Upload UAVs&ecology_keywords.csv

UAVs&ecology_keywords.csv

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+id,link (APA),link,titile,authors,year,keywords,topic,citations ,abstract,abstract_ru,prompt,topic_generated_keywords
+1,"Estrada, M. A. R., & Ndoma, A. (2019). The uses of unmanned aerial vehicles–UAV’s-(or drones) in social logistic: Natural disasters response and humanitarian relief aid. Procedia Computer Science, 149, 375-383.
+",l__l,The uses of unmanned aerial vehicles –UAV’s- (or drones) in social logistic: Natural disasters response and humanitarian relief aid,"Estrada, M. A. R., & Ndoma, A.",2019,Drones; UAV’s; Natural disasters; Policy modelling; Indicators;,Natural disasters and emergencies,132,"This paper evaluates the crucial role of unmanned aerial vehicles –UAV’s- (or Drones) in the case of natural disasters response and humanitarian relief aid. The primary objective of this paper is to evaluate how unmanned aerial vehicles –UAV’s- (or Drones) in the present or near future can help survivors in the case of a tsunami, earthquake, flooding, and any natural disaster. Initially, we assume that in any natural disaster always exist the high possibility of damage to the infrastructure, transportation systems, telecommunications systems access, and basic services immediately. This research proposes three areas the uses of unmanned aerial vehicles –UAV’s- (or Drones) in the case of natural disasters response and humanitarian relief aid. These are (i) the aerial monitoring post-natural disaster damage evaluation, (ii) the natural disaster logistic and cargo delivery, (iii) the post-natural disaster aerial assessment.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+2,"Hildmann, H., & Kovacs, E. (2019). Using unmanned aerial vehicles (UAVs) as mobile sensing platforms (MSPs) for disaster response, civil security and public safety. Drones, 3(3), 59.",l__l,"Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety","Hildmann, H., & Kovacs, E.",2019,review; UAV; drone; UAV-Swarms; Intelligent Autonomous Systems; smart city; civil security; public safety; disaster response; Mobile Sensing Platform; applications; challenges,Natural disasters and emergencies,186,"The use of UAVs in areas ranging from agriculture over urban services to entertainment or simply as a hobby has rapidly grown over the last years. Regarding serious/commercial applications, UAVs have been considered in the literature, especially as mobile sensing/actuation platforms (i.e., as a delivery platform for an increasingly wide range of sensors and actuators). With regard to timely, cost-effective and very rich data acquisition, both, NEC Research as well as TNO are pursuing investigations into the use of UAVs and swarms of UAVs for scenarios where high-resolution requirements, prohibiting environments or tight time constraints render traditional approaches ineffective. In this review article, we provide a brief overview of safety and security-focused application areas that we identified as main targets for industrial and commercial projects, especially in the context of intelligent autonomous systems and autonomous/semi-autonomously operating swarms. We discuss a number of challenges related to the deployment of UAVs in general and to their deployment within the identified application areas in particular. As such, this article is meant to serve as a review and overview of the literature and the state-of-the-art, but also to offer an outlook over our possible (near-term) future work and the challenges that we will face there.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+3,"Nedjati, A., Vizvari, B., & Izbirak, G. (2016). Post-earthquake response by small UAV helicopters. Natural Hazards, 80, 1669-1688.",l__l,Post-earthquake response by small UAV helicopters,"Nedjati, A., Vizvari, B., & Izbirak, G. ",2015,Emergency response; Earthquake; UAV helicopter; Relief distribution; Disasters;,Natural disasters and emergencies,134,"The aim of this study was to introduce a new relief distribution system for emergency response after an earthquake. Extensive literature is used to explain the ground transportation difficulties following a disaster and some possible alternative choices. Due to roads blockage and time limits in the disaster response phase, small unmanned aerial vehicle (UAV) helicopter can be utilized as relief distributors. The use of UAV helicopters for humanitarian purposes and even for commercial purposes by some companies has been discussed in several publications. Yet no one has considered an autonomous small UAV helicopter network for post-earthquake relief distribution in highly populated cities. In an applied sense, the study offers a system that helps earthquake response managers in the early hours following an earthquake. The suggested system is a synthesis of many new technical results achieved in the area of earthquake detection, data fusion, and UAV helicopters. The relief distribution system by medium-scale UAV helicopters is investigated in Tehran metropolis as a case study for the worst-case earthquake scenario. The outcomes reveal that the system has efficient capability for urban areas with high population density. The proposed aerial distribution system can supply a large amount of demand in a small amount of time. Moreover, it works without disturbing the ground transportation system and may turn out to be managed as a complementary system for serving the affected people in non-accessible areas.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+4,"Khan, A., Gupta, S., & Gupta, S. K. (2022). Emerging UAV technology for disaster detection, mitigation, response, and preparedness. Journal of Field Robotics, 39(6), 905-955.",l__l,"Emerging UAV technology for disaster detection, mitigation, response, and preparedness","Khan, A., Gupta, S., & Gupta, S. K.",2022,UAVs; disaster management; wireless communication; challenges; applications,Natural disasters and emergencies,35,"In recent years, Unmanned Aerial Vehicles (UAVs) have gained significant interest in research groups due to the wide range of applications, such as disaster surveillance and monitoring, rescue operations, military, civil, and so forth. UAVs are most often used to fulfill both user's services and requirements such as wireless communication facilities to end-users, as a relay node to extend the coverage of the core network, and so forth. UAVs are versatile in design and can cover larger areas, contrary to the Tethered Balloon and Loon Balloon systems. Generally in any natural or human-made disaster, there is a high potential risk of damage to buildings, transport systems, communication systems, and basic services. During heavy disasters like landslides, forest fires, floods, earthquakes, and so forth, the conventional terrestrial communication system gets destroyed, and people face many problems. In this case, UAVs prove to offer a better solution to provide fast, cost-effective, easy to deploy, and secure wireless communication to the victims. But there are some issues like interference between UAVs and other base stations, coordination between UAVs, Quality of Service requirements, Size, Weight, and Power limitation, delay, coverage, positioning of UAVs, and so forth. This study article mainly highlighted these issues and try to present the recent developments of the state-of-the-art to overcome these issues. In UAV communications, with an increasing emphasis on how UAVs can be integrated with different technologies, such as the Internet of Things, Wireless Sensor Network, Heterogeneous Network, and Cloud computing. The primary aim of this article is to examine how UAVs can assist survivors in floods, earthquakes, tsunamis, or in any natural or human-made disaster situations, either in the present or soon. Also, it focused on various applications of UAVs in disaster management (DM). It underlines the significance of UAVs in DM and their advantages. It also focuses on the various issues and challenges faced by the UAV-based infrastructure and security issues and gives future directions.