Special Issue "Applications of Full Waveform Lidar"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Prof. Dr. Luis A. Ruiz
Website SciProfiles
Guest Editor
GeoEnvironmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022-Valencia, Spain.
Interests: Lidar for forest structure analysis; 3D fire behaviour models; Object-based feature extraction and classification; Land use/land cover change analysis
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Special Issue Information

Dear Colleagues,

LiDAR Full-waveform (FW) systems allow for the registration of the complete wave as the energy pulse interacts with the object. This is particularly relevant in forest environments, since they are able to capture continuous information from the top of the canopy to the ground. Currently, the main use of LiDAR FW is focused on forest applications, where new methods for forest ecology management, forest structure characterization, fuel variables mapping and quantifying understory vegetation using LiDAR FW are under development. However, other applications have also benefited from using this technology, such as land use/land cover urban and agricultural classification, topographic modelling or archaeological prospection. Although software processing tools are still incipient, new FW sensors are coming out, not only aerial but also terrestrial, underwater and mobile, opening the scope of potential applications of this emerging technology.

The purpose of this Special Issue is to bring the state-of-the-art in LiDAR FW applications with different system types, in the development of new processing methods, algorithms and tools, and in the integration of LiDAR with other sensors and data sets to optimize its performance. Review papers and research contributions are both welcomed.

Prof. Luis A. Ruiz
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind 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 semimonthly 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 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Forest ecology and structure assessment
  • Wildfire prevention and fuel estimates
  • Urban classification
  • Topographic applications
  • Agricultural applications
  • LiDAR full-waveform processing methods and software
  • New full-waveform sensors
  • Integration of LiDAR full-waveform with other data/sensors
  • Terrestrial, underwater and mobile full-waveform LiDAR systems

Published Papers (2 papers)

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Research

Open AccessArticle
Improvement of Full Waveform Airborne Laser Bathymetry Data Processing based on Waves of Neighborhood Points
Remote Sens. 2019, 11(10), 1255; https://doi.org/10.3390/rs11101255 - 27 May 2019
Cited by 2
Abstract
Measurements of the topography of the sea floor are one of the main tasks of hydrographic organizations worldwide. The occurrence of any disaster in maritime traffic can contaminate the environment for many years. Therefore, increasing attention is being paid to the development of [...] Read more.
Measurements of the topography of the sea floor are one of the main tasks of hydrographic organizations worldwide. The occurrence of any disaster in maritime traffic can contaminate the environment for many years. Therefore, increasing attention is being paid to the development of effective methods for the detection and monitoring of possible obstacles on the transport route. Bathymetric laser scanners record the full waveform reflected from the object (target). Its transformation allows to obtain information about the water surface, water column, seabed, and the objects on it. However, it is not possible to identify subsequent returns among all waves, leading to a loss of information about the situation under the water. On the basis of the studies conducted, it was concluded that the use of a secondary analysis of a full waveform of the airborne laser bathymetry allowed for the identification of objects on the seabed. It allowed us to detect further points in the point cloud, which are necessary in the identification of objects on the seabed. The results of the experiment showed that, among the area of experiment where objects on the seabed were located, the number of points increased between 150 and 550% and the altitude accuracy of the seabed elevation model even by 50% to the level of 0.30 m with reference to sonar data depending of types of objects. Full article
(This article belongs to the Special Issue Applications of Full Waveform Lidar)
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Open AccessArticle
Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data
Remote Sens. 2018, 10(11), 1832; https://doi.org/10.3390/rs10111832 - 19 Nov 2018
Cited by 10
Abstract
Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation [...] Read more.
Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches. Full article
(This article belongs to the Special Issue Applications of Full Waveform Lidar)
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