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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.

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 --- 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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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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