UAV Or Drones For Remote Sensing Applications BEST
Remote sensing with unmanned aerial vehicles (UAVs) is a game-changer in precision agriculture. It offers unprecedented spectral, spatial, and temporal resolution, but can also provide detailed vegetation height data and multiangular observations. In this article, we review the progress of remote sensing with UAVs in drought stress, in weed and pathogen detection, in nutrient status and growth vigor assessment, and in yield prediction. To transfer this knowledge to everyday practice of precision agriculture, future research should focus on exploiting the complementarity of hyperspectral or multispectral data with thermal data, on integrating observations into robust transfer or growth models rather than linear regression models, and on combining UAV products with other spatially explicit information.
UAV or Drones for Remote Sensing Applications
In this section, you will become familiar with and understand the different civilian and commercial applications of the UAS as it stands today. UAS applications that concern us the most are the remote sensing applications. Here, the UAS is replacing manned aircraft as an acquisition platform. Remote sensors such cameras and LiDAR systems are shrunk in size and weight to make them more suitable for the lightweight small UAS as was mentioned in the Payload section of Lesson 2. Remote sensing applications derived from sensors onboard a UAS are more or less similar to the applications that one can expect from a manned system. Manned aircraft can carry larger and heavier payload, which open the door for additional applications that required large sensors such as IFSAR. Reported applications for the UAS include the following:
Unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains.
The term drone has been used from the early days of aviation, being applied to remotely-flown target aircraft used for practice firing of a battleship's guns, such as the 1920s Fairey Queen and 1930s de Havilland Queen Bee. Later examples included the Airspeed Queen Wasp and Miles Queen Martinet, before ultimate replacement by the GAF Jindivik. The term remains in common use. In addition to the software, autonomous drones also employ a host of advanced technologies that allow them to carry out their missions without human intervention, such as cloud computing, computer vision, artificial intelligence, machine learning, deep learning, and thermal sensors. For recreational uses, an aerial photography drone (as opposed to a UAV) is an aircraft that has first-person video, autonomous capabilities, or both.
Solar-powered atmospheric satellites ("atmosats") designed for operating at altitudes exceeding 20 km (12 miles, or 60,000 feet) for as long as five years could potentially perform duties more economically and with more versatility than low Earth orbit satellites. Likely applications include weather drones for weather monitoring, disaster recovery, Earth imaging and communications.
Drones are ideally suited to capturing aerial shots in photography and cinematography, and are widely used for this purpose. Small drones avoid the need for precise coordination between pilot and cameraman, with the same person taking on both roles. However, big drones with professional cine cameras, there is usually a drone pilot and a camera operator who controls camera angle and lens. For example, the AERIGON cinema drone which is used in film production in big blockbuster movies is operated by 2 people. Drones provide access to dangerous, remote or otherwise inaccessible sites.
The implementation of the Class Identification Label serves a crucial purpose in the regulation and operation of drones. The label is a verification mechanism designed to confirm that drones within a specific class meet the rigorous standards set by administrations for design and manufacturing. These standards are necessary to ensure the safety and reliability of drones in various industries and applications.
The rapid development and growth of drones as a remote sensing platforms as well as advances in the miniaturization of instruments and data systems, have resulted in an increasing uptake of this technology in the urban areas and remote sensing social community. This paper attempt to review a development of UAV/Drone remote sensing applications in urban areas and it can resolve issues respectively. The classification, design methods and challenges has been discussed appropriately and at the end we suit to urban applications. We found that the evolution of UAV/drone based remote sensing is efficient to solve an urban issues nowadays simultaneously ensure the sustainability and resiliency of urban areas.
