Assessing the Fires Impact on Vegetation Cover Using Remote Sensing Data: Indonesia Case Study

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Elizaveta Andreevna Grigorets
Anna Igorevna Kurbatova
Yaroslav Nikolaevich Vasyunin
Vasily Konstantinovich Lobanov
Riri Fitri Sari
Kseniya Yurievna Mikhaylichenko
Elizaveta Vyacheslavovna Anikina
Anastasia Mikhailovna Kupriyanova


Wildfires in Indonesia have become abnormally frequent due to the human-driven degradation of forest and agricultural lands, as well as climate change. The authors analyze recent studies that provide evidence for an increase in the fire hazard to various ecosystems in Indonesia (forests, peatlands, agricultural lands) considering changes in climatic and meteorological parameters of the environment. This work establishes a relationship between burnt areas, measured by Moderate Resolution Imaging Spectroradiometer (MODIS), and the following parameters, retrieved from the Reanalysis v5 (ERA5) ECMWF dataset: monthly precipitation amount, temperature at a height of 2 m above sea level, soil temperature in the upper layer (0 to 7 cm depth), water content in the upper soil layer (0 to 7 cm depth), specific air humidity, zonal wind speed, meridional wind speed, and a standard deviation of precipitation. The authors reveal a correlation and a direct dependence of wildfires on the potential factors influencing the area: air temperature and soil temperature. It is assumed to be associated with the rainfall type, winds (speed, direction, and oscillations), improper land use, and the El Niño–Southern Oscillation.


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How to Cite
Grigorets, E., Kurbatova, A., Vasyunin, Y., Lobanov, V., Sari, R. F., Mikhaylichenko, K., Anikina, E., & Kupriyanova, A. (2023). Assessing the Fires Impact on Vegetation Cover Using Remote Sensing Data: Indonesia Case Study. Advances in Systems Science and Applications, 23(1), 50-60.
Author Biographies

Yaroslav Nikolaevich Vasyunin, Paititi Research, London, United Kingdom

Yaroslav develops and implements artificial intelligence applications using the Google Cloud Platform. He is fueled by profound expertise of 10+ years in the extraction of value from complex geographic data, such as satellite imagery. He holds an engineering degree with a specialty in Remote Sensing in Natural Resource Exploration. He is also a Google Cloud Certified Professional Machine Learning Engineer. As a Geospatial Data Scientist, Yaroslav plays a crucial role in Paititi Research - an independent self-funded international effort. They aim to discover ancient human-made structures in the Peruvian uninhabited forests by applying geospatial technologies and data

Vasily Konstantinovich Lobanov, RUDN University, Moscow, Russian Federation


Elizaveta Vyacheslavovna Anikina, RUDN University, Moscow, Russian Federation