Fire risk prediction using remote sensed products: A case of Cambodia

Photogrammetric Engineering and Remote Sensing

Volumn 83, Issue 1, 2017, Pages 19-25

  Yu, Bo ^a   Chen, Fang ^a,b   Li, Bin ^a   Wang, Li ^a   Wu, Mingquan ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; DEFORESTATION; FIRE HAZARDS; FORECASTING; LEARNING SYSTEMS; MEAN SQUARE ERROR; METEOROLOGY; REGRESSION ANALYSIS; REMOTE SENSING; RISKS;

CORRELATION COEFFICIENT; FIRE OCCURRENCE PROBABILITY; FIRE OCCURRENCES; MACHINE LEARNING METHODS; METEOROLOGICAL DATA; RANDOM FORESTS; REGRESSION MODEL; USER EXPERIENCE;

FIRES;

DISASTER MANAGEMENT; ERROR CORRECTION; FIRE MANAGEMENT; FOREST FIRE; MACHINE LEARNING; MONSOON; PROBABILITY; REGRESSION ANALYSIS; REMOTE SENSING; RISK ASSESSMENT;

CAMBODIA;

EID: 85018711183     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.83.1.19     Document Type: Article

References (54)

1. Akther, M.S., and Q.K. Hassan, 2011. Remote sensing-based assessment of fire danger conditions over boreal forest, Selected Topics in Applied Earth Observations and Remote Sensing, Journal of IEEE, 4(4):992–999.
2. Alexander, M., and F. Cole, 2001. Rating fire danger in Alaska ecosystems, Fireline, 12:2–3.
3. Alexander, M., and L. Fogarty, 2002. A pocket card for predicting fire behaviour in grasslands under severe burning conditions.
4. Ark, P., 2003. Logging threatens Cambodian tragedy, UN, World Environment News.
5. Breiman, L., 2001. Random Forests, Machine Learning, 45(1):5–32.
6. Burgan, R., P. Andrews, L. Bradshaw, C. Chase, R. Hartford and D. Latham, 1997. WFAS: wildland fire assessment system, Fire Management Notes, 57(2):14–17.
7. Chatenoux, B., and P. Peduzzi, 2013. Biomass fires: Preliminary estimation of ecosystems global economic losses.
8. Chowdhury, E.H., and Q.K. Hassan, 2013. Use of remote sensingderived variables in developing a forest fire danger forecasting system, Natural Hazards, 67(2):321–334.
9. Chowdhury, E.H., and Q.K. Hassan, 2015. Operational perspective of remote sensing-based forest fire danger forecasting systems, ISPRS Journal of Photogrammetry and Remote Sensing, 104:224–236.
10. de Groot, W.J., R.D. Field, M.A. Brady, O. Roswintiarti, and M. Mohamad, 2007. Development of the Indonesian and Malaysian fire danger rating systems, Mitigation and Adaptation Strategies for Global Change, 12(1):165–180.
11. Deeming, J.E., R.E. Burgan, and J.D. Cohen, 1977. The national fire-danger rating system-1978., USDA Forest Service General Technical Report INTUS (USA). No. 39.
12. Dowdy, A.J., and C. Scientific, 2009. Australian fire weather as represented by the McArthur forest fire danger index and the Canadian forest fire weather index, Citeseer.
13. Eduardo, E. M., Y. E. Shimabukuro, G. Felipe, B. Arcoverde, A. Lima, 2009. Forest fire risk mapping in the Brazilian Amazon using MODIS images and artificial neural networks, Proceedings of the Simposio Brasileiro de Sensoriamento Remoto, Natal, Brasil, pp. 1425–1432.
14. Fensholt, R., and I. Sandholt, 2003. Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment, Remote Sensing of Environment, 87(1):111–121.
15. Flannigan, M., B. Stocks, M. Turetsky, and M. Wotton, 2009. Impacts of climate change on fire activity and fire management in the circumboreal forest, Global Change Biology, 15(3):549–560.
16. Forsberg, N.T., B.M. Longo, K. Baxter, and M. Boutté, 2012. Wildfire smoke exposure: A guide for the nurse practitioner, The Journal for Nurse Practitioners, 8(2):98–106.
17. Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sensing of Environment, 114(1):168–182.
18. Gao, B.-C., 1996. NDWI -A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58(3):257–266.
19. Gitelson, A., R. Stark, U. Grits, D. Rundquist, Y. Kaufman, and D. Derry, 2002. Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction, International Journal of Remote Sensing, 23(13):2537–2562.
20. Granstrom, A., and J. Schimmel, 1998. Assessment of the Canadian forest fire danger system for Swedish fuel conditions, Rescue Services Agency, Stockholm, Sweden, 34 p.
21. Henion, A., and L. Luo, 2013. Future extreme wildfires may be fueled by climate change, http://msutoday.msu.edu/news/2013/futureextreme- wildfires-may-be-fueled-by-climate-c hange/, Michigan State University (last date accessed: 08 December 2016).
22. Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones, and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas, International Journal of Climatology, 25(15):1965–1978.
23. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83(1):195–213.
24. Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25(3):295–309.
25. Jarvis, A., H.I. Reuter, A. Nelson, and E. Guevara, 2008. Hole-filled SRTM for the globe version 4, CGIAR-CSI SRTM 90m Database, URL: http://www.cgiar-csi.org/data/srtm-90m-digital-elevationdatabase- v4-1 (last date accessed: 08 December 2016).
26. Jones, R.T., D.P. Ribbe, P.B. Cunningham, J.D. Weddle, and A.K. Langley, 2002. Psychological impact of fire disaster on children and their parents, Behavior Modification, 26(2):163–186.