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+5,"Clark, D. G., Ford, J. D., & Tabish, T. (2018). What role can unmanned aerial vehicles play in emergency response in the Arctic: A case study from Canada. PLoS One, 13(12), e0205299.",l__l,What role can unmanned aerial vehicles play in emergency response in the Arctic: A case study from Canada,"Clark, D. G., Ford, J. D., & Tabish, T.",2018,search and rescue; backcountry medical response; Canadian Arctic; unmanned aerial vehicles; emergency response,Natural disasters and emergencies,51,"This paper examines search and rescue and backcountry medical response constraints in the Canadian Arctic and potential for unmanned aerial vehicles (UAV) to aid in response and preparedness. Semi-structured interviews (n = 18) were conducted with search and rescue responders, Elders, and emergency management officials to collect data on current emergency response and potential for UAV use. UAV test flights (n = 17) were undertaken with community members. We analyzed five years of weather data to examine UAV flight suitability. Numerous challenges face Arctic search and rescue and backcountry emergency response. Changing social and environmental conditions were described as increasing vulnerability to backcountry emergencies. Responders desired additional first aid and emergency training. Legal and weather restrictions were found to limit where, when and who could fly UAVs. UAVs were demonstrated to have potential benefits for hazard monitoring but not for SAR or medical response due to legal restrictions, weather margins, and local capacity. We find that communities are ill-prepared for ongoing SAR demands, let alone a larger disaster. There are numerous limitations to the use of consumer UAVs by Arctic communities. Prevention of backcountry medical emergencies, building resilience to disasters, and first responder training should be prioritized over introducing UAVs to the response system.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+6,"Erdelj, M., Natalizio, E., Chowdhury, K. R., & Akyildiz, I. F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24-32.",l__l,Help from the Sky: Leveraging UAVs for Disaster Management,"Erdelj, M., Natalizio, E., Chowdhury, K. R., & Akyildiz, I. F.",2017,unmanned aerial vehicles; UAVs; wireless sensor networks; disaster management; drones; mobile; pervasive computing; disaster management; robotics; distributed systems; big data; Internet/Web technologies; networking; first response;,Natural disasters and emergencies,916,"This article presents a vision for future unmanned aerial vehicles (UAV)-assisted disaster management, considering the holistic functions of disaster prediction, assessment, and response. Here, UAVs not only survey the affected area but also assist in establishing vital wireless communication links between the survivors and nearest available cellular infrastructure. A perspective of different classes of geophysical, climate-induced, and meteorological disasters based on the extent of interaction between the UAV and terrestrially deployed wireless sensors is presented in this work, with suitable network architectures designed for each of these cases. The authors outline unique research challenges and possible solutions for maintaining connected aerial meshes for handoff between UAVs and for systems-specific, security- and energy-related issues. This article is part of a special issue on drones.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+7,"Jin, W., Yang, J., Fang, Y., & Feng, W. (2020, July). Research on application and deployment of UAV in emergency response. In 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) (pp. 277-280). IEEE.",l__l,Research on Application and Deployment of UAV in Emergency Response,"Jin, W., Yang, J., Fang, Y., & Feng, W.",2020,UAVs; Emergency Management; Emergency Communication; Aerial Remote Sensing;,Natural disasters and emergencies,29,"With the continuous development and maturity of related technologies in recent years, unmanned aerial vehicles (UAVs) are increasingly used in the fields of industry and public safety. This article first discusses the various demands for the emergency response mechanism. Next, a capability matrix of UAVs and payloads is created comprising various capacities required in response to various disasters and accidents. Combining the regional disaster susceptibility, traffic inconvenience index and terrain complexity coefficient, recommendations are further provided for the deployment of UAVs and payloads in various regions. Based on the above analysis, this article finally puts forward suggestions on the application of UAVs in implementing emergency response mechanism.",todo,UAVs in natural disaster response,"""natural"", ""disasters"", ""relief"", ""modelling"",""atmospheric"", ""emergency"", ""earthquake"", ""rescue"""
+8,"Jońca, J., Pawnuk, M., Bezyk, Y., Arsen, A., & Sówka, I. (2022). Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review. Sustainability, 14(18), 11516.",l__l,Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review,"Jońca, J., Pawnuk, M., Bezyk, Y., Arsen, A., & Sówka, I. ",2022,unmanned aerial vehicle; gas sensors; electronic noses; atmospheric pollution,Air pollution,23,"Ambient air quality is a complex issue that depends on multiple interacting factors related to emissions coming from energy production and use, transportation, industrial processes, agriculture, and waste and wastewater treatment sectors. It is also impacted by adverse meteorological conditions, pollutants concentrations, their transport and dispersion in the atmosphere, and topographic constraints. Therefore, air pollutants distribution is not uniform and their monitoring at proper temporal and spatial resolution is necessary. Drone-borne analytical instrumentation can fulfill these requirements. Thanks to the rapid development in the drone manufacturing sector as well as in the field of portable detectors construction, applications of unmanned aerial vehicles (UAVs) for atmospheric pollution monitoring are growing. The purpose of this work is to give an overview of this matter. Therefore, this paper contains basic information on UAVs (i.e., description of different types of drones with their