Active fire monitoring and fire danger potential detection from space: A review

Abstract

Wildland fire is both one of the major natural hazards and a natural process for ecosystem persistence. Accurate assessment of fire danger potential and timely detection of active fires are critical for fire fighting and fuel management. Space-borne measurements have become the primary approaches for these efforts. Many research works have been conducted and some data products have been generated for practical applications. This paper presents a review of the major sensors and algorithms for active fire monitoring and fire danger potential detection from space. Major sensors and their characteristics, physical principles of the major algorithms are summarized. Limitations of these algorithms and future improvements are also discussed.

References

1. Alonso M, Camarasa A, Chuvieco E, et al. (1996). Estimating temporal dynamicsof fuel moisture content of Mediterranean species from NOAA-AVHRRdata. EARSEL Advances in Remote Sensing, 4, 9–24
2. Arino O, Mellinotte J M (1998). The 1993Africa fire map, Int. J. Rem. Sens.19, 2019–2023
3. Barnes W L, Barnes R A, Holmes A W (1996). Characterization and calibrationresults from the Visible and Infrared Scanner (VIRS) for the TropicalRainfall Measuring Mission (TRMM). Proceedingsof the Advanced and Next–Generation Satellites II, Taormina, Italy, 23–26 1996, 266–276
4. Bradshaw L S, Deeming J E, Burgan R E, et al. (1984). The 1978 national fire dangerrating system: technical documentation. General Technical Report INT-169.U.S.D.A. Forest Service, Intermountain Forest and RangeExperiment Station, Ogden, Utah
5. Burgan R E, Klaver R W, Klaver J M (1998). Fuel models and fire potential fromsatellite and surface observations. InternationalJournal of Wildland Fires, 8 (3), 159–170. doi:10.1071/WF9980159
6. Burgan R E (1996). Use of remotely sensed data for fire danger estimation. EARSeL Advances in Remote Sensing, 4 (4), 1–8
7. Ceccato P, Gobron N, Flasse S, et al. (2002). Designing a spectral indexto estimate vegetation water content from remote sensing data: Part1. Theoretical approach. Remote Sensing of Environment, 82, 188–197. doi:10.1016/S0034-4257(02)00037-8
8. Chuvieco E, Aguado I, Cocero D, et al. (2003b). Design of an empiricalindex to estimate fuel moisture content from NOAA-AVHRR images inforest fire danger studies. InternationalJournal of Remote Sensing, 24(8), 1621–1637. doi:10.1080/01431160210144660
9. Chuvieco E, Cocero D, Riano D, et al. (2004). Combining NDVI and SurfaceTemperature for the estimation of live fuel moisture content in forestfire danger rating. Remote Sensing of Environment, 92, 322–331. doi:10.1016/j.rse.2004.01.019
10. Chuvieco E, Deshayes M, Stach N, et al. (1999). Short-term fire risk: foliagemoisture content estimation from satellite data. In Remote Sensingof Large Wildfires in the Eiropean Mediterrranean basin, (E. Chuvieco Ed.), Berlin: Springer-Verlag
11. Chuvieco E, Martin M P (1994). A simplemethod for fire growth monitoring using AVHRR channel 3 data, Int. J. Rem. Sens. 15: 3141–3146
12. Chuvieco E, Riaño D, Van Wagtendok J, et al. (2003a). Fuel Loads and Fuel TypeMapping. In Wildland fire danger estimationand mapping: The Role of Remote Sensing Data, (E. Chuvieco, Ed.). Singapore: World Scientific Publishing, 119–142
13. Combal B, Baret F, Weiss M, et al. (2002). Retrieval of canopy biophysicalvariables from bidirectional reflectance–using prior informationto solve the ill-posed inverse problem. Remote Sensing of Environment, 84, 1–15. doi:10.1016/S0034-4257(02)00035-4
14. Croft T A (1973). Burning waste gas in oil fields. Nature, 245, 375–376. doi:10.1038/245375a0
15. Dasgupta S, Qu J J, Hao X (2006). Design of a Susceptibility Indexfor fire risk monitoring. IEEE Geoscienceand Remote Sensing Letters, 3(1), 140–144. doi:10.1109/LGRS.2005.858484
