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Remote Sensing of Above Ground Biomass

Remote Sensing of Above Ground Biomass

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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

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Keywords

  • above ground biomass
  • above-ground biomass
  • aboveground biomass
  • ALOS2
  • alpine grassland conservation
  • alpine meadow grassland
  • anthropogenic disturbance
  • applicability evaluation
  • AquaCrop model
  • Atriplex nummularia
  • Bidirectional Reflectance Distribution Factor
  • Biomass
  • broadleaves
  • carbon inventory
  • carbon mitigation
  • chlorophyll index
  • CIRed-edge
  • Climate Change
  • conifer
  • correlation coefficient
  • dry biomass
  • dry matter index
  • ecological policies
  • Error analysis
  • estimation accuracy
  • field spectrometry
  • foliage projective cover
  • Food security
  • forage crops
  • forest above ground biomass (AGB)
  • forest biomass
  • forest structure information
  • fractional vegetation cover
  • grass biomass
  • grazing exclusion
  • grazing management
  • ground-based remote sensing
  • inversion model
  • Land Surface Phenology
  • Landsat
  • LiDAR
  • light detection and ranging (LiDAR)
  • Livestock
  • mapping
  • mixed forest
  • MODIS
  • MODIS time series
  • multi-angle remote sensing
  • n/a
  • NDLMA
  • NDVI
  • Niger
  • particle swarm optimization
  • pasture biomass
  • random forest
  • rangeland productivity
  • regional sustainability
  • Regression analysis
  • Remote sensing
  • Rice
  • Sahel
  • sensor fusion
  • short grass
  • spectral index
  • SPLSR
  • stem volume
  • TerraSAR-X
  • ultrasonic sensor
  • vegetation index
  • vegetation indices
  • Wambiana grazing trial
  • Wetlands
  • winter wheat
  • yield

Links

DOI: 10.3390/books978-3-03921-210-1

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