the vegetation density in the lower strata. Besides structural information, UAV-SfM derived indicators, such as canopy cover, can provide relevant ecological information as the indicators are related to structural, functional, and/or compositional aspects, such as biomass or compositional dominance. Although UAV-SfM techniques cannot replace the wealth of data collected during ground-based forest inventories, its strength lies in the three-dimensional (3D) monitoring of the tree canopy at cm-scale resolution, and the versatility of the technique to provide multi-temporal datasets of the horizontal and vertical forest structure.

Determinants of riparian fire severity in two Oregon fires, USA

2008 ◽  
Vol 38 (7) ◽  
pp. 1959-1973 ◽  
Author(s):  
Jessica E. Halofsky ◽  
David E. Hibbs
Keyword(s):  
Random Sampling ◽  
Sampling Design ◽  
Fire Severity ◽  
Mineral Soil ◽  
Basal Area ◽  
Fire Effects ◽  
Forest Stand ◽  
Riparian Areas ◽  
Fuel Component ◽  
Crown Scorch

We sought to understand how vegetation indicators and local topographic factors interact to influence riparian fire severity in two recent fires in Oregon, USA. A stratified random sampling design was used to select points in a range of fire severity classes, forest stand ages, and stream sizes in each fire. At each point, plots were sampled in riparian areas and adjacent uplands. Fire severity was assessed in each plot, and measurements were made of factors that have been found to influence riparian fire severity. Understory fire severity (percent exposed mineral soil and bole char height) was significantly lower in riparian areas compared with adjacent uplands in both fires, suggesting a decoupling in understory fire effects in riparian areas versus uplands. However, overstory fire severity (percent crown scorch and percent basal area mortality) was similar in riparian areas and adjacent uplands in both fires. Fire severity in riparian areas was most strongly associated with upland fire severity. In addition, vegetation indicators, particularly those describing riparian fine fuel component and species composition, were strong predictors of riparian fire severity. Consistency in factors controlling fire severity in the two fires suggests that controls on riparian fire severity may be similar in other regions.

scholarly journals Obstacle-Aware Indoor Pathfinding Using Point Clouds

2019 ◽  
Vol 8 (5) ◽  
pp. 233 ◽  
Author(s):  
Lucía Díaz-Vilariño ◽  
Pawel Boguslawski ◽  
Kourosh Khoshelham ◽  
Henrique Lorenzo
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Indoor Environments ◽  
Aged People ◽  
Emergency Assistance ◽  
Indoor Scenes ◽  
Mobility Skills

With the rise of urban population, updated spatial information of indoor environments is needed in a growing number of applications. Navigational assistance for disabled or aged people, guidance for robots, augmented reality for gaming, and tourism or training emergency assistance units are just a few examples of the emerging applications requiring real three-dimensional (3D) spatial data of indoor scenes. This work proposes the use of point clouds for obstacle-aware indoor pathfinding. Point clouds are firstly used for reconstructing semantically rich 3D models of building structural elements in order to extract initial navigational information. Potential obstacles to navigation are classified in the point cloud and directly used to correct the path according to the mobility skills of different users. The methodology is tested in several real case studies for wheelchair and ordinary users. Experiments show that, after several iterations, paths are readapted to avoid obstacles.

scholarly journals The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds

2020 ◽  
Vol 12 (11) ◽  
pp. 1885 ◽  
Author(s):  
Paul-Mark DiFrancesco ◽  
David Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Laser Scanning ◽  
Rock Slope ◽  
Spatial Information ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Multiscale Model ◽  
Computation Method ◽  
Highly Active ◽  
Optimal Projection

Rockfall inventories are essential to quantify a rockfall activity and characterize the hazard. Terrestrial laser scanning and advancements in processing algorithms have resulted in three-dimensional (3D) semi-automatic extraction of rockfall events, permitting detailed observations of evolving rock masses. Currently, multiscale model-to-model cloud comparison (M3C2) is the most widely used distance computation method used in the geosciences to evaluate 3D changing features, considering the time-sequential spatial information contained in point clouds. M3C2 operates by computing distances using points that are captured within a projected search cylinder, which is locally oriented. In this work, we evaluated the effect of M3C2 projection diameter on the extraction of 3D rockfalls and the resulting implications on rockfall volume and shape. Six rockfall inventories were developed for a highly active rock slope, each utilizing a different projection diameter which ranged from two to ten times the point spacing. The results indicate that the greatest amount of change is extracted using an M3C2 projection diameter equal to, or slightly larger than, the point spacing, depending on the variation in point spacing. When the M3C2 projection diameter becomes larger than the changing area on the rock slope, the change cannot be identified and extracted. Inventory summaries and illustrations depict the influence of spatial averaging on the semi-automated rockfall extraction, and suggestions are made for selecting the optimal projection diameter. Recommendations are made to improve the methods used to semi-automatically extract rockfall from sequential point clouds.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Megan M. Friggens ◽  
Rachel A. Loehman ◽  
Connie I. Constan ◽  
Rebekah R. Kneifel
Keyword(s):  
New Mexico ◽  
Fire Severity ◽  
Fire Effects ◽  
Burn Severity ◽  
Fire Season ◽  
Archaeological Sites ◽  
Archaeological Record ◽  
Human Environment ◽  
Jemez Mountains ◽  
Archaeological Materials

