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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.

The leaf area index (LAI)- as one of the most important vegetation biophysical variables, has been retrieved in vegetation canopies using data from different remote sensing platforms. LAI was recently proposed as a remote sensing-enabled essential biodiversity variable. To our knowledge, however, the retrieval of the LAI using hyperspectral thermal infrared (i.e., TIR 8-14 m) data has been addressed only under controlled laboratory conditions and has not yet been accomplished using thermal infrared hyperspectral data acquired from an airborne platform. Therefore, the primary goal of this study is to determine the accuracy of LAI prediction using thermal infrared hyperspectral data acquired from an airborne platform. The field campaign was conducted in July 2017 in the Bavarian Forest National Park in southeast Germany, and biophysical parameters, including LAI, were measured for 36 plots. Concurrently, thermal hyperspectral data were obtained using the Twin Otter aircraft operated by NERC-ARF (i.e., the U.K. Natural Environment Research Council- Airborne Research Facility) and the AISA Owl sensor. LAI was retrieved using an artificial neural network Levenberg-Marquardt algorithm. The results indicated that thermal infrared hyperspectral data could estimate LAI with relatively high accuracy (R= 0.734, RMSE=0.554). The study showed the significance of using an artificial neural network. It proved the possibility of using hyperspectral thermal infrared data to estimate vegetation biophysical properties at the canopy level and over a large forest area.