[S-RD20] Cutting-edge sensing technology applied to geology and resource exploration

[SRD20-P05] Determination of vegetation’s red edge slope and Leaf Area Index using hyperspectral LCTF camera

Bayartsetseg Oyungerel², Bayarsaikhan Uudus³, Yukihiro Takahashi¹, Erdenebaatar Dashdondog², Turtogtokh Tumenjargal², *Begzsuren Tumendemberel^1,2 (1.Department of Cosmosciences, Graduate School of Science, Hokkaido University, 2.Department of Physics, School of Arts and Sciences, National University of Mongolia, 3.Department of Biology, School of Arts and Sciences, National University of Mongolia)

Keywords:Spectral reflectance, Red edge, Leaf Area Index (LAI), Liquid Crystal Tunable Filter camera , Remote sensing , Environmental

Within the framework of this research, a new algorithm was designed to determine the plant’s red edge slope and the Leaf Area Index (LAI) based on their spectral characteristics using a hyperspectral LCTF (Liquid Crystal Tunable Filter) camera. In the first week of July 2022, we took pictures of field vegetation in several fields in the Khar Yamaat Nature Reserve with the LCTF camera. From the collected data, the light reflectance spectrum of many species of growing plants was distinguished with the help of image processing, and with that, we created a spectrum dataset of 21 different species. From the generated dataset, we designed an algorithm to determine plant species or dry grass based on the red edge slope, which works with approximately 95% accuracy. The algorithm also calculates the leaf area index, and healthy plants are distinguished from damaged grass and non-plant species. The results of this work demonstrate that vegetation can be classified by vegetation’s red edge slope using hyperspectral cameras. In the future, it will be possible to determine the location and number of growing plants using the algorithm we developed on the images taken by modern satellites and drones.