Special Issue "LiDAR"
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A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 December 2009)
Special Issue Editor
Special Issue Information
Dear Colleagues,
The last decade has seen the widespread adoption of lidar (Light Detection and Ranging) 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.
Dr. Alistair M. S. Smith
Guest Editor
Submission
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. 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 800 CHF (Swiss Francs).
Keywords
- lidar
- terrestrial lidar
- ground lidar
- satellite based
- GLAS
- DESDyni
- aerial lidar
- EEARL
- near-infrared lidar
- DEMs
Published Papers (12 papers)
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Received: 1 September 2009; in revised form: 16 October 2009 / Accepted: 20 October 2009 / Published: 27 October 2009
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Abstract: Recent years have seen the progression of light detection and ranging (lidar) from the realm of research to operational use in natural resource management. Numerous government agencies, private industries, and public/private stakeholder consortiums are planning or have recently acquired large-scale acquisitions, and a national U.S. lidar acquisition is likely before 2020. Before it is feasible for land managers to integrate lidar into decision making, resource assessment, or monitoring across the gambit of natural resource applications, consistent standards in project planning, data processing, and user-driven products are required. This paper introduces principal lidar acquisition parameters, and makes recommendations for project planning, processing, and product standards to better serve natural resource managers across multiple disciplines.
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Received: 31 August 2009; in revised form: 22 October 2009 / Accepted: 9 November 2009 / Published: 11 November 2009
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Abstract: Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future.
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Received: 21 September 2009; in revised form: 9 October 2009 / Accepted: 11 November 2009 / Published: 19 November 2009
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Abstract: The high-resolution Experimental Advanced Airborne Research LIDAR (EAARL) is a new technology for cross-environment surveys of channels and floodplains. EAARL measurements of basic channel geometry, such as wetted cross-sectional area, are within a few percent of those from control field surveys. The largest channel mapping errors are along stream banks. The LIDAR data adequately support 1D and 2D computational fluid dynamics models and frequency domain analyses by wavelet transforms. Further work is needed to establish the stream monitoring capability of the EAARL and the range of water quality conditions in which this sensor will accurately map river bathymetry.
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Received: 18 October 2009; in revised form: 24 November 2009 / Accepted: 8 December 2009 / Published: 17 December 2009
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Abstract: Unlike mobile survey systems, stationary survey systems are given very little direct georeferencing attention. Direct Georeferencing is currently being used in several mobile applications, especially in terrestrial and airborne LiDAR systems. Georeferencing of stationary terrestrial LiDAR scanning data, however, is currently performed indirectly through using control points in the scanning site. The indirect georeferencing procedure is often troublesome; the availability of control stations within the scanning range is not always possible. Also, field procedure can be laborious and involve extra equipment and target setups. In addition, the conventional method allows for possible human error due to target information bookkeeping. Additionally, the accuracy of this procedure varies according to the quality of the control used. By adding a dual GPS antenna apparatus to the scanner setup, thereby supplanting the use of multiple ground control points scattered throughout the scanning site, we mitigate not only the problems associated with indirect georeferencing but also induce a more efficient set up procedure while maintaining sufficient precision. In this paper, we describe a new method for determining the 3D absolute orientation of LiDAR point cloud using GPS measurements from two antennae firmly mounted on the optical head of a stationary LiDAR system. In this paper, the general case is derived where the orientation angles are not small; this case completes the theory of stationary LiDAR direct georeferencing. Simulation and real world field experimentation of the prototype implementation suggest a precision of about 0.05 degrees (~1 milli-radian) for the three orientation angles.
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Received: 7 January 2010; in revised form: 2 February 2010 / Accepted: 3 February 2010 / Published: 11 February 2010
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Abstract: Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 × 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range.
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Received: 4 January 2010; in revised form: 20 February 2010 / Accepted: 27 February 2010 / Published: 22 March 2010
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Abstract: This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.
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Received: 26 January 2010; in revised form: 8 March 2010 / Accepted: 16 March 2010 / Published: 23 March 2010
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Abstract: Over the last few years, 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 system’s raw measurements, which may not be always 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 the existing limitations are introduced. The first procedure, denoted as “Simplified method”, makes use of the LiDAR point cloud from parallel LiDAR strips acquired by a steady platform (e.g., fixed wing aircraft) over an area with moderately varying elevation. The second procedure, denoted as “Quasi-rigorous method”, can deal with non-parallel strips, but requires time-tagged LiDAR point cloud and navigation data (trajectory position only) acquired by a steady platform. With the widespread adoption of LAS format and easy access to trajectory information, this data requirement is not a problem. The proposed methods can be applied in any type of terrain coverage without the need for control surfaces and are relatively easy to implement. Therefore, they can be used in every flight mission if needed. Besides, the proposed procedures require minimal interaction from the user, which can be completely eliminated after minor extension of the suggested procedure.
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Received: 5 January 2010; in revised form: 15 March 2010 / Accepted: 30 March 2010 / Published: 9 April 2010
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Abstract: Automotive particulate matter (PM) causes deleterious effects on health and visibility. Physical and chemical properties of PM also influence climate change. Roadside remote sensing of automotive emissions is a valuable option for assessing the contribution of individual vehicles to the total PM burden. LiDAR represents a unique approach that allows measuring PM emissions from in-use vehicles with high sensitivity. This publication reviews vehicle emission remote sensing measurements using ultraviolet LiDAR and transmissometer systems. The paper discusses the measurement theory and documents examples of how these techniques provide a unique perspective for exhaust emissions of individual and groups of vehicles.
