[U-04] Geospatial Applications for Societal Benefits

[U04-P09] Optimizing Observation Angles for Vegetation capturing in the Gobi Desert Using multispectral LCTF Imaging

Namuun Gantulga¹、Yukihiro Takahashi²、Baljmaa Byambakhand ¹、Bayarsaikhan Uudus³、Erdenebaatar Dashdondog¹、Turtogtokh Tumenjargal¹、*Begzsuren Tumendemberel^1,4 (1.Department of Physics, School of Arts and Sciences, National University of Mongolia、2.Department of Cosmosciences, Graduate School of Science, Hokkaido University, Japan 、3.Department of Biology, School of Arts and Sciences, National University of Mongolia、4.Institute of Physics and Technology, Academy of Sciences, Mongolia)

キーワード：Spectral reflectance, Vegetation imaging , Multispectral imaging, The Gobi desert , Remote sensing

The Gobi Desert is known for its extreme landscapes, yet it supports a variety of plant and animal life, including rare and endangered species. In satellite images, the Gobi often appears as a dry and empty desert, but its appearance changes depending on the viewing angle. From certain angles, the land looks green and covered with vegetation, while from others, it appears mostly sandy. Because of this unique feature, it is important to study how observation angles affect vegetation detection in this region.
This study explores the light reflection properties of Gobi vegetation using a multispectral or hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Field measurements were taken at different azimuth angles (0°–300°, every 60°) and zenith angles (0°–60°, every 20°) to fully understand how light interacts with the land. Each captured image contained detailed color and light data from the terrain.
The results showed that images taken directly from above (nadir view) reflected mostly sand, while images taken from an angle (oblique view) captured more vegetation details. This suggests that viewing the Gobi steppe from oblique angles provides a clearer picture of its biological features. These findings may help improve remote sensing techniques for studying desert landscapes and identifying vegetation more accurately.