Special Issue "Individual Tree Detection and Characterisation from UAV Data"

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

Deadline for manuscript submissions: 29 August 2020.

Special Issue Editor

Dr. Luke Wallace
E-Mail Website
Guest Editor
School of Science, Geospatial Science, RMIT University, Australia
Interests: 3D remote sensing; remote sensing of forested environments; laser scanning; vegetation structure; wildfire
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) and associated sensors are providing us with data with spatial resolutions not previously available from an airborne source. One area of forest remote sensing that is benefiting greatly from this increased resolution is the detection and characterisation of individual trees. Individual tree information is required to inform a number of diverse fields, including forestry, habitat mapping and ecology, urban forestry and fire behaviour modelling.

Whilst UAV data has the potential to improve our understanding at the level of the individual tree, a number of challenges remain to be addressed. This Special Issue invites prospective authors to submit papers that address challenges within the field of individual tree detection and characterisation using UAVs. Potential topics include:

  • new algorithms for the extraction of individual tree information from UAV-based data;
  • new methods for characterising the three-dimensional (3D) tree structure once individual trees have been identified;
  • species and health identification of individual trees;
  • the impact of data collection parameters on individual tree detection and characterisation;
  • comparisons of detection and characterisation accuracies between different sensors as well as between UAVs and other platforms; and
  • sensor fusion to improve individual tree detection and characterisation.

Dr. Luke Wallace
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 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 2000 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

  • individual tree detection
  • forest remote sensing
  • LiDAR
  • unmanned aerial vehicle (UAV)
  • drones
  • sensor fusion

Published Papers (1 paper)

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Research

Open AccessArticle
Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks
Remote Sens. 2019, 11(21), 2585; https://doi.org/10.3390/rs11212585 - 04 Nov 2019
Abstract
Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce [...] Read more.
Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration. Full article
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
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