Estimation of Fire Area in Iranian Vegetation Using MODIS and Alos-2 Data

Document Type : Original Article

Authors

1 M.Sc. Student of Remote Sensing and GIS, Faculty of Geography, University of Tehran

2 M.Sc. Student of Remote Sensing and GIS, Faculty of Geography, University of Yazd

Abstract

Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for their management and development. The proliferation of remote sensing images such as the active fire products of the Terra and Aqua satellites over the past two decades has been one of the most essential methods in detecting these fires. However, the active fire product of the MODIS sensor in previous studies has shown that they alone do not provide good results in fire-affected areas. Therefore, it is necessary to evaluate vegetation with basic maps. The aim of this study was to investigate two types of plant products and to discover the active fire of MODIS sensor and FNF-JAXA forest and non-forest cover maps for better separation of burnt areas of vegetation in Iran between July 1 and 160 2020. The results show the highest area of fire on Julius 144 with more than 49 thousand hectares and Julius 128 with more than 45 thousand hectares. However, the largest area of the fire, forest cover is estimated at 120 to 160 in 2020 with more than 14 thousand hectares. Khuzestan province had the highest area of fires in the period under study that most of these areas in agricultural lands and the three provinces of Fars, Kohgiluyeh and Boyer-Ahmad and Bushehr had the highest area of fires in forest cover. The highest frequency of fires was observed in agricultural lands, the main reason for which could be human intervention. The evaluation of the results showed that the use of the FNF-JAXA product (accuracy of 87.4% and a Kappa coefficient of 0.85) compared to MODIS products (accuracy of 80.3% and a Kappa coefficient of 0.78) in the separation of forest areas has better capabilities. However, the ability of MODIS products to distinguish between pasture and agricultural vegetation is an important advantage, which the FNF-JAXA product does not have. In general, the findings of the research show that the MODIS product and FNF-JAXA maps can be used as reference maps to distinguish different types of vegetation that are subject to fire, in damage assessment and management.

Keywords

References

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Iranian Journal of Remote Sensing & GIS
Volume 15, Issue 3 - Serial Number 59
September 2023
Pages 103-124

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