Traditional field monitoring and pest scouting techniques rely on manual sampling and are time-consuming and labor-intensive. To address this issue, several remote sensing technologies and techniques have been developed (Ampatzidis et al. 2017). Recently, small unmanned aerial vehicles (UAVs) equipped with several sensors became cost-effective solutions for rapid and non-destructive field data collection (Matese et al. 2015; Pajares 2015). Small UAVs can be used for high-resolution image acquisition at low cost compared to other sensing technologies (e.g., airborne and satellite imaging). Data collected from UAVs are used for photogrammetry processing to create orthomosaics, plant stress maps (e.g., NDVI maps), digital surface models (DSM) and 3D models (de Castro et al. 2018). Artificial intelligence (AI) algorithms have been developed to process the UAV-collected data to estimate plant water needs, monitor crop health status, and detect weeds, pests, and diseases (Abdulridha et al. 2019; Matese et al. 2013; Nebiker et al. 2008). For example, Garcia-Ruiz et al. (2013) developed a UAV-based technique to monitor tree stress in groves utilizing multispectral imaging, and Ampatzidis and Partel (2019) developed an AI-based technique to count citrus trees and tree gaps, measure tree height and canopy size, and develop individual tree NDVI maps. Acquiring high-resolution data is a critical step for precision agriculture applications. In order to acquire high-resolution UAV-based data and carefully create a flight path, several steps should be followed (Xiang and Tian 2011). This document presents steps that are necessary for a succesful UAV flight and data collection.
Anita Simic Milas is an associate professor at Bowling Green State University. Simic Milas received her M.Sc. and Ph.D. degrees in remote sensing from the University of Toronto. She was a session/senior lecturer at the University of Toronto, Ryerson University, and the University of Victoria in Canada, and Wuhan University in China. Simic Milas worked in environmental engineering field and as a scientist at the Canada Centre for Remote Sensing for several years. She is the founder of the SPatial LITeracy - SPLIT Remote Sensing, an international educational program for early career professionals and graduate students.
Her research is related to remote sensing applications in vegetation science (forest and agriculture), hydrology and water quality with expertise in applications of optical remote sensing including satellite and airborne / drone data. Simic Milas is actively involved with AmericaView, and she is the Editor of the Drones section of IJRS since 2018.
Under the program SPatial LITeracy - SPLIT Remote Sensing prof. Simic Milas organizes international summer schools. This intensive and advanced 6-day learning program is the best insights into recent remote sensing techniques learned from top international professors and researchers through hands-on sessions and lectures. For more information see the website:
TABLE 1. Summary of existing UAS aquatic remote sensing literature including the UAS sensor(s) used, radiometric quantity studied (where Rrs represents UAS derived remote sensing reflectance and RUAS represents UAS derived total reflectance), whether the study accounted for surface reflected radiance (LSR), and the water quality parameter(s) derived.
Previously, understanding of the fluvial process from the ecological, morphological, and hydrodynamic perspectives has largely relied on the limited scale of in-situ field observation or the sparse spatial and temporal scale of satellite-based remote sensing. However, with the recent advent of unmanned aerial vehicles (UAVs) and concurrent advances in sensor technology, measurement campaign has been revolutionized and the view of rivers has fundamentally changed from the local scale to the holistic scale; the perspective has shifted from a static to a dynamic one. UAVs can provide a fine spatial and temporal resolution of measurements with a relatively low cost, which, as compared to conventional satellite or pilot-controlled airborne systems, can be more suitable for the analysis of fluvial processes in narrow rivers and small lakes. In this paper, we comprehensively review and document various crucial achievements driven by UAVs-based remote sensing in fluvial environments, among a variety of other relevant applications. Specifically, the paper highlights the UAV-based fluvial remote sensing in terms of riparian vegetation, hazardous aquatic algae blooms, submerged morphology, water-surface slope, sediment, flow velocity, and disasters, including flood inundation mapping.
Drones or unmanned aircraft systems (UAS) are fundamentally changing aviation, and the FAA is committed to working to fully integrate drones into the National Airspace System (NAS). Safety and security are top priorities for the FAA and remote identification (remote ID) of drones is crucial to our integration efforts. 041b061a72