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Remote sensing of vegetationalong a ...

Bodinger, Christian Julian.

Remote sensing of vegetationalong a latitudinal gradient in Chile /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Remote sensing of vegetationby Christian Julian Bodinger.
Reminder of title:	along a latitudinal gradient in Chile /
Author:	Bodinger, Christian Julian.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden :2019.
Description:	xxiii, 108 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Geoinformatics.
Online resource:	https://doi.org/10.1007/978-3-658-25120-8
ISBN:	9783658251208$q(electronic bk.)

Remote sensing of vegetationalong a latitudinal gradient in Chile /
Bodinger, Christian Julian.

Remote sensing of vegetationalong a latitudinal gradient in Chile /[electronic resource] :by Christian Julian Bodinger. - Wiesbaden :Springer Fachmedien Wiesbaden :2019. - xxiii, 108 p. :ill., digital ;24 cm. - BestMasters,2625-3577. - BestMasters..

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.

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 Bodinger 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 Bodinger holds a M.Sc. in Physical Geography from the University of Tubingen, 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.

ISBN: 9783658251208$q(electronic bk.)

Standard No.: 10.1007/978-3-658-25120-8doiSubjects--Topical Terms:

769868
Geoinformatics.

LC Class. No.: QK46.5.R44 / B865 2019

Dewey Class. No.: 580.285

Remote sensing of vegetationalong a latitudinal gradient in Chile /
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520 $a 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 Bodinger 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 Bodinger holds a M.Sc. in Physical Geography from the University of Tubingen, 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.
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