27. Kales, S.N., E.S. Soteriades, C.A. Christophi, and D.C. Christiani, 2007. Emergency duties and deaths from heart disease among firefighters in the United States, New England Journal of Medicine, 356(12):1207–1215.
28. Kamran, K.V., K. Omrani, and S.S. Khosroshahi, 2014. Forest fire risk assessment using multi-criteria analysis: A case study Kaleybar Forest, International Conference on Agriculture, Environment and Biological Sciences, Turkey, pp. 30–33.
29. Kandya, A., M. Kimothi, R. Jadhav, and J. Agarwal, 1998. Application of geographic information system in identification of’fireprone’areas- a feasibility study in parts of Junagadh (Gujarat), Indian Forester, 124(7):531–536.
30. Lee, B., M. Alexander, B. Hawkes, T. Lynham, B. Stocks, and P. Englefield, 2002. Information systems in support of wildland fire management decision making in Canada, Computers and Electronics in Agriculture, 37(1):185–198.
31. Liaw, A., and M. Wiener, 2002. Classification and regression by Random Forest, R news, 2(3):18–22.
32. Loehman, R.A., J.A. Clark, and R.E. Keane, 2011. Modeling effects of climate change and fire management on western white pine (Pinus monticola) in the Northern Rocky Mountains, USA, Forests, 2(4):832–860.
33. Longley, P., 2005. Geographic Information Systems and Science, John Wiley & Sons.
34. Luke, R.H., and A.G. McArthur, 1978. Bushfires in Australia, A.G.P. Service, Canberra, p. 359.
35. Maeda, E.E., A.R. Formaggio, Y.E. Shimabukuro, G.F.B. Arcoverde, and M.C. Hansen, 2009. Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks, International Journal of Applied Earth Observation and Geoinformation, 11(4):265–272.
36. Mangan, R.J., 2007. Wildland Firefighter Fatalities in the United States: 1990-2006, USDA Forest Service Technology and Development Center.
37. Nesterov, V., 1949. Forest fire danger and methods of its determination, USSR State Industry Press, Goslesbumizdat, Moscow [in Russian].
38. Nobleman, M.T., 2003. Cambodia, Mankato, Minnesota: Bridgestone Books, 7 p.
39. Oldford, S., B. Leblon, D. Maclean, and M. Flannigan, 2006. Predicting slow-drying fire weather index fuel moisture codes with NOAA-AVHRR images in Canada’s northern boreal forests, International Journal of Remote Sensing, 27(18):3881–3902.
40. Pausas, J.G., and S. Paula, 2012. Fuel shapes the fire-climate relationship: Evidence from Mediterranean ecosystems, Global Ecology and Biogeography, 21(11):1074–1082.
41. Pinty, B., and M. Verstraete, 1992. GEMI: A non-linear index to monitor global vegetation from satellites, Vegetatio, 101(1):15–20.
42. Realmuto, V., S. Hook, M. Foote, I. Csiszar, P. Dennison, L. Giglio, M. Ramsey, R. Vaughan, M. Wooster, and R. Wright, 2011. HyspIRI high-temperature saturation study, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California.
43. Roberts, D., P. Dennison, S. Peterson, S. Sweeney, and J. Rechel, 2006. Evaluation of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California, Journal of Geophysical Research: Biogeosciences, 111(G4).
44. San-Miguel-Ayanz, J., P. Barbosa, G. Liberta, G. Schmuck, E. Schulte, and P. Bucella, 2003. The European forest fire information system: A European strategy towards forest fire management, Proceedings of the Proceedings of the 3rd International Wildland Fire Conference, Sydney, Australia.
45. Serrano, L., S.L. Ustin, D.A. Roberts, J.A. Gamon, and J. Penuelas, 2000. Deriving water content of chaparral vegetation from AVIRIS data, Remote Sensing of Environment, 74(3):570–581.
46. Sonka, M., V. Hlavac, and R. Boyle, 2014. Image Processing, Analysis, and Machine Vision, Cengage Learning.
47. Sow, M., C. Mbow, C. Hély, R. Fensholt, and B. Sambou, 2013. Estimation of herbaceous fuel moisture content using vegetation indices and land surface temperature from MODIS data, Remote Sensing, 5(6):2617–2638.
48. Stefanidou, M., S. Athanaselis, and C. Spiliopoulou, 2008. Health impacts of fire smoke inhalation, Inhalation Toxicology, 20(8):761–766.
49. Stocks, B.J., T. Lynham, B. Lawson, M. Alexander, C.V. Wagner, R. McAlpine, and D. Dube, 1989. Canadian forest fire danger rating system: An overview, The Forestry Chronicle, 65(4):258–265.
50. Taylor, S., 2001. Considerations for applying the Canadian Forest Fire Danger Rating System in Argentina, Canadian Forest Service, Pacific Forestry Centre, Natural Resources Canada, BC Ministry of Forests Contract Ref. (14020-90):02.
51. Tropical Rainfall Measurement Mission Project (TRMM). Daily TRMM and Others Rainfall Estimate (3B42 V7 derived), Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC).
52. Venevsky, S., K. Thonicke, S. Sitch, and W. Cramer, 2002. Simulating fire regimes in human-dominated ecosystems: Iberian Peninsula case study, Global Change Biology, 8(10):984–998.
53. Vidal, A., and C. Devaux-Ros, 1995. Evaluating forest fire hazard with a Landsat TM derived water stress index, Agricultural and Forest Meteorology, 77(3):207–224.
54. Viegas, D.X., G. Bovio, A. Ferreira, A. Nosenzo, and B. Sol, 2000. Comparative study of various methods of fire danger evaluation in southern Europe, International Journal of Wildland Fire, 9(4):235–246.