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Article

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

1
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
2
Department of Natural Resources and Environment and Institute of Agriculture, Natural Recourses and Extension, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
3
Department of Geography, University of California, Santa Barbara, CA 93106, USA
4
Alaska Fire Science Consortium, International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Henning Buddenbaum
Remote Sens. 2021, 13(9), 1693; https://doi.org/10.3390/rs13091693
Received: 6 March 2021 / Revised: 23 April 2021 / Accepted: 23 April 2021 / Published: 27 April 2021
(This article belongs to the Special Issue Imaging Spectroscopy of Forest Ecosystems)
Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest. View Full-Text
Keywords: simulation; hyperspectral; UPDM; spectral reconstruction; boreal forest simulation; hyperspectral; UPDM; spectral reconstruction; boreal forest
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MDPI and ACS Style

Badola, A.; Panda, S.K.; Roberts, D.A.; Waigl, C.F.; Bhatt, U.S.; Smith, C.W.; Jandt, R.R. Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska. Remote Sens. 2021, 13, 1693. https://doi.org/10.3390/rs13091693

AMA Style

Badola A, Panda SK, Roberts DA, Waigl CF, Bhatt US, Smith CW, Jandt RR. Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska. Remote Sensing. 2021; 13(9):1693. https://doi.org/10.3390/rs13091693

Chicago/Turabian Style

Badola, Anushree, Santosh K. Panda, Dar A. Roberts, Christine F. Waigl, Uma S. Bhatt, Christopher W. Smith, and Randi R. Jandt 2021. "Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska" Remote Sensing 13, no. 9: 1693. https://doi.org/10.3390/rs13091693

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