Application of Remote Sensing Techniques in Determining the Risk Taking Level of Different Seasons on Fire Generation in Terms of NDVI Index During the Year Case Study: Golestan Province, Iran
- 1 Karaj Environmental Faculty, Iran
- 2 Yazd University, Iran
- 3 Islamic Azad University, Iran
- 4 Isfahan University of Technology, Iran
- 5 Texas A and M University, United States
- 6 University of Thessaly, Volos, Greece
Copyright: © 2020 Akram Karimi, Sara Abdollahi, Kaveh Ostad-Ali-Askari, Saeid Eslamian, Vijay P. Singh and Nicolas R. Dalezios. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Knowledge of the nature of seasons and months in terms of the fire risk is very important in environmental planning, land management and forest resource management in order to achieve the sustainable development. One of the applications of remote sensing in this regard is the continuous monitoring of the zone to detect changes. Currently, the vegetation mapping is used to generate information for macro and micro planning. In order to monitor changes across the Golestan province forests through different seasons in 2000-2015, all images of MOD13Q1 MODIS were prepared during this period. Then, the images of the Normalized Difference Vegetation Index (NDVI) were prepared for the four seasons and twelve months of the year. The classification of the indices included lands covered with excellent, moderate, weak and very poor coverage was conducted in order to investigate the changes. Then, the comparison was then performed by LAND FIRE points and the validity of the classification results was evaluated. It was concluded that the seasons of the year from high risk to low risk were winter, summer, fall and spring, respectively. In the high-risk season, winter, January was the most dangerous month and in the low risk season, spring, may was the lowest month of the year.
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- Forest Cover Surface Changes
- NDVI Vegetation Index
- Remote Sensing