16. Dasgupta S, Qu J J, Hao X (2007). Evaluating remotely sensed live fuelmoisture estimations for fire behavior predictions in Georgia. Remote Sensing of Environment, 108: 2, 138–150. doi:10.1016/j.rse.2006.06.023
17. Dozier J (1981). A method for satellite identification of surface temperaturefields of subpixel resolution, Remote Sensingof Environment, 11, 221–229
18. Dwyer E, Gregoire J M, Malingreau J P (1998). A global analysis of vegetation firesusing satellite images: Spatial and temporal dynamics, Ambio, 27, 175–181
19. Elvidge C D, Pack D W, Prins E, et al. (1998). Wildfire Detection withMeteorological Satellite Data: Results from New Mexico during Juneof 1996 using GOES, AVHRR, and DMSP-OLS, Ed. Lunetta, R. S and C.D. Elvidge. Remote Sensing Change Detection: Environmental Monitoring Methodsand Applications, Ann Arbor Press, 103–121
20. Eva H, Flasse S (1996). Contextualand muliple-threshold algorithms for regional active fire detectionwith AVHRR data, Rem. Sens. Rev.14, 333–351
21. Flannigan M D, Vonder Haar T H (1986). Forestfire monitoring using NOAA satellite AVHRR,Can. J. For. Res. 16, 975–982
22. Flasse S P, Ceccato P (1996). A contextualalgorithm for AVHRR fire detection, Int.J.Rem. Sens.17, 419–424
23. Franca J R, Brustet J M, Fontan J (1995). Multispectral remote sensing of biomassburning in West Africa, J. Atmos. Chem. 22, 81–110
24. Gao B C (1996). NDWI- a normalized difference water index for remotesensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266. doi:10.1016/S0034-4257(96)00067-3
25. Giglio L, Descloitres J, Justice C O, et al. (2003). An enhanced contextual firedetection algorithm for MODIS. Remote Sensingof Environment, 87, 273–282. doi:10.1016/S0034-4257(03)00184-6
26. Giglio L, Kendall J D, Justice C O (1999). Evaluation of global fire detectionusing simulated AVHRR infrared data, Int.J. Rem. Sens., 20, 1947–1985. doi:10.1080/014311699212290
27. González F A, Cuevas J M, Casanova J L, et al. (1997). A forest fire risk assessmentusing NOAA-AVHRR images in the Valencia area, eastern Spain. International Journal of Remote Sensing, 18, 2201–2207. doi:10.1080/014311697217837
28. Guo G, Zhou M (2004). UsingMODIS land surface temperature to evaluate forest fire risk of northeastchina. IEEE Geoscience and Remote sensingletters, 1(2), 98–100. doi:10.1109/LGRS.2004.826550
29. Hao X, Qu J J (2007). Retrievalof Real-time Live Fuel Moisture Content Using MODIS Measurements. Remote Sensing of Environment, 108: 2, 130–137. doi:10.1016/j.rse.2006.09.033
30. Hardisky M A, Lemas V, Smart R M (1983). The influence of soil salinity, growthform, and leaf moisture on the spectral reflectance of spartina alternifoliacanopies. Photogrammetric Engineering andRemote Sensing, 49, 77–83
31. Hunt E R, Rock B N (1989). Detectionof changes in leaf water content using near- and middle infrared reflectances. Remote Sensing of Environment, 30, 43–54. doi:10.1016/0034-4257(89)90046-1
32. Hunt E R, Rock B N, Nobel P S (1987). Measurement of leaf relative watercontent by infrared reflectance. RemoteSensing of Environment, 22, 429–435. doi:10.1016/0034-4257(87)90094-0
33. Illera P, Fernandez A, Delgado J A (1996). Temporal evolution of the NDVI asan indicator of forest fire danger. InternationalJournal of Remote Sensing, 17, 1093–1105. doi:10.1080/01431169608949072
34. Jackson R D (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology, 24, 265–287. doi:10.1146/annurev.py.24.090186.001405
35. Jackson R D, Idso S B, Reginato R J, et al. (1981). Canopy temperature as acrop water stress indicator. Water ResourcesResearch, 17, 1133–1138. doi:10.1029/WR017i004p01133
36. Jacquemoud S (1993). Inversion of the PROSPECT + SAIL canopy reflectancemodel from AVIRIS equivalent spectra: theoretical study. Remote Sensing of Environment, 44, 281–292. doi:10.1016/0034-4257(93)90022-P
37. Jacquemoud S, Baret F, Andrieu B, et al. (1995). Extraction of vegetationbiophysical parameters by inversion of the PROSPECT + SAIL modelson sugar beet canopy reflectance data. Application to TM and AVIRISsensors. RemoteSensing of Environment, 52, 163–172. doi:10.1016/0034-4257(95)00018-V
