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12 pages, 3864 KiB

Open AccessArticle

Vertical Profiles of PM_2.5 and O₃ Measured Using an Unmanned Aerial Vehicle (UAV) and Their Relationships with Synoptic- and Local-Scale Air Movements

by Hyemin Hwang, Ju Eun Lee, Seung A. Shin, Chae Rim You, Su Hyun Shin, Jong-Sung Park and Jae Young Lee

Remote Sens. 2024, 16(9), 1581; https://doi.org/10.3390/rs16091581 - 29 Apr 2024

Viewed by 427

Abstract

The vertical air pollutant concentrations and their relationships with synoptic- and local-scale air movement have been studied. This study measured the vertical profiles of PM_2.5 and O₃ using an unmanned aerial vehicle during summer in South Korea and analyzed the characteristics [...] Read more.

The vertical air pollutant concentrations and their relationships with synoptic- and local-scale air movement have been studied. This study measured the vertical profiles of PM_2.5 and O₃ using an unmanned aerial vehicle during summer in South Korea and analyzed the characteristics of the measured profiles. To understand the impact of synoptic air movements, we generated and categorized the 48 h air trajectories based on HYSPLIT, and we analyzed how the vertical profiles varied under different categories of long-range transport. We found that the vertical PM_2.5 concentration has a positive gradient with altitude when more polluted air was transported from China or North Korea and has negative gradient when cleaner air was transported from the East Sea. Unlike PM_2.5, the O₃ concentration did not depend significantly on the long-range transport scenario because of the short photochemical lifetime of O₃ during summer. For local-scale air movements, we found no significant impact of local wind on the measured profiles. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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17 pages, 3089 KiB

Open AccessArticle

Polarized Light Pollution of Fixed-Tilt Photovoltaic Solar Panels Measured by Drone-Polarimetry and Its Visual-Ecological Importance

by Péter Takács, Dénes Száz, Balázs Bernáth, István Pomozi and Gábor Horváth

Remote Sens. 2024, 16(7), 1177; https://doi.org/10.3390/rs16071177 - 28 Mar 2024

Viewed by 980

Abstract

Specific polarized light pollution (PLP) means the adverse influences of strongly and horizontally polarized light reflected from smooth and dark artificial surfaces on polarotactic water-seeking aquatic insects. Typical PLP sources are photovoltaic panels. Using drone-based imaging polarimetry, in a solar panel farm, we [...] Read more.

Specific polarized light pollution (PLP) means the adverse influences of strongly and horizontally polarized light reflected from smooth and dark artificial surfaces on polarotactic water-seeking aquatic insects. Typical PLP sources are photovoltaic panels. Using drone-based imaging polarimetry, in a solar panel farm, we measured the reflection-polarization patterns of fixed-tilt photovoltaic panels from the viewpoint of flying polarotactic aquatic insects, which are the most endangered targets and potential victims of such panels. We found that the temporal changes in PLP were complementary for the two orthogonal viewing directions relative to the panel rows. The estimated magnitude plp of the polarized light pollution of solar panels viewed parallel to the panel rows was the highest (primary peak plp = 49–58% after sunrise and secondary peak plp = 35–48% prior to sunset) at low solar elevations, after sunrise and at or prior to sunset, when many aquatic insect species fly and seek water bodies. On the other hand, the PLP of solar panels viewed perpendicular to the panel rows was the highest (plp = 29–35%) at the largest solar elevations, near noon, when numerous flying aquatic insect species also seek water. Solar panel farms near wetlands can, therefore, be dangerous for these insects. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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23 pages, 17121 KiB

Open AccessArticle

The Mapping of Alpha-Emitting Radionuclides in the Environment Using an Unmanned Aircraft System

by Pablo Royo, Arturo Vargas, Tania Guillot, David Saiz, Jonathan Pichel, Daniel Rábago, María Amor Duch, Claudia Grossi, Maksym Luchkov, Volker Dangendorf and Faton Krasniqi

Remote Sens. 2024, 16(5), 848; https://doi.org/10.3390/rs16050848 - 29 Feb 2024

Cited by 1 | Viewed by 734

Abstract

The protection of first responders from radioactive contamination with alpha emitters that may result from a radiological accident is of great complexity due to the short range of alpha particles in the air of a few centimeters. To overcome this issue, for the [...] Read more.

