Thermal infrared airborne hyperspectral data for vegetation land cover classification in a mixed temperate forest

H.K. Korir*, E. Neinavaz, A.K. Skidmore (Contributor), R. Darvishzadeh (Contributor)

*Corresponding author for this work

Research output: Contribution to conferenceAbstractAcademic

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Abstract

Land cover, which is an essential climate variable and a remote sensing-enabled essential biodiversity variable is important for understanding terrestrial ecosystems functioning. Many studies have investigated forest land cover classification using remote sensing data from the visible, near, and short-wave infrared (VNIR-SWIR, 0.4- 2.5 μm) regions. However, to our knowledge, no study has addressed forest land cover classification using thermal infrared (TIR, 8-14 μm) hyperspectral data. In this study, for the first time, we present the preliminary assessment of vegetation classification using TIR hyperspectral data. TIR hyperspectral images (7.5 – 12.5 μm) were acquired by EUFAR aircraft using the AISA Owl sensor in July 2017 in Bavaria Forest National Park, Germany. In addition, fieldwork was conducted in 2017, concurrent to the flight campaign as well as in 2020 and 2021, and vegetation types were recorded in 92 plots. Canopy emissivity spectra were extracted for three vegetation classes namely, coniferous, broadleaves, and mixed classes. The extracted emissivity spectra were further used to classify three vegetation classes by means of a supervised Random Forest classifier. The results confirmed the expected capabilities of hyperspectral TIR data to produce an acceptable land cover map with an overall accuracy of 66%. The study showed that for coniferous class the most important spectral bands for classification were
wavelengths 8.9 μm, between 9.7 – 9.9 μm and 10.3 μm. While for broadleaves there were,10.2 μm, 10.8 μm, and between 11.0 – 11.4 μm bands. The findings of this study show the possibility of using airborne hyperspectral TIR data for forest land cover classification. However, further investigation should be done applying other machine learning and deep learning techniques to examine the potential of TIR hyperspectral data for land cover classification.
Original languageEnglish
Pages116-116
Publication statusPublished - 24 Jun 2022
Event12th EARSeL Workshop on Imaging Spectroscopy 2022 - Potsdam, Germany, Potsdam, Germany
Duration: 22 Jun 202224 Jun 2022
Conference number: 12
https://is.earsel.org/workshop/12-IS-Potsdam2022/

Conference

Conference12th EARSeL Workshop on Imaging Spectroscopy 2022
Country/TerritoryGermany
CityPotsdam
Period22/06/2224/06/22
Internet address

Keywords

  • Canopy emissivity
  • Thermal infrared
  • Hyperspectral
  • Airborne data
  • Land cover Classification
  • Random forest

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