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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 1800 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 (4 papers)

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Research

Open AccessArticle Exploring Multispectral ALS Data for Tree Species Classification
Remote Sens. 2018, 10(2), 183; doi:10.3390/rs10020183
Received: 15 November 2017 / Revised: 19 January 2018 / Accepted: 23 January 2018 / Published: 26 January 2018
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Abstract
Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm,
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Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58°27′18.35″N, 13°39′8.03″E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user’s and producer’s accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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Open AccessArticle Tree Death Not Resulting in Gap Creation: An Investigation of Canopy Dynamics of Northern Temperate Deciduous Forests
Remote Sens. 2018, 10(1), 121; doi:10.3390/rs10010121
Received: 15 November 2017 / Revised: 9 January 2018 / Accepted: 15 January 2018 / Published: 17 January 2018
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Abstract
Several decades of research have shown that canopy gaps drive tree renewal processes in the temperate deciduous forest biome. In the literature, canopy gaps are usually defined as canopy openings that are created by partial or total tree death of one or more
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Several decades of research have shown that canopy gaps drive tree renewal processes in the temperate deciduous forest biome. In the literature, canopy gaps are usually defined as canopy openings that are created by partial or total tree death of one or more canopy trees. In this study, we investigate linkages between tree damage mechanisms and the formation or not of new canopy gaps in northern temperate deciduous forests. We studied height loss processes in unmanaged and managed forests recovering from partial cutting with multi-temporal airborne Lidar data. The Lidar dataset was used to detect areas where canopy height reduction occurred, which were then field-studied to identify the tree damage mechanisms implicated. We also sampled the density of leaf material along transects to characterize canopy structure. We used the dataset of the canopy height reduction areas in a multi-model inference analysis to determine whether canopy structures or tree damage mechanisms most influenced the creation of new canopy gaps within canopy height reduction areas. According to our model, new canopy gaps are created mainly when canopy damage enlarges existing gaps or when height is reduced over areas without an already established dense sub-canopy tree layer. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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Open AccessArticle Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures
Remote Sens. 2018, 10(1), 39; doi:10.3390/rs10010039
Received: 16 November 2017 / Revised: 22 December 2017 / Accepted: 23 December 2017 / Published: 26 December 2017
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Abstract
A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with
[...] Read more.
A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM) using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW) algorithms. Subsequently, the Random forests (RF) and Conditional inference forests (CF) models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak) and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR) using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF) and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR) and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due to the similar characteristics of blue oak and interior live oak. Uncertainty estimates from the BMLR method compensate for this downside by providing classification results in a probabilistic sense and rendering users with more confidence in interpreting and applying classification results to real-world tasks such as forest inventory. Overall, this study recommends the CF method for feature selection and suggests that BMLR could be a superior alternative to classical machining learning methods. Full article
(This article belongs to the Special Issue Lidar for Forest Science and Management)
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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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