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Drones
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Ecological Applications of Drone-Based Remote Sensing-II
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Ecological Applications of Drone-Based Remote Sensing-II

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5602

Special Issue Editor

Dr. Adam Cross

E-Mail Website
Guest Editor
School of Molecular and Life Sciences, Curtin University, Perth 6845, Australia
Interests: ecological restoration; seed biology; community ecology and phytosociology; freshwater aquatic ecosystems; conservation biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last decade has seen an exponential increase in the application of remote sensing to ecological monitoring research, in a diverse range of fields. While cost and availability have traditionally constrained the use of remote sensing in research projects, recent technological development has seen drones (UAVs, UAS, RPAS) become increasingly popular sensing tools that have greatly expanded research capacity. Significant translation research has seen drones transition from tools of predominantly agricultural application to their being employed with increasing novelty to address complex ecological questions, particularly in the monitoring of biological communities and in assessing the trajectory of ecological recovery. Drones are becoming smaller, cheaper, and capable of mounting a wider variety of sensors to collect a greater diversity and volume of data. However, despite the trend of drone-mounted sensors being used in novel ways to monitor a wide variety of environmental factors, they remain often applied to highly specific aims or questions and do not consider the wide potential for capturing associated ecological data.

Activities directed at returning ecological functioning to degraded ecosystems are being undertaken at increasing scale around the world, as we must achieve a net gain in the extent and function of indigenous ecosystems in coming decades if ambitious global targets relating to sustainable development and biodiversity preservation are to be met. However, ecological restoration is a complex process requiring detailed subsequent monitoring over long time periods to ensure that predetermined goals are being met and to inform adaptive management in situations where trajectories are unsatisfactory. Given the increasing spatial and temporal scales of ecological recovery projects, the demand for more rapid and accurate methods of predicting restoration trajectory is growing.

This Special Issue aims to present a selection of studies experimentally applying drones to ecological research questions, particularly in the context of conservation, rehabilitation, and ecological restoration. Significantly more research is required to improve the potential of UAVs as ecological monitoring tools. Many areas of application remain predominantly unexplored, for example, examination of the capacity to monitor at very fine scales; accurate assessments of the health and performance of non-agricultural plants; monitoring and tracking of the development of individual plants; reliable classification of species from complex native plant communities; and assessments of fauna behaviour and ecology.

Dr. Adam Cross
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.

Keywords

  • remote sensing
  • ecology rehabilitation
  • ecological restoration
  • conservation communities

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Published Papers (3 papers)

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Research

20 pages, 4012 KiB  
Article
Pollution from Highways Detection Using Winter UAV Data
by Gabriel A. Baah, Igor Yu. Savin and Yuri I. Vernyuk
Drones 2023, 7(3), 178; https://doi.org/10.3390/drones7030178 - 6 Mar 2023
Cited by 3 | Viewed by 1533
Abstract
This study identified and evaluated the association between metal content and UAV data to monitor pollution from roadways. A total of 18 mixed snow samples were collected at the end of winter, utilizing a 1 m long and 10 cm wide snow collection [...] Read more.
This study identified and evaluated the association between metal content and UAV data to monitor pollution from roadways. A total of 18 mixed snow samples were collected at the end of winter, utilizing a 1 m long and 10 cm wide snow collection tube, from either side of the Caspian Highway (Moscow-Tambo-Astrakhan) in Moscow. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to examine the chemical composition of the samples, yielding 35 chemical elements (metals). UAV data and laboratory findings were calculated and examined. Regression estimates demonstrated the possibility of using remote sensing data to identify Al, Ba, Fe, K, and Na metals in snow cover near roadways due to dust dispersal. This discovery supports the argument that UAV sensing data can be utilized to monitor air pollution from roadways. Full article
(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing-II)
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6 pages, 3276 KiB  
Communication
A New Method for Surveying the World’s Smallest Class of Dragonfly in Wetlands Using Unoccupied Aerial Vehicles
by Hideyuki Niwa and Takumi Hirata
Drones 2022, 6(12), 427; https://doi.org/10.3390/drones6120427 - 17 Dec 2022
Cited by 5 | Viewed by 1707
Abstract
Field surveys in wetlands are limited by the difficulty in accessing the site, hazards during surveys, and the risk of disturbing the ecosystem. Thus, the use of unoccupied aerial vehicles (UAVs) can overcome these limiting factors and can assist in monitoring small organisms, [...] Read more.
Field surveys in wetlands are limited by the difficulty in accessing the site, hazards during surveys, and the risk of disturbing the ecosystem. Thus, the use of unoccupied aerial vehicles (UAVs) can overcome these limiting factors and can assist in monitoring small organisms, such as plants and insects, that are unique to wetlands, aiding in wetland management and conservation. This study aimed to demonstrate the effectiveness of a survey method that uses a small drone equipped with a telephoto lens to monitor dragonflies, which are unique to wetlands and have been difficult to survey quantitatively, especially in large wetlands. In this study, the main target species of dragonflies was Nannophya pygmaea, which is the world’s smallest dragonfly (about 20 mm long). The study area was Mizorogaike wetland (Kita Ward, Kyoto City, Japan). The UAV was flown at a low speed at an altitude of 4 m to 5 m, and images were taken using 7× telephoto lens on Mavic 3 (7× optical and 4× digital). A total of 107 dragonflies of seven species were identified from the photographs taken by the drone. N. pygmaea, about 20 mm long, was clearly identified. Eighty-five dragonflies belonging to N. pygmaea were identified from the images. Thus, by using a small drone equipped with a telephoto lens, the images of N. pygmaea were captured, and the effects of downwash and noise were reduced. The proposed research method can be applied to large wetlands that are difficult to survey in the field, and can thus provide new and important information pertaining to wetland management and conservation. This research method is highly useful for monitoring wetlands as it is non-invasive, does not require the surveyor to enter the wetland, requires little research effort, and can be repeated. Full article
(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing-II)
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17 pages, 7904 KiB  
Article
Automated Detection Method to Extract Pedicularis Based on UAV Images
by Wuhua Wang, Jiakui Tang, Na Zhang, Xuefeng Xu, Anan Zhang and Yanjiao Wang
Drones 2022, 6(12), 399; https://doi.org/10.3390/drones6120399 - 6 Dec 2022
Cited by 4 | Viewed by 1740
Abstract
Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts [...] Read more.
Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of multi-class classifiers is better than that of one-class classifiers, but it requires all classes that occur in the image to be exhaustively assigned labels. Among the one-class classifiers that only need to label positive or positive and labeled data, the PUL has the highest F score of 0.9878. (2) PUL performs the most robustly to change features in one-class classifiers. All one-class classifiers prove that the green band is essential for extracting Pedicularis. (3) The parameters of the PUL are easy to tune, and the training time is easy to control. Therefore, PUL is a promising one-class classification method for Pedicularis extraction, which can accurately identify the distribution range of Pedicularis to promote grassland administration. Full article
(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing-II)
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