The protection of first responders from radioactive contamination with alpha emitters that may result from a radiological accident is of great complexity due to the short range of alpha particles in the air of a few centimeters. To overcome this issue, for the first time, a system mounted on a UAS for the near-real-time remote measurement of alpha particles has been developed, tested, and calibrated. The new system, based on an optical system adapted to be installed on a UAS in order to measure the UV-C fluorescence emitted by alpha particles in the air, has been tested and calibrated, carried out in the laboratory and in field experiments using UV-C LEDs and ²⁴¹Am sources. In experimental flights, the probability of detecting a point source was determined to be approximately 60%. In the case of a surface extended source, a detection efficiency per unit surface activity of 10 counts per second per MBq cm⁻² was calculated. A background count rate of UV-C of around 26 ± 28 s⁻¹ for an integration time of 0.1 s was measured during flights, which led to a decision threshold surface activity of 5 MBq cm⁻². Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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25 pages, 15999 KiB

Open AccessArticle

Framework for Autonomous UAV Navigation and Target Detection in Global-Navigation-Satellite-System-Denied and Visually Degraded Environments

by Sebastien Boiteau, Fernando Vanegas and Felipe Gonzalez

Remote Sens. 2024, 16(3), 471; https://doi.org/10.3390/rs16030471 - 25 Jan 2024

Cited by 1 | Viewed by 1361

Abstract

Autonomous Unmanned Aerial Vehicles (UAVs) have possible applications in wildlife monitoring, disaster monitoring, and emergency Search and Rescue (SAR). Autonomous capabilities such as waypoint flight modes and obstacle avoidance, as well as their ability to survey large areas, make UAVs the prime choice [...] Read more.

Autonomous Unmanned Aerial Vehicles (UAVs) have possible applications in wildlife monitoring, disaster monitoring, and emergency Search and Rescue (SAR). Autonomous capabilities such as waypoint flight modes and obstacle avoidance, as well as their ability to survey large areas, make UAVs the prime choice for these critical applications. However, autonomous UAVs usually rely on the Global Navigation Satellite System (GNSS) for navigation and normal visibility conditions to obtain observations and data on their surrounding environment. These two parameters are often lacking due to the challenging conditions in which these critical applications can take place, limiting the range of utilisation of autonomous UAVs. This paper presents a framework enabling a UAV to autonomously navigate and detect targets in GNSS-denied and visually degraded environments. The navigation and target detection problem is formulated as an autonomous Sequential Decision Problem (SDP) with uncertainty caused by the lack of the GNSS and low visibility. The SDP is modelled as a Partially Observable Markov Decision Process (POMDP) and tested using the Adaptive Belief Tree (ABT) algorithm. The framework is tested in simulations and real life using a navigation task based on a classic SAR operation in a cluttered indoor environment with different visibility conditions. The framework is composed of a small UAV with a weight of 5 kg, a thermal camera used for target detection, and an onboard computer running all the computationally intensive tasks. The results of this study show the robustness of the proposed framework to autonomously explore and detect targets using thermal imagery under different visibility conditions. Devising UAVs that are capable of navigating in challenging environments with degraded visibility can encourage authorities and public institutions to consider the use of autonomous remote platforms to locate stranded people in disaster scenarios. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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19 pages, 7699 KiB

Open AccessArticle

Quantifying Multi-Scale Performance of Geometric Features for Efficient Extraction of Insulators from Point Clouds

by Jie Tang, Junxiang Tan, Yongyong Du, Haojie Zhao, Shaoda Li, Ronghao Yang, Tao Zhang and Qitao Li

Remote Sens. 2023, 15(13), 3339; https://doi.org/10.3390/rs15133339 - 29 Jun 2023

Cited by 2 | Viewed by 1063

Abstract

Insulator extraction from images or 3D point clouds is an important part of automatic power inspection by unmanned airborne vehicles (UAVs), which is vital for improving the efficiency of inspection and the stability of power grids. However, for point cloud data, many challenges, [...] Read more.

Insulator extraction from images or 3D point clouds is an important part of automatic power inspection by unmanned airborne vehicles (UAVs), which is vital for improving the efficiency of inspection and the stability of power grids. However, for point cloud data, many challenges, such as the diversity of pylon shape and insulator type, complex topology, and similarity of structures, were not tackled with the study of power element extraction. To efficiently identify the small insulators from complex power transmission corridor (PTC) scenarios, this paper proposes a robust extraction method by fusing multi-scale neighborhood and multi-feature entropy weighting. The pylon head is segmented according to the aspect ratio of horizontal slices following the locating of the pylons based on the height difference and continuous vertical distribution firstly. Aiming to quantify the different contributions of features in decision-making and better segment insulators, a feature evaluation system combined with information entropy, eigen entropy-based optimal neighborhood selection, and designed multi-scale features is constructed to identify suspension insulators and tension insulators. In the optimization step, a region erosion and growing method is proposed to segment complete insulator strings by enlarging the perspectives to obtain more object representations. The extraction results of 82 pylons with 654 insulators demonstrate that the proposed method is suitable for different pylon shapes and sizes. The identification accuracy of the whole line achieves 98.23% and the average F1 score is 90.98%. The proposed method can provide technical support for automatic UAV inspection and pylon reconstruction. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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14 pages, 5296 KiB

Open AccessCommunication

Self-Supervised Monocular Depth Estimation Using Global and Local Mixed Multi-Scale Feature Enhancement Network for Low-Altitude UAV Remote Sensing

by Rong Chang, Kailong Yu and Yang Yang

Remote Sens. 2023, 15(13), 3275; https://doi.org/10.3390/rs15133275 - 26 Jun 2023

Cited by 3 | Viewed by 1728

Abstract

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple [...] Read more.

