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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.

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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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

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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

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Globally, fire regimes are being altered by changing climatic conditions and land use changes. This has the potential to drive species extinctions and cause ecosystem state changes, with a range of consequences for ecosystem services. Accurate prediction of the risk of forest fires over short timescales (weeks or months) is required for land managers to target suppression resources in order to protect people, property, and infrastructure, as well as fire-sensitive ecosystems. Over longer timescales, prediction of changes in forest fire regimes is required to model the effect of wildfires on the terrestrial carbon cycle and subsequent feedbacks into the climate system.This was the motivation to publish this book, which is focused on quantifying and modelling the risk factors of forest fires. More specifically, the chapters in this book address four topics: (i) the use of fire danger metrics and other approaches to understand variation in wildfire activity; (ii) understanding changes in the flammability of live fuel; (iii) modeling dead fuel moisture content; and (iv) estimations of emission factors.The book will be of broad relevance to scientists and managers working with fire in different forest ecosystems globally.

Research & information: general --- Biology, life sciences --- Forestry & related industries --- fire danger rating --- fire management --- fire regime --- fire size --- fire weather --- Portugal --- critical LFMC threshold --- forest/grassland fire --- radiative transfer model --- remote sensing --- southwest China --- acid rain --- aerosol --- biomass burning --- forest fire --- PM2.5 --- direct estimation --- meteorological factor regression --- moisture content --- time lag --- forest fire driving factors --- forest fire occurrence --- random forest --- forest fire management --- China --- Cupressus sempervirens --- fire risk --- fuels --- fuel moisture content --- mass loss calorimeter --- Seiridium cardinale --- vulnerability to wildfires --- disease --- alien pathogen --- allochthonous species --- introduced fungus --- drying tests --- humidity diffusion coefficients --- wildfire --- prescribed burning --- modeling --- drought --- flammability --- fuel moisture --- leaf water potential --- plant traits --- climate change --- MNI --- fire season --- fire behavior --- crown fire --- fire modeling --- senescence --- foliar moisture content --- canopy bulk density --- fire danger --- fire weather patterns --- RCP --- FWI system --- SSR --- occurrence of forest fire --- machine learning --- variable importance --- prediction accuracy --- epicormic resprouter --- eucalyptus --- fire severity --- flammability feedbacks --- temperate forest

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Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

optical sensor --- spectral analysis --- response surface sampling --- sensor evaluation --- electromagnetic induction --- multivariate water quality parameters --- mandarin orange --- crop inspection platform --- SPA-MLR --- object tracking --- feature selection --- simultaneous measurement --- diseases --- genetic algorithms --- processing of sensed data --- electrochemical sensors --- thermal image --- ECa-directed soil sampling --- handheld --- recognition patterns --- salt concentration --- clover-grass --- bovine embedded hardware --- weed control --- soil --- field crops --- vineyard --- connected dominating set --- water depth sensors --- SS-OCT --- wheat --- striped stem-borer --- silage --- geostatistics --- detection --- NIR hyperspectral imaging --- electronic nose --- machine learning --- virtual organizations of agents --- packing density --- data validation and calibration --- dataset --- Wi-SUN --- temperature sensors --- geoinformatics --- gas sensor --- X-ray fluorescence spectroscopy --- vegetable oil --- photograph-grid method --- Vitis vinifera --- WSN distribution algorithms --- laser-induced breakdown spectroscopy --- irrigation --- quality assessment --- energy efficiency --- wireless sensor network (WSN) --- geo-information --- Fusarium --- texture features --- weeds --- discrimination --- big data --- soil moisture sensors --- meat spoilage --- land cover --- stereo imaging --- near infrared sensors --- biological sensing --- compound sensor --- pest management --- moisture --- plant localization --- heavy metal contamination --- artificial neural networks --- spectral pre-processing --- moisture content --- apparent soil electrical conductivity --- data fusion --- semi-arid regions --- smart irrigation --- back propagation model --- wireless sensor network --- energy balance --- light-beam --- fluorescent measurement --- agriculture --- precision agriculture --- deep learning --- spectroscopy --- hulled barely --- dielectric probe --- RPAS --- water supply network --- rice leaves --- mobile app --- gradient boosted machines --- hyperspectral camera --- one-class --- nitrogen --- LiDAR --- total carbon --- chemometrics analysis --- rice --- agricultural land --- on-line vis-NIR measurement --- CARS --- obstacle detection --- stratification --- neural networks --- regression estimator --- Kinect --- proximity sensing --- distributed systems --- pest --- noninvasive detection --- texture feature --- soil mapping --- classification --- soil salinity --- visible and near-infrared reflectance spectroscopy --- germination --- computer vision --- hyperspectral imaging --- diffusion --- dielectric dispersion --- UAS --- random forests --- case studies --- total nitrogen --- thermal imaging --- cameras --- dry matter composition --- near-infrared --- salt tolerance --- deep convolutional neural networks --- soil type classification --- water management --- preprocessing methods --- wireless sensor networks (WSN) --- remote sensing image classification --- precision plant protection --- radar --- spatial variability --- GF-1 satellite --- plant disease --- naked barley --- leaf area index --- CIE-Lab --- change of support --- radiative transfer model --- 3D reconstruction --- plant phenotyping --- vine --- near infrared --- vegetation indices --- remote sensing --- greenhouse --- time-series data --- scattering --- sensor --- crop area --- speckle --- spatial data --- grapevine breeding --- wide field view --- partial least squares-discriminant analysis --- spiking --- area frame sampling --- chromium content --- machine-learning --- RGB-D sensor --- pest scouting --- PLS --- Capsicum annuum --- spatial-temporal model --- drying temperature --- boron tolerance --- ambient intelligence --- laser wavelength --- fuzzy logic --- dynamic weight --- landslide --- management zones --- real-time processing --- event detection --- crop monitoring --- apple shelf-life --- rice field monitoring --- wireless sensor --- birth sensor --- proximal sensor