scholarly journals Toward a Novel Laser-Based Approach for Estimating Snow Interception

2020 ◽  
Vol 12 (7) ◽  
pp. 1146 ◽  
Author(s):  
Micah Russell ◽  
Jan U. H. Eitel ◽  
Andrew J. Maguire ◽  
Timothy E. Link
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Primary Objective ◽  
Snow Accumulation ◽  
Future Research ◽  
Snow Density ◽  
Canopy Interception ◽  
Terrestrial Lidar ◽  
Large Trees

Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium-sized trees, and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception volume with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled the comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ −1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data in difficult to access terrain, and calibrating snow interception models to new forest types around the globe.

scholarly journals Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 527 ◽  
Author(s):  
Alvaro Lau ◽  
Kim Calders ◽  
Harm Bartholomeus ◽  
Christopher Martius ◽  
Pasi Raumonen ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Point Clouds ◽  
Validation Data ◽  
Terrestrial Lidar ◽  
Allometric Models ◽  
Crown Diameter ◽  
National Monitoring ◽  
Large Trees

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

scholarly journals CLASSIFICATION OF MOBILE TERRESTRIAL LIDAR POINT CLOUD IN URBAN AREA USING LOCAL DESCRIPTORS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1117-1122 ◽  
Author(s):  
M. Zaboli ◽  
H. Rastiveis ◽  
A. Shams ◽  
B. Hosseiny ◽  
W. A. Sarasua
Keyword(s):  
Urban Area ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Automated Analysis ◽  
Support Vector ◽  
Terrestrial Lidar ◽  
Local Descriptors ◽  
3D Point Clouds

Abstract. Automated analysis of three-dimensional (3D) point clouds has become a boon in Photogrammetry, Remote Sensing, Computer Vision, and Robotics. The aim of this paper is to compare classifying algorithms tested on an urban area point cloud acquired by a Mobile Terrestrial Laser Scanning (MTLS) system. The algorithms were tested based on local geometrical and radiometric descriptors. In this study, local descriptors such as linearity, planarity, intensity, etc. are initially extracted for each point by observing their neighbor points. These features are then imported to a classification algorithm to automatically label each point. Here, five powerful classification algorithms including k-Nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Neural Network, and Random Forest (RF) are tested. Eight semantic classes are considered for each method in an equal condition. The best overall accuracy of 90% was achieved with the RF algorithm. The results proved the reliability of the applied descriptors and RF classifier for MTLS point cloud classification.

Urban energetics applications and Geomatic technologies in a Smart City perspective

2015 ◽  
Vol 6 (1) ◽  
pp. 19-29 ◽  
Author(s):  
G. Bitelli ◽  
P. Conte ◽  
T. Csoknyai ◽  
E. Mandanici
Keyword(s):  
Smart City ◽  
Laser Scanning ◽  
Near Infrared ◽  
Three Dimensional ◽  
Energy Sector ◽  
Primary Objective ◽  
Airborne Lidar ◽  
City Management ◽  
Urban Context ◽  
Research Areas

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

scholarly journals Weighing trees with lasers: advances, challenges and opportunities

2018 ◽  
Vol 8 (2) ◽  
pp. 20170048 ◽  
Author(s):  
M. I. Disney ◽  
M. Boni Vicari ◽  
A. Burt ◽  
K. Calders ◽  
S. L. Lewis ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Tree Form ◽  
Form And Function ◽  
Challenges And Opportunities ◽  
And Function ◽  
Non Destructive

Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.

scholarly journals Living in the clouds: conceptual reconstructions of harbour structures

2018 ◽  
Vol 8 (4) ◽  
pp. 405-419
Author(s):  
Elizabeth Anne Shotton
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Heritage Management ◽  
Historical Narrative ◽  
Web Based ◽  
Content Type ◽  
Terrestrial Lidar ◽  
Sea Levels ◽  
Rising Sea Levels ◽  
Scan Data

Purpose The harbours of Ireland, under threat from deterioration and rising sea levels, are being documented using terrestrial LiDAR augmented by archival research to develop comprehensive histories and timeline models for public dissemination. While methods to extract legible three-dimensional models from scan data have been developed and such operational formats for heritage management are imperative, the need for this format in interpretive visualisations should be reconsidered. The paper aims to discuss these issues. Design/methodology/approach Interpretive visualisations are forms of history making, where factual evidence is drawn together with conjecture to illustrate a plausible account of events, and differentiation between fact and conjecture is the key to their intellectual transparency. A procedure for superimposing conjectural reconstructions, generated using Rhinoceros and CloudCompare, on original scan data in Cyclone and visualised on a web-based viewer is discussed. Findings Embellishing scan data with conjectural elements to visualise the evolution of harbours is advantageous for both research and public dissemination. The accuracy and density of the scans enables the interrogation of the harbour form and the irregular details, the latter in danger of generalisation if translated into parametric or mesh format. Equally, the ethereal quality of the point cloud conveys a sense of tentativeness, consistent with a provisional hypothesis. Finally, coding conjectural elements allows users to intuit the difference between fact and historical narrative. Originality/value While various web-based point clouds viewers are used to disseminate research, the novelty here is the potential to develop didactic representations using point clouds that successfully capture a provisional thesis regarding each harbour’s evolution in an intellectually transparent manner to enable further inquiry.

