Development of the spectral forest index in the Khangai region, Mongolia using Sentinel-2 imagery

Abstract

Abstract Mongolian forests have low productivity and growth and are vulnerable to disturbances. Additionally, it is difficult to control and evaluate the forested areas. Therefore, satellite data and surveillance methods are needed to study mountain forests. This study aimed to determine the changes in the main forest cover classes of Khangal soum using remote sensing and geographical information system datasets. A spectral forest index (SFI) using Sentinel-2 imagery was developed for forest cover estimations and applied to the study area during 2015–2020. The SFI was based on the forest index (FI) and the concept of Dark Objects. Each SFI was compared to existing vegetation indices (ratio vegetation index, normalized difference vegetation index, leaf area index, and forest index) for forest data analysis. The highest correlation was with SFI2. The SFI2 data agreed with the national forest inventory (NFI) 2018 data. The SFI2 of the forest area was set at 1.2, which was confirmed with 90.4% confidence. Overall, SFI2 is suitable for land cover/land use changes and forest classification, monitoring, and management in Mongolia and could be crucial for estimating the boundary of forested areas depending on the forest cover and species in the region.