38. Ji Y, Stocker E (2002). An overviewof the TRMM/TSDIS fire algorithm and product. International Journal of Remote Sensing, 23, 3285–3303. doi:10.1080/01431160110070816
39. Justice C O, Dowty P (editors) (1994). IGBP-DIS satellite fire detection algorithm workshop technical report. IGBP-DIS Working Paper 9, NASA/GSFC, Greenbelt,Maryland, USA, February1993
40. Justice C O, Kendall J D, Dowty P R, et al. (1996). Satellite remote sensingof fires during the SAFARI campaign using NOAA advanced very highresolution radiometer data, J. Geophy. Res, 101, 23851–23863
41. Justice C O, Giglio L, Roy D, et al. (2002). The MODIS fire products:algorithm, preliminary validation and utilization. Remote Sensing of Environment, 83, 244–262. doi:10.1016/S0034-4257(02)00076-7
42. Kaufman Y J, Kleidman R G, King M D (1998b). SCAR-B fires in the tropics: propertiesand remote sensing from EOS-MODIS. Journalof Geophysical Research, 103, 31955–31968. doi:10.1029/98JD02460
43. Kaufman Y J, Justice C O, Flynn L, et al. (1998a). Potential global fire monitoringfrom EOS-MODIS. Journal of GeophysicalResearch, 103, 32215–32238. doi:10.1029/98JD01644
44. Kaufman Y J, Tucker C J, Fung I (1990). Remote sensing of biomass burningin the tropics. Journal of GeophysicalResearch, 95, 9927–9939. doi:10.1029/JD095iD07p09927
45. Kauth R J, Thomas G S (1976). The tasseledCap -- A graphic description of the spectral-temporal developmentof agricultural crops as Seen by LANDSAT. Proceedings of the Symposium on Machine Processing of Remotely SensedData, Purdue University of West Lafayette, Indiana, 41–51
46. Keane R E, Burgan R, Van Wagtendonk J (2001). Mapping wildland fuels for fire managementacross multiple scales: Integrating remote sensing, GIS, and biophysicalmodeling. International Journal of WildlandFire, 10, 301–319. doi:10.1071/WF01028
47. Keane R E, Mincemoyer S A, Schmindt K M, et al. (2000). Mapping vegetation and fuelsfor fire management on the Gila National Forest Complex, New Mexico.General Technical Report RMRS-GTR-46-CD, Ogden, UT, USDA Forest Service. Rocky Mountain ResearchStation
48. Kennedy P J, Belward A S, Gregoire J M (1994). An improved approach to fire monitoringin West Africa using AVHRR data, Int. J. Remote Sens, 15, 2235–2255. doi:10.1080/01431169408954240
49. Kidwell K B (comp. anded.) (1998). NOAA Polar Orbiter Data (TIROS-N,NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, and NOAA-14)Users Guide: Washington, D.C., NOAA/NESDIS
50. Kummerow C, Barnes W, Kozu T, et al. (1998). The tropical rainfall measuringmission (TRMM) sensor package. Journalof Atmospheric and Oceanic Technology, 15, 808–816. doi:10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2
51. Langaas S (1992). Temporal and spatial distribution of Savannah firesin Senegal and the Gambia, West Africa, 1989-90, derived from muilti-temporalAVHRR night images. Int. J. Wildland Fire, 2, 21–36. doi:10.1071/WF9920021
52. Langaas S (1995). A critical review of sub-resolution redetection techniquesand principles using thermal satellite data. PhD Dissertation,Department of Geography, University of Oslo, Norway
53. Lasaponara R, Cuomo V, Macchiato M F, et al. (2003). A self-adaptive algorithmbased on AVHRR multitemporal data analysis for small active fire detection. International Journal of Remote Sensing, 24, 1723–1749. doi:10.1080/01431160210144723
54. Lee T M, Tag P M (1990). Improveddetection of hotspots using the AVHRR 3.7 um channel. Bull. Amer. Meteoro. Soc. 71, 1722–1730
55. Li Z, Cihlar J, Moreau L, Huang F, et al. (1997). Monitoring fire activities in the boreal ecosystem,J. Geophy. Res. 102, 29611–29624
56. Li Z, Nadon S, Cihlar J (2000). Satellite detection of Canadian borealforest fires: Development and application of an algorithm, Int. J. Rem. Sens. 21, 3057–3069
