Special Issue "Drones for Ecology and Conservation"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Angelica Maria Almeyda Zambrano
Website
Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, USA
Interests: Sustainability science; Unmanned aerial vehicles (UAVs); GatorEye
Dr. Eben North Broadbent
Website
Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, USA
Interests: unmanned aerial vehicles (UAVs); forest ecology; airborne sensors; GatorEye
Special Issues and Collections in MDPI journals
Dr. Ana Paula Dalla Corte
Website
Guest Editor
Forest and Wood Science Center, Department of Forest Sciences, Federal University of Paraná (UFPR), Brazil
Interests: lidar remote sensing; digital aerial photogrammetry; tropical and forest plantations; forest inventory and spatial analysis
Dr. Carlos Alberto Silva
Website
Guest Editor
1. University of Maryland College Park, Department of Geographical Sciences, USA;
2. School of Forest Resources and Conservation, University of Florida, USA
Interests: lidar and hyperspectral remote sensing; tropical forest structure and ecology; industrial forest plantations, algorithms and tools development; data integration and change detection

Special Issue Information

Dear Colleagues,

Recent increases in the use of drone-borne sensors for ecological and conservation-related applications have been motivated by reduced costs, increased availability, new and enhanced passive and active sensors (e.g., hyperspectral and lidar), and the development of sophisticated fusion algorithms. Data have moved beyond mapping and monitoring ecosystem flora structure and composition, to directly mapping wildlife, and now to improve understanding of advanced community ecological, conservation biology, and forest ecology theory and application, and human dimensions of sustainability in varied landscape mosaics. In this Special Issue, we invite submissions from the broad ecological and applied conservation community, including but not limited to forest ecologists, wildlife biologists, conservation biologists, land-use and land-cover experts, and sustainability science researchers, who use drone-borne sensors ranging from small and low-cost systems (e.g., DJI Phantom) to complex multisensor fusion platforms (e.g., www.gatoreye.org).

We will be accepting review articles, technical notes, and research contributions. Specifically, innovative themes such as the following subtopics described below will be welcome:

  • Use of UAV-LiDAR for parameters attribute at landscape levels and individual trees;
  • Application of Unmanned aerial vehicles (UAV) and photogrammetry 3D for helping in the development of natural sciences;
  • Integration of remote sensors for the structural representation of forest and trees;
  • Integration of platforms for analysis of distribution and density of fauna and flora species;
  • Development of methodologies using UAV data approaches for applied conservation;
  • Use of drone-borne data for assessing sustainability as relates to human–environment interactions and land use and land cover change (LULCC).

Other themes linked to the title of the Special Edition “Drones for Ecology and Conservation” are also welcome. Please feel free to get in touch with us if you have any questions.

Dr. Angelica Maria Almeyda Zambrano
Dr. Eben North Broadbent
Dr. Ana Paula Dalla Corte
Dr. Carlos Alberto Silva
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.

Published Papers (1 paper)

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Research

Open AccessArticle
Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil
Remote Sens. 2020, 12(11), 1754; https://doi.org/10.3390/rs12111754 - 29 May 2020
Abstract
Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural [...] Read more.
Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m2). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: UAV-based mapping of dune plant invasions in the Mediterranean coast

Authors: MARZIALETTI Flavio, FRATE Ludovico, DE SIMONE Walter, ACOSTA Alicia T.R., CARRANZA Maria Laura

Keywords: Acacia saligna, Carpobrotus sp., coastal dunes, multi-temporal analysis, multispectral images, species flowering, unmanned aerial vehicles

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