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Special Issue "Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Quantitative Methods and Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2019)

Special Issue Editors

Guest Editor
Dr. Qinghua Guo

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Website | E-Mail
Interests: remote sensing, lidar applications, GIS, UAV, climate change and terrestrial ecosystem
Guest Editor
Dr. Yanjun Su

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Science, Beijing, China
Website | E-Mail
Interests: remote sensing; LiDAR; GIS; climate change; terrestrial ecosystems; forest structures

Special Issue Information

Dear Colleagues,

Biodiversity offers a foundation to various ecosystem services (e.g., provisioning services, regulating services, culture services) that are critical to human beings. Tropical, temperate, and boreal forests serve a diverse set of habitats for wildlife, and, consequently, harbor the majority of terrestrial species. However, in recent decades, forest wildlife abundancy and diversity have been diminishing due to the threat caused by human activities and global climate change. The importance of conserving forest wildlife abundance and diversity has been increasingly recognized, and numerous efforts have been made to improve our understanding of forest wildlife behavior. Forest vegetation biophysical features and three-dimensional (3D) structures have been demonstrated to be valuable inputs for mapping wildlife distribution. Therefore, accurate and continuous mapping of these forest parameters are essential for assessing forest wildlife habitats. Light detection and ranging (LiDAR) can provide highly accurate 3D forest structures from individual-tree to forest-stand scales, and optical imagery contains abundant spectral and textural information for mapping vegetation types and bio-physiological parameters. The integration of these two datasets opens a new era for mapping forest vegetation, both vertically and horizontally, and, therefore, assessing and protecting wildlife habitats.

This Special Issue of Forests emphasizes forest vegetation mapping through the integration of LiDAR and optical imagery, and how it can be used to assess the quality of wildlife habitats. Research articles may focus on, but are not limited to, topics such as new approaches of forest vegetation mapping for wildlife habitat assessment based on LiDAR and/or optical data, data fusion algorithms on improving the vegetation mapping accuracy for wildlife habitat assessment, and addressing how the integration of LiDAR data and optical imagery affects the niche modelling and wildlife habitat assessment results. Application studies regarding forest biodiversity and wildlife habitat management and conservation with the help of LiDAR data and optical imagery are also welcome.

Prof. Qinghua Guo
Dr. Yanjun Su
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 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.

Keywords

  • LiDAR
  • aerial imagery
  • multispectral imagery
  • forest
  • vegetation mapping
  • wildlife habitat
  • biodiversity

Published Papers (4 papers)

