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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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Each year, disasters such as storms, floods, fires, volcanoes, earthquakes, and epidemics cause thousands of casualties and tremendous damage to property around the world, displacing tens of thousands of people from their homes and destroying their livelihoods. The majority of these casualties and property loss could be prevented if better information were available regarding the onset and course of such disasters. Several remote sensing technologies, such as meteorological and Earth observation satellites, communication satellites, and satellite-based positioning, supported by geoinformation technologies, offer the potential to contribute to improved prediction and monitoring of potential hazards, risk mitigation, and disaster management which, in turn, would lead to sharp reductions in losses to life and property. This book explores most of the scientific issues related to spatially supported disaster management and its integration with geographical information system technologies in different disaster examples and scales. Dealing with disasters over space and time represents a long-lasting theme, now approached by means of innovative techniques and modelling approaches. Several priorities for actions are outlined toward preventing new and reduce existing disaster risks, including understanding disaster risk, strengthening disaster risk governance for management of disaster risk, investing in disaster reduction for resilience, and enhancing disaster preparedness for effective response. This book presents ideas to address the challenges facing different components of spatial patterns related to ecological processes, and the published articles extended versions of selected presentations from the Gi4DM Conference in 2019 in Prague.

Research & information: general --- Environmental economics --- mapping impact --- tidal flood --- hydrodynamic model --- solar salt farming --- drone --- computer vision --- point clouds --- machine learning --- CNN --- GAN --- first responder --- RECONASS --- INACHUS --- multi-hazard --- susceptibility mapping --- developing urban settlements --- landslide --- flood --- logistic regression --- Mamdani fuzzy algorithm --- M-AHP --- cartographic symbols --- map symbology --- crisis map --- comparative analysis --- taxonomy --- graphic design --- availability --- promulgation --- sharing --- standardisation --- Black sea --- sea level change --- tide gauge --- satellite altimetry --- GNSS --- post-fire management --- forest regeneration --- fire severity mapping --- multispectral imagery --- Sentinel-2A --- unmanned aerial vehicles --- Parrot SEQUOIA --- climate change --- fuzzy logic --- GIS, household --- Index method --- sea level rise --- vulnerability

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

History of engineering & technology --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill–Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching–learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- n/a --- Hill-Tsai failure criterion --- teaching-learning

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

History of engineering & technology --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting