Using Drones for Individual Tree Detection (ITD) and Its Applications

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 3217

Special Issue Editors


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Guest Editor
Department of Vegetal Production and Forestry Science, Universitat de Lleida, Lleida, Spain
Interests: Wildfire; satellite remote sensing; extreme weather events; fire management; fire ecology; global change; burn severity
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Forest and Wood Science Center, Department of Forest Sciences, Federal University of Paraná (UFPR), Curitiba, Brazil
Interests: LiDAR remote sensing; digital aerial photogrammetry; tropical and forest plantations; forest inventory and spatial analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Forest Restoration Scientist, Maryland/Washington D.C. Chapter, The Nature Conservancy, Cumberland, MD, USA
Interests: forest restoration and monitoring; forest resilience; forest fire management; UAV remote sensing; climate mitigation and adaptation; Monitoring, Evaluation and Learning (MEL)

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Guest Editor Assistant
Department of Geography, University of California, Berkeley, CA, USA
Interests: drones; LiDAR; satellite remote sensing; tropical forests; forest management and modeling; individual tree detection; forest carbon science; machine learning; biodiversity conservation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Morobe Development Foundation, Lae 00411, Papua New Guinea
Interests: UAV remote sensing; environmental management; individual tree detection; forest data analysis; data science and machine learning

Special Issue Information

Dear Colleagues,

This Special Issue aims to promote and support advancements in research activities in the domain of individual tree detection (ITD), which has benefited tremendously from the proliferation of low-cost unmanned aerial vehicles (UAVs) and data science industry in recent years. Currently, ITD finds applications in multiple sub-sectors within forestry, including forest health monitoring, biomass mapping, forest growth modeling, deforestation tracking, disturbance and recovery analysis, canopy gap quantification, UAV-supported seed sowing, species diversity estimation, biodiversity conservation, land-use/land-change analysis, natural resource management, restoration assessment, pest detection, and three-dimensional (3D) canopy structure analysis, among others. More and more use case scenarios are emerging day-by-day with the support of state-of-the-art technological amalgamations such as UAV-LiDAR (light detection and ranging) and data fusion strategies. However, there are still a couple of issues that need attention, such as model transferability across sites, the development of novel ITD algorithms that are applicable to dense canopies, and wall-to-wall mapping with the integration of UAV data with other open-source satellite/LiDAR data sources of differing resolutions (e.g., NASA GEDI).

We hereby invite authors to consider this open-access SI to publish their original and/or review articles in the broad field of forest remote sensing and associated spheres related to ITD and its applications.

With gratitude,

Dr. Eben Broadbent
Dr. Adrian Cardil
Dr. Ana Paula Dalla Corte
Dr. Pabodha Galgamuwa G.A.
Guest Editors

Midhun (Mikey) Mohan
Shaurya Bajaj
Guest Editor Assistants

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 submissions that pass pre-check are 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. Drones 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 2600 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

  • forest health monitoring and management
  • forest attributes mensuration and modeling
  • individual tree crown delineation
  • species classification
  • biomass mapping
  • pest management
  • 3D point clouds
  • UAV-LiDAR
  • multispectral and hyperspectral data
  • data fusion, machine learning and deep learning
  • time series analysis

Published Papers (1 paper)

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Research

10 pages, 3454 KiB  
Article
Effects of Flight and Smoothing Parameters on the Detection of Taxus and Olive Trees with UAV-Borne Imagery
by Sam Ottoy, Nikolaos Tziolas, Koenraad Van Meerbeek, Ilias Aravidis, Servaas Tilkin, Michail Sismanis, Dimitris Stavrakoudis, Ioannis Z. Gitas, George Zalidis and Alain De Vocht
Drones 2022, 6(8), 197; https://doi.org/10.3390/drones6080197 - 8 Aug 2022
Cited by 7 | Viewed by 1875
Abstract
Recent technical and jurisdictional advances, together with the availability of low-cost platforms, have facilitated the implementation of unmanned aerial vehicles (UAVs) in individual tree detection (ITD) applications. UAV-based photogrammetry or structure from motion is an example of such a low-cost technique, but requires [...] Read more.
Recent technical and jurisdictional advances, together with the availability of low-cost platforms, have facilitated the implementation of unmanned aerial vehicles (UAVs) in individual tree detection (ITD) applications. UAV-based photogrammetry or structure from motion is an example of such a low-cost technique, but requires detailed pre-flight planning in order to generate the desired 3D-products needed for ITD. In this study, we aimed to find the most optimal flight parameters (flight altitude and image overlap) and processing options (smoothing window size) for the detection of taxus trees in Belgium. Next, we tested the transferability of the developed marker-controlled segmentation algorithm by applying it to the delineation of olive trees in an orchard in Greece. We found that the processing parameters had a larger effect on the accuracy and precision of ITD than the flight parameters. In particular, a smoothing window of 3 × 3 pixels performed best (F-scores of 0.99) compared to no smoothing (F-scores between 0.88 and 0.90) or a window size of 5 (F-scores between 0.90 and 0.94). Furthermore, the results show that model transferability can still be a bottleneck as it does not capture management induced characteristics such as the typical crown shape of olive trees (F-scores between 0.55 and 0.61). Full article
(This article belongs to the Special Issue Using Drones for Individual Tree Detection (ITD) and Its Applications)
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