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The use of unmanned aerial vehicles (UAVs) plays an important role in supporting human activities. Man is concentrating more and more on intellectual work, and trying to automate practical activities as much as possible in order to increase their efficiency. In this regard, the use of drones is increasingly becoming a key aspect of this automation process, offering many advantages, including agility, efficiency and reduced risk, especially in dangerous missions. Hence, this Special Issue focuses on applications, platforms and services where UAVs can be used as facilitators for the task at hand, also keeping in mind that security should be addressed from its different perspectives, ranking from communications security to operational security, and furthermore considering privacy issues.

History of engineering & technology --- computer vision --- oil well working condition --- real-time detection --- sort --- unmanned aerial vehicle (UAV) --- YOLOv3 --- UAV --- autonomous landing --- vision-based --- ArduSim --- ArUco marker --- blind signature --- security --- MEC --- UAVs --- FANET --- 5G --- IoT --- Mutual authentication --- Privacy --- Traceable --- BAN logic --- coverage model --- human mobility model --- UAVs/drones positioning --- energy model --- UAS --- horizon --- undistortion --- FPGA --- sense-and-avoid --- LoRaWAN --- Unmanned Aerial Vehicles --- topology control --- virtual spring forces --- firefighting communications --- n/a

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The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites.The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and temporal resolution of the products obtained.More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products.This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry.

Technology: general issues --- unmanned aerial vehicle --- urban LULC --- GEOBIA --- multiscale classification --- unmanned aircraft system (UAS) --- deep learning --- super-resolution (SR) --- convolutional neural network (CNN) --- generative adversarial network (GAN) --- structure-from-motion --- photogrammetry --- remote sensing --- UAV --- 3D-model --- surveying --- vertical wall --- snow --- remotely piloted aircraft systems --- structure from motion --- lidar --- forests --- orthophotography --- construction planning --- sustainable construction --- urbanism --- BIM --- building maintenance --- unmanned aerial vehicle (UAV) --- structure-from-motion (SfM) --- ground control points (GCP) --- accuracy assessment --- point clouds --- corridor mapping --- UAV photogrammetry --- terrain modeling --- vegetation removal --- unmanned aerial vehicles --- power lines --- image-based reconstruction --- 3D reconstruction --- unmanned aerial systems --- time series --- accuracy --- reproducibility --- orthomosaic --- validation --- drone --- GNSS RTK --- precision --- elevation --- multispectral imaging --- vegetation indices --- nutritional analysis --- correlation --- optimal harvest time --- UAV images --- monoscopic mapping --- stereoscopic plotting --- image overlap --- optimal image selection --- n/a

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This book is the first literature collection focused on the development and implementation of unmanned aircraft systems (UAS) and their integration with sensors for atmospheric measurements on Earth. The research covered in the book combines chemical, physical, and meteorological measurements performed in field campaigns, as well as conceptual and laboratory work. Useful examples for the development of platforms and autonomous systems for environmental studies are provided, which demonstrate how careful the operation of sensors aboard UAS must be to gather information for remote sensing in the atmosphere. The work serves as a key collection of articles to introduce the topic to new researchers interested in the field, guide future studies, and motivate measurements to improve our understanding of the Earth’s complex atmosphere.

Research & information: general --- unmanned aerial vehicles (UAV) --- drones --- geostatistics --- atmospheric physics --- meteorology --- spatial sampling --- unmanned aerial vehicles --- unmanned aerial systems, turbulence --- atmospheric boundary layer --- TK-1G sounding rocket --- near space --- data analysis --- remote sensing --- unmanned aerial systems --- atmospheric composition --- sensors --- UAS --- RPAS --- ALADINA --- airborne turbulence --- radiation measurements --- aerosol measurements --- field experiments --- validation methods --- unmanned aircraft --- meteorological observation --- stable atmospheric boundary layer --- turbulence --- remotely piloted aircraft systems (RPAS) --- ground-based in-situ observations --- boundary layer remote sensing --- Arctic --- polar --- sea ice --- n/a --- source estimation --- methane emissions --- natural gas --- leak surveys --- inverse emissions --- MONITOR --- UAV --- LDAR --- air pollution --- unmanned aerial vehicle (UAV) --- PM2.5 --- meteorological condition --- long-distance transport --- satellite data --- RMLD-UAV --- methane --- mass flux --- leak rate quantification --- wind speed and direction estimation algorithms --- flow probes --- airspeed measurement --- small unmanned aircraft systems (sUAS)

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The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry.

