MAPPING FOREST IN DARKHAN-UUL PROVINCE, MONGOLIA THROUGH SENTINEL-2 SPECTRAL INDICES
Bayanmunkh Norovsuren1,6, Ochirkhuyag Lkhamjav 2,3,6, Tsolmon Altanchimeg3,6, Ulziisaikhan Ganbold4,6, Bayarmaa Enkhbold4,6, Gankhuyag Purev5,6, Tserennadmid Bataa5, Battuya Sanjaakhand5,6*

1Spatial data analysis department, Center for policy research and analysis, Ulaanbaatar, Mongolia (bayanmunkh.n[at]cpra.ub.gov.mn)
2 Department of Civil Engineering, National Central University, Taoyuan 32017, Taiwan (ROC) (olkhamjav[at]g.ncu.edu.tw)
3 Institute of Geography and Geoecology, Mongolian Academy of Science, Ulaanbaatar, Mongolia (tsolmon_a[at]mas.ac.mn- ochirkhuyag_l[at]mas.ac.mn)
4 School of Geology and Mining Engineering, Mongolian University of Science and Technology (ulziis[at]must.edu.mn- ebayarmaa[at]must.edu.mn )
5 Mongolian University of Life Science, Ulaanbaatar, Mongolia (battuya[at]muls.edu.mn)
6 Mongolian Geospatial Association, Ulaanbaatar 15141, Mongolia (info[at]geomedeelel.mn)

Abstract

Analyzing forest health in Mongolia through spectral indices derived from Sentinel-2 satellite data provides an innovative and effective approach for environmental monitoring and management. The study area, located in the northern region of Mongolia, is dominated by mixed forest ecosystems. This research utilizes high-resolution, multi-spectral imagery from Sentinel-2 satellites, part of the European Space Agency^s Copernicus program, to assess forest health across Darkhan-Uul Province. The primary objective is to compare changes in the main forest canopy classes within this region.

We calculated key spectral indices-including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and the Spectral Forest Index (SFI)-to assess forest health, moisture content, and stress factors. The methodological framework involved acquiring and preprocessing Sentinel-2 images to correct for atmospheric disturbances and cloud cover, calculating relevant spectral indices, and analyzing these indices to derive insights into forest health status.

Results demonstrate that Sentinel-2 data significantly improved forest canopy health mapping accuracy by approximately 10% compared to conventional methods. Forest area extent was determined by assessing canopy health conditions, and degraded forest areas were identified in several locations. These findings will inform forest management planning and contribute to enhanced forestry practices in Darkhan-Uul Province,

Keywords: Remote sensing, forest health, forest spectral index, forest health management

Topic: Topic B: Applications of Remote Sensing