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Special Issue "LiDAR Remote Sensing of Forest Resources"

A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: closed (15 September 2016)

Special Issue Editors

Guest Editor
Dr. Chris Hopkinson

Department of Geography, University of Lethbridge, Lethbridge, Alberta, T1K3M4, Canada
Website | E-Mail
Interests: airborne and terrestrial LiDAR; forest resources; change detection; water resources
Guest Editor
Dr. Laura Chasmer

Department of Geography, University of Lethbridge, Lethbridge, Alberta, T1K3M4, Canada
Website | E-Mail
Interests: airborne LiDAR; multi-spectral classification; ecosystem monitoring; carbon assessment
Guest Editor
Dr. Craig Mahoney

Department of Geography, University of Lethbridge, Lethbridge, Alberta, T1K3M4, Canada
Website | E-Mail
Interests: satellite and airborne LiDAR; full waveform analysis; machine learning forest classification

Special Issue Information

Dear Colleagues,

The field of LiDAR remote sensing of forest resources originated in 1980s and 1990s with low-resolution, large footprint sensors. For the last 20 years, commercial mapping sensors have been available to provide comparatively high-resolution discrete return sampling of canopy structure, and during this period techniques to exploit the 3D nature of these data structures have proliferated. From 2003 to 2010, ICESat (Ice, Cloud, and land Elevation Satellite)’s Geoscience Laser Altimeter System provided orbital LiDAR coverage of forest environments, allowing the construction of global forest products; work that is anticipated to continue with new missions, such as ICESat II and GEDI (Global Ecosystem Dynamics Investigation). During the last decade, terrestrial sensors have complimented the overviews provided by airborne and satellite data by providing within-canopy observations of fine structural elements down to foliage element level. Within the last year, we have witnessed something of a paradigm shift in this field, with the availability of multi-channel sensors that allow for combined active multi-spectral and geometric representation of canopy environments from a single sensor. Consequently, LiDAR is, in many ways, a mainstream technology in remote sensing forest attribute mapping, while in others new techniques and data products are emerging or yet to be developed.

Meanwhile, and in addition to the obvious commercial values presented by forests environments, the growing realisation that forests are critical and threatened elements in our global ecosystem is evident in the many national and international initiatives directed at sustainable forest management and conservation. Increasingly, we are seeing the emergence of frameworks that value and account for forest attributes, such as Carbon, instead of simply merchantable volume, or ecosystem services instead of just commercial yield. Moreover, we are now at point in time where national ecosystem monitoring networks or observatories are recognising LiDAR’s ability to map and scale critical forest attributes from stem to stand to nation and beyond.

This special issue seeks manuscript submissions that showcase integrative usages of LiDAR technology and data fusion in the pursuit of forest attribute scaling and/or the generation of methods and data products that demonstrate holistic ecosystem classifications and functional assessments. In particular, papers presenting new LiDAR-based Carbon assessment or monitoring methods and/or linkages between forest resources and surrounding terrestrial ecosystem processes are of high interest.

Dr. Chris Hopkinson
Dr. Laura Chasmer
Dr. Craig Mahoney
Guest Editors

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. Forests 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 1200 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

  • LiDAR data fusion
  • Scaling
  • Carbon
  • Ecosystem services
  • Biomass
  • Forest resources
  • Sustainable management

Published Papers (6 papers)