advantages and disadvantages) and analytical instrumentation (i.e., low-cost gas sensors, multi-sensor systems, electronic noses, high-accuracy optical analyzers, optical particle counters, radiation detectors) used for the monitoring of airborne pollution. Different ways of payload integration are addressed and examples of commercially available solutions are given. Examples of applications of drone-borne analytical systems for pollution monitoring coming from natural (i.e., volcanoes, thawing permafrost, wildfires) and anthropological (i.e., urbanization and industrialization; extraction, transport and storage of fossil fuels; exploitation of radioactive materials; waste and wastewater treatment; agriculture) sources are also described. Finally, the current limitations and future perspectives are discussed. Although there is a great potential for drones applications in the field of atmospheric pollution monitoring, several limitations should be addressed in the coming years. Future research should focus on improving performances of available analytical instrumentation and solving problems related to insufficient payload capacity and limited flight time of commonly used drones. We predict that applications of drone-assisted measurements will grow in the following years, especially in the field of odor pollution monitoring.","Качество атмосферного воздуха представляет собой сложную проблему, которая зависит от множества взаимодействующих факторов, связанных с выбросами в секторах производства и использования энергии, транспорта, промышленных процессов, сельского хозяйства, а также секторов очистки отходов и сточных вод. На него также влияют неблагоприятные метеорологические условия, концентрации загрязняющих веществ, их перенос и рассеивание в атмосфере, а также топографические ограничения. Таким образом, распределение загрязнителей воздуха не является равномерным, и необходим их мониторинг с надлежащим временным и пространственным разрешением. Аналитические приборы, переносимые дронами, могут удовлетворить эти требования. Благодаря быстрому развитию сектора производства дронов, а также строительства портативных детекторов, растет применение беспилотных летательных аппаратов (БПЛА) для мониторинга загрязнения атмосферы. Цель данной работы – дать общее представление об этом вопросе. Таким образом, этот документ содержит основную информацию о БПЛА (т. е. описание различных типов дронов с их преимуществами и недостатками) и аналитических приборов (т. е. недорогих датчиков газа, мультисенсорных систем, электронных носов, высокоточных оптических анализаторов, оптические счетчики частиц, детекторы радиации), используемые для мониторинга загрязнения воздуха. Рассматриваются различные способы интеграции полезной нагрузки и приводятся примеры коммерчески доступных решений. Примеры применения беспилотных аналитических систем для мониторинга загрязнения природного (т. е. вулканов, таяния вечной мерзлоты, лесных пожаров) и антропологического (т. е. урбанизации и индустриализации; добычи, транспортировки и хранения ископаемого топлива; эксплуатации радиоактивных материалов; отходов и Очистка сточных вод; сельское хозяйство) также описаны источники. Наконец, обсуждаются текущие ограничения и будущие перспективы. Хотя существует большой потенциал применения дронов в области мониторинга загрязнения атмосферы, в ближайшие годы необходимо устранить некоторые ограничения. Будущие исследования должны быть сосредоточены на улучшении характеристик имеющихся аналитических приборов и решении проблем, связанных с недостаточной грузоподъемностью и ограниченным временем полета широко используемых дронов. Мы прогнозируем, что в ближайшие годы применение измерений с помощью дронов будет расти, особенно в области мониторинга загрязнения запахами.",UAVs for air pollution monitoring,"""gas"", ""pollution"", ""air"", ""sensors"", ""environmental"", ""monitoring"", ""optical"", ""atmospheric"",""emissions"""
+9,"Bolla, G. M., Casagrande, M., Comazzetto, A., Dal Moro, R., Destro, M., Fantin, E., ... & Lorenzini, E. C. (2018, June). ARIA: Air pollutants monitoring using UAVs. In 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) (pp. 225-229). IEEE.",l__l,ARIA: Air Pollutants Monitoring Using UAVs,"Bolla, G. M., Casagrande, M., Comazzetto, A., Dal Moro, R., Destro, M., Fantin, E., ... & Lorenzini, E. C.",2018,UAVs; drone swarms; Wireless Sensor Network; low-cost ambient sensors; air pollution; environmental monitoring;,Air pollution,49,"UAV technology, and specifically networks and swarms, have been an open research topic for many years. This is because of their potentially huge benefits at an affordable cost in a wide range of tasks. UAVs are commonly used in public and private fields. Their usefulness still comes with a price and some serious limitations, not completely surpassed yet. There are many proposals for protocols and algorithms trying to improve drone swarms but they are massively dependent on the actual scenarios and use cases. UAVs can fill a gap in modern Wireless Sensor Network, mostly because of their mobility and ability to fly at different heights. While drone swarms are prolific research topics, there is almost no interest investigating benefits of vertical deployment of this technology. It is proven that air pollution changes abruptly even at relatively short distances, both horizontally and vertically. We aim to provide a new tool to study air quality at different heights that even private citizens could afford. We will present an overview of ARIA project, a vertical drone swarm for air pollution monitoring. Considering the scope of our project, we will only utilize low-cost UAVs and sensors. Knowing the hard challenges inherent to UAVs and environmental monitoring, we're looking at preliminary results but we think this will become a valuable proof-of-concept for further research.",todo,UAVs for air pollution monitoring,"""gas"", ""pollution"", ""air"", ""sensors"", ""environmental"", ""monitoring"", ""optical"", ""atmospheric"",""emissions"""
+10,"Alvear, O., Zema, N. R., Natalizio, E., & Calafate, C. T. (2017). Using UAV-based systems to monitor air pollution in areas with poor accessibility. Journal of advanced Transportation, 2017.",l__l,Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility,"Alvear, O., Zema, N. R., Natalizio, E., & Calafate, C. T.",2017,air pollution monitoring; UAVs; off-the-shelf sensors; Pollution-driven UAV Control (PdUC) algorithm; chemotaxis metaheuristic; particle swarm optimization; pollution maps,Air pollution,147,"Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.",todo,UAVs for air pollution monitoring,"""gas"", ""pollution"", ""air"", ""sensors"", ""environmental"", ""monitoring"", ""optical"", ""atmospheric"",""emissions"""
