Abstract

Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change. With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land–atmosphere exchanges of water and energy at regional scales in an economically feasible way. Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research. UAF’s newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery. The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes. In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST). The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska’s Arctic and boreal regions. This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012.

Highlights

  • Warming in high latitudes is causing widespread melting of snow, ice, and permafrost degradation across Arctic tundra and boreal ecosystems, changing plant communities and increasing wildfire and drought occurrence [1,2,3,4,5,6,7,8,9,10]

  • We present a freely-available medium-resolution land surface temperature (LST) Arctic dataset derived from Landsat imagery (Landsat-5 TM and Landsat-7 Enhanced Thematic Mapper (ETM)+) from 2009 to 2013 over Alaska’s boreal forest, processed at the University of Alaska Fairbanks’s (UAF’s)

  • The Hyperspectral Imaging Laboratory (HyLab) facility consists of a lab for instrument calibration and laboratory spectroscopy as well as much-needed local airborne and satellite multispectral, hyperspectral, and thermal imaging capability for Arctic regions using the NEO HySpex sensors in combination with a Forward Looking Infrared Radiometer (FLIR) camera

Read more

Summary

Introduction

Warming in high latitudes is causing widespread melting of snow, ice, and permafrost degradation across Arctic tundra and boreal ecosystems, changing plant communities and increasing wildfire and drought occurrence [1,2,3,4,5,6,7,8,9,10]. We present a freely-available medium-resolution LST Arctic dataset derived from Landsat imagery (Landsat-5 TM and Landsat-7 Enhanced Thematic Mapper (ETM)+) from 2009 to 2013 over Alaska’s boreal forest, processed at the University of Alaska Fairbanks’s (UAF’s). Hyperspectral Imaging Laboratory (HyLab) through a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities to retrieve land surface temperature (LST). This dataset is currently growing for other regions in Alaska, and will be used to retrieve spatio-temporal surface energy fluxes as well as other land surface processes in Arctic ecosystems

High-Latitude Thermal and Hyperspectral Laboratory
Image Data
Image Metadata
Land Surface Temperature Retrieval
Land Surface Temperature Evaluation
Plans for Expanding the LST Arctic Dataset
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.