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Research

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Open AccessArticle Non-Destructive, Laser-Based Individual Tree Aboveground Biomass Estimation in a Tropical Rainforest
Forests 2017, 8(3), 86; doi:10.3390/f8030086
Received: 7 November 2016 / Revised: 8 March 2017 / Accepted: 13 March 2017 / Published: 17 March 2017
PDF Full-text (5285 KB) | HTML Full-text | XML Full-text
Abstract
Recent methods for detailed and accurate biomass and carbon stock estimation of forests have been driven by advances in remote sensing technology. The conventional approach to biomass estimation heavily relies on the tree species and site-specific allometric equations, which are based on destructive
[...] Read more.
Recent methods for detailed and accurate biomass and carbon stock estimation of forests have been driven by advances in remote sensing technology. The conventional approach to biomass estimation heavily relies on the tree species and site-specific allometric equations, which are based on destructive methods. This paper introduces a non-destructive, laser-based approach (terrestrial laser scanner) for individual tree aboveground biomass estimation in the Royal Belum forest reserve, Perak, Malaysia. The study area is in the state park, and it is believed to be one of the oldest rainforests in the world. The point clouds generated for 35 forest plots, using the terrestrial laser scanner, were geo-rectified and cleaned to produce separate point clouds for individual trees. The volumes of tree trunks were estimated based on a cylinder model fitted to the point clouds. The biomasses of tree trunks were calculated by multiplying the volume and the species wood density. The biomasses of branches and leaves were also estimated based on the estimated volume and density values. Branch and leaf volumes were estimated based on the fitted point clouds using an alpha-shape approach. The estimated individual biomass and the total above ground biomass were compared with the aboveground biomass (AGB) value estimated using existing allometric equations and individual tree census data collected in the field. The results show that the combination of a simple single-tree stem reconstruction and wood density can be used to estimate stem biomass comparable to the results usually obtained through existing allometric equations. However, there are several issues associated with the data and method used for branch and leaf biomass estimations, which need further improvement. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Open AccessArticle Predictive Modeling of Black Spruce (Picea mariana (Mill.) B.S.P.) Wood Density Using Stand Structure Variables Derived from Airborne LiDAR Data in Boreal Forests of Ontario
Forests 2016, 7(12), 311; doi:10.3390/f7120311
Received: 23 September 2016 / Revised: 21 November 2016 / Accepted: 29 November 2016 / Published: 8 December 2016
Cited by 1 | PDF Full-text (3728 KB) | HTML Full-text | XML Full-text
Abstract
Our objective was to model the average wood density in black spruce trees in representative stands across a boreal forest landscape based on relationships with predictor variables extracted from airborne light detection and ranging (LiDAR) point cloud data. Increment core samples were collected
[...] Read more.
Our objective was to model the average wood density in black spruce trees in representative stands across a boreal forest landscape based on relationships with predictor variables extracted from airborne light detection and ranging (LiDAR) point cloud data. Increment core samples were collected from dominant or co-dominant black spruce trees in a network of 400 m2 plots distributed among forest stands representing the full range of species composition and stand development across a 1,231,707 ha forest management unit in northeastern Ontario, Canada. Wood quality data were generated from optical microscopy, image analysis, X-ray densitometry and diffractometry as employed in SilviScan™. Each increment core was associated with a set of field measurements at the plot level as well as a suite of LiDAR-derived variables calculated on a 20 × 20 m raster from a wall-to-wall coverage at a resolution of ~1 point m−2. We used a multiple linear regression approach to identify important predictor variables and describe relationships between stand structure and wood density for average black spruce trees in the stands we observed. A hierarchical classification model was then fitted using random forests to make spatial predictions of mean wood density for average trees in black spruce stands. The model explained 39 percent of the variance in the response variable, with an estimated root mean square error of 38.8 (kg·m−3). Among the predictor variables, P20 (second decile LiDAR height in m) and quadratic mean diameter were most important. Other predictors describing canopy depth and cover were of secondary importance and differed according to the modeling approach. LiDAR-derived variables appear to capture differences in stand structure that reflect different constraints on growth rates, determining the proportion of thin-walled earlywood cells in black spruce stems, and ultimately influencing the pattern of variation in important wood quality attributes such as wood density. A spatial characterization of variation in a desirable wood quality attribute, such as density, enhances the possibility for value chain optimization, which could allow the forest industry to be more competitive through efficient planning for black spruce management by including an indication of suitability for specific products as a modeled variable derived from standard inventory data. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Open AccessArticle Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest
Forests 2016, 7(12), 307; doi:10.3390/f7120307
Received: 7 September 2016 / Revised: 30 November 2016 / Accepted: 1 December 2016 / Published: 7 December 2016
Cited by 1 | PDF Full-text (4111 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanning is a promising technique for efficient and accurate, remote-based forest inventory, due to its capacity for direct measurement of the three-dimensional structure of vegetation. The main objective of this study was to test the usability and accuracy of an individual
[...] Read more.
Airborne laser scanning is a promising technique for efficient and accurate, remote-based forest inventory, due to its capacity for direct measurement of the three-dimensional structure of vegetation. The main objective of this study was to test the usability and accuracy of an individual tree detection approach, using reFLex software, in the evaluation of forest variables. The accuracy assessment was conducted in a selected type of multilayered deciduous forest in southern Italy. Airborne laser scanning data were taken with a YellowScan Mapper scanner at an average height of 150 m. Point density reached 30 echoes per m2, but most points belonged to the first echo. The ground reference data contained the measured positions and dimensions of 445 trees. Individual tree-detection rates were 66% for dominant, 48% for codominant, 18% for intermediate, and 5% for suppressed trees. Relative root mean square error for tree height, diameter, and volume reached 8.2%, 21.8%, and 45.7%, respectively. All remote-based tree variables were strongly correlated with the ground data (R2 = 0.71–0.79). At the stand-level, the results show that differences ranged between 4% and 17% for stand height and 22% and 40% for stand diameter. The total growing stock differed by −43% from the ground reference data, and the ratios were 64% for dominant, 58% for codominant, 36% for intermediate, and 16% for suppressed trees. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Open AccessArticle Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching
Forests 2016, 7(11), 255; doi:10.3390/f7110255
Received: 13 July 2016 / Revised: 15 October 2016 / Accepted: 22 October 2016 / Published: 28 October 2016
Cited by 3 | PDF Full-text (6178 KB) | HTML Full-text | XML Full-text
Abstract
Accurate information on both the current stock and future growth and yield of forest resources is critical for sustainable forest management. We demonstrate a novel approach to utilizing airborne laser scanning (ALS)-derived forest stand attributes to determine future growth and yield of six
[...] Read more.
Accurate information on both the current stock and future growth and yield of forest resources is critical for sustainable forest management. We demonstrate a novel approach to utilizing airborne laser scanning (ALS)-derived forest stand attributes to determine future growth and yield of six attributes at a sub-stand (25 m grid cell) level of detail: dominant height (HMAX), Lorey’s height (HL), quadratic mean diameter (QMD), basal area (BA), whole stem volume (V), and trees per hectare (TPH). The approach is designed to find the most appropriate matching yield curve and project the attributes to the age of 80 years. Comparisons to conventional plot-level projections resulted in relative mean differences of 13.4% (HMAX), −27.1% (HL), 18.8% (QMD), 12.0% (BA), 18.6% (V), and −17.5% (TPH). The respective relative root mean squared difference values were: 31.1%, 38.4%, 19.8%, 19.8%, 21.8%, and 38.4%. Differences were driven mostly by stand-level age and site index. The uncertainty of cell-level yield curve assignment was used to refine stand-level summaries. The novel contribution of this study is in the application of growth and yield models at the cell level, combined with the use of ALS-derived attributes to optimize yield curve selection via template matching. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Open AccessArticle Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data
Forests 2016, 7(6), 122; doi:10.3390/f7060122
Received: 15 April 2016 / Revised: 3 June 2016 / Accepted: 4 June 2016 / Published: 11 June 2016
Cited by 4 | PDF Full-text (2985 KB) | HTML Full-text | XML Full-text
Abstract
In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of
[...] Read more.
In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of tree species classification of urban forests using aerial-based HyMap hyperspectral imagery and light detection and ranging (LiDAR) data. First, we conducted an object-based image analysis (OBIA) to segment individual tree crowns present in LiDAR-derived Canopy Height Models (CHMs). Then, hyperspectral values for individual trees were extracted from HyMap data for band reduction through Minimum Noise Fraction (MNF) transformation which allowed us to reduce the data to 20 significant bands out of 118 bands acquired. Finally, we compared several different classifications using Random Forest (RF) and Multi Class Classifier (MCC) methods. Seven tree species were classified using all 118 bands which resulted in 46.3% overall classification accuracy for RF versus 79.6% for MCC. Using only the 20 optimal bands extracted through MNF, both RF and MCC achieved an increase in overall accuracy to 87.0% and 88.9%, respectively. Thus, the MNF band selection process is a preferable approach for tree species classification when using hyperspectral data. Further, our work also suggests that RF is heavily disadvantaged by the high-dimensionality and noise present in hyperspectral data, while MCC is more robust when handling high-dimensional datasets with small sample sizes. Our overall results indicated that individual tree species identification in urban forests can be accomplished with the fusion of object-based LiDAR segmentation of crowns and hyperspectral characterization. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Review

