Remote Sensing of Vegetation : Along a Latitudinal Gradient in Chile / by Christian Julian Bödinger

Von:

Bödinger, Christian Julian [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: BestMasters | SpringerLink BücherVerlag: Wiesbaden : Springer Fachmedien Wiesbaden, 2019Beschreibung: Online-Ressource (XXIII, 108 p. 1 illus, online resource)ISBN:

9783658251208

Schlagwörter:

Andere physische Formen: 9783658251192 | 9783658251215 | Erscheint auch als: 978-3-658-25119-2 Druck-Ausgabe | Printed edition: 9783658251192 | Printed edition: 9783658251215 DDC-Klassifikation:

910.285

LOC-Klassifikation:

GA102.4.R44

DOI: DOI: 10.1007/978-3-658-25120-8Online-Ressourcen:

Zusammenfassung: 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 sensingZusammenfassung: 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 ENSOPPN: PPN: 1048365387Package identifier: Produktsigel: ZDB-2-EES | ZDB-2-SEB | ZDB-2-SXEE

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