scholarly journals Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 53 ◽  
Author(s):  
Buters ◽  
Belton ◽  
Cross
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Substrate Surface ◽  
Morphological Characteristics ◽  
Ecological Monitoring ◽  
Image Resolution ◽  
Automated Classification ◽  
Aerial Vehicles ◽  
Novel Approach ◽  
Plant Life Cycle ◽  
Ecological Recovery

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.

scholarly journals Attitude estimation using horizon detection in thermal images

2018 ◽  
Vol 10 (4) ◽  
pp. 352-361 ◽  
Author(s):  
Adrian Carrio ◽  
Hriday Bavle ◽  
Pascual Campoy
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Infinite Horizon ◽  
Attitude Estimation ◽  
Thermal Image ◽  
Image Sensors ◽  
Image Resolution ◽  
Estimation Methods ◽  
Operation Conditions ◽  
Aerial Vehicles ◽  
Horizon Detection

The lack of redundant attitude sensors represents a considerable yet common vulnerability in many low-cost unmanned aerial vehicles. In addition to the use of attitude sensors, exploiting the horizon as a visual reference for attitude control is part of human pilots’ training. For this reason, and given the desirable properties of image sensors, quite a lot of research has been conducted proposing the use of vision sensors for horizon detection in order to obtain redundant attitude estimation onboard unmanned aerial vehicles. However, atmospheric and illumination conditions may hinder the operability of visible light image sensors, or even make their use impractical, such as during the night. Thermal infrared image sensors have a much wider range of operation conditions and their price has greatly decreased during the last years, becoming an alternative to visible spectrum sensors in certain operation scenarios. In this paper, two attitude estimation methods are proposed. The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image. The resulting line is then used to estimate the pitch and roll angles using an infinite horizon line model. The second method uses deep learning to predict attitude angles using raw pixel intensities from a thermal image. For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system. Both methods presented are proven to be valid for redundant attitude estimation, providing RMS errors below 1.7° and running at up to 48 Hz, depending on the chosen method, the input image resolution and the available computational capabilities.

scholarly journals Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration

2019 ◽  
Vol 11 (10) ◽  
pp. 1180 ◽  
Author(s):  
Todd M. Buters ◽  
Philip W. Bateman ◽  
Todd Robinson ◽  
David Belton ◽  
Kingsley W. Dixon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Unmanned Aerial Vehicles ◽  
Ecological Restoration ◽  
Large Scale ◽  
Ecological Monitoring ◽  
Policy Framework ◽  
Strong Positive Correlation ◽  
Aerial Vehicles ◽  
The Future ◽  
Thermal Cameras

The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future.

scholarly journals Modern means of assessing the impact of emergencies on the environmental condition of the ground layer of atmosphere

2021 ◽  
Vol 280 ◽  
pp. 09017
Author(s):  
Anastasiia Turevych ◽  
Svitlana Madzhd ◽  
Larysa Cherniak ◽  
Anatoliy Pavlyuk ◽  
Vincent Ojeh
Keyword(s):  
Air Quality ◽  
Unmanned Aerial Vehicles ◽  
Ecological Monitoring ◽  
Hazardous Substances ◽  
Operational Monitoring ◽  
Aerial Vehicles ◽  
Raising Awareness ◽  
Functional Scheme ◽  
Surrounding Areas ◽  
The Impact

The problem of emergencies will not leave humanity as long as it exists, and therefore it is necessary to at least create conditions under which it is possible to reduce the risks of injuries, diseases and deaths of people who are in the emergency zone. This can be achieved by raising awareness of the nature of the emergency, the hazardous substances that are released in connection with it. Theoretical analysis of various remote means of assessing the impact of emergencies of man-made areas on the ecological state of the atmospheric air of the surrounding areas. It has been found that the use of remote sensing equipment greatly simplifies the procedure of operational monitoring of the environment during emergencies, as well as contributes to the health of professionals. A comparison of different remote means of environmental monitoring of air quality was performed: In particular, stationary automatic stations, mobile automatic stations, probes, and unmanned aerial vehicles (UAVs) were compared. It is proposed to use UAVs as remote means of operational monitoring of air quality. The functional scheme of UAV system implementation for the needs of operative ecological monitoring is offered. The legal features of the use of unmanned aerial vehicles as remote means of monitoring air quality during emergencies are analyzed.

scholarly journals Javan langur responses to the repeated exposure of ground survey and novel stimulus, unmanned aerial vehicles

2021 ◽  
Vol 948 (1) ◽  
pp. 012006
Author(s):  
D A Rahman ◽  
Y Setiawan ◽  
A A A F Rahman ◽  
T R Martiyani
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Behavioral Responses ◽  
Ecological Monitoring ◽  
Local Fauna ◽  
Policy Makers ◽  
Linear Mixed Effects ◽  
Aerial Vehicles ◽  
Ground Survey ◽  
New Type ◽  
Generalized Linear Mixed Effects

Abstract The use of small Unmanned Aerial Vehicles (UAVs; a.k.a “drones”) for ecological monitoring, conservation campaign, and management is increasing enormously. UAVs operate at low altitudes (<150 m) and in any terrain; thus, they are susceptible to interact with local fauna, generating a new type of anthropogenic disturbance that has not been systematically evaluated. Both policy-makers and practitioners require data about the potential impacts of UAVs on natural biota, but few studies exist. The research aims to compare behavioral responses from ground-based surveys vs. UAVs flights. Moreover, we conducted two experiments of UAVs overflights, specifically aiming to assess the responses of Trachypithecus auratus. Between January and March 2021, we conducted 24 UAVs flight approaches and 12 ground surveys at Mount Halimun-Salak National Park, Indonesia. We applied generalized linear mixed-effects models and Kruskal-Wallis tests to 364 behavioral scores obtained from two independent observers. When directly compared, the detection time was higher using UAVs (χ2 = 38.50; df= 1; p < 0.050), and behavioral responses by Javan langur to UAVs overflights at > 30 m were different from responses to ground surveys were more intense. Finally, we suggest data-driven best practices for UAVs use and the design of future UAVs-wildlife response studies.