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple images, while UAV depth estimation can complete the task with just one image, thus having higher efficiency and lower cost. This study aims to use deep learning to estimate depth from a single UAS low-altitude remote sensing image. We propose a novel global and local mixed multi-scale feature enhancement network for monocular depth estimation in low-altitude remote sensing scenes, which exchanges information between feature maps of different scales during the forward process through convolutional operations while maintaining the maximum scale feature map. At the same time, we propose a Global Scene Attention (GSA) module in the decoder part of the depth network, which can better focus on object edges, distinguish foreground and background in the UAV field of view, and ultimately demonstrate excellent performance. Finally, we design several loss functions for the low-altitude remote sensing field to constrain the network to reach its optimal state. We conducted extensive experiments on public dataset UAVid 2020, and the results show that our method outperforms state-of-the-art methods. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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19 pages, 9518 KiB

Open AccessArticle

Coastal Dune Invaders: Integrative Mapping of Carpobrotus sp. pl. (Aizoaceae) Using UAVs

by Michele Innangi, Flavio Marzialetti, Mirko Di Febbraro, Alicia Teresa Rosario Acosta, Walter De Simone, Ludovico Frate, Michele Finizio, Priscila Villalobos Perna and Maria Laura Carranza

Remote Sens. 2023, 15(2), 503; https://doi.org/10.3390/rs15020503 - 14 Jan 2023

Cited by 8 | Viewed by 2152

Abstract

Coastal dune ecosystems are highly threatened, and one of the strongest pressures is invasive alien plants (IAPs). Mitigating the negative effects of IAPs requires development of optimal identification and mapping protocols. Remote sensing offers innovative tools that have proven to be very valuable [...] Read more.

Coastal dune ecosystems are highly threatened, and one of the strongest pressures is invasive alien plants (IAPs). Mitigating the negative effects of IAPs requires development of optimal identification and mapping protocols. Remote sensing offers innovative tools that have proven to be very valuable for studying IAPs. In particular, unmanned aerial vehicles (UAVs) can be very promising, especially in the study of herbaceous invasive species, yet research in UAV application is still limited. In this study, we used UAV images to implement an image segmentation approach followed by machine learning classification for mapping a dune clonal invader (Carpobrotus sp. pl.), calibrating a total of 27 models. Our study showed that: (a) the results offered by simultaneous RGB and multispectral data improve the prediction of Carpobrotus; (b) the best results were obtained by mapping the whole plant or its vegetative parts, while mapping flowers was worse; and (c) a training area corresponding to 20% of the total area can be adequate for model building. Overall, our results highlighted the great potential of using UAVs for Carpobrotus mapping, despite some limitations imposed by the particular biology and ecology of these taxa. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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16 pages, 32218 KiB

Open AccessTechnical Note

Target Positioning for Complex Scenes in Remote Sensing Frame Using Depth Estimation Based on Optical Flow Information

by Linjie Xing, Kailong Yu and Yang Yang

Remote Sens. 2023, 15(4), 1036; https://doi.org/10.3390/rs15041036 - 14 Feb 2023

Viewed by 1401

Abstract

UAV-based target positioning methods are in great demand in fields, such as national defense and urban management. In previous studies, the localization accuracy of UAVs in complex scenes was difficult to be guaranteed. Target positioning methods need to improve the accuracy with guaranteed [...] Read more.

UAV-based target positioning methods are in great demand in fields, such as national defense and urban management. In previous studies, the localization accuracy of UAVs in complex scenes was difficult to be guaranteed. Target positioning methods need to improve the accuracy with guaranteed computational speed. The purpose of this study is to improve the accuracy of target localization while using only UAV information. With the introduction of depth estimation methods that perform well, the localization errors caused by complex terrain can be effectively reduced. In this study, a new target position system is developed. The system has these features: real-time target detection and monocular depth estimation based on video streams. The performance of the system is tested through several target localization experiments in complex scenes, and the results proved that the system can accomplish the expected goals with guaranteed localization accuracy and computational speed. Full article

(This article belongs to the Special Issue Drone Remote Sensing II)

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