Remote Sensing of Vegetation [electronic resource] : Along a Latitudinal Gradient in Chile /
Bödinger, Christian Julian.author.
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.
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 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.
Remote sensing.
Physical geography.
Environmental geography.
Remote Sensing/Photogrammetry.
Physical Geography.
Environmental Geography.