+11,"Villa, T. F., Salimi, F., Morton, K., Morawska, L., & Gonzalez, F. (2016). Development and validation of a UAV based system for air pollution measurements. Sensors, 16(12), 2202.",l__l,Development and Validation of a UAV Based System for Air Pollution Measurements,"Villa, T. F., Salimi, F., Morton, K., Morawska, L., & Gonzalez, F.",2016,UAV remote gas sensing; downwash effect; air quality; hexacopter; optical sensor; air pollution; particle number concentration monitor,Air pollution,207,"Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design decisions which need to be made to enable representative data collection, in particular the location of the air sampler or air sensor intake. The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions. The research included two different tests: (1) evaluate the air flow behavior of a hexacopter, its downwash and upwash effect, by measuring air speed along three axes to determine the location where the sensors should be mounted; (2) evaluate the use of gas sensors for CO2, CO, NO2 and NO, and the PNC monitor (DISCmini) to assess the efficiency and performance of the UAV based system by measuring emissions from a diesel engine. The air speed behavior map produced by test 1 shows the best mounting point for the sensors to be alongside the UAV. This position is less affected by the propeller downwash effect. Test 2 results demonstrated that the UAV propellers cause a dispersion effect shown by the decrease of gas and PN concentration measured in real time. A Linear Regression model was used to estimate how the sensor position, relative to the UAV center, affects pollutant concentration measurements when the propellers are turned on. This research establishes guidelines on how to develop a UAV system to measure point source emissions. Such research should be undertaken before any UAV system is developed for real world data collection.",todo,UAVs for air pollution monitoring,"""gas"", ""pollution"", ""air"", ""sensors"", ""environmental"", ""monitoring"", ""optical"", ""atmospheric"",""emissions"""
+12,"Gu, Q., R. Michanowicz, D., & Jia, C. (2018). Developing a modular unmanned aerial vehicle (UAV) platform for air pollution profiling. Sensors, 18(12), 4363.",l__l,Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling,"Gu, Q., R. Michanowicz, D., & Jia, C.",2018, Unmanned aerial vehicle; drone; air pollution; air monitoring; modular design,Air pollution,91,"The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs.",todo,UAVs for air pollution monitoring,"""gas"", ""pollution"", ""air"", ""sensors"", ""environmental"", ""monitoring"", ""optical"", ""atmospheric"",""emissions"""
+13,"Li, B., Cao, R., Wang, Z., Song, R. F., Peng, Z. R., Xiu, G., & Fu, Q. (2019). Use of multi-rotor unmanned aerial vehicles for fine-grained roadside air pollution monitoring. Transportation research record, 2673(7), 169-180.",l__l,Use of Multi-Rotor Unmanned Aerial Vehicles for Fine-Grained Roadside Air Pollution Monitoring,"Li, B., Cao, R., Wang, Z., Song, R. F., Peng, Z. R., Xiu, G., & Fu, Q.",2019,vehicle exhaust gas; urban air pollutants; multi-rotor UAVs; atmospheric environment monitoring system; diffusion patterns,Air pollution,33,"With increasing numbers of motor vehicles, vehicle exhaust gas has become one of the most important sources of urban air pollutants. After being emitted from the motor vehicle, exhaust gas spreads through the air along the road and is gradually deposited in the surrounding area, having an adverse impact on pedestrians and residents. At present, most research on vehicle exhaust directly measures the total emissions from the exhaust pipe or monitors the time variation of air pollutants at the roadside by setting roadside monitoring stations. The spatial resolution of these two methods is very low, however, and it is impossible to describe accurately the diffusion patterns of exhaust gas in the atmosphere after discharge. Some scholars have conducted research on the quality of roadside air by hand-held portable devices, but these are limited by the speed of travel, and the spatial and temporal resolution of the acquired data is also very low. By using multi-rotor unmanned aerial vehicles (UAVs) and portable equipment, this study demonstrates an atmospheric environment monitoring system based on multi-rotor UAV by designing corresponding hardware circuits and software programs. With flexible requirements for takeoff or landing sites and high maneuverability of multi-rotor UAVs, the system increases the capability for high resolution spatial and temporal monitoring of the diffusion of traffic-emitted pollutants. The system can conduct fixed-point measurement by hovering, and can also measure air pollutants in complex urban terrain, providing an innovation in the study of vehicle exhaust gas diffusion.",todo,UAVs for air pollution monitoring,"""gas"", ""pollution"", ""air"", ""sensors"", ""environmental"", ""monitoring"", ""optical"", ""atmospheric"",""emissions"""
+14,"Zang, W., Lin, J., Wang, Y., & Tao, H. (2012, June). Investigating small-scale water pollution with UAV remote sensing technology. In World Automation Congress 2012 (pp. 1-4). IEEE.",l__l,Investigating small-scale water pollution with UAV Remote Sensing Technology,"Zang, W., Lin, J., Wang, Y., & Tao, H.",2012,Unmanned Aerial Vehicle (UAV); Remote Sensing; Water pollution Investigation; Southwest China;,Water pollution,80,"Water environment monitoring is an important part of environmental monitoring. Due to the constraints of natural condition and time and temporal factors, the traditional monitoring method has some limitations. In recent years, remote Sensing technologies were widely applied in the investigation and monitoring of various pollutions in aquatic environments. Water pollution events like sediment pollution occur frequently in Southwest China. Hence, it is urgent to timely conduct water pollution investigation in target region and takes necessary measures to treat identified water pollution events. Field investigation is reliable but too expensive and time-consuming. Satellite or manned aerial remote sensing have the advantages of region-scale coverage and moderate timeliness, but restricted by the factors of heavy mist, low image resolution, poor human safety, and prohibitive cost. Unmanned Aerial Vehicle (UAV) remote sensing is a very promising approach for water pollution investigation in regional scope, as giving consideration to both accuracy and spatial coverage. In this paper, we demonstrate our practice and experiences in applying UAVs in the investigation of aquatic environment pollution from the aspects of UAV customization and modification, and UAV imagery Interpretation and analysis.",todo,UAVs in water pollution detection,"""remote"", ""sensing"", ""water"", ""pollution"", ""quality"", ""monitoring"", ""investigation"", ""machinelearning"""
