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Special Issue "Lidar for Forest Science and Management"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (15 November 2017)

Special Issue Editors

Guest Editor
Prof. Valerie A. Thomas

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 307A, 310 West Campus Dr, Blacksburg, VA 24061, USA
Website | E-Mail
Interests: forest ecosystem remote sensing; forest disturbance; multitemporal analysis; LiDAR; imaging spectroscopy; data fusion
Guest Editor
Prof. Randolph H. Wynne

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 319, 310 West Campus Dr, Blacksburg, VA 24061, USA
Website | E-Mail
Phone: 540-231-7811
Interests: applications of remote sensing to forestry; natural resource management; ecological modeling; and Earth system science

Special Issue Information

Dear Colleagues,

Associated with SilviLaser 2017 (http://www.cpe.vt.edu/silvilaser2017/), hosted in Blacksburg, Virginia at Virginia Tech in October 2017, this Special Issue will be focused on lidar applications for assessing and managing forest ecosystems. Allied technologies, such as phodar, are welcome, as are any methods of acquisition (piloted or unpiloted; airborne, space borne and terrestrial). We particularly welcome technological or analytical advances that have strong potential to improve forest science and silviculture.

Prof. Valerie A. Thomas
Prof. Randolph H. Wynne
Guest Editor

Manuscript Submission Information

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. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 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 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 1600 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.

Published Papers (1 paper)

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Research

Open AccessArticle Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs
Remote Sens. 2017, 9(11), 1101; doi:10.3390/rs9111101
Received: 22 September 2017 / Revised: 14 October 2017 / Accepted: 26 October 2017 / Published: 28 October 2017
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Abstract
Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed
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Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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