scholarly journals A UAV-Based Thermal-Imaging Approach for the Monitoring of Urban Landfills

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 55
Author(s):  
Giovanni Tanda ◽  
Marco Balsi ◽  
Paolo Fallavollita ◽  
Valter Chiarabini
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Thermal Imaging ◽  
Preliminary Evaluation ◽  
Production Potential ◽  
Thermal Anomalies ◽  
Aerial Vehicles ◽  
Innovative Method ◽  
Novel Approach ◽  
Imaging Approach ◽  
Methane Production Potential

The monitoring of waste disposal sites is important in order to minimize leakages of biogas, produced by anaerobic digestion and potentially explosive and detrimental to the environment. In this research, thermal imaging from unmanned aerial vehicles (UAVs) has been proposed as a diagnostic tool to monitor urban landfills. Since the anaerobic decomposition produces heat along with biogas, thermal anomalies recorded over the soil are likely to be associated with local biogas escaping from the landfill terrain and leaving a local thermal print. A simple and novel approach, based only on the processing of thermal maps gathered by the remote sensing surveys, has been proposed for the estimation of the fugitive methane emissions from landfills. Two case studies, concerning two Italian landfills, have been presented. For one of them (Mount Scarpino, Genoa), significant thermal anomalies were identified during several UAV flights and the relevant thermal images processed to obtain a rough estimation of the associated methane leakages. For the second landfill (Scala Erre, Sassari), the thermal map did not reveal any anomaly attributable to local biogas emission. Despite some limitations outlined in the paper, the present approach is proposed as an innovative method to identify significant biogas leakages from an urban landfill and to provide a preliminary evaluation of the methane production potential.

scholarly journals Unmanned aerial vehicles (UAVs)‐based thermal infrared (TIR) mapping, a novel approach to assess groundwater discharge into the coastal zone

2016 ◽  
Vol 14 (11) ◽  
pp. 725-735 ◽  
Author(s):  
Eunhee Lee ◽  
Heesung Yoon ◽  
Sung Pil Hyun ◽  
William C. Burnett ◽  
Dong‐Chan Koh ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Coastal Zone ◽  
Groundwater Discharge ◽  
Thermal Infrared ◽  
Aerial Vehicles ◽  
Novel Approach

scholarly journals Feature-Based Stereo Vision Relative Positioning Strategy for Formation Control of Unmanned Aerial Vehicles

2020 ◽  
Vol 9 (2) ◽  
pp. 1613-1617
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Formation Control ◽  
Vision System ◽  
Ground Truth ◽  
Flight Test ◽  
Relative Positioning ◽  
Ground Truth Data ◽  
Aerial Vehicles ◽  
Novel Approach ◽  
Positioning Strategy

As inspired by birds flying in flocks, their vision is one of the most critical components to enable them to respond to their neighbor’s motion. In this paper, a novel approach in developing a Vision System as the primary sensor for relative positioning in flight formation of a Leader-Follower scenario is introduced. To use the system in real-time and on-board of the unmanned aerial vehicles (UAVs) with up to 1.5 kilograms of payload capacity, few computing platforms are reviewed and evaluated. The study shows that the NVIDIA Jetson TX1 is the most suited platform for this project. In addition, several different techniques and approaches for developing the algorithm is discussed as well. As per system requirements and conducted study, the algorithm that is developed for this Vision System is based on Tracking and On-Line Machine Learning approach. Flight test has been performed to check the accuracy and reliability of the system, and the results indicate the minimum accuracy of 83% of the vision system against ground truth data.

scholarly journals Estimating the population size of migrating Tibetan antelopes Pantholops hodgsonii with unmanned aerial vehicles

Oryx ◽  
2018 ◽  
Vol 54 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Jianbo Hu ◽  
Xiaomin Wu ◽  
Mingxing Dai
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Population Size ◽  
Population Data ◽  
Image Resolution ◽  
Aerial Photographs ◽  
Migration Routes ◽  
Long Distance ◽  
Aerial Vehicles ◽  
High Concentrations ◽  
Tibetan Antelope

AbstractData on the distribution and population size of the Near Threatened Tibetan antelope Pantholops hodgsonii are necessary to protect this species. Ground-based count surveys are usually carried out from a long distance to avoid disturbing the sensitive animals, and on calving grounds or along migration routes where they are seasonally concentrated. This can result in underestimation of population sizes if terrain features obstruct the view and high concentrations of animals make estimating numbers difficult. Here we test the efficacy of unmanned aerial vehicles (UAVs) for gathering population data for the Tibetan antelope. We conducted the study south of a known calving ground, at the foot of Sewu Snow Mountain, in the Chang Tang National Nature Reserve, China. The UAV did not appear to disturb the animals and resulted in more accurate counts than ground-based observations. A total of 23,063 Tibetan antelopes were identified in twelve orthoimages derived from c. 4,000 aerial photographs. In the first flight area 7,671 females and 4,353 calves were identified (proportion of calves: 36.2%). In the second flight area 7,989 females and 3,050 calves were identified (proportion of calves: 27.6%). Two flights over the same area revealed the direction and speed of moving Tibetan antelope groups. Image resolution, which can be controlled with flight planning, was an important factor in determining the animals’ visibility in the photos. We found that UAV-based surveys outperformed ground-based surveys, and that larger UAVs are preferable for this application.

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