Mapping Wildfire Dynamics in Eastern Mongolia: Integrating Remote Sensing for Sustainable Resource Management

DOI

10.2991/978-94-6463-928-5_19How to use a DOI?

Keywords

Wildfire; Spatiotemporal Analysis; Eastern Mongolia; MODIS; Risk Assessment

Abstract

This study presents a comprehensive spatiotemporal analysis of wildfire patterns across Mongolia from January 2005 to December 2024 using satellite imagery, covering a 20-year period of observed data. The study reveals characteristics of wildfire frequency, distribution, and trends at national and provincial levels, with a focus on eastern provinces, which are the most prone to wildfires. Wildfire patch centroids were extracted and analyzed using spatial clustering techniques, kernel density estimation, and emerging hotspot analysis via space-time cube tools. Results show that while fire points are scattered across western Mongolia, the largest burned areas and most persistent hotspots are concentrated in the eastern steppe regions, particularly near borders. Temporal trend analysis revealed that spring (March-May) is the peak fire season, accounting for 62% of burned areas, followed by summer (21%) and autumn (16%). Although the total number of fires shows a decreasing trend, the intensity and extent of individual fire events remain significant, especially in 2012, 2015, and 2023. Comparative assessment with official fire incident reports from the Mongolian Statistical Information Service highlights both consistencies and discrepancies with satellite-derived data. The findings contribute to a better understanding of wildfire dynamics in Mongolia and support improved fire risk management and policy development. To assess wildfire risk and its environmental drivers, the MaxEnt model was applied using presence-only fire patch data and environmental variables such as vegetation index (NDVI), land surface temperature, precipitation (SPI), wind speed, and slope. The model results revealed that land surface temperature and wind speed were the most influential predictors of wildfire occurrence in Eastern Mongolia, indicating that dry, warm, and windy conditions significantly increase fire probability. The model results effectively delineate high-risk zones, providing valuable insights for fire prevention and management.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Enkhjin Enkhbold
AU  - Gantuya Ganbat
AU  - Nilanchal Patel
PY  - 2025
DA  - 2025/12/25
TI  - Mapping Wildfire Dynamics in Eastern Mongolia: Integrating Remote Sensing for Sustainable Resource Management
BT  - Proceedings of the 3rd International Conference Resources and Technology (RESAT 2025)
PB  - Atlantis Press
SP  - 260
EP  - 274
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-928-5_19
DO  - 10.2991/978-94-6463-928-5_19
ID  - Enkhbold2025
ER  -