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Received: 25 January 2010; in revised form: 9 March 2010 / Accepted: 13 April 2010 / Published: 15 April 2010
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Abstract: LiDAR-derived digital elevation models can reveal road networks located beneath dense forest canopy. This study tests the accuracy of forest road characteristics mapped using LiDAR in the Santa Cruz Mountains, CA. The position, gradient, and total length of a forest haul road were accurately extracted using a 1 m DEM. In comparison to a field-surveyed centerline, the LiDAR-derived road exhibited a positional accuracy of 1.5 m, road grade measurements within 0.53% mean absolute difference, and total road length within 0.2% of the field-surveyed length. Airborne LiDAR can provide thorough and accurate road inventory data to support forest management and watershed assessment activities.
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Received: 10 July 2010; in revised form: 19 August 2010 / Accepted: 20 August 2010 / Published: 1 September 2010
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Abstract: A significant portion of the Canadian Maritime coastline has been surveyed with airborne Light Detection and Ranging (LiDAR). The purpose of these surveys has been to map the risk of flooding from storm surges and projected long-term sea‑level rise from climate change and to include projects in all three Maritime Provinces: Prince Edward Island, New Brunswick, and Nova Scotia. LiDAR provides the required details in order to map the flood inundation from 1 to 2 m storm surge events, which cause coastal flooding in many locations in this region when they occur at high tide levels. The community of Annapolis Royal, Nova Scotia, adjacent to the Bay of Fundy, has been surveyed with LiDAR and a 1 m DEM (Digital Elevation Model) was constructed for the flood inundation mapping. Validation of the LiDAR using survey grade GPS indicates a vertical accuracy better than 30 cm. A benchmark storm, known as the Groundhog Day storm (February 1–3, 1976), was used to assess the flood maps and to illustrate the effects of different sea-level rise projections based on climate change scenarios if it were to re-occur in 100 years time. Near shore bathymetry has been merged with the LiDAR and local wind observations used to model the impact of significant waves during this benchmark storm. Long-term (ca. greater than 30 years) time series of water level observations from across the Bay of Fundy in Saint John, New Brunswick, have been used to estimate return periods of water levels under present and future sea-level rise conditions. Results indicate that under current sea-level rise conditions this storm has a 66 year return period. With a modest relative sea-level (RSL) rise of 80 cm/century this decreases to 44 years and, with a possible upper limit rise of 220 cm/century, this decreases further to 22 years. Due to the uncertainty of climate change scenarios and sea-level rise, flood inundation maps have been constructed at 10 cm increments up to the 9 m contour which represents an upper flood limit estimate in 100 years, based on the highest predicted tide, plus a 2 m storm surge and a RSL of 220 cm/century.
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Received: 25 September 2010; in revised form: 12 October 2010 / Accepted: 8 November 2010 / Published: 23 November 2010
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Abstract: Topography and land cover characteristics can have significant effects on infiltration, runoff, and erosion processes on watersheds. The ability to model the timing and routing of surface water and erosion is affected by the resolution of the digital elevation model (DEM). High resolution ground-based Light Detecting and Ranging (LiDAR) technology can be used to collect detailed topographic and land cover characteristic data. In this study, a method was developed to remove vegetation from ground-based LiDAR data to create high resolution DEMs. Research was conducted on intensively studied rainfall–runoff plots on the USDA-ARS Walnut Gulch Experimental Watershed in Southeast Arizona. LiDAR data were used to generate 1 cm resolution digital surface models (DSM) for 5 plots. DSMs created directly from LiDAR data contain non-surface objects such as vegetation cover. A vegetation removal method was developed which used a slope threshold and a focal mean filter method to remove vegetation and create bare earth DEMs. The method was validated on a synthetic plot, where rocks and vegetation were added incrementally. Results of the validation showed a vertical error of ±7.5 mm in the final DEM.
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Received: 7 October 2010; in revised form: 7 December 2010 / Accepted: 8 December 2010 / Published: 17 December 2010
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Abstract: The active Séchilienne landslide (Isère, France) has been continuously monitored by tacheometry, radar and extensometry devices for 25 years. Indeed, if the 3 mil. m3 of rocks in the active zone named ―Ruines‖ fell down, the debris would dam the Romanche valley. The breaking of the dam by overtopping and rapid erosion would bring a catastrophic flood and other dramatic consequences throughout the valley. Given the rockfall hazard in the most active zone, it is impossible to use targets in this area: Only reflectorless remote sensing techniques can provide information. A time-series of seven Terrestrial Laser Scanner (TLS) point clouds acquired between 2004 and 2007 enable us to monitor the 3D displacements of the whole scanned area, although point coverage is not homogeneous. From this sequential monitoring, the volume of registered collapses can be deduced and the landslide movement along the main geological structures can be inferred. From monitoring associated subsidence and toppling observed on TLS data, it can be deduced that blocks rearrangements are linked to structural settings and that the Séchilienne landslide is complex. To conclude, TLS point clouds enable an accurate monitoring of the evolution of the inaccessible "Ruines" area and, proven its ability to provide reliable kinematic information, even in areas where on-site instrumentation is infeasible.
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Last update: 5 April 2011
