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Remotely sensed data from either air- or spaceborne platforms are often leveraged for archaeological or more general cultural heritage goals. However, despite the steady developments in remote sensing technology over the past three decades, the thoughtful integration of data sources and methods into theoretically aware archaeological practice remains relatively underdeveloped. This volume contains nine contributions which, each in their way, address different theoretical dislocations and practical shortcomings in the use of remote sensing products within archaeological practice. These contributions provide the reader with food for thought on these challenges, and so contribute to archaeological remote sensing as a more mature interdisciplinary field characterised by explicit, thoughtful, and theoretically engaged approaches to understanding the past.

Biography & True Stories --- Archaeology --- relief mapping --- visualization --- blend modes --- digital elevation model --- airborne laser scanning --- lidar --- archaeological prospection --- deep learning --- citizen science --- The Netherlands --- archaeology --- arid environments --- satellite remote sensing --- lithological mapping --- lithic procurement --- chert sourcing --- Landsat 8 --- GIS --- ALS --- amplitude --- radiometric calibration --- reflectance --- Sicily --- transfer learning --- historic mining --- heritage management --- LiDAR --- hyperspectral data --- submerged areas --- cultural heritage monitoring --- anomaly detection --- MNF --- radiative transfer model --- Martin Heidegger --- technology --- mimesis --- remote sensing archaeology --- cultural context --- archaeological remote sensing --- satellite mission design --- satellite archaeology --- archaeological survey --- cropmarks --- empirical knowledge --- alluvial sediments --- geomorphological/pedological background --- soil spatial infrastructure --- statistical methods --- n/a

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Remotely sensed data from either air- or spaceborne platforms are often leveraged for archaeological or more general cultural heritage goals. However, despite the steady developments in remote sensing technology over the past three decades, the thoughtful integration of data sources and methods into theoretically aware archaeological practice remains relatively underdeveloped. This volume contains nine contributions which, each in their way, address different theoretical dislocations and practical shortcomings in the use of remote sensing products within archaeological practice. These contributions provide the reader with food for thought on these challenges, and so contribute to archaeological remote sensing as a more mature interdisciplinary field characterised by explicit, thoughtful, and theoretically engaged approaches to understanding the past.

Biography & True Stories --- Archaeology --- relief mapping --- visualization --- blend modes --- digital elevation model --- airborne laser scanning --- lidar --- archaeological prospection --- deep learning --- citizen science --- The Netherlands --- archaeology --- arid environments --- satellite remote sensing --- lithological mapping --- lithic procurement --- chert sourcing --- Landsat 8 --- GIS --- ALS --- amplitude --- radiometric calibration --- reflectance --- Sicily --- transfer learning --- historic mining --- heritage management --- LiDAR --- hyperspectral data --- submerged areas --- cultural heritage monitoring --- anomaly detection --- MNF --- radiative transfer model --- Martin Heidegger --- technology --- mimesis --- remote sensing archaeology --- cultural context --- archaeological remote sensing --- satellite mission design --- satellite archaeology --- archaeological survey --- cropmarks --- empirical knowledge --- alluvial sediments --- geomorphological/pedological background --- soil spatial infrastructure --- statistical methods --- relief mapping --- visualization --- blend modes --- digital elevation model --- airborne laser scanning --- lidar --- archaeological prospection --- deep learning --- citizen science --- The Netherlands --- archaeology --- arid environments --- satellite remote sensing --- lithological mapping --- lithic procurement --- chert sourcing --- Landsat 8 --- GIS --- ALS --- amplitude --- radiometric calibration --- reflectance --- Sicily --- transfer learning --- historic mining --- heritage management --- LiDAR --- hyperspectral data --- submerged areas --- cultural heritage monitoring --- anomaly detection --- MNF --- radiative transfer model --- Martin Heidegger --- technology --- mimesis --- remote sensing archaeology --- cultural context --- archaeological remote sensing --- satellite mission design --- satellite archaeology --- archaeological survey --- cropmarks --- empirical knowledge --- alluvial sediments --- geomorphological/pedological background --- soil spatial infrastructure --- statistical methods

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River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow. Accurate discharge measurements are critical for a range of applications including water supply, navigation, recreation, management of in-stream habitat, and the prediction and monitoring of floods and droughts. However, the traditional stream gage networks that provide such data are sparse and declining. Remote sensing represents an appealing alternative for obtaining streamflow information. Potential advantages include greater efficiency, expanded coverage, increased measurement frequency, lower cost and reduced risk to field personnel. In addition, remote sensing provides opportunities to examine long river segments with continuous coverage and high spatial resolution. To realize these benefits, research must focus on the remote measurement of flow velocity, channel geometry and their product: river discharge. This Special Issue fostered the development of novel methods for retrieving discharge and its components, and thus stimulated progress toward an operational capacity for streamflow monitoring. The papers herein address all aspects of the remote measurement of streamflow—estimation of flow velocity, bathymetry (water depth), and discharge—from various types of remotely sensed data acquired from a range of platforms: manned and unmanned aircraft, satellites, and ground-based non-contact sensors.

