Feature Papers for Drones in Ecology Section

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 15696

Special Issue Editor

Special Issue Information

Dear Colleagues,

As Section Editor-in-Chief, I am pleased to announce a Special Issue entitled “Feature Papers for Section Drones in Ecology”. This Special Issue welcomes high-quality papers from the general ecology and applied conservation community, including but not limited to forest, freshwater, and marine ecologists, wildlife and conservation biologists, land-use and land-cover experts, and sustainability science researchers who use drone-borne sensors ranging from small and low-cost systems with single sensors to complex multi-sensor fusion platforms. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts are particularly welcome. For more scope information, you may check https://www.mdpi.com/journal/drones/sections/drones_in_ecology

Dr. Eben Broadbent
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 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.

Published Papers (4 papers)

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Research

30 pages, 41427 KiB  
Article
Autonomous Surveying of Plantation Forests Using Multi-Rotor UAVs
by Tzu-Jui Lin and Karl A. Stol
Drones 2022, 6(9), 256; https://doi.org/10.3390/drones6090256 - 16 Sep 2022
Cited by 3 | Viewed by 2304
Abstract
Modern plantation forest procedures still rely heavily on manual data acquisition in the inventory process, limiting the quantity and quality of the collected data. This limitation in collection performance is often due to the difficulty of traversing the plantation forest environment on foot. [...] Read more.
Modern plantation forest procedures still rely heavily on manual data acquisition in the inventory process, limiting the quantity and quality of the collected data. This limitation in collection performance is often due to the difficulty of traversing the plantation forest environment on foot. This work presents an autonomous system for exploring plantation forest environments using multi-rotor UAVs. The proposed method consists of three parts: waypoint selection, trajectory generation, and trajectory following. Waypoint selection is accomplished by estimating the rows’ locations within the environment and selecting points between adjacent rows. Trajectory generation is completed using a non-linear optimization-based constant speed planner and the following is accomplished using a model predictive control approach. The proposed method is tested extensively in simulation against various procedurally generated forest environments, with results suggesting that it is robust against variations within the scene. Finally, flight testing is performed in a local plantation forest, demonstrating the successful application of our proposed method within a complex, uncontrolled environment. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Ecology Section)
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18 pages, 14743 KiB  
Article
High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)
by Ana Paula Dalla Corte, Ernandes M. da Cunha Neto, Franciel Eduardo Rex, Deivison Souza, Alexandre Behling, Midhun Mohan, Mateus Niroh Inoue Sanquetta, Carlos Alberto Silva, Carine Klauberg, Carlos Roberto Sanquetta, Hudson Franklin Pessoa Veras, Danilo Roberti Alves de Almeida, Gabriel Prata, Angelica Maria Almeyda Zambrano, Jonathan William Trautenmüller, Anibal de Moraes, Mauro Alessandro Karasinski and Eben North Broadbent
Drones 2022, 6(2), 48; https://doi.org/10.3390/drones6020048 - 16 Feb 2022
Cited by 11 | Viewed by 5146
Abstract
Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which [...] Read more.
Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Ecology Section)
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14 pages, 18047 KiB  
Article
Water Hyacinth (Eichhornia crassipes) Detection Using Coarse and High Resolution Multispectral Data
by Luís Pádua, Ana M. Antão-Geraldes, Joaquim J. Sousa, Manuel Ângelo Rodrigues, Verónica Oliveira, Daniela Santos, Maria Filomena P. Miguens and João Paulo Castro
Drones 2022, 6(2), 47; https://doi.org/10.3390/drones6020047 - 15 Feb 2022
Cited by 21 | Viewed by 4510
Abstract
Efficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect [...] Read more.
Efficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth (Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose, high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy (OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks (OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with a UAV-based survey. Combining both datasets and even considering the different resolutions, it was possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to be an effective way to assess the effects of the mitigation/control measures taken in the study areas. Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor their changes over time. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Ecology Section)
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39 pages, 4843 KiB  
Article
A Control Algorithm for Early Wildfire Detection Using Aerial Sensor Networks: Modeling and Simulation
by André M. Rocha, Pedro Casau and Rita Cunha
Drones 2022, 6(2), 44; https://doi.org/10.3390/drones6020044 - 11 Feb 2022
Cited by 4 | Viewed by 2820
Abstract
This work presents an algorithm for an Aerial Sensor Network (ASN) composed of fixed-wing Unmanned Aerial Vehicles (UAVs) that performs surveillance and detects the early signs of a wildfire in a given territory. The main goal is to cover a given area while [...] Read more.
This work presents an algorithm for an Aerial Sensor Network (ASN) composed of fixed-wing Unmanned Aerial Vehicles (UAVs) that performs surveillance and detects the early signs of a wildfire in a given territory. The main goal is to cover a given area while prioritizing areas of higher fire hazard risk. The proposed algorithm is scalable to any number of aircraft and can use any kind of fire hazard risk map as long as it contains bounded and nonnegative values. Two different dynamical models associated with the movement of fixed-wing UAVs are proposed, tested, and compared through simulations. Lastly, we propose a workflow to size the ASN in order to maximize the probability of detection of wildfires for a particular risk profile. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Ecology Section)
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