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Albedo is a known and documented phenomenon, defined as the reflectivity of a surface, i.e., the ratio of reflected light energy to incident light energy. It is a dimensionless quantity, used in particular in agro-forestry, urban environment, cryosphere and geology. It is an Essential Climate Variable (ECV), deemed extremely meaningful to compute the earth heat balance. The albedo of natural surfaces varies largely, especially in the visible, with the lowest values found for water bodies and dense vegetation canopies and the highest values for desert and snow. It also changes with the angular distribution and spectral composition of the incident radiation and with the surface moisture. Satellite observations allow consistent measuring of the surface albedo at continental scale over a short period of time. Long-term series of surface albedo are good indicators of climate change, especially over glaciers and polar caps. On the other hand, the albedo of bare soil provides a good diagnostic of their degradation. The reliability of satellite albedo is verified against ground-based radiometers and UAV, which also serves to calibrate the instruments embarked on space-borne observing systems and check the quality of the atmospheric correction.

surface albedo --- urbanization --- vegetation variation --- climate change --- DMSP --- albedo --- land use --- remote sensing --- Unmanned Aerial Vehicles --- vegetation indices --- snow --- climate --- Unmanned Aerial Vehicle (UAV) --- landscape --- consumer-grade camera --- radiometric calibration --- sea ice --- VIIRS --- Arctic --- PROMICE --- GC-NET --- validation --- AVHRR --- BRDF --- MODIS --- VJB --- LTDR --- directional correction --- spatial representativeness --- semivariogram --- Landsat --- HLS --- Sentinel 2 --- SURFRAD --- OzFlux --- directional hemispherical reflectance --- bi-hemispherical reflectance --- tower albedometer --- CGLS --- MISR --- upscaling --- bare soil albedo --- MODIS albedo --- contiguous United States --- soil line --- Landsat albedo --- soil moisture --- land surface albedo --- time series --- high spatio-temporal resolution --- EnKF --- spectral unmixing --- empirical modeling --- linear endmember --- forest cover --- forest management --- forest structure --- BRDF/Albedo --- NDSI Snow Cover --- 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.

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 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.

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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Coastal areas are remarkable regions with high spatiotemporal variability. A large population is affected by their physical and biological processes—resulting from effects on tourism to biodiversity and productivity. Coastal ecosystems perform several critical ecosystem services and functions, such as water oxygenation and nutrients provision, seafloor and beach stabilization (as sediment is controlled and trapped within the rhizomes of the seagrass meadows), carbon burial, as areas for nursery, and as refuge for several commercial and endemic species. Knowledge of the spatial distribution of marine habitats is prerequisite information for the conservation and sustainable use of marine resources. Remote sensing from UAVs to spaceborne sensors is offering a unique opportunity to measure, analyze, quantify, map, and explore the processes on the coastal areas at high temporal frequencies. This Special Issue on “Application of Remote Sensing in Coastal Areas” is specifically addresses those successful applications—from local to regional scale—in coastal environments related to ecosystem productivity, biodiversity, sea level rise.

satellite remote sensing --- Landsat --- coastline --- barrier island --- morphological change --- coastal ocean --- Photon-counting lidar --- MABEL --- land cover --- remote sensing --- signal photons --- ground settlement --- marine reclamation land --- time series InSAR --- Sentinel-1 --- Xiamen New Airport --- Pleiades --- photogrammetry --- LiDAR --- RTK-GPS --- beach topography --- cliff coastlines --- time-series analysis --- terrestrial laser scanner --- southern Baltic Sea --- non-parametric Bayesian network --- satellite-derived bathymetry --- hydrography --- CubeSats --- hypertemporal --- zones of confidence --- PlanetScope --- vegetation mapping --- dunes --- unmanned aerial system --- pixel-based classification --- object-based classification --- dune vegetation classification --- coastal monitoring --- multispectral satellite images --- multi-temporal NDVI --- pixels based supervised classification --- Random Forest --- harmonization --- shoreline mapping --- semi-global subpixel localization --- intensity integral error --- polarimetric SAR --- polarimetric decomposition --- ship detection --- Euclidean distance --- mutual information --- new feature --- Bohai sea ice --- sea ice extent --- OLCI imagery --- sea ice information index --- waterline extraction --- sub-pixel --- surface water mapping --- data cube --- contour extraction --- water extraction --- water indices --- thresholding --- Coastal process --- wind wake --- heat advection --- multi-sensor --- ASAR --- oceanic thermal response --- Hainan Island --- coastal remote sensing --- habitat mapping --- unmanned aerial vehicle (UAV) --- unmanned aircraft system (UAS) --- drone --- object-based image analysis (OBIA) --- UAS data acquisition --- n/a

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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.

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