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Unmanned aerial vehicles (UAV) have already become an affordable and cost-efficient tool to quickly map a targeted area for many emerging applications in the arena of ecological monitoring and biodiversity conservation. Managers, owners, companies, and scientists are using professional drones equipped with high-resolution visible, multispectral, or thermal cameras to assess the state of ecosystems, the effect of disturbances, or the dynamics and changes within biological communities inter alia. We are now at a tipping point on the use of drones for these type of applications over natural areas. UAV missions are increasing but most of them are testing applicability. It is time now to move to frequent revisiting missions, aiding in the retrieval of important biophysical parameters in ecosystems or mapping species distributions. This Special Issue shows UAV applications contributing to a better understanding of biodiversity and ecosystem status, threats, changes, and trends. It documents the enhancement of knowledge in ecological integrity parameters mapping, long-term ecological monitoring based on drones, mapping of alien species spread and distribution, upscaling ecological variables from drone to satellite images: methods and approaches, rapid risk and disturbance assessment using drones, mapping albedo with UAVs, wildlife tracking, bird colony and chimpanzee nest mapping, habitat mapping and monitoring, and a review on drones for conservation in protected areas.

Pinus nigra --- unmanned aerial vehicles (UAVs) --- biological conservation --- precision --- flight altitude --- accuracy --- multiscale approach --- low-cost UAV --- LTER --- small UAV --- ecological monitoring --- Sequoia --- long-term monitoring --- albedo --- image processing --- vegetation indices --- Tanzania --- ground-truth --- Sentinel-2 --- biodiversity threats --- field experiments --- effective management --- great apes --- drone --- ecological integrity --- multispectral --- rice crops --- conservation --- protected areas --- survey --- response surface --- aerial survey --- bird censuses --- multispectral mapping --- drones --- UAS --- hyperspectral --- UAV --- random forest --- Pinus sylvestris --- NDVI --- UAVs --- Parrot Sequoia --- supervised classification --- drone mapping --- RPAS --- greenness index --- image resolution --- Plegadis falcinellus --- Motus --- biodiversity --- Landsat 8 --- Sentinel --- boreal forest --- phenology --- LTSER --- western swamphen --- Parrot SEQUOIA --- native grassland --- forêt Montmorency --- drought --- forest regeneration --- radio-tracking

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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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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 --- 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

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Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome.

Research & information: general --- Savanna --- alternative stable states --- MODIS VCF --- land surface temperature --- albedo --- Cerrado --- Amazon --- vegetation type --- optical --- sar --- synergism --- mapping --- savanna --- post oak --- vegetation index --- ecosystem function --- phenology --- encroachment --- evergreen --- deciduous --- vegetation --- grassland --- fractional cover --- trend --- ecoregion --- bare soil --- livestock --- production systems --- remote sensing --- vegetation dynamics --- vegetation persistence --- conservation --- savannas --- Africa --- vegetation indices --- oak-grass savanna --- hydrology --- Sentinel-2 --- land cover --- grasslands --- forests --- monitoring --- random forest --- spectral indexes --- vegetation seasonality --- aboveground biomass --- Cerrado ecosystem --- SAR --- allometry --- biomass --- carbon --- cost-effective --- LiDAR --- TLS --- plant water availability --- tree phenology --- phenocams --- MODIS --- terrestrial laser scanning (TLS) --- Above Ground Biomass (AGB) --- 3D point cloud --- vegetation structure --- Savanna --- alternative stable states --- MODIS VCF --- land surface temperature --- albedo --- Cerrado --- Amazon --- vegetation type --- optical --- sar --- synergism --- mapping --- savanna --- post oak --- vegetation index --- ecosystem function --- phenology --- encroachment --- evergreen --- deciduous --- vegetation --- grassland --- fractional cover --- trend --- ecoregion --- bare soil --- livestock --- production systems --- remote sensing --- vegetation dynamics --- vegetation persistence --- conservation --- savannas --- Africa --- vegetation indices --- oak-grass savanna --- hydrology --- Sentinel-2 --- land cover --- grasslands --- forests --- monitoring --- random forest --- spectral indexes --- vegetation seasonality --- aboveground biomass --- Cerrado ecosystem --- SAR --- allometry --- biomass --- carbon --- cost-effective --- LiDAR --- TLS --- plant water availability --- tree phenology --- phenocams --- MODIS --- terrestrial laser scanning (TLS) --- Above Ground Biomass (AGB) --- 3D point cloud --- vegetation structure

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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

NDLMA --- n/a --- multi-angle remote sensing --- TerraSAR-X --- above ground biomass --- stem volume --- regression analysis --- ground-based remote sensing --- sensor fusion --- pasture biomass --- grazing management --- livestock --- mixed forest --- SPLSR --- estimation accuracy --- Bidirectional Reflectance Distribution Factor --- forage crops --- Land Surface Phenology --- climate change --- vegetation index --- dry biomass --- mapping --- rangeland productivity --- vegetation indices --- error analysis --- broadleaves --- remote sensing --- applicability evaluation --- ultrasonic sensor --- chlorophyll index --- alpine meadow grassland --- forest biomass --- anthropogenic disturbance --- fractional vegetation cover --- alpine grassland conservation --- carbon mitigation --- conifer --- short grass --- grazing exclusion --- MODIS time series --- random forest --- aboveground biomass --- NDVI --- AquaCrop model --- inversion model --- wetlands --- field spectrometry --- spectral index --- yield --- foliage projective cover --- lidar --- correlation coefficient --- Sahel --- biomass --- dry matter index --- Niger --- Landsat --- grass biomass --- particle swarm optimization --- winter wheat --- carbon inventory --- rice --- forest structure information --- MODIS --- light detection and ranging (LiDAR) --- ALOS2 --- ecological policies --- above-ground biomass --- Wambiana grazing trial --- food security --- forest above ground biomass (AGB) --- Atriplex nummularia --- regional sustainability --- CIRed-edge