+15,"Chen, B., Mu, X., Chen, P., Wang, B., Choi, J., Park, H., ... & Yang, H. (2021). Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data. Ecological Indicators, 133, 108434.",l__l,Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data,"Chen, B., Mu, X., Chen, P., Wang, B., Choi, J., Park, H., ... & Yang, H.",2021,UAV; remote sensing; Water quality; inversion; Machine learning; Urban river;,Water pollution,44,"Urban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing water quality monitoring in urban rivers. In this paper, based on unmanned aerial vehicles (UAV) images and measured water quality data, the genetic algorithm_extreme gradient boosting (GA_XGBoost) algorithm is used to model water quality parameters in the study area, combined with its characteristics of supporting urban river polymorphism learning and semantic feature analysis. The results show that the coefficient of determination (R2) of GA_XGBoost algorithm for chlorophyll a (Chla), total phosphorous (TP), total nitrogen (TN), ammonia–nitrogen (NH3-N) and turbidity (TUB) is 0.855, 0.699, 0.787, 0.694, and 0.597, respectively, indicating a high precision and the predicted results are consistent with the measured data. Meanwhile, this paper compares the GA_XGBoost model with other algorithms: Deep Neural Network (DNN), Random Forest, genetic algorithm_RandomForest (GA_RandomForest), adaptive boosting (AdaBoost) and genetic algorithm_adaptive boosting (GA_AdaBoost), and the performance of the GA_XGBoost model is better. At the same time, data from different periods have been added to verify the model’s applicability. Moreover, based on the inversion results, analyze from the point of view of point source pollution, non-point source pollution, etc., to further investigate the influencing factors that cause urban river pollution. The current method has important practical significance for promoting the intelligent and automatic level of water environment monitoring technology in ecological environmental protection and urban water resources protection.",todo,UAVs in water pollution detection,"""remote"", ""sensing"", ""water"", ""pollution"", ""quality"", ""monitoring"", ""investigation"", 'machinelearning'"
+16,"Koparan, C., Koc, A. B., Privette, C. V., & Sawyer, C. B. (2018). In situ water quality measurements using an unmanned aerial vehicle (UAV) system. Water, 10(3), 264.",l__l,In Situ Water Quality Measurements Using an Unmanned Aerial Vehicle (UAV) System,"Koparan, C., Koc, A. B., Privette, C. V., & Sawyer, C. B.",2018,water quality; in situ; remote sampling; UAV; integrated sensors,Water pollution,139,"An unmanned aerial vehicle-assisted water quality measurement system (UAMS) was developed for in situ surface water quality measurement. A custom-built hexacopter was equipped with an open-source electronic sensors platform to measure the temperature, electrical conductivity (EC), dissolved oxygen (DO), and pH of water. Electronic components of the system were coated with a water-resistant film, and the hexacopter was assembled with flotation equipment. The measurements were made at thirteen sampling waypoints within a 1.1 ha agricultural pond. Measurements made by an open-source multiprobe meter (OSMM) attached to the unmanned aerial vehicle (UAV) were compared to the measurements made by a commercial multiprobe meter (CMM). Percent differences between the OSMM and CMM measurements for DO, EC, pH, and temperature were 2.1%, 3.43%, 3.76%, and <1.0%, respectively. The collected water quality data was used to interpret the spatial distribution of measurements in the pond. The UAMS successfully made semiautonomous in situ water quality measurements from predetermined waypoints. Water quality maps showed homogeneous distribution of measured constituents across the pond. The concept presented in this paper can be applied to the monitoring of water quality in larger surface waterbodies.",todo,UAVs in water pollution detection,"""remote"", ""sensing"", ""water"", ""pollution"", ""quality"", ""monitoring"", ""investigation"", ""machinelearning"""
+17,"Wang, L., Yue, X., Wang, H., Ling, K., Liu, Y., Wang, J., ... & Song, H. (2020). Dynamic inversion of inland aquaculture water quality based on UAVs-WSN spectral analysis. Remote Sensing, 12(3), 402.",l__l,Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis,"Wang, L., Yue, X., Wang, H., Ling, K., Liu, Y., Wang, J., ... & Song, H.",2020,UAVs; WSN; spectral; remote sensing; water quality; dynamic inversion; deep learning;,Water pollution,32,"The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R^2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R^2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values:  R^2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.",todo,UAVs in water pollution detection,"""remote"", ""sensing"", ""water"", ""pollution"", ""quality"", ""monitoring"", ""investigation"", ""machinelearning"""
+18,"Cheng, K. H., Chan, S. N., & Lee, J. H. (2020). Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs). Marine Pollution Bulletin, 152, 110889.",l__l,Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs),"Cheng, K. H., Chan, S. N., & Lee, J. H.",2020,phytoplankton; water discolouration; fish kills; water quality monitoring; chlorophyll-a concentration,Water pollution,53,"The explosive growth of phytoplankton under favorable conditions in subtropical coastal waters can lead to water discolouration and massive fish kills. Traditional water quality monitoring relies on manual field sampling and laboratory analysis of chlorophyll-a (Chl-a) concentration, which is resources intensive and time consuming. The cloudy weather of Hong Kong also precludes using satellite images for algal blooms monitoring. This study for the first time demonstrates the use of an Unmanned Aerial Vehicle (UAVs) to quantitatively map surface water Chl-a distribution in coastal waters from a low altitude. An estimation model for Chl-a concentration from visible images taken by a digital camera on a UAV has been developed and validated against one-year field data. The cost-effective and robust technology is able to map the spatial and temporal variations of Chl-a concentration during an algal bloom. The proposed method offers a useful complement to traditional field monitoring for fisheries management.",todo,UAVs in water pollution detection,"""remote"", ""sensing"", ""water"", ""pollution"", ""quality"", ""monitoring"", ""investigation"", ""machinelearning"""
+19,"Cillero Castro, C., Domínguez Gómez, J. A., Delgado Martín, J., Hinojo Sánchez, B. A., Cereijo Arango, J. L., Cheda Tuya, F. A., & Díaz-Varela, R. (2020). An UAV and satellite multispectral data approach to monitor water quality in small reservoirs. Remote Sensing, 12(9), 1514.",l__l,An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs,"Cillero Castro, C., Domínguez Gómez, J. A., Delgado Martín, J., Hinojo Sánchez, B. A., Cereijo Arango, J. L., Cheda Tuya, F. A., & Díaz-Varela, R.",2020,satellite; water quality; multispectral imagery; UAV; eutrophication; monitoring,Water pollution,75,"A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The goal is to evaluate the performance of a multi-platform approach for the trophic state monitoring with images obtained with MultiSpectral Sensors on board satellites Sentinel 2 (S2A and S2B), Landsat 8 (L8) and UAV. We assessed the performance of three different sensors (MultiSpectral Instrument (MSI), Operational Land Imager (OLI) and Rededge Micasense) for retrieving the pigment chlorophyll-a (chl-a), as a quantitative descriptor of phytoplankton biomass and trophic level. The study was conducted in a waterbody affected by cyanobacterial blooms, one of the most important eutrophication-derived risks for human health. Different empirical models and band indices were evaluated. Spectral band combinations using red and near-infrared (NIR) bands were the most suitable for retrieving chl-a concentration (especially 2 band algorithm (2BDA), the Surface Algal Bloom Index (SABI) and 3 band algorithm (3BDA)) even though blue and green bands were useful to classify UAV images into two chl-a ranges. The results show a moderately good agreement among the three sensors at different spatial resolutions (10 m., 30 m. and 8 cm.), indicating a high potential for the development of a multi-platform and multi-sensor approach for the eutrophication monitoring of small reservoirs.",todo,UAVs in water pollution detection,"""remote"", ""sensing"", ""water"", ""pollution"", ""quality"", ""monitoring"", ""investigation"", ""machinelearning"""