Jump to: Research

Open AccessReview Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification
Forests 2016, 7(9), 198; doi:10.3390/f7090198
Received: 19 May 2016 / Revised: 15 August 2016 / Accepted: 31 August 2016 / Published: 6 September 2016
Cited by 3 | PDF Full-text (771 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In recent years, small-footprint full-waveform airborne laser scanning has become readily available and established for vegetation studies in the fields of forestry, agriculture and urban studies. Independent of the field of application and the derived final product, each study uses features to classify
[...] Read more.
In recent years, small-footprint full-waveform airborne laser scanning has become readily available and established for vegetation studies in the fields of forestry, agriculture and urban studies. Independent of the field of application and the derived final product, each study uses features to classify a target object and to assess its characteristics (e.g., tree species). These laser scanning features describe an observable characteristic of the returned laser signal (e.g., signal amplitude) or a quantity of an object (e.g., height-width ratio of the tree crown). In particular, studies dealing with tree species classification apply a variety of such features as input. However, an extensive overview, categorization and comparison of features from full-waveform airborne laser scanning and how they relate to specific tree species are still missing. This review identifies frequently used full-waveform airborne laser scanning-based point cloud and waveform features for tree species classification and compares the applied features and their characteristics for specific tree species detection. Furthermore, limiting and influencing factors on feature characteristics and tree classification are discussed with respect to vegetation structure, data acquisition and processing. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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