51,706 research outputs found
Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review
Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earthβs surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earthβs surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future
Autonomous monitoring framework for resource-constrained environments
Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD
Sensitive and specific detection of E. coli using biomimetic receptors in combination with a modified heat-transfer method
We report on a novel biomimetic sensor that allows sensitive and specific detection of Escherichia colt (E. coli) bacteria in a broad concentration range from 10(2) up to 10(6) CFU/mL in both buffer fluids and relevant food samples (i.e. apple juice). The receptors are surface-imprinted polyurethane layers deposited on stainless-steel chips. Regarding the transducer principle, the sensor measures the increase in thermal resistance between the chip and the liquid due to the presence of bacteria captured on the receptor surface. The low noise level that enables the low detection limit originates from a planar meander element that serves as both a heater and a temperature sensor. Furthermore, the experiments show that the presence of bacteria in a liquid enhances the thermal conductivity of the liquid itself. Reference tests with a set of other representative species of Enterobacteriaceae, closely related to E. coli, indicate a very low cross-sensitivity with a sensor response at or below the noise level
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Migrating eastern North Pacific gray whale call and blow rates estimated from acoustic recordings, infrared camera video, and visual sightings.
During the eastern North Pacific gray whale 2014-2015 southbound migration, acoustic call recordings, infrared blow detections, and visual sightings were combined to estimate cue rates, needed to convert detections into abundance. The gray whale acoustic call rate ranged from 2.3-24 calls/whale/day during the peak of the southbound migration with an average of 7.5 calls/whale/day over both the southbound and northbound migrations. The average daily calling rate increased between 30 December-13 February. With a call rate model, we estimated that 4,340 gray whales migrated south before visual observations began on 30 December, which is 2,829 more gray whales than used in the visual estimate, and would add approximately 10% to the abundance estimate. We suggest that visual observers increase their survey effort to all of December to document gray whale presence. The infrared camera blow rate averaged 49 blows/whale/hour over 5-8 January. Probability of detection of a whale blow by the infrared camera was the same at night as during the day. However, probability of detection decreased beyond 2.1 km offshore, whereas visual sightings revealed consistent whale densities up to 3 km offshore. We suggest that future infrared camera surveys use multiple cameras optimised for different ranges offshore
Multistage, multiband and sequential imagery to identify and quantify non-forest vegetation resources
Earth Resources photographs from Apollo 6, 7, and 9 and photographs taken during Gemini 4, were used in the research along with high altitude and conventional aerial photography. A unified land use and resource analysis system was devised and used to develop a mapping legend. The natural vegetation, land use, macrorelief, and landforms of northern Maricopa County, Arizona, were analyzed and inventoried. This inventory was interpreted in relation to the critical problem of urban expansion and agricultural production in the study area. The central thrust of the research program has been to develop methods for use of space and small-scale, high-altitude aerial photography to develop information for land use planning and resource allocation decisions
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λ°λΌμ, λ³Έ μ°κ΅¬λ μ΄νμκ³Ό μ€νμ μ΄λ―Έμ§λ₯Ό νμ©ν λλ¬Ό μλ νμ§ λ°©λ²μ κ°λ°κ³Ό, κ°λ°λ λ°©λ²μ΄ μ΄μ λ°©λ²λ€μ νκ· μ΄μμ μ νλμ ν¨κ» νμ₯μμ μ€μκ°μΌλ‘ μ¬μ©λ μ μλλ‘ νλ κ²μ λͺ©νλ‘ νλ€.For wildlife detection and monitoring, traditional methods such as direct observation and capture-recapture have been carried out for diverse purposes. However, these methods require a large amount of time, considerable expense, and field-skilled experts to obtain reliable results. Furthermore, performing a traditional field survey can result in dangerous situations, such as an encounter with wild animals. Remote monitoring methods, such as those based on camera trapping, GPS collars, and environmental DNA sampling, have been used more frequently, mostly replacing traditional survey methods, as the technologies have developed. But these methods still have limitations, such as the lack of ability to cover an entire region or detect individual targets.
To overcome those limitations, the unmanned aerial vehicle (UAV) is becoming a popular tool for conducting a wildlife census. The main benefits of UAVs are able to detect animals remotely covering a wider region with clear and fine spatial and temporal resolutions. In addition, by operating UAVs investigate hard to access or dangerous areas become possible. However, besides these advantages, the limitations of UAVs clearly exist. By UAV operating environments such as study site, flying height or speed, the ability to detect small animals, targets in the dense forest, tracking fast-moving animals can be limited. And by the weather, operating UAV is unable, and the flight time is limited by the battery matters. Although detailed detection is unavailable, related researches are developing and previous studies used UAV to detect terrestrial and marine mammals, avian and reptile species.
The most common type of data acquired by UAVs is RGB images. Using these images, machine-learning and deep-learning (MLβDL) methods were mainly used for wildlife detection. MLβDL methods provide relatively accurate results, but at least 1,000 images are required to develop a proper detection model for specific species. Instead of RGB images, thermal images can be acquired by a UAV. The development of thermal sensor technology and sensor price reduction has attracted the interest of wildlife researchers. Using a thermal camera, homeothermic animals can be detected based on the temperature difference between their bodies and the surrounding environment. Although the technology and data are new, the same MLβDL methods were typically used for animal detection. These ML-DL methods limit the use of UAVs for real-time wildlife detection in the field.
Therefore, this paper aims to develop an automated animal detection method with thermal and RGB image datasets and to utilize it under in situ conditions in real-time while ensuring the average-above detection ability of previous methods.Abstract I
Contents IV
List of Tables VII
List of Figures VIII
Chapter 1. Introduction 1
1.1 Research background 1
1.2 Research goals and objectives 10
1.2.1 Research goals 10
1.2.2 Research objectives 11
1.3 Theoretical background 13
1.3.1 Concept of the UAV 13
1.3.2 Concept of the thermal camera 13
Chapter 2. Methods 15
2.1 Study site 15
2.2 Data acquisition and preprocessing 16
2.2.1 Data acquisition 16
2.2.2 RGB lens distortion correction and clipping 19
2.2.3 Thermal image correction by fur color 21
2.2.4 Unnatural object removal 22
2.3 Animal detection 24
2.3.1 Sobel edge creation and contour generation 24
2.3.2 Object detection and sorting 26
Chapter 3. Results 30
3.1 Number of counted objects 31
3.2 Time costs of image types 33
Chapter 4. Discussion 36
4.1 Reference comparison 36
4.2 Instant detection 40
4.3 Supplemental usage 41
4.4 Utility of thermal sensors 42
4.5 Applications in other fields 43
Chapter 5. Conclusions 47
References 49
Appendix: Glossary 61
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Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports
Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques
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