Reprint

Earth Observation (EO), Remote Sensing (RS), and Geoinformation (GI) Applications in Svalbard

Edited by
May 2023
510 pages
  • ISBN978-3-0365-7418-9 (Hardback)
  • ISBN978-3-0365-7419-6 (PDF)

This book is a reprint of the Special Issue Earth Observation (EO), Remote Sensing (RS), and Geoinformation (GI) Applications in Svalbard that was published in

Engineering
Environmental & Earth Sciences
Summary

Earth observation (EO), remote sensing (RS), and geoinformation (GI) technologies play a critical role in the study of Svalbard's environment, providing insights into the region's changes and supporting decision-making processes. This reprint presents a comprehensive overview of the applications of EO and RS technologies in Svalbard, covering a wide range of topics related to the environment. It includes contributions from leading experts in the field, providing insights into the current state of the art and future directions for research. The reprint starts by introducing the status of EO and RS in Svalbard, providing a solid foundation for readers new to the field. It then delves into specific applications of these technologies, including the monitoring of glaciers, snow cover, and permafrost using ground-, space-, and air-based RS platforms. Overall, this book aims to provide a comprehensive overview of the applications of EO and RS technologies in Svalbard, highlighting their importance in understanding and addressing the challenges faced by the region. It will be a valuable resource for researchers, students, policymakers, and practitioners in the fields of environmental science, geography, and remote sensing.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
snow cover; remote sensing; sea ice variability; vegetation growth; arctic climate change; Arctic aerosol; aerosol transport; aged aerosol; aerosol modification; aerosol optical properties; aerosol microphysical properties; aerosol remote sensing; microphysical inversion; aerosol radiative effect; Arctic radiative budget; earth observation; remote sensing; COVID-19; Svalbard; earth system science; SIOS; polar regions; snow cover; remote sensing; snow modelling; MODIS; Sentinel-2; permafrost; active layer; InSAR; time series; ground displacement; ground temperature; displacement progression; thaw progression; Arctic; Svalbard; Sentinel-2; NDVI; time-series; onset of growth; Svalbard; classifier; disturbance; drone; ecological monitoring; GLCM; herbivore; random forest; Svalbard; winter climate effect; grubbing; Arctic clouds; cirrus clouds; ice clouds; lidar; ocean eddies; marginal ice zone; sea ice; SAR imaging; Fram Strait; Svalbard; Greenland Sea; Hopen Island; Arctic Ocean; tidewater glaciers; surface elevation changes; glacier geometry; Svalbard; structure-from-motion; terrestrial laser scanning; digital elevation model; Svalbard; SIOS; ICESat-2; laser altimetry; kinematic GPS experiments; glaciology; surge glaciers; svalbard; density dimension algorithm for ice surfaces; airborne validation of satellite data; lake ice; remote sensing; Svalbard; MODIS; Sentinel-1; water temperature; glacier facies; atmospheric correction; pansharpening; WorldView-2; Ny-Ålesund; Chandra–Bhaga basin; target detection; supervised classification; remote sensing; Sentinel-1 and Sentinel-2; time series analysis; snow melt; Svalbard; tundra; plant phenology; snow cover; ice cover; Arctic; Antarctic; spectral reflectance; hyperspectral data; ocean colour; coastal darkening; SPM; sediment plumes; Arctic coast; remote sensing; regional tuning; coastal ecosystems; land-ocean-interaction; riverine inputs; geographic object-based image analysis; atmospheric correction; pansharpening; WorldView-2; Ny-Ålesund; Chandra–Bhaga basin; glacier surface facies; surface facies of glaciers; pixel-based image analysis; geographic object-based image analysis; atmospheric corrections; pansharpening; image processing routines; n/a