Forest is an important part of the earth's terrestrial biosphere. At present, the coverage area of all kinds of forest land in the world is about 5 billion hectares, accounting for about 1 / 3 of the land area. The distribution of these forests in the world is extremely uneven, of which Latin America is the largest, with a forest area of 550 million hectares, accounting for about 21% of the world's forest area; The second is North America, with a forest area of 470 million hectares, accounting for about 19% of the world's forest area. Today, the world's forest area is decreasing at the rate of 18 million hectares every year. According to the statistics of the food and Agriculture Organization of the United Nations, half of the world's forests have been lost since 1950 (mainly in developing countries). At that time,  the world's forest area accounted for about 1 / 41 of the land area, which decreased to 1 / 5 in 1978 and 1 / 6 in 2000, about 2.1 billion hectares. Some scholars estimate that the trend of forest reduction will continue until 2020, when it will drop to 1 / 7, leaving only 1.8 billion hectares. Especially in developing countries.

Nowadays, forest fires occur frequently, which has a great destructive effect on forest and animal husbandry production. Scientific assessment of forest grassland fire risk is one of the effective means to prevent fires, reduce fire hazards and scientifically formulate fire prevention and extinguishing measures.

At present, fire risk assessment mainly depends on the observation data obtained by meteorological stations. Remote sensing data has obvious advantages in a wide range of spatial information than meteorological observation data, especially in the acquisition of vegetation information.

Karimi had a research in 1995-2017, and the modeling and zoning data of fire risk prediction were extracted and analyzed descriptively. The results indicate that RS and GIS are effective tools in the study of fire risk prediction, Fuzzy Analytic Hierarchy Process(AHP) zoning method was more practical among the applied methods, and the plant moisture stress measurement was the most efficient among the remote sensing indices.

                                                                      Figure 1. Forest fire risk coefficient through Francilla method.

More information, please click the link: https://systems.enpress-publisher.com/index.php/JGC/article/view/618 

References:

[1] https://wenwen.sogou.com/z/q863618785.htm

[2] Karimi A, Abdollahi S, Ostad-Ali-Sskari K, et al. Evaluating Models and Effective Factors Obtained from Remote Sensing (RS) and Geographic Information System (GIS) in the Prediction of Forest Fire Risk, Structured Review. Journal of Geography and Cartography, 2020; 3(1): 1-11.