+20,"Sliusar, N., Filkin, T., Huber-Humer, M., & Ritzkowski, M. (2022). Drone technology in municipal solid waste management and landfilling: A comprehensive review. Waste Management, 139, 1-16.",l__l,Drone technology in municipal solid waste management and landfilling: A comprehensive review,"Sliusar, N., Filkin, T., Huber-Humer, M., & Ritzkowski, M.",2022,unmanned aerial vehicles; municipal solid waste landfills; dumpsites; landfill gas emissions; environmental safety; remote sensing data; geographic information systems,Household waste ,45,"The paper discusses the experience of using unmanned aerial vehicles (UAV) in the management of municipal solid waste landfills and dumpsites. Although the use of drones at waste disposal sites (WDS) has a more than ten-year history, the active application of these technologies has increased in the last 3–4 years. The paper analyzes scientific publications of 2010–2021 (July) and identifies the main WDS management task groups for which the solution of UAV can be used. It illustrates that most of the research is devoted to studying spatial and volumetric characteristics of landfills, which is connected with the practical needs. About a quarter of the publications focus on monitoring the emissions of landfill gas or its individual components, mainly methane. Issues of a comprehensive assessment of the technological and environmental safety of landfills and dumps are covered in the scientific literature fragmentarily and insufficiently. At the same time, the current level of technologies for collecting and processing remote sensing air data (UAV, sensors for aerial imagery, software for photogrammetric processing of aerial imagery data, geographic information systems (GIS)) makes it possible to identify and assess many environmental effects of landfills and dumps and to monitor compliance with the standards for the landfills operation, which could bring management of these facilities to a fundamentally different level. Promising areas of further research in the field of UAV application at WDS are indicated: development of processes for automatic interpretation of aerial imagery materials; product analysis of photogrammetric data processing in a GIS environment, etc.",todo,review UAVs in waste management,"""city"", ""dump"", ""emissions"", ""safety"", ""remote"", ""sensing"", ""garbage"",""household"", ""waste"", ""recycling"""
+21,"Filkin, T., Sliusar, N., Ritzkowski, M., & Huber-Humer, M. (2021). Unmanned aerial vehicles for operational monitoring of landfills. Drones, 5(4), 125.",l__l,Unmanned Aerial Vehicles for Operational Monitoring of Landfills,"Filkin, T., Sliusar, N., Ritzkowski, M., & Huber-Humer, M.",2021,monitoring of landfills; unmanned aerial vehicle; aerial imagery,Household waste,19,"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.",todo,review UAVs in waste management,"""city"", ""dump"", ""emissions"", ""safety"", ""remote"", ""sensing"", ""garbage"",""household"", ""waste"", ""recycling"""
+22,"Baiocchi, V., Napoleoni, Q., Tesei, M., Servodio, G., Alicandro, M., & Costantino, D. (2019). UAV for monitoring the settlement of a landfill. European Journal of Remote Sensing, 52(sup3), 41-52.",l__l,UAV for monitoring the settlement of a landfill,"Baiocchi, V., Napoleoni, Q., Tesei, M., Servodio, G., Alicandro, M., & Costantino, D.",2019,Uav; sfm; waste dump; dsm; dem; landfill; Latina;,Household waste,35,"Remote-pilot aircraft are developing very rapidly and their potential in the various fields is often still to be fully investigated. The possibility to fly over the areas to be surveyed without the need to access the areas themselves makes the use of UAVs in some cases certainly preferable for safety reasons, as has already been tested for the management of post-disaster areas. Waste landfills are small sites where contact with waste itself must be limited and scientific experimentation on surveying this specific type of site is currently limited. The results obtained in other types of sites or infrastructures are not automatically applied to waste landfills due to the specific geometrical characteristics and texture of the images that can be obtained at sites like these. In this work, a test on an exhausted landfill has been carried out with attention to the accurate survey of a large number of control points necessary for a correct assessment of the final geometric accuracy. The use of ground control points and checkpoints has allowed the separate evaluation of precision and accuracy, which are very close to those obtained with the most common methods for these sites, such as laser scanning and total stations.",todo,review UAVs in waste management,"""city"", ""dump"", ""emissions"", ""safety"", ""remote"", ""sensing"", ""garbage"",""household"", ""waste"", ""recycling"""
+23,"Verma, V., Gupta, D., Gupta, S., Uppal, M., Anand, D., Ortega-Mansilla, A., ... & Goyal, N. (2022). A deep learning-based intelligent garbage detection system using an unmanned aerial vehicle. Symmetry, 14(5), 960.",l__l,A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle,"Verma, V., Gupta, D., Gupta, S., Uppal, M., Anand, D., Ortega-Mansilla, A., ... & Goyal, N.",2022,convolutional neural network; data augmentation; deep learning; garbage image symmetry; unmanned aerial vehicle,Household waste,35,"A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.",todo,review UAVs in waste management,"""city"", ""dump"", ""emissions"", ""safety"", ""remote"", ""sensing"", ""garbage"",""household"", ""waste"", ""recycling"""
+24,"Son, S. W., Kim, D. W., Sung, W. G., & Yu, J. J. (2020). Integrating UAV and TLS approaches for environmental management: A case study of a waste stockpile area. Remote Sensing, 12(10), 1615.",l__l,Integrating UAV and TLS Approaches for Environmental Management: A Case Study of a Waste Stockpile Area,"Son, S. W., Kim, D. W., Sung, W. G., & Yu, J. J. ",2020,integration; point cloud; terrestrial laser scanning; unmanned aerial vehicle; volume computation,Household waste,50,"Abstract
