Predicting fire hazard areas using vegetation indexes, case study: Forests of Golestan Province, Iran

Akram Karimi, Sara Abdollahi, Kaveh Ostad-Ali-Askari, Saeid Eslamian, Vijay P. Singh

Article ID: 451
Vol 4, Issue 1, 2021

VIEWS - 1896 (Abstract) 733 (PDF)


Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.


Time Series; MODIS Sensor; Threshold; NDWI

Full Text:



1. Hedayati M. Review Forestry Spruce Something North of Iran. Journal of Forest and pasture NO55 2003.

2. Yin H, Kong F, Li X. RS and GIS-based forest fire risk zone mapping in da hinggan mountains. Chinese Geographical Science 2004; 14(3): 251–257.

3. Shariati N. The role and position of forests and pastures and watershed management for the management, development and supply of raw materials of wood and paper industry of the country [Master’s thesis] (in Persian). Gorgan: Gorgan University; 2008.

4. Ardakani S, Voldazoj M, Mohamadzade AM. Spectroscopic spectroscopic characterization of fire and field objectives for identification and separation in remote sensing data [PhD thesis]. Tehran: Khaje Naseerdin Toosi University of Technology, Geomechanics faculty; 2010.

5. Jazirei M. Forest conservation (in Persian). Tehran: Tehran Publication and Printing Institute; 2005. p. 230.

6. Merino-de-Miguel S, Huesca M, González-Alonso F. Modis reflectance and active fire data for burn mapping and assessment at regional level. Ecological Modelling 2010; 221(1): 67–74.

7. Portillo-Quintero C, Sanchez-Azofeifa A, Marcos do Espirito-Santo M. Monitoring deforestation with MODIS Active Fires in Neotropical dry forests: An analysis of local-scale assessments in Mexico, Brazil and Bolivia. Journal of Arid Environments 2013; 97: 150–159.

8. Xu D, Dai L, Shao G, et al. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research 2005; 16(3): 169–174.

9. Sharma MSD, V. Hoa MEPV, Cuong ETV, et al. Forest Fire Risk Zonation for Jammu District forest division using Remote Sensing and GIS. 7th FIG Regional Conference, Spatial Data Serving People: Land Governance and the Environment – Building the Capacity. Hanoi, Vietnam; 2009. p. 19–22.

10. Chuvieco E, Congalton RG. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment 1989; 29(2): 147–159.

11. Mohammadi F, Shabani N, Pourhashemi M. Preparation of forest fire hazard map using GIS and AHP in part of Pave forest. Journal of Forest and Poplar Research 2010; 18(4): 569–586.

12. Adhami Mojaddar MH, Mousavir A, Honardoost F. 1390. Fire hazard zonation using GIS, AHP case study-Caspian forests of Northern Iran-Golestan Province (in Persian) 2012.

13. Shorfi S. Investigation and zoning of susceptible forest areas using RS and GIS (in Persian). National Conference on Forests of Central Zagros; 2012 Nov 26-28; Adelaide. Arlington: American Public Power Association; 2012.

14. Nezhad YA, Jafarabadi DM. Comparison of fire damage of two broad-leaved and broad-leaved broadleaf masses mixed with leaf needles in Golestan Province (in Persian). International Conference on Climate Change and Tree Chronology in Caspian Ecosystems; 2009 June 2; Washington D.C. Arlington Heights: The Heartland Institute; 2009.

15. Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988; 25(3): 295–309.

16. Murta Özbayoğlu A, Bozer R. Estimation of the burned area in forest fires using computational intelligence techniques. Procedia Computer Science 2012; 12(1): 282–287.

17. Adab H, Kanniah KD, Solaimani K. GIS-based probability assessment of fire risk in grassland and forested landscapes of Golestan Province, Iran. 2011 International Conference on Environmental and Computer Science(ICECS 2011); 2011 Sep 16; Singapore. Singapore: IACSIT Press; 2011. p. 66.

18. Hunt ERJr, Rock BN. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sensing of Environment 1989; 30(1): 43–54.

19. Rouse JW, Haas RW, Schell JA, et al. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Type III, Final Report; 1973 Apr-Sep; Maryland. Greenbelt, MD: NASA/GSFC; 1974. p. 1–69.

20. Clevers JGPW. Application of a weighted infrared vegetation index for estimating leaf. Area Index by Correcting for Soil Moisture Remote Sensing of Environment 1989; 29(1): 25–37.

21. Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 1996; 55(2): 95–107.

22. Gao BC. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 1996; 38(3): 257–266.

23. Ceccato P, Gobron N, Flasse S, et al. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sensing of Environment 2002; 82(2-3): 188–197.

24. Ghobadi GJ, Gholizadeh B, Dashliburun OM. Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (Case study, Golestan Province). International Journal of Agricultural Crop Science 2012; 4(12): 818–824.

25. Sowmya SV, Somashekar RK. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology 2010; 31(6): 969–974.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This site is licensed under a Creative Commons Attribution 4.0 International License.