Abstract Background Wildfires of uncharacteristic severity, a consequence of climate changes and accumulated fuels, can cause amplified or novel impacts to archaeological resources. The archaeological record includes physical features associated with human activity; these exist within ecological landscapes and provide a unique long-term perspective on human–environment interactions. The potential for fire-caused damage to archaeological materials is of major concern because these resources are irreplaceable and non-renewable, have social or religious significance for living peoples, and are protected by an extensive body of legislation. Although previous studies have modeled ecological burn severity as a function of environmental setting and climate, the fidelity of these variables as predictors of archaeological fire effects has not been evaluated. This study, focused on prehistoric archaeological sites in a fire-prone and archaeologically rich landscape in the Jemez Mountains of New Mexico, USA, identified the environmental and climate variables that best predict observed fire severity and fire effects to archaeological features and artifacts. Results Machine learning models (Random Forest) indicate that topography and variables related to pre-fire weather and fuel condition are important predictors of fire effects and severity at archaeological sites. Fire effects were more likely to be present when fire-season weather was warmer and drier than average and within sites located in sloped, treed settings. Topographic predictors were highly important for distinguishing unburned, moderate, and high site burn severity as classified in post-fire archaeological assessments. High-severity impacts were more likely at archaeological sites with southern orientation or on warmer, steeper, slopes with less accumulated surface moisture, likely associated with lower fuel moistures and high potential for spreading fire. Conclusions Models for predicting where and when fires may negatively affect the archaeological record can be used to prioritize fuel treatments, inform fire management plans, and guide post-fire rehabilitation efforts, thus aiding in cultural resource preservation.

scholarly journals Virtual Archaeology of Death and Burial: A Procedure for Integrating 3D Visualization and Analysis in Archaeothanatology

2021 ◽  
Vol 7 (1) ◽  
pp. 540-555
Author(s):  
Hayley L. Mickleburgh ◽  
Liv Nilsson Stutz ◽  
Harry Fokkens
Keyword(s):  
Spatial Information ◽  
3D Visualization ◽  
Rapid Development ◽  
Three Dimensional ◽  
3D Models ◽  
The Body ◽  
Test Case ◽  
Archaeology Of Death ◽  
Human Burials ◽  
3D Information

Abstract The reconstruction of past mortuary rituals and practices increasingly incorporates analysis of the taphonomic history of the grave and buried body, using the framework provided by archaeothanatology. Archaeothanatological analysis relies on interpretation of the three-dimensional (3D) relationship of bones within the grave and traditionally depends on elaborate written descriptions and two-dimensional (2D) images of the remains during excavation to capture this spatial information. With the rapid development of inexpensive 3D tools, digital replicas (3D models) are now commonly available to preserve 3D information on human burials during excavation. A procedure developed using a test case to enhance archaeothanatological analysis and improve post-excavation analysis of human burials is described. Beyond preservation of static spatial information, 3D visualization techniques can be used in archaeothanatology to reconstruct the spatial displacement of bones over time, from deposition of the body to excavation of the skeletonized remains. The purpose of the procedure is to produce 3D simulations to visualize and test archaeothanatological hypotheses, thereby augmenting traditional archaeothanatological analysis. We illustrate our approach with the reconstruction of mortuary practices and burial taphonomy of a Bell Beaker burial from the site of Oostwoud-Tuithoorn, West-Frisia, the Netherlands. This case study was selected as the test case because of its relatively complete context information. The test case shows the potential for application of the procedure to older 2D field documentation, even when the amount and detail of documentation is less than ideal.

Robotic motion compensation for bone movement, using ultrasound images

2015 ◽  
Vol 42 (5) ◽  
pp. 466-474 ◽  
Author(s):  
P.M.B. Torres ◽  
P. J. S. Gonçalves ◽  
J.M.M. Martins
Keyword(s):  
Motion Compensation ◽  
Optical Measurement ◽  
Three Dimensional ◽  
Point Clouds ◽  
Robotic System ◽  
Ultrasound Images ◽  
Bone Drilling ◽  
Content Type ◽  
3D Point Clouds ◽  
Robotic Motion

Purpose – The purpose of this paper is to present a robotic motion compensation system, using ultrasound images, to assist orthopedic surgery. The robotic system can compensate for femur movements during bone drilling procedures. Although it may have other applications, the system was thought to be used in hip resurfacing (HR) prosthesis surgery to implant the initial guide tool. The system requires no fiducial markers implanted in the patient, by using only non-invasive ultrasound images. Design/methodology/approach – The femur location in the operating room is obtained by processing ultrasound (USA) and computer tomography (CT) images, obtained, respectively, in the intra-operative and pre-operative scenarios. During surgery, the bone position and orientation is obtained by registration of USA and CT three-dimensional (3D) point clouds, using an optical measurement system and also passive markers attached to the USA probe and to the drill. The system description, image processing, calibration procedures and results with simulated and real experiments are presented and described to illustrate the system in operation. Findings – The robotic system can compensate for femur movements, during bone drilling procedures. In most experiments, the update was always validated, with errors of 2 mm/4°. Originality/value – The navigation system is based entirely on the information extracted from images obtained from CT pre-operatively and USA intra-operatively. Contrary to current surgical systems, it does not use any type of implant in the bone to track the femur movements.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

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