+A methodology for optimal volume computation for the environmental management of waste stockpiles was derived by integrating the terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) technologies. Among the UAV-based point clouds generated under various flight scenarios, the most accurate point cloud was selected for analysis. The root mean square errors (RMSEs) of the TLS- and UAV-based methods were 0.202 and 0.032 m, respectively, and the volume computation yielded 41,226 and 41,526 m3, respectively. Both techniques showed high accuracy but also exhibited drawbacks in terms of their spatial features and efficiency. The TLS and UAV methods required 800 and 340 min, respectively, demonstrating the high efficiency of the UAV method. The RMSE and volume obtained using the TLS/UAV fusion model were calculated as 0.030 m and 41,232 m3, respectively. The UAV approach generally yielded high point cloud accuracy and volume computation efficiency.",todo,review UAVs in waste management,"""city"", ""dump"", ""emissions"", ""safety"", ""remote"", ""sensing"", ""garbage"",""household"", ""waste"", ""recycling"""
+25,"Beaver, J. T., Baldwin, R. W., Messinger, M., Newbolt, C. H., Ditchkoff, S. S., & Silman, M. R. (2020). Evaluating the use of drones equipped with thermal sensors as an effective method for estimating wildlife. Wildlife Society Bulletin, 44(2), 434-443.",l__l,Evaluating the Use of Drones Equipped with Thermal Sensors as an Effective Method for Estimating Wildlife,"Beaver, J. T., Baldwin, R. W., Messinger, M., Newbolt, C. H., Ditchkoff, S. S., & Silman, M. R.",2020,thermal drone; population survey; white-tailed deer; thermal sensor; aerial surveys,Infrared and thermal mapping,65,"Drones equipped with thermal sensors have shown ability to overcome some of the limitations often associated with traditional human-occupied aerial surveys (e.g., low detection, high operational cost, human safety risk). However, their accuracy and reliability as a valid population technique have not been adequately tested. We tested the effectiveness of using a miniaturized thermal sensor equipped to a drone (thermal drone) for surveying white-tailed deer (Odocoileus virginianus) populations using a captive deer population with a highly constrained (hereafter, known) abundance (151–163 deer, midpoint 157 [87–94 deer/km2, midpoint 90 deer/km2]) at Auburn University's deer research facility, Alabama, USA, 16–17 March 2017. We flew 3 flights beginning 30 minutes prior to sunrise and sunset (1 morning and 2 evening) consisting of 15 nonoverlapping parallel transects (18.8 km) using a small fixed-wing aircraft equipped with a nonradiometric thermal infrared imager. Deer were identified by 2 separate observers by their contrast against background thermal radiation and body shape. Our average thermal drone density estimate (69.8 deer/km2, 95% CI = 52.2–87.6), was 78% of the mean known value of 90.2 deer/km2, exceeding most sighting probabilities observed with thermal surveys conducted using human-occupied aircraft. Thermal contrast between animals and background was improved during evening flights and our drone-based density estimate (82.7 deer/km2) was 92% of the mean known value. This indicates that time of flight, in conjunction with local vegetation types, determines thermal contrast and influences ability to distinguish deer. The method provides the ability to perform accurate and reliable population surveys in a safe and cost-effective manner compared with traditional aerial surveys and is only expected to continue to improve as sensor technology and machine learning analytics continue to advance. Furthermore, the precise replicability of autonomous flights at future dates results in methodology with superior spatial precision that increases statistical power to detect population trends across surveys. © 2020 The Wildlife Society.",todo,review UAVs for thermal sensing in ecology,"""population"", ""aerial"", ""surveys"", ""thermal"", ""remote"",""sensing"", ""infrared"",""mapping"""
+26,"Melis, M. T., Da Pelo, S., Erbì, I., Loche, M., Deiana, G., Demurtas, V., ... & Scaringi, G. (2020). Thermal remote sensing from UAVs: A review on methods in coastal cliffs prone to landslides. Remote Sensing, 12(12), 1971.
+",l__l,Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides,"Melis, M. T., Da Pelo, S., Erbì, I., Loche, M., Deiana, G., Demurtas, V., ... & Scaringi, G.",2020,coastal landslides; thermal remote sensing; UAV; infrared thermography,Infrared and thermal mapping,48,"Coastal retreat is a non-recoverable phenomenon that—together with a relevant proneness to landslides—has economic, social and environmental impacts. Quantitative data on geological and geomorphologic features of such areas can help to predict and quantify the phenomena and to propose mitigation measures to reduce their impact. Coastal areas are often inaccessible for sampling and in situ surveys, in particular where steeply sloping cliffs are present. Uses and capability of infrared thermography (IRT) were reviewed, highlighting its suitability in geological and landslides hazard applications. Thanks to the high resolution of the cameras on the market, unmanned aerial vehicle-based IRT allows to acquire large amounts of data from inaccessible steep cliffs. Coupled structure-from-motion photogrammetry and coregistration of data can improve accuracy of IRT data. According to the strengths recognized in the reviewed literature, a three-step methodological approach to produce IRTs was proposed.",todo,review UAVs for thermal sensing in ecology,"""population"", ""aerial"", ""surveys"", ""thermal"", ""remote"",""sensing"", ""infrared"",""mapping"""
+27,"Rhodes, M. W., Bennie, J. J., Spalding, A., ffrench‐Constant, R. H., & Maclean, I. M. (2022). Recent advances in the remote sensing of insects. Biological Reviews, 97(1), 343-360.",l__l,Recent advances in the remote sensing of insects,"Rhodes, M. W., Bennie, J. J., Spalding, A., ffrench‐Constant, R. H., & Maclean, I. M.",2022,remote sensing; ecological research; entomological research; insects; spatial resolution,Infrared and thermal mapping,32,"Remote sensing has revolutionised many aspects of ecological research, enabling spatiotemporal data to be collected in an efficient and highly automated manner. The last two decades have seen phenomenal growth in capabilities for high-resolution remote sensing that increasingly offers opportunities to study small, but ecologically important organisms, such as insects. Here we review current applications for using remote sensing within entomological research, highlighting the emerging opportunities that now arise through advances in spatial, temporal and spectral resolution. Remote sensing can be used to map environmental variables, such as habitat, microclimate and light pollution, capturing data on topography, vegetation structure and composition, and luminosity at spatial scales appropriate to insects. Such data can also be used to detect insects indirectly from the influences that they have on the environment, such as feeding damage or nest structures, whilst opportunities for directly detecting insects are also increasingly available. Entomological radar and light detection and ranging (LiDAR), for example, are transforming our understanding of aerial insect abundance and movement ecology, whilst ultra-high spatial resolution drone imagery presents tantalising new opportunities for direct observation. Remote sensing is rapidly developing into a powerful toolkit for entomologists, that we envisage will soon become an integral part of insect science.
