Special Issue "LiDAR"

Guest Editor
Dr. Alistair M. S. Smith
College of Natural Resources, University of Idaho, Moscow, ID 83844, USA
Website: http://www.RemoteEarth.net
E-Mail:
Interests: vegetation structure from LiDAR; disturbance and natural hazards monitoring; object-orientated remote sensing; wavelet analysis

Lidar (Light Detection and Ranging)

The last decade has seen the widespread adoption of lidar in terrestrial Earth Observation remote sensing. lidar datasets have been developed for applications in urban environments (buildings, bridges, highways, etc), for mining and geological applications, emergency management (landslides, floodplain mapping, hurricane damage assessment, etc), land cover change and global biogeochemical cycling (biomass, ecological impacts, etc), amongst others.

This special issue is open to all forms of terrestrial lidar research including, Ground scanners or Terrestrial Laser Scanners (TLS); aerial sensors including green-red (like EEARL) lidars used to map riparian areas and near-infrared lidars that characterize ground surfaces (so DEMs) and vegetation, and satellite based research or planned activities from sensors such as GLAS or DESDyni.

We encourage submissions that substantially improve algorithms to generate digital surface models such as for buildings, vegetation, ground surfaces, or stream channel morphology. Applications of these data and assessments of accuracy to help reduce uncertainties in global biogeochemical budgets are also encouraged.

All manuscripts should be submitted to remotesensing@mdpi.org with a copy to the Guest Editor. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this Open Access journal is 300 CHF per accepted paper. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

lidar; terrestrial lidar; ground lidar; satellite based; GLAS, DESDyni, aerial lidar; EEARL; near-infrared lidar; DEMs

Tentative Title: On the Use of LiDAR Data to Estimate Forest Biomass: A Review
Author: Damiano Gianelle
Centro di Ecologia Alpina; E-mail: gianelle@cealp.it
Abstract: To be added soon

Type of Paper: Article
Title: Monitoring Automotive Particulate Matter Emissions with LIDAR: A Review
Authors: Claudio Mazzoleni¹ and Hans Moosmüller²
Affiliations: ¹ Physics Department, Michigan Technological University, Houghton, MI, USA
² Desert Research Institute, Reno, NV, USA
Abstract: Particulate matter (PM) emissions from vehicles cause deleterious health effects, impair visibility, damage buildings of historic and artistic value, and affect the earth’s radiative forcing and climate change. Assessing the contribution of individual vehicles and classes of vehicles to the total PM burden in a urban environment is complicated by the many parameters that control individual vehicle emissions such as: vehicle age and maintenance, vehicle type, vehicle weight, fuel type, driving conditions, and meteorological conditions. Often a very small number of highly emitting vehicles dominates the total vehicular PM emissions, making statistically accurate measurements very challenging. Laboratory studies are generally prohibitively costly and time-consuming for large vehicle fleets and they poorly represent real-world conditions. Therefore, other techniques are needed to determine average emissions of large fleets that include a representative number of highly emitting vehicles. Among the few currently available techniques, remote sensing is the most cost-effective and allows stratifying PM emission factors by many parameters potentially controlling them.
Remote sensing techniques for automotive emissions place the sensor at the roadside and allow for the characterization of PM and gaseous emissions of up to 10,000 individual vehicles per day. Roadside remote sensing also has some limitations, including that emissions from each vehicle are characterized only during the very short time period while the vehicle passes by the system (typically a 0.5 s snapshots), limiting the diversity of vehicle operating conditions encountered at each measurement site. Remote sensing of automotive PM emissions has been particularly challenging. Approaches include the measurement of light extinction and scattering by the emitted PM at specified wavelengths. LIDAR represents a unique approach that allows to measure the light backscattered from the emitted PM and thereby to range-resolve the PM concentrations behind the vehicle. Due to the short range, it is easy to directly measure the total PM light extinction in addition to the backscatter signal. The added PM extinction measurement facilitates a more accurate inversion of the LIDAR signal, allowing for quantitative results even when optically thick plumes are encountered.
This publication briefly introduces and reviews roadside remote sensing for vehicle emission measurements and then focuses on reviewing backward elastic scattering LIDAR and transmissometer techniques for the roadside measurement of PM emission factors and their applications to individual vehicles and vehicle fleets.

Title: Alternative Methodologies for LiDAR System Calibration
Author: Ayman Habib et al.; E-mail: ahabib@ucalgary.ca
Abstract: LiDAR has become a popular technology for the direct acquisition of topographic information. In spite of the increasing utilization of this technology in several applications, its accuracy potential has not been fully explored. Most of current LiDAR calibration techniques are based on empirical and proprietary procedures that demand the original observations (GPS, IMU, and the laser measurements) or at least the trajectory and time-tagged point cloud, which may not be directly available to the end-user. As a result, we can still observe systematic discrepancies between conjugate surface elements in overlapping LiDAR strips. In this paper, two alternative calibration procedures that overcome existing limitations are introduced. The first presented method – denoted as “Simplified Method” – makes use of the LiDAR point cloud from parallel LiDAR strips. The second method – denoted as “Quasi-Rigorous Method” – can deal with non-parallel strips, but requires time-tagged LiDAR point cloud and navigation data (trajectory position only).