Prediction of leaf area index using hyperspectral thermal infrared imagery over the mixed temperate forest

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.