+
+",todo,review UAVs for thermal sensing in ecology,"""population"", ""aerial"", ""surveys"", ""thermal"", ""remote"",""sensing"", ""infrared"",""mapping"""
+28,"Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., ... & Toth, B. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote sensing, 10(4), 641.",l__l,On the Use of Unmanned Aerial Systems for Environmental Monitoring,"Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., ... & Toth, B.",2018,UAS; remote sensing; environmental monitoring; precision agriculture; vegetation indices; soil moisture; river monitoring,Infrared and thermal mapping,634,"Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.
+",todo,review UAVs for thermal sensing in ecology,"""population"", ""aerial"", ""surveys"", ""thermal"", ""remote"",""sensing"", ""infrared"",""mapping"""
+29,"Mangewa, L. J., Ndakidemi, P. A., & Munishi, L. K. (2019). Integrating UAV technology in an ecological monitoring system for community wildlife management areas in Tanzania. Sustainability, 11(21), 6116.",l__l,"Integrating UAV Technology in an Ecological Monitoring System for Community Wildlife Management Areas in Tanzania
+",todo,review UAVs for thermal sensing in ecology,"""population"", ""aerial"", ""surveys"", ""thermal"", ""remote"",""sensing"", ""infrared"",""mapping"""
+30,"Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3(2), 40.",l__l,A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses,"Barbedo, J. G. A.",2019,drone; UAV; UAS; precision agriculture; stress; crop; orchard,Agricultural Mapping and Surveying,247,"Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.
+",todo,review UAVs for thermal sensing in ecology,"""precision"", ""agriculture"", ""crop"", ""water"",""farming"", ""landscapes"",""land"",""monitoring"",""mapping"""
+31,"Messina, G., & Modica, G. (2020). Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook. Remote Sensing, 12(9), 1491.",l__l,Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook,"Messina, G., & Modica, G.",2020,unmanned aerial vehicles (UAVs); remote sensing (RS); thermal UAV RS; thermal infrared (TIR); precision agriculture (PA); crop water stress monitoring; plant disease detection; yield estimation; vegetation status monitoring,Agricultural Mapping and Surveying,186,"Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.",todo,review UAVs for thermal sensing in ecology,"""precision"", ""agriculture"", ""crop"", ""water"",""farming"", ""landscapes"",""land"",""monitoring"",""mapping"""
+32,"Yao, H., Qin, R., & Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications—A review. Remote Sensing, 11(12), 1443.",l__l,Unmanned Aerial Vehicle for Remote Sensing Applications—A Review,"Yao, H., Qin, R., & Chen, X.",2019,UAVs; remote sensing applications; data analysis;,Agricultural Mapping and Surveying,481,"The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.
+33,"Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349.",l__l,A Review on UAV-Based Applications for Precision Agriculture †,"Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G.",2019,remote sensing; IoT; UAV; UAS; Unmanned Aerial Vehicle; Unmanned Aerial System; image processing; Precision Agriculture; Smart Farming; review;,Agricultural Mapping and Surveying,713,"Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.
+",todo,review UAVs for agricultural mapping and land use planning,"""precision"", ""agriculture"", ""crop"", ""water"",""farming"", ""landscapes"",""land"",""monitoring"",""mapping"""
+34,"Basiri, A., Mariani, V., Silano, G., Aatif, M., Iannelli, L., & Glielmo, L. (2022). A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture. The Journal of Navigation, 75(2), 364-383.",l__l,"A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture
+
+",todo,review UAVs for agricultural mapping and land use planning,"""precision"", ""agriculture"", ""crop"", ""water"",""farming"", ""landscapes"",""land"",""monitoring"",""mapping"""
+35,"Librán-Embid, F., Klaus, F., Tscharntke, T., & Grass, I. (2020). Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes-A systematic review. Science of the total environment, 732, 139204.",l__l,"Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes - A systematic review
+","Librán-Embid, F., Klaus, F., Tscharntke, T., & Grass, I.",2020,biodiversity-friendly agricultural landscapes; unmanned aerial vehicles; UAV applications; biodiversity conservation; agricultural land monitoring,Agricultural Mapping and Surveying,87,"The development of biodiversity-friendly agricultural landscapes is of major importance to meet the sustainable development challenges of our time. The emergence of unmanned aerial vehicles (UAVs), i.e. drones, has opened a new set of research and management opportunities to achieve this goal. On the one hand, this review summarizes UAV applications in agricultural landscapes, focusing on biodiversity conservation and agricultural land monitoring, based on a systematic review of the literature that resulted in 550 studies. Additionally, the review proposes how to integrate UAV research in these fields and point to new potential applications that may contribute to biodiversity-friendly agricultural landscapes. UAV-based imagery can be used to identify and monitor plants, floral resources and animals, facilitating the detection of quality habitats with high prediction power. Through vegetation indices derived from their sensors, UAVs can estimate biomass, monitor crop plant health and stress, detect pest or pathogen infestations, monitor soil fertility and target patches of high weed or invasive plant pressure, allowing precise management practices and reduced agrochemical input. Thereby, UAVs are helping to design biodiversity-friendly agricultural landscapes and to mitigate yield-biodiversity trade-offs. In conclusion, UAV applications have become a major means of biodiversity conservation and biodiversity-friendly management in agriculture, while latest developments, such as the miniaturization and decreasing costs of hyperspectral sensors, promise many new applications for the future.
+
+",todo,review UAVs for agricultural mapping and land use planning,"""precision"", ""agriculture"", ""crop"", ""water"",""farming"", ""landscapes"",""land"",""monitoring"",""mapping"""