Research & information: general --- estuary --- morphology --- rapid assessment --- bathymetry --- flow velocity --- salinity --- tool --- remotely-sensed imagery --- small unmanned aerial system (sUAS) --- river flow --- thermal infrared imagery --- particle image velocimetry --- lidar bathymetry --- fluvial --- geomorphology --- change detection --- remotely piloted aircraft system --- refraction correction --- structure-from-motion photogrammetry --- water surface elevation --- topographic error --- machine learning --- UAV LiDAR --- airborne laser bathymetry --- full waveform processing --- performance assessment --- high resolution hydro-mapping --- remote sensing --- rivers --- discharge --- hydrology --- modelling --- ungauged basins --- Alaska --- river --- PIV --- large-scale particle image velocimetry --- LSPIV --- surface velocity --- river discharge --- Doppler radar --- pulsed radar --- probability concept --- water temperature --- salmonids --- Pend Oreille River --- thermal infrared (TIR) --- acoustic Doppler current profiler (ADCP) --- channel bathymetry --- cold-water refuge --- dam --- flooding --- high-water marks (HWMs) --- small unmanned aircraft systems (sUAS) --- drone --- photogrammetry --- hydraulic modeling --- aerial photography --- surveying --- inundation --- Landsat --- streamflow --- flow frequency --- satellite revisit time --- flow regime

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River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow. Accurate discharge measurements are critical for a range of applications including water supply, navigation, recreation, management of in-stream habitat, and the prediction and monitoring of floods and droughts. However, the traditional stream gage networks that provide such data are sparse and declining. Remote sensing represents an appealing alternative for obtaining streamflow information. Potential advantages include greater efficiency, expanded coverage, increased measurement frequency, lower cost and reduced risk to field personnel. In addition, remote sensing provides opportunities to examine long river segments with continuous coverage and high spatial resolution. To realize these benefits, research must focus on the remote measurement of flow velocity, channel geometry and their product: river discharge. This Special Issue fostered the development of novel methods for retrieving discharge and its components, and thus stimulated progress toward an operational capacity for streamflow monitoring. The papers herein address all aspects of the remote measurement of streamflow—estimation of flow velocity, bathymetry (water depth), and discharge—from various types of remotely sensed data acquired from a range of platforms: manned and unmanned aircraft, satellites, and ground-based non-contact sensors.

Research & information: general --- estuary --- morphology --- rapid assessment --- bathymetry --- flow velocity --- salinity --- tool --- remotely-sensed imagery --- small unmanned aerial system (sUAS) --- river flow --- thermal infrared imagery --- particle image velocimetry --- lidar bathymetry --- fluvial --- geomorphology --- change detection --- remotely piloted aircraft system --- refraction correction --- structure-from-motion photogrammetry --- water surface elevation --- topographic error --- machine learning --- UAV LiDAR --- airborne laser bathymetry --- full waveform processing --- performance assessment --- high resolution hydro-mapping --- remote sensing --- rivers --- discharge --- hydrology --- modelling --- ungauged basins --- Alaska --- river --- PIV --- large-scale particle image velocimetry --- LSPIV --- surface velocity --- river discharge --- Doppler radar --- pulsed radar --- probability concept --- water temperature --- salmonids --- Pend Oreille River --- thermal infrared (TIR) --- acoustic Doppler current profiler (ADCP) --- channel bathymetry --- cold-water refuge --- dam --- flooding --- high-water marks (HWMs) --- small unmanned aircraft systems (sUAS) --- drone --- photogrammetry --- hydraulic modeling --- aerial photography --- surveying --- inundation --- Landsat --- streamflow --- flow frequency --- satellite revisit time --- flow regime --- estuary --- morphology --- rapid assessment --- bathymetry --- flow velocity --- salinity --- tool --- remotely-sensed imagery --- small unmanned aerial system (sUAS) --- river flow --- thermal infrared imagery --- particle image velocimetry --- lidar bathymetry --- fluvial --- geomorphology --- change detection --- remotely piloted aircraft system --- refraction correction --- structure-from-motion photogrammetry --- water surface elevation --- topographic error --- machine learning --- UAV LiDAR --- airborne laser bathymetry --- full waveform processing --- performance assessment --- high resolution hydro-mapping --- remote sensing --- rivers --- discharge --- hydrology --- modelling --- ungauged basins --- Alaska --- river --- PIV --- large-scale particle image velocimetry --- LSPIV --- surface velocity --- river discharge --- Doppler radar --- pulsed radar --- probability concept --- water temperature --- salmonids --- Pend Oreille River --- thermal infrared (TIR) --- acoustic Doppler current profiler (ADCP) --- channel bathymetry --- cold-water refuge --- dam --- flooding --- high-water marks (HWMs) --- small unmanned aircraft systems (sUAS) --- drone --- photogrammetry --- hydraulic modeling --- aerial photography --- surveying --- inundation --- Landsat --- streamflow --- flow frequency --- satellite revisit time --- flow regime

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Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security).

Research & information: general --- hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire --- hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire