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.

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.

THE USE OF DECONVOLUTION TECHNIQUE FOR THE ANALYSIS OF GAMMA SPECTROMETRY DATA FROM FIELD MONITORING USING UNMANNED AERIAL VEHICLES

2019 ◽  
Vol 186 (2-3) ◽  
pp. 284-287
Author(s):  
Jaroslav Klusoň ◽  
Lenka Thinová
Keyword(s):  
Data Analysis ◽  
Unmanned Aerial Vehicles ◽  
Gamma Spectrometry ◽  
Long Distance ◽  
Air Kerma ◽  
Aerial Vehicles ◽  
Deconvolution Technique ◽  
Scan Data ◽  
Deconvolution Techniques ◽  
Short Time

ABSTRACT Airborne gamma spectrometry is an effective tool for prompt monitoring and mapping of large areas contaminated after NPP accident, radionuclides leakage cases, an impact of uranium ore mining and processing, etc. Airborne spectrometry data analysis using deconvolution technique enables to calculate air kerma rates and/or radionuclides concentrations as well as identification of radionuclides. Application of this technique on the airborne data (from manned as well as an unmanned survey using drones) is rather specific due to the requirements for short time of one scan data acquisition, a relatively long distance from the source and small detector size, due to the limited payload of the usually used drones. Application of deconvolution techniques for analysis of spectra with very poor statistics, methods and possibilities to improve the processing of such spectra are discussed.

scholarly journals Genetic Resilience of a Once Endangered Species, Tibetan Antelope (Pantholops hodgsonii)

2019 ◽  
Author(s):  
Yue Shi ◽  
Jiarui Chen ◽  
Jianping Su ◽  
Tongzuo Zhang ◽  
Samuel K. Wasser
Keyword(s):  
Genetic Diversity ◽  
Gene Flow ◽  
Population Size ◽  
Effective Population Size ◽  
Genetic Bottleneck ◽  
Migration Routes ◽  
Effective Population ◽  
Human Disturbances ◽  
Population Reduction ◽  
Tibetan Antelope

AbstractPopulation reduction is generally assumed to reduce the population’s genetic diversity and hence its ability to adapt to environmental change. However, if life history traits that promote gene flow buffer populations from such impacts, conservation efforts should aim to maintain those traits in vulnerable species. Tibetan antelope (Pantholops hodgsonii) has experienced population reduction by 95% due to poaching during the 20th century. We hypothesize that opportunities for gene flow provided by their sex-specific migration buffered their genetic diversity from the poaching impacts. We measured the mtDNA (control region, CR) and nuDNA (microsatellites or STRs) diversity, population differentiation, along with the change in effective population size (pre-poaching era vs. post-poaching era) and tested for a genetic bottleneck. Our results showed that Tibetan antelope maintained considerable genetic diversity in both mtDNA CR and STR markers (Hd = 0.9970 and Hobs = 0.8446, respectively), despite a marked reduction in post-poaching effective population size 368.9 (95% CI of 249.3 - 660.6) compared to the pre-poaching average (4.93×103 - 4.17×104). Post-poached populations also had low population structure and showed no evidence of a genetic bottleneck. Pairwise Fst values using CR haplotype frequencies were higher than those using STR allele frequencies, suggesting different degrees of gene flow mediated by females and males. This study suggests that the Tibetan antelope’s sex-specific migration buffered their loss of genetic diversity in the face of severe demographic decline. These findings highlight the importance of recognizing the traits likely to maintain genetic diversity and promoting conservation efforts that allow them to be exercised. For Tibetan antelope, this requires assuring that their migration routes remain unobstructed by growing human disturbances while continuing to enforce anti-poaching law enforcement efforts.

scholarly journals Smart Search System of Autonomous Flight UAVs for Disaster Rescue

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6810
Author(s):  
Donggeun Oh ◽  
Junghee Han
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Base Stations ◽  
Localization Algorithm ◽  
Tracking Accuracy ◽  
Long Distance ◽  
Search System ◽  
Autonomous Flight ◽  
Shadow Area ◽  
Aerial Vehicles

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

scholarly journals Using Unmanned Aerial Vehicles (UAV) for free-roaming dog population size estimation

2019 ◽  
Author(s):  
Charlotte Warembourg ◽  
Monica Berger-González ◽  
Danilo Alvarez ◽  
Filipe Maximiano Sousa ◽  
Alexis López Hernández ◽  
...  
Keyword(s):  
Statistical Model ◽  
Unmanned Aerial Vehicles ◽  
Population Size ◽  
Census Data ◽  
Population Size Estimation ◽  
Domestic Dog ◽  
Estimation Methods ◽  
Size Estimation ◽  
Aerial Vehicles ◽  
Capture Recapture

AbstractPopulation size estimation is performed for several reasons including disease surveillance and control, for example to design adequate control strategies such as vaccination programs or to estimate a vaccination campaign coverage. In this study, we aimed at assessing the benefits and challenges of using Unmanned Aerial Vehicles (UAV) to estimate the size of free-roaming domestic dog (FRDD) populations and compare the results with two regularly used methods for population estimations: a Bayesian statistical model based on capture-recapture data and the human:dog ratio estimation. Three studies sites of one square kilometer were selected in Petén department, Guatemala. UAV flight were conducted twice during two consecutive days per study site. The UAV’s camera was set to regularly take pictures and cover the entire surface of the selected areas. A door-to-door survey was conducted in the same areas, all available dogs were marked with a collar and owner were interviewed. Simultaneously to the UAV’s flight, transect walks were performed and the number of collared and non-collared dogs were recorded. Data collected during the interviews and the number of dogs counted during the transect walks informed a Bayesian statistical model. The number of dogs counted on the UAV’s pictures and the estimates given by the Bayesian statistical model, as well as the estimates derived from using a 5:1 human:dog ratio were compared to dog census data. FRDD could be detected using the UAV’s method. However, the method lacked of sensitivity, which could be overcome by choosing the flight timing and the study area wisely, or using infrared camera or automatic detection of the dogs. We also suggest to combine UAV and capture-recapture methods to obtain reliable FRDD population size estimated. This publication may provide helpful directions to design dog population size estimation methods using UAV.

scholarly journals Unmanned Aerial Vehicles for Operational Monitoring of Landfills

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 125
Author(s):  
Timofey Filkin ◽  
Natalia Sliusar ◽  
Marco Ritzkowski ◽  
Marion Huber-Humer
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Aerial Imagery ◽  
Aerial Photographs ◽  
Engineering Systems ◽  
Aerial Vehicles ◽  
Municipal Solid Waste Landfills ◽  
Landfill Management ◽  
Visually Based ◽  
Solid Waste Landfills

This study justifies the prospect of using aerial imagery from unmanned aerial vehicles (UAVs) for technological monitoring and operational control of municipal solid waste landfills. It presents the results of surveys (aerial imagery) of a number of Russian landfills, which were carried out using low-cost drones equipped with standard RGB cameras. In the processing of aerial photographs, both photogrammetric data processing algorithms (for constructing orthophotoplans of objects and 3D modeling) and procedures for thematic interpretation of photo images were used. Thematic interpretation was carried out based on lists of requirements for the operating landfills (the lists were compiled on the basis of current legislative acts). Thus, this article proposes framework guidelines for the complex technological monitoring of landfills using relatively simple means of remote control. It shows that compliance with most of the basic requirements for landfill operations, which are listed in both Russian and foreign regulation, can be controlled by unmanned aerial imagery. Thus, all of the main technological operations involving waste at landfills (placement, compaction, intermediate isolation) are able to be controlled remotely; as well as compliance with most of the design and planning requirements associated with the presence and serviceability of certain engineering systems and structures (collection systems for leachate and surface wastewater, etc.); and the state of the landfill body. Cases where the compliance with operating standards cannot be monitored remotely are also considered. It discusses the advantages of air imagery in comparison with space imagery (detail of images, operational efficiency), as well as in comparison with ground inspections (speed, personnel safety). It is shown that in many cases, interpreting the obtained aerial photographs for technological monitoring tasks does not require special image processing and can be performed visually. Based on the analysis of the available world experience, as well as the results of the study, it was concluded that unmanned aerial imagery has great potential for solving problems of waste landfill management.

scholarly journals Software Package for Searching People and Vehicles Using Unmanned Aerial Vehicles

2021 ◽  
Author(s):  
Dmitriy Bulatitskiy ◽  
Andrey Selifontov ◽  
Sergey Shevchenko ◽  
Victor Filippov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Software Package ◽  
Training Sample ◽  
Aerial Photographs ◽  
Emergency Situations ◽  
Aerial Vehicles ◽  
Processing Data ◽  
Network Operation ◽  
Search Operation ◽  
Technical Solutions

The paper considers the relevance of using a single software package for processing images made by unmanned aerial vehicles and coordinating the actions of search participants during operations to search for people and vehicles in the natural environment. An approach to the creation of such a package is proposed, its architecture is developed, the technologies used in the development and the main technical solutions are described. An algorithm for processing data for a search operation is developed and implemented. The package core, which recognizes search objects on aerial photographs, is built on a convolutional artificial neural network YOLOv5. The characteristics of the network operation depending on the structure of the training sample and the network parameters are studied. The experimental operation of the package by one division of the crisis management center of the Ministry of Emergency Situations of Russia in Bryansk region is described.

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 Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xuejun Zhang ◽  
Yang Liu ◽  
Yu Zhang ◽  
Xiangmin Guan ◽  
Daniel Delahaye ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Risk Model ◽  
Risk Estimation ◽  
Population Data ◽  
National Airspace System ◽  
Expected Number ◽  
Aerial Vehicles ◽  
Real Scenario ◽  
Ground Impact ◽  
Rotary Wing

This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.

Sign in / Sign up

Export Citation Format

Share Document