A new drone laser scanning benchmark dataset for characterization of single-tree and forest biophysical properties

2021 ◽  
Author(s):  
Puliti Stefano ◽  
Grant D. Pears ◽  
Michael S. Watt ◽  
Edward Mitchard ◽  
Iain McNicol ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Method Development ◽  
Three Dimensional ◽  
Point Clouds ◽  
Benchmark Dataset ◽  
Validation Dataset ◽  
Tree Structures ◽  
Single Tree ◽  
Individual Trees

<p>Survey-grade drone laser scanners suitable for unmanned aerial vehicles (UAV-LS) allow the efficient collection of finely detailed three-dimensional information of tree structures. This data type allows forests to be resolved into discrete individual trees and has shown promising results in providing accurate in-situ observations of key forestry variables. New and improved approaches for analyzing UAV-LS point clouds have to be developed to transform the vast amounts of data from UAV-LS into actionable insights and decision support. Many different studies have explored various methods for automating single tree detection, segmentation, parsing into different tree components, and measurement of biophysical variables (e.g., diameter at breast height). Despite the considerable efforts dedicated to developing automated ways to process UAV-LS data into useful data, current methods tend to be tailored to small datasets, and it remains challenging to evaluate the performance of different algorithms based on a consistent validation dataset. To fill this knowledge gap and to further advance our ability to measure forests from UAV-LS data, we present a new benchmarking dataset. This data is composed of manually labelled UAV-LS data acquired a number of continents and biomes which span tropical to boreal forests. The UAV-LS data was collected exclusively used survey-grade sensors such as the Riegl VUX and mini-VUX series which are characterized by a point density in the range 1 – 10 k points m<sup>2</sup>. Currently, such data represent the state-of-the-art in aerial laser scanning data. The benchmark data consists of a library of single-tree point clouds, aggregated to sample plots, with each point classified as either stem, branch, or leaves. With the objective of releasing such a benchmark dataset as a public asset, in the future, researchers will be able to leverage such pre-existing labelled trees for developing new methods to measure forests from UAV-LS data. The availability of benchmarking datasets represents an important driver for enabling the development of robust and accurate methods. Such a benchmarking dataset will also be important for a consistent comparison of existing or future algorithms which will guide future method development.</p>

scholarly journals Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress

2020 ◽  
Vol 12 (6) ◽  
pp. 942 ◽  
Author(s):  
Maria Rosaria De Blasiis ◽  
Alessandro Di Benedetto ◽  
Margherita Fiani
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Road Traffic ◽  
Single Point ◽  
Three Dimensional ◽  
Point Clouds ◽  
Road Surface ◽  
Important Indicator ◽  
The Road ◽  
Laser Systems

The surface conditions of road pavements, including the occurrence and severity of distresses present on the surface, are an important indicator of pavement performance. Periodic monitoring and condition assessment is an essential requirement for the safety of vehicles moving on that road and the wellbeing of people. The traditional characterization of the different types of distress often involves complex activities, sometimes inefficient and risky, as they interfere with road traffic. The mobile laser systems (MLS) are now widely used to acquire detailed information about the road surface in terms of a three-dimensional point cloud. Despite its increasing use, there are still no standards for the acquisition and processing of the data collected. The aim of our work was to develop a procedure for processing the data acquired by MLS, in order to identify the localized degradations that mostly affect safety. We have studied the data flow and implemented several processing algorithms to identify and quantify a few types of distresses, namely potholes and swells/shoves, starting from very dense point clouds. We have implemented data processing in four steps: (i) editing of the point cloud to extract only the points belonging to the road surface, (ii) determination of the road roughness as deviation in height of every single point of the cloud with respect to the modeled road surface, (iii) segmentation of the distress (iv) computation of the main geometric parameters of the distress in order to classify it by severity levels. The results obtained by the proposed methodology are promising. The procedures implemented have made it possible to correctly segmented and identify the types of distress to be analyzed, in accordance with the on-site inspections. The tests carried out have shown that the choice of the values of some parameters to give as input to the software is not trivial: the choice of some of them is based on considerations related to the nature of the data, for others, it derives from the distress to be segmented. Due to the different possible configurations of the various distresses it is better to choose these parameters according to the boundary conditions and not to impose default values. The test involved a 100-m long urban road segment, the surface of which was measured with an MLS installed on a vehicle that traveled the road at 10 km/h.

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