57. Li Z, Kaufman Y J, Ithoku C, et al. (2001). A review of AVHRR-basedactive fire detection algorithms: Principles, limitations, and recommendations,in Global and Regional Vegetation Fire Monitoring From Space: Planningand Coordinated International Effort, edited by F. Ahern, J. G. Goldammer, and C. Justice, pp. 199–225, SPB Acad., The Hague, 2001
58. López A S, Gonzalzez F, Llop R, et al. (1991). An evaluation of the utilityof NOAA AVHRR images for monitoring forest fire risk in Spain. International Journal of Remote Sensing, 12, 1841–1851. doi:10.1080/01431169108955213
59. López A S, San-Miguel-Ayanz J Burgan R E (2002). Integrationof satellite sensor data and meteorological observations for evaluationof forest fire risk at the pan-European scale. International Journal of Remote Sensing, 23(13), 2713–2719. doi:10.1080/01431160110107761
60. Lunetta R S, Elvidge C D (1998). RemoteSensing Change Detection: Environmental Monitoring Methodsand Applications, Ann Arbor Press 106–108
61. Malingreau J P (1990). The Contribution of Remote Sensing to the Global Monitoringof Fires in Tropical and Sub-tropical Ecosystems. In: Fire in the Tropical Biota, edited by J. G. Goldammer, 337-370. Berlin: Springer-Verlag
62. Martin P, Ceccato P, Flasse S, et al. (1999). I. Fire detection and firegrowth monitoring using satellite data. Remote Sensing of Large Wildfiresin the European Mediterranean Basin, Chuvieco, E., (Ed.). Springer-Verlag Berlin/Heidelberg 3-540-65767-3, XII, 212
63. Maselli F, Romanelli S, Botta L, et al. (2003). Use of NOAA AVHRR NDVI imagesfor the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment, 86, 187–197. doi:10.1016/S0034-4257(03)00099-3
64. Matson M, Dozier J (1981). Identificationof subresolution high temperature sources using a thermal IR sensor.Photogramm. Eng. Remote Sens. 47, 1311–1318
65. Matson M, Schneider S R, Aldridge B, et al. (1984). Fire detection using theNOAA-series satellites, NOAA Tech. Rep. NESDIS 7, 34 pp.,Natl. Oceanic and Atmos. Admin., Washington, D. C.
66. Matson M, Stephens G, Robinson J (1987). Fire detection using data from theNOAA-N satellites, Int. J. Remote Sens. 8(7), 961–970
67. Mbow C, Goïta K, Bénié GB (2004). Spectral indicesfor fire risk assessment in savanna ecosystems. Remote Sensing of Environment, 91, 1–13. doi:10.1016/j.rse.2003.10.019
68. McNamara D, Stephens G, Ruminski M, et al. (2004). The Hazard Mapping System(HMS) - NOAA's multi-sensor fire and smoke detection program usingenvironmental satellites (Invited Presentation), 13th Conference on Satellite Meteorology and Oceanography, Norfolk,VA, September
69. Moran M S, Clarke T R, Inoue Y, et al. (1994). Estimation crop water deficitusing the relation between surface– air temperature and spectralvegetation index. Remote Sensing of Environment, 49, 246–263. doi:10.1016/0034-4257(94)90020-5
70. Paltridge G W, Barber J (1988). Monitoringgrassland dryness and fire potential in Australia with NOAA/AVHRRdata. Remote Sensing of Environment, 25, 381–394. doi:10.1016/0034-4257(88)90110-1
71. Peñuelas J, Filella I, Biel C, et al. (1993). The reflectance at the 950-970nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887–1905. doi:10.1080/01431169308954010
72. Peñuelas J, Filella I, Serrano L, et al. (1996). Cell wall elasticity andwater index (R970nm/R900nm) in wheat under different nitrogen availabilities. International Journal of Remote Sensing, 17(2), 378–382
73. Peñuelas J, Pinol J, Ogaya R, et al. (1997). Estimation of plant waterconcentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869–2875. doi:10.1080/014311697217396
74. Prins E, Schmetz J, Flynn L, et al. (2001a). Overview of current andfuture diurnal active fire monitoring using a suite of internationalgeostationary satellites. Global and RegionalWildfire Monitoring: Current Status and Future Plans, SPB AcademicPublishing, The Hague, Netherlands, 145–170
75. Prins E M, Feltz J M, Menzel W P, et al. (1998). An overview of GOES-8 diurnalfire and smoke results for SCAR-B and the 1995 fire season in SouthAmerica. J. Geo. Res. 103, 31821–31836