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Research

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Open AccessArticle Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China
Forests 2018, 9(12), 746; https://doi.org/10.3390/f9120746
Received: 23 October 2018 / Revised: 22 November 2018 / Accepted: 28 November 2018 / Published: 29 November 2018
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Abstract
Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio (α) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods [...] Read more.
Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio ( α ) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods into LAI. Although optical methods for estimating the α of forest canopy have been proposed, their performance has never been assessed. In this study, five Larix gmelinii Rupr. forest plots with contrasting plot characteristics (i.e., tree age, tree height, management activities, stand density, and site conditions) were selected. The performance of two commonly used optical methods, namely, multispectral canopy imager (MCI) and digital hemispherical photography (DHP), in estimating the α of L. gmelinii forest plots was evaluated by using the reference α of the selected forest plots. The reference α of forest plots was measured via destructive method by harvesting two or three representative trees in each plot. Large variations were observed amongst the reference α of the selected forest plots (ranging from 0% to 56%). These α were also highly correlated with the site conditions and management activities in these plots. The effective α ( α e ) or α estimated using the leaf-on and leaf-off periods MCI or DHP images with or without consideration of the clumping effects of canopy element and woody components were 1.57 to 4.63 times the reference α in the five plots. The overestimation of α or α e was mainly caused by the preferential shading of woody components by the shoots in the leaf-on canopy. Accurate α estimates for the L. gmelinii forest plots with errors of less than 20% can be obtained from MCI when the clumping effects of canopy element and woody components are considered in the estimation. Full article
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Open AccessArticle Detecting the Competition between Moso Bamboos and Broad-Leaved Trees in Mixed Forests Using a Terrestrial Laser Scanner
Forests 2018, 9(9), 520; https://doi.org/10.3390/f9090520
Received: 9 August 2018 / Revised: 23 August 2018 / Accepted: 24 August 2018 / Published: 29 August 2018
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Abstract
The growth of individual trees in a forest is affected by many factors, a crucial one being the intensity of competition among trees, because it affects the spatial structure of the forest and is in turn influenced by silvicultural practices. In a mixed [...] Read more.
The growth of individual trees in a forest is affected by many factors, a crucial one being the intensity of competition among trees, because it affects the spatial structure of the forest and is in turn influenced by silvicultural practices. In a mixed forest in particular, the growth of trees is affected by multiple interactions. To analyse the competition between moso bamboo (Phyllostachys pubescens (Pradelle) Mazel ex J.Houz.) and broad-leaved trees in a mixed forest, data were extracted by sampling six spots within such a forest using terrestrial laser scanning (TLS). The convex hull algorithm was used for calculating the overlap volume between the crowns of the broad-leaved trees and the bamboo canopy. Bamboos growing at least 3 m away from any of the broad-leaved trees were the most numerous and the diameter at breast height (DBH) is larger than those growing closer than that, which suggests that broad-leaved trees suppressed the growth of bamboo if they are closer but promote it beyond 3 m up to a point at which the distance is too great for any such effect. The modified Hegyi’s competition index was constructed based on the canopy factor, which may better describe the competitive interaction among the trees and bamboos. Using TLS can enhance our understanding of the competition among trees in mixed forests and help in planning the spatial structure of such forests in general and provide a benchmark for choosing planting distances in particular. Full article
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Open AccessArticle Estimating Stand Density in a Tropical Broadleaf Forest Using Airborne LiDAR Data
Forests 2018, 9(8), 475; https://doi.org/10.3390/f9080475
Received: 28 June 2018 / Revised: 31 July 2018 / Accepted: 1 August 2018 / Published: 4 August 2018
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Abstract
Forest-related statistics, including forest biomass, carbon sink, and the prevention of forest fires, can be obtained by estimating stand density. In this study, a dataset with the laser pulse density of 225.5 pulses/m2 was obtained using airborne laser scanning in a tropical [...] Read more.
Forest-related statistics, including forest biomass, carbon sink, and the prevention of forest fires, can be obtained by estimating stand density. In this study, a dataset with the laser pulse density of 225.5 pulses/m2 was obtained using airborne laser scanning in a tropical broadleaf forest. Three digital surface models (DSMs) were generated using first-echo, last-echo, and highest first-echo data. Three canopy height models (CHMs) were obtained by deducting the digital elevation model from the three DSMs. The cell sizes (Csizes) of the CHMs were 1, 0.5, and 0.2 m. In addition, stand density was estimated using CHM data and following the local maximum method. The stand density of 35 sample regions was acquired via in-situ measurement. The results indicated that the root-mean-square error ( R M S E ) ranged between 1.68 and 2.43; the R M S E difference was only 0.78, indicating that stand density was effectively estimated in both cases. Furthermore, regression models were used to correct the error in stand density estimations; the R M S E after correction was called R M S E . A comparison of the R M S E and R M S E showed that the average value decreased from 12.35 to 2.66, meaning that the regression model could effectively reduce the error. Finally, a comparison of the effects of different laser pulse densities on the R M S E value showed that, in order to obtain the minimum R M S E for stand density, the laser pulse density must be greater than 10, 30, and 125 pulses/m2 at Csizes of 1, 0.5, and 0.2 m, respectively. Full article
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Review

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Open AccessReview A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment
Forests 2019, 10(1), 1; https://doi.org/10.3390/f10010001
Received: 23 November 2018 / Revised: 16 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
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Abstract
With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species [...] Read more.
With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs. Full article
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