Research & information: general --- forest road inventory --- total station --- global navigation satellite system --- point cloud --- precision density --- positional accuracy --- efficiency --- mangrove sustainability --- deforestation depletion --- anthropogenic --- natural water balance --- Southeast Asia --- Phoracantha spp. --- unmanned aerial vehicle (UAV) --- multispectral imagery --- vegetation index --- thresholding analysis --- Large Scale Mean-Shift Segmentation (LSMS) --- Random Forest (RF) --- forest mask --- validation --- probability sampling --- remote sensing --- earth observations --- forestry --- accuracy assessment --- forest classification --- forested catchment --- hydrological modeling --- SWAT model --- DEM --- airborne laser scanning --- deep learning --- Landsat --- national forest inventory --- stand volume --- bark beetle --- Ips typographus L. --- pest --- change detection --- forest damage --- spruce --- Sentinel-2 --- damage mapping --- multi-temporal regression --- mangrove --- replanting --- restoration --- analytic hierarchy process --- UAV --- DJI drone --- machine learning --- forest canopy --- canopy gaps --- canopy openings percentage --- satellite indices --- Elastic Net --- beech–fir forests --- pixel-based supervised classification --- random forest --- support vector machine --- gray level cooccurrence matrix (GLCM) --- principal component analysis (PCA) --- WorldView-3 --- wildfires --- MaxENT --- risk modeling --- GIS --- multi-scale analysis --- Yakutia --- Artic --- Siberia --- phenology modelling --- forest disturbance --- forest monitoring --- bark beetle infestation --- forest management --- time series analysis --- satellite imagery --- landsat time series --- growing stock volume --- forest inventory --- harmonic regression --- n/a --- beech-fir forests

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This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- UAS --- multiple sensors --- vegetation index --- leaf nitrogen accumulation --- plant nitrogen accumulation --- pasture quality --- airborne hyperspectral imaging --- random forest regression --- sun-induced chlorophyll fluorescence (SIF) --- SIF yield indices --- upward --- downward --- leaf nitrogen concentration (LNC) --- wheat (Triticum aestivum L.) --- laser-induced fluorescence --- leaf nitrogen concentration --- back-propagation neural network --- principal component analysis --- fluorescence characteristics --- canopy nitrogen density --- radiative transfer model --- hyperspectral --- winter wheat --- flooded rice --- pig slurry --- aerial remote sensing --- vegetation indices --- N recommendation approach --- Mediterranean conditions --- nitrogen --- vertical distribution --- plant geometry --- remote sensing --- maize --- UAV --- multispectral imagery --- LNC --- non-parametric regression --- red-edge --- NDRE --- dynamic change model --- sigmoid curve --- grain yield prediction --- leaf chlorophyll content --- red-edge reflectance --- spectral index --- precision N fertilization --- chlorophyll meter --- NDVI --- NNI --- canopy reflectance sensing --- N mineralization --- farmyard manures --- Triticum aestivum --- discrete wavelet transform --- partial least squares --- hyper-spectra --- rice --- nitrogen management --- reflectance index --- multiple variable linear regression --- Lasso model --- Multiplex®3 sensor --- nitrogen balance index --- nitrogen nutrition index --- nitrogen status diagnosis --- precision nitrogen management --- terrestrial laser scanning --- spectrometer --- plant height --- biomass --- nitrogen concentration --- precision agriculture --- unmanned aerial vehicle (UAV) --- digital camera --- leaf chlorophyll concentration --- portable chlorophyll meter --- crop --- PROSPECT-D --- sensitivity analysis --- UAV multispectral imagery --- spectral vegetation indices --- machine learning --- plant nutrition --- canopy spectrum --- non-destructive nitrogen status diagnosis --- drone --- multispectral camera --- SPAD --- smartphone photography --- fixed-wing UAV remote sensing --- random forest --- canopy reflectance --- crop N status --- Capsicum annuum --- proximal optical sensors --- Dualex sensor --- leaf position --- proximal sensing --- cross-validation --- feature selection --- hyperparameter tuning --- image processing --- image segmentation --- nitrogen fertilizer recommendation --- supervised regression --- RapidSCAN sensor --- nitrogen recommendation algorithm --- in-season nitrogen management --- nitrogen use efficiency --- yield potential --- yield responsiveness --- standard normal variate (SNV) --- continuous wavelet transform (CWT) --- wavelet features optimization --- competitive adaptive reweighted sampling (CARS) --- partial least square (PLS) --- grapevine --- hyperparameter optimization --- multispectral imaging --- precision viticulture --- RGB --- multispectral --- coverage adjusted spectral index --- vegetation coverage --- random frog algorithm --- active canopy sensing --- integrated sensing system --- discrete NIR spectral band data --- soil total nitrogen concentration --- moisture absorption correction index --- particle size correction index --- coupled elimination