Special Issue "Advances in LiDAR Remote Sensing for Forestry and Ecology"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Jason Drake
Website
Guest Editor
USDA Forest Service and Florida A&M University, USA
Interests: LiDAR remote sensing; ecological modeling; geospatial analysis; UAV applications
Dr. Paul Medley
Website
Guest Editor
USDA Forest Service and Florida A&M University, USA
Interests: geospatial analysis and tool development; UAV applications; wildlife and fisheries

Special Issue Information

Dear Colleagues,

LiDAR technology is evolving very rapidly with applications in fields such as survey/engineering, natural resource assessment, manufacturing/design, autonomous vehicle navigation and even consumer electronics such as the latest iPad Pro which uses LiDAR for 3D scanning and augmented reality applications. In forestry and ecology LiDAR has proven to be an invaluable tool for assessment and monitoring of forest structure and condition. Advancements such as the miniaturization of LiDAR sensors so that they can be flown on UAVs and the new Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station open up many exciting opportunities at scales ranging from 10s of hectares to the globe.    

We invite authors to submit research papers on new and innovative applications using LiDAR remote sensing in forestry, ecology, and related fields for this special issue. This special issue is open to all types of LiDAR including terrestrial, UAV, aerial, and spaceborne sensors. Research involving the synergy of LiDAR with other sensors is encouraged, as well as innovative methods for processing and extracting useful information from LiDAR point clouds (e.g., machine learning techniques). A synthesis or review paper may also be considered which provides a thorough overview of the current methods, applications, and related best practices (e.g., recommendations for cost effective collection and processing of LiDAR data for forestry and ecological applications). We look forward to your submissions to this special issue.

Dr. Jason Drake
Dr. Paul Medley
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. 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

  • LiDAR
  • UAV LiDAR
  • Airborne LiDAR
  • Terrestrial LiDAR
  • Forestry
  • Ecology
  • Forest management

Published Papers (3 papers)

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Open AccessArticle
Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry
Remote Sens. 2020, 12(21), 3649; https://doi.org/10.3390/rs12213649 - 06 Nov 2020
Abstract
Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple [...] Read more.
Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Open AccessArticle
Radiometric Calibration for Incidence Angle, Range and Sub-Footprint Effects on Hyperspectral LiDAR Backscatter Intensity
Remote Sens. 2020, 12(17), 2855; https://doi.org/10.3390/rs12172855 - 02 Sep 2020
Abstract
Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle [...] Read more.
Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle and sub-footprint play a significant role. Further studies on the influence of the range, incidence angle and sub-footprint are needed to improve the accuracy of backscatter intensity data as it is important for vegetation structural and biochemical information estimation. In this paper, we investigated the effects on the laser backscatter intensity and developed a practical correction method for HSL data. We established a laser ratio calibration method and a reference target-based method for HSL and investigated the calibration procedures for the mixed measurements of the effects of the incident angle, range and sub-footprint. Results showed that the laser ratio at the red-edge and near-infrared laser wavelengths has higher accuracy and simplicity in eliminating range, incident angle and sub-footprint effects and can significantly improve the backscatter intensity discrepancy caused by these effects. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Open AccessTechnical Note
UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy
Remote Sens. 2020, 12(22), 3829; https://doi.org/10.3390/rs12223829 - 21 Nov 2020
Abstract
High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and [...] Read more.
High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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