rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Remote Sensing of Vegetation
    sub title: Along a Latitudinal Gradient in Chile
  name:
    name part: Bödinger, Christian Julian.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIII, 108 p. 1 illus. online resource.
  abstract: How is the vegetation distribution influencing the erosion and surface formation in the different eco zones of Chile? To answer this question, it is mandatory to possess fundamental knowledge about plant species habitats, occurrence and their dynamics. In his study Christian Bödinger utilizes satellite imagery in combination with machine learning to derive maps of land use and land cover (LULC) in four study sites along a climatic gradient and to monitor vegetation using monthly Normalized Difference Vegetation Index (NDVI) time series. The findings contribute to a better understanding of climate impacts on Chilean vegetation and serve as a basis of landscape evolution models. Contents TanDEM-X DEM, Sentinel Optical and Radar Data, Landsat Surface Reflectance Machine Learning Using SVMs and Random Forest Statistical Time-Series Evaluation Maps of Land Use and Cover (LULC) Time-Series Showing the Impact of ENSO Target Groups Scientists, lecturers and students in the field of geology and ecology Geoscientists and Ecologists with a focus on remote sensing About the Author Christian Bödinger holds a M.Sc. in Physical Geography from the University of Tübingen, Germany. His focus in research lies on remote sensing and image analysis for environmental applications. He is currently working for a company focusing on aquatic remote sensing.
  table of contents: TanDEM-X DEM, Sentinel Optical and Radar Data, Landsat Surface Reflectance -- Machine Learning Using SVMs and Random Forest -- Statistical Time-Series Evaluation -- Maps of Land Use and Cover (LULC) -- Time-Series Showing the Impact of ENSO.
  note: by Christian Julian Bödinger.
  subject:
    topic: Remote sensing
  subject:
    topic: Physical geography
  subject:
    topic: Environmental geography
  subject:
    topic: Remote Sensing/Photogrammetry
  subject:
    topic: Physical Geography
  subject:
    topic: Environmental Geography
  classification: GA102.4.R44
  classification: G70.39-70.6
  classification: 910.285
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: BestMasters
  identifier: 9783658251208
  identifier: https://doi.org/10.1007/978-3-658-25120-8
  record info:
    record creation date: 190124
    record change date: 20191022112046.0
    record identifier: 978-3-658-25120-8