76. Prins E M, Feltz J, Schmidt C (2001b). An overview of active fire detectionand monitoring using meteorological satellites. Proc. 11th Conferenceon Satellite Meteorology and Oceanography, Madison, WI, Amer. Meteor. Soc. 1–8
77. Prins E M, Menzel W P (1992). GeostationarySatellite detection of biomass burning in South America. Int. J. of Remote Sensing, 13, 2783–2799. doi:10.1080/01431169208904081
78. Prins E M, Menzel W P (1994). Trendsin South American biomass burning detected with the GOES visible infraredspin scan radiometer atmospheric sounder from 1983 to 1991. Jour. Geo. Res. 99, 16719–16735
79. Prosper-Laget V, Douguedroit A, Guinot J P (1995). Mapping the risk of forest fire occurrenceusing NOAA satellite information. EARSeLAdvances in Remote Sensing, 4, 30–38
80. Pu R, Gong P, Li Z, et al. (2004). A dynamic algorithm forwildfire mapping with NOAA/AVHRR data, International Journal of Wildland Fire, 13, 275–285
81. Qu J J, Justice C O (2007). The EastFIREspecial edition: Selected papers from the EastFIRE Conference on theApplication of Remote Sensing to Fire Research in the Eastern UnitedStates, Remote Sensing of Environment, 108:2, 121–122
82. Rauste Y, Herland E, Frelander H, et al. (1997). Satellite-based forest firedetection for fire control in boreal forests. International Journal of Remote Sensing, 18, 2641–2656. doi:10.1080/014311697217512
83. Riaño D, Chuvieco E, Salas J, et al. (2002). Generation of fuel typemaps from Landsat TM images and ancillary data in Mediterranean ecosystems. Canadian Journal of Forest Research, 32(8), 1301–1315. doi:10.1139/x02-052
84. Ritter N (1994). MISR DID (DTED Intermediate Dataset) Subroutine LibraryProgrammer's Guide. Jet Propulsion Laboratory,Pasadena, CA
85. Robinson J M (1991). Fire from space: Global fire evaluation using infraredremote sensing,Int. J. Rem. Sen. 12, 3–24
86. Rothermel R C (1972). A mathematical model for predicting fire spread in wildlandfuels. Research Paper, INT-115, U.S.D.A. Forest Service, Intermountain Forest and Range Experiment Station,Ogden, Utah
87. Scott J H, Reinhardt E D (2001). Assessingcrown fire potential by linking models of surface and crown fire behavior. USDA Forest Service Rocky Mountain Research StationResearch Paper Rmrs (RP-29)
88. Setzer A W, Pereira M C (1991). Operationaldetection of fires in Brazil with NOAA-AVHRR, in Proceedings 24th International Symposium on Remote Sensing of theEnvironment, 469–482, Rio de Janeiro, Brazil
89. Sudiana D, Kuze H, Takeuchi N, et al. (2003). Assessing forest fire potentialin Kalimantan Island, Indonesia using satellite and surface weatherdata. International Journal of WildlandFire, 12(2),175–184. doi:10.1071/WF02035
90. Townshend J R G, Justice C O, Skole D, et al. (1994). The 1 km resolution globaldataset: needs of the International Geosphere–Biosphere Programme. International Journal of Remote Sensing, 15, 3417–3441. doi:10.1080/01431169408954338
91. Tucker C J (1977). Asymptotic nature of grass canopy spectral reflectance. Applied Optics, 16 (5), 1151–1156
92. Vidal A, Pinglo F, Durand H, et al. (1994). Evaluation of a temporalfire risk index in mediterranean forests from NOAA thermal IR. Remote Sensing of Environment, 49, 296–303. doi:10.1016/0034-4257(94)90024-8
93. Wang W, Qu J J, Liu Y, et al. (2007). An improved algorithm forsmall and cool fire detection using MODIS data: a preliminary studyin the southeastern United States. Remotesensing of Environment, 108:2, 163–170. doi:10.1016/j.rse.2006.11.009
94. Zarco-Tejada P L J, Rueda C A, Ustin S L (2003). Water content estimation in vegetationwith MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, 109–124. doi:10.1016/S0034-4257(02)00197-9
95. Zipoli G, Costantini R, Romanelli S, et al. (2000). Use of satellite and ancillarydata for the evaluation of structural and meteorological forest firerisks in Tuscany (Central Italy). Proceedings of ECAC 2000, Pisa,16–20 October 2000. Florence, Italy: IATACNR, On CD-ROM