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This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research.

Environmental science, engineering & technology --- Land Surface Temperature (LST) --- satellite retrievals of LST --- LST from GOES satellites --- land surface temperature --- drones --- unmanned aerial vehicles --- thermal remote sensing --- MODIS --- Bayesian Maximum Entropy --- interpolation --- Himalaya --- air temperature --- topography --- Landsat --- split window algorithm --- TIRS --- thermal --- Landsat 8 --- stray light correction --- split-window algorithm --- single-channel algorithm --- AMSR2 --- annual cycle parameters --- random forest --- cloudy sky LST --- evapotranspiration --- data fusion --- field-scale --- machine-learning --- physical model --- Sentinel-2 --- Sentinel-3 --- Downscaling --- thermal infrared --- disaggregation --- Copernicus --- hyperspectral thermal infrared --- spectral smoothness --- temperature-emissivity separation --- sensitivity analysis --- noise --- land surface temperature (LST) --- daytime LST --- nighttime LST --- validation --- land surface emissivity (LSE) --- single channel algorithm --- radiative transfer equation --- mono window algorithm --- SURFRAD data --- GK2A --- split-window method --- BSRN --- LST --- downscaling --- LSA-SAF --- Sentinel 2 --- DEM --- spatial averaging biases --- land surface emissivity --- measurement uncertainties --- emissivity box method --- Fourier transform infrared spectrometer --- portable spectrometer --- n/a

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The papers included in this Special Issue tackle multiple aspects of how cities, districts, and buildings could evolve along with climate change and how this would impact our way of conceiving and applying design criteria, policies, and urban plans. Despite the multidisciplinary nature of the collection, some transversal take-home messages emerge: • Today’s energy-efficient paradigms may lose their virtuosity in the future unless accurate estimates of future scenarios are used to design modelling platforms and to inform legislative frameworks; • Acting at the local scale is key. Future climate change adaptation will be implemented at the local level. Overlooking regional and local specificities will contribute to inaccurate and inefficient action plans. As such, the smaller scale will become vital in predicting future urban metabolic rates and corresponding comfort-driven strategies; • Energy poverty, heat vulnerability, and social injustice are emerging as critical factors for planning and acting for future-proof cities on par of micro- and meso-climatological factors; • Given that the impacts of climate change will persist for many years, adaptation to this phenomenon should be prioritized by removing any prominent barrier and by enabling combinations of different mitigation technologies. These topics will receive a global reach in few decades, since also developing and underdeveloped countries are starting their fight against local climate change, with cities at the forefront.

outdoor space --- thermal environment --- radiation environment --- wind environment --- heat-related mortality --- built environment --- urban resilience --- extreme heat --- climate change --- urban heat island --- heat stress from outside --- indoor environments --- tropics --- multi-level office buildings --- coastal cities --- Mediterranean climate --- urban heat island intensity --- sample year --- climate change adaptation --- barriers --- focus group discussion --- Tehran --- structural equation modeling --- urban management --- near-zero energy buildings --- future scenarios --- energy efficiency --- adaptive comfort --- long-term performance --- urban heat --- Australia --- UHI effect --- mitigation --- bushfire smoke --- indoor air quality --- filtration --- building envelope --- energy --- future weather data --- building energy performance --- thermal comfort --- statistical downscaling of climate models --- dynamical downscaling of climate models --- urban modelling --- cities --- buildings --- decarbonization --- urbanisation --- climate --- densification --- population --- temperature --- n/a

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This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research.

Environmental science, engineering & technology --- Land Surface Temperature (LST) --- satellite retrievals of LST --- LST from GOES satellites --- land surface temperature --- drones --- unmanned aerial vehicles --- thermal remote sensing --- MODIS --- Bayesian Maximum Entropy --- interpolation --- Himalaya --- air temperature --- topography --- Landsat --- split window algorithm --- TIRS --- thermal --- Landsat 8 --- stray light correction --- split-window algorithm --- single-channel algorithm --- AMSR2 --- annual cycle parameters --- random forest --- cloudy sky LST --- evapotranspiration --- data fusion --- field-scale --- machine-learning --- physical model --- Sentinel-2 --- Sentinel-3 --- Downscaling --- thermal infrared --- disaggregation --- Copernicus --- hyperspectral thermal infrared --- spectral smoothness --- temperature-emissivity separation --- sensitivity analysis --- noise --- land surface temperature (LST) --- daytime LST --- nighttime LST --- validation --- land surface emissivity (LSE) --- single channel algorithm --- radiative transfer equation --- mono window algorithm --- SURFRAD data --- GK2A --- split-window method --- BSRN --- LST --- downscaling --- LSA-SAF --- Sentinel 2 --- DEM --- spatial averaging biases --- land surface emissivity --- measurement uncertainties --- emissivity box method --- Fourier transform infrared spectrometer --- portable spectrometer

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Management of water resources in large rivers basins typically differs in important ways from management in smaller basins. While in smaller basins the focus of water resources management may be on project implementation, irrigation and drainage management, water use efficiency and flood operations; in larger basins, because of the greater complexity and competing interests, there is often a greater need for long-term strategic river basin planning across sectors and jurisdictions, and considering social, environmental, and economic outcomes. This puts a focus on sustainable development, including consumptive water use and non-consumptive water uses, such as inland navigation and hydropower. It also requires the consideration of hard or technical issues—data, modeling, infrastructure—as well as soft issues of governance, including legal frameworks, policies, institutions, and political economy. Rapidly evolving technologies could play a significant role in managing large basins. This Special Issue of Water traverses these hard and soft aspects of managing water resources in large river basins through a series of diverse case studies from across the globe that demonstrate recent advances in both technical and governance innovations in river basin management.

multi-objective competition mechanism --- cascade reservoirs operation --- copula function --- Pareto set --- hydropower --- dam --- damage --- ecosystem --- conservation measures --- environmental assessment --- environmental flows --- GIS --- integrated water resources management --- river basin planning --- Ganga River --- India --- participatory modelling --- conjunctive water use --- hydrologic modelling --- co-production --- development assistance --- hydrological modelling --- irrigation --- IWRM --- rational choice --- stakeholder participation --- scenario analysis --- water governance --- transboundary waters --- cooperation --- integrated water resource management --- Brahmaputra River Basin --- South Asia --- SMAP --- passive microwave soil moisture --- soil moisture downscaling --- digital platforms --- stakeholder engagement --- equitable water sharing --- UN watercourse convention --- international and transboundary rivers --- Nile River basin --- disruptive technology --- river basins --- large basins --- water security --- water resources management --- water data --- information technology --- analytics --- n/a

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

Research & information: general --- convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN