A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

Remote Sensing of Environment

Volumn 136, Issue , 2013, Pages 455-468

  Yebra, Marta ^a   Dennison, Philip E ^b   Chuvieco, Emilio ^c   Riaño, David ^d,e   Zylstra, Philip ^f   Hunt, E Raymond ^g   Danson, F Mark ^h   Qi, Yi ^b   Jurdao, Sara ^c  

^a CSIRO   (Australia)

^b UNIVERSITY OF UTAH   (United States)

^c UNIVERSITY OF ALCALÁ   (Spain)

^d CSIC Instituto de Economia Geografia y Demografia IEGD   (Spain)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF WOLLONGONG   (Australia)

^g AGRICULTURAL RESEARCH SERVICE   (United States)

^h UNIVERSITY OF SALFORD   (United Kingdom)

Author keywords

Dry matter content; Fire occurrence; Fire risk; Live fuel moisture content; Radiative transfer models; Spectral indices; Vegetation water content; Water thickness; Wildfire

Indexed keywords

DRY MATTER CONTENT; FIRE OCCURRENCES; FIRE RISKS; LIVE FUEL MOISTURE; RADIATIVE TRANSFER MODEL; SPECTRAL INDICES; VEGETATION WATER CONTENT; WILDFIRE;

ESTIMATION; FUELS; LIQUIDS; PLANTS (BOTANY); REMOTE SENSING; RISK ASSESSMENT; RISK PERCEPTION; WATER ABSORPTION;

FIRES;

ABSORPTION; ADAPTATION; ALGORITHM; ASSESSMENT METHOD; BIOCHEMICAL COMPOSITION; DROUGHT STRESS; DRY MATTER; EXPERIMENTAL STUDY; FUEL; LANDSCAPE; MOISTURE CONTENT; MONITORING; RADIATIVE TRANSFER; REMOTE SENSING; RISK FACTOR; SPATIOTEMPORAL ANALYSIS; SPECTRAL REFLECTANCE; WATER CONTENT; WILDFIRE;

EID: 84879537165     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2013.05.029     Document Type: Review

References (158)

1. Al-Moustafa T., Armitage R.P., Danson F.M. Mapping fuel moisture content in upland vegetation using airborne hyperspectral imagery. Remote Sensing of Environment 2012, 127:74-83.
2. Alexander M.E., Cruz M.G. Assessing the effect of foliar moisture on the spread rate of crown fires. International Journal of Wildland Fire 2012, 10.1071/WF12008.
3. Anderson M.C., Hain C., Wardlow B., Pimstein A., Mecikalski J.R., Kustas W.P. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. Journal of Climate 2011, 24:2025-2044.
4. Anderson M.C., Norman J.M., Mecikalski J.R., Otkin J.A., Kustas W.P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of Geophysical Research-Atmospheres 2007, 112.
5. Anderson S.A.J., Anderson W.R. Ignition and fire spread thresholds in gorse (Ulex europaeus). International Journal of Wildland Fire 2010, 19:589-598.
6. Asner G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 1998, 64:234-253.
7. Beaudoin A., Vidal A., Desbois N., Debaux-Ros C. Monitoring the water status of Mediterranean forests using ERS-1 to support fire risk prevention. IEEE Transactions on Geoscience and Remote Sensing 1995, 2:963-966.
8. Blackburn G.A. Wavelet decomposition of hyperspectral data: A novel approach to quantifying pigment concentrations in vegetation. International Journal of Remote Sensing 2007, 28:2831-2855.
9. Bossert J.E., Linn R.R., Reisner J.M., Winterkamp J.L., Dennison P.E., Roberts D.A. Coupled atmosphere fire behavior model sensitivity to spatial fuels characterization. Third symposium on fire and forest meteorology, Amer. Meteor. Soc. 80th Annual Meeting Long Beach, CA 2000, 21-26.
10. Boulet G., Chehbouni A., Gentine P., Duchemin B., Ezzahar J., Hadria R. Monitoring water stress using time series of observed to unstressed surface temperature difference. Agricultural and Forest Meteorology 2007, 146:159-172.
11. Bowyer P., Danson F.M. Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level. Remote Sensing of Environment 2004, 92:297-308.
12. Bradstock R.A., Cohn J.S., Gill A.M., Bedward M., Lucas C. Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather. International Journal of Wildland Fire 2009, 18:932-943.
13. Burgan R.E. Estimating live fuel moisture for the 1978 National Fire Danger Rating System. Research paper. INT-RP-226 1979, USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, (16 pp.).
14. Burgan R.E., Klaver R.W., Klaver J.M. Fuel models and fire potential from satellite and surface observations. International Journal of Wildland Fire 1998, 8:159-170.
15. Burrows N.D. Fire behaviour in jarrah forest fuels. CALMScience 1999, 3:31-84.
16. Caccamo G., Chisholm L.A., Bradstock R.A., Puotinen M.L. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment 2011, 115:2626-2639.
17. Caccamo G., Chisholm L.A., Bradstock R.A., Puotinen M.L. Using remotely-sensed fuel connectivity patterns as a tool for fire danger monitoring. Geophysical Research Letters 2012, 39:L01302.
18. Caccamo G., Chisholm L.A., Bradstock R.A., Puotinen M.L., Pippen B.G. Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data. International Journal of Wildland Fire 2012, 21:257-269.
19. Castro F.X., Tudela A., Gabriel E., Montserrat D., Canyameres E., Segarra M. Evolution of live fuel moisture in Mediterranean forest. V international conference on forest fire research 2006, Elsevier BV, Figuera da Foz, Amsterdam. D.X. Viegas (Ed.).
20. Ceccato P. Estimation of vegetation water content using remote sensing for the assessment of fire risk occurrence and burning efficiency. Phylosophy 2001, 168. University of Greenwich, Greenwich.
21. Ceccato P., Flasse S., Gregoire J.M. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sensing of Environment 2002, 82:198-207.
22. Ceccato P., Flasse S., Tarantola S., Jacquemoud S., Grégoire J.M. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment 2001, 77:22-33.
23. Ceccato P., Gobron N., Flasse S., Pinty B., Tarantola S. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment 2002, 82:188-197.
24. Cheney N.P., Gould J.S., Catchpole W.R. Prediction of fire spread in grasslands. International Journal of Wildland Fire 1998, 8:1-13.
25. Cheng T., Rivard B., Sanchez-Azofeifa A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment 2011, 115:659-670.
26. Cheng Y.-B., Ustin S.L., Riano D., Vanderbilt V.C. Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona. Remote Sensing of Environment 2008, 112:363-374.
27. Chladil M.A., Nunez M. Assessing grassland moisture and biomass in Tasmania. The application of remote sensing and empirical models for a cloudy environment. International Journal of Wildland Fire 1995, 5:165-171.
28. Chuvieco E., Aguado I., Cocero D., Riaño D. Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR analysis in forest fire danger studies. International Journal of Remote Sensing 2003, 24:1621-1637.
29. Chuvieco E., Aguado I., Dimitrakopoulos A. Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment. Canadian Journal of Forest Research-Revue Canadienne de Recherche Forestiere 2004, 34(11):2284-2293.
30. Chuvieco E., Aguado I., Jurdao S., Pettinari M.L., Yebra M., Salas J., et al. Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire 2012, 10.1071/WF12052.
31. Chuvieco E., Cocero D., Aguado I., Palacios-Orueta A., Prado E. Improving burning efficiency estimates through satellite assessment of fuel moisture content. Journal of Geophysical Research-Atmospheres 2004, 109(D14S07):1-8. 10.1029/2003JD003467.
32. Chuvieco E., Cocero D., Riaño D., Martín M.P., Martínez-Vega J., de la Riva J., et al. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment 2004, 92:322-331.
33. Chuvieco E., De Santis A., Riaño D., Halligan K. Simulation approaches for burn severity estimation using remotely sensed images. Journal of Fire Ecology 2007, 3:129-150.
34. Chuvieco E., González I., Verdú F., Aguado I., Yebra M. Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. International Journal of Wildland Fire 2009, 18:430-441.
35. Chuvieco E., Riaño D., Aguado I., Cocero D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing 2002, 23:2145-2162.
36. Chuvieco E., Yebra M., Jurdao S., Aguado I., Salas F.J., García M., et al. Field fuel moisture measurements on Spanish study sites 2011, Department of Geography, Alcala de Henares. U.o.A. Department of Geography, Spain (Ed.).
37. Colombo R., Meroni M., Marchesi A., Busetto L., Rossini M., Giardino C., et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing of Environment 2008, 112:1820-1834.
38. Combal B., Baret F., Weiss M. Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies. Agronomie 2002, 22:205-215.
39. Combal B., Baret F., Weiss M., Trubuil A., Mace D., Pragne're A., et al. Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem. Remote Sensing of Environment 2002, 84:1-15.
40. Danson F.M., Bowyer P. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment 2004, 92:309-321.
41. Dasgupta S., Qu J.J., Hao X., Bhoi S. Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA. Remote Sensing of Environment 2007, 108:138-150.
42. Davies G.M., Legg C.J., Smith A.A., MacDonald A.J. Rate of spread of fires in Calluna vulgaris-dominated moorlands. Journal of Applied Ecology 2009, 46:1054-1063.
43. De Santis A., Chuvieco E. Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models. Remote Sensing of Environment 2007, 108:422-435.
44. Deeming J.E., Burgan R.E., Cohen J.D. The National Fire-Danger Rating System - 1978. General technical report INT-39 1978, USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, (63 pp.).
45. Dennison P.E., Moritz M.A. Critical live fuel moisture in chaparral ecosystems: A threshold for fire activity and its relationship to antecedent precipitation. International Journal of Wildland Fire 2009, 18:1021-1027.
46. Dennison P.E., Moritz M.A., Taylor R.S. Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California. International Journal of Wildland Fire 2008, 17:18-27.
47. Dennison P.E., Roberts D.A., Peterson S.H., Rechel J. Use of Normalized Difference Water Index for monitoring live fuel moisture. International Journal of Remote Sensing 2005, 26:1035-1042.
48. Desbois N., Deshayes M., Beudoin A. Protocol for fuel moisture content measurements. A review of remote sensing methods for the study of large wildland fires 1997, 61-72. Departamento de Geografía, Universidad de Alcalá, Alcalá de Henares. E. Chuvieco (Ed.).
49. Dimitrakopoulos A., Papaioannou K.K. Flammability assessment of Mediterranean forest fuels. Fire Technology 2001, 37:143-152.
50. Dimitrakopoulos A.P., Bemmerzouk A.M. Predicting live herbaceous moisture content from a seasonal drought index. International Journal of Biometeorology 2003, 47:73-79.
51. Feret J.-B., François C., Asner G.P., Gitelson A.A., Martin R.E., Bidel L.P.R., et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment 2008, 112:3030-3043.
52. Finney M.A. FARSITE: Fire area simulator - Model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4 1998, (Fort Collins, CO. 51 pp.).
53. Fletcher T.H., Pickett B.M., Smith S.G., Spittle G.S., Woodhouse M.M., Haake E., et al. Effects of moisture on ignition behavior of moist California chaparral and Utah leaves. Combustion Science and Technology 2007, 179:1183-1203.
54. Fourty T., Baret F. Vegetation water and dry matter contents estimated from top-of-the atmosphere reflectance data: A simulation study. Remote Sensing of Environment 1997, 61:34-45.
55. Gao B.C. NDWI. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 1996, 58:257-266.
56. Garabedian P., Paul R. Partial differential equations 1964, Wiley, New York.
57. Garcia M., Chuvieco E., Nieto H., Aguado I. Combining AVHRR and meteorological data for estimating live fuel moisture content. Remote Sensing of Environment 2008, 112:3618-3627.
58. Garnier E., Navas M.L. A trait-based approach to comparative functional plant ecology: Concepts, methods and applications for agroecology. A review. Agronomy for Sustainable Development 2012, 32:365-399.
59. Gastellu-Etchegorry J.P., Martin E., Gascon F. DART: A 3D model for simulating satellite images and studying surface radiation budget. Remote Sensing of Environment 2004, 25:73-96.
60. Gaulton R., Danson F.M., Ramirez F.A., Gunawan O. The potential of dual-wavelength laser scanning for estimating vegetation moisture content. Remote Sensing of Environment 2013, 132:32-39.
61. Gellie N., Gibos K., Johnson K. Relationship between severe landscape dryness and large destructive fires in Victoria. VI international conference on forest fire research 2010, ADAI, Coimbra, Portugal. X. Viegas (Ed.).
62. Giglio L., Descloitres J., Justice C.O., Kauffmam J.B. An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment 2003, 87:273-282.
63. Gillon D., Dauriac F., Deshayes M., Valette J.C., Moro C. Estimation of foliage moisture content using near infrared reflectance spectroscopy. Agricultural and Forest Meteorology 2004, 124:51-62.
64. Gitelson A., Kaufmam J.Y., Stark R., Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 2002, 80:76-87.
65. Gonzalez-Dugo V., Zarco-Tejada P., Berni J.A.J., Suarez L., Goldhamer D., Fereres E. Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent. Agricultural and Forest Meteorology 2012, 154:156-165.
66. Gould J.S., McCaw W.L., Cheney N.P., Ellis P.F., Knight I.K., Sullivan A.L. Project Vesta - Fire in dry eucalypt forest: Fuel structure, fuel dynamics and fire behaviour 2007, Ensis-CSIRO, Canberra ACT, and Department of Environment and Conservation, Perth WA.
67. Hao X.J., Qu J.J. Retrieval of real-time live fuel moisture content using MODIS measurements. Remote Sensing of Environment 2007, 108:130-137.
68. Hardisky M.A., Klemas V., Smart R.M. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 1983, 49:77-83.
69. Hardy C.C., Burgan R.E. Evaluation of NDVI for monitoring live moisture in three vegetation types of the Western U.S. Photogrammetric Engineering and Remote Sensing 1999, 65:603-610.
70. Huang J., Chen D., Cosh M.H. Sub-pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites. International Journal of Remote Sensing 2009, 30:2075-2104.
71. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83:195-213.
72. Huete A.R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988, 25:295-309.
73. Imanishi J., Sugimoto K., Morimoto Y. Detecting drought status and LAI of two Quercus species canopies using derivative spectra. Computers and Electronics in Agriculture 2004, 43:109-129.
74. Jackson R.D., Idso S.B., Reginato R.J., Pinter P.J. Canopy temperature as a crop water stress indicator. Water Resources Research 1981, 17:1133-1138.
75. Jacquemoud S. PROSPECT: A model to leaf optical properties spectra. Remote Sensing of Environment 1990, 34:74-91.
76. Jacquemoud S., Baret F., Andrieu B., Danson F.M., Jaggard K. Extraction of vegetation biophysical parameters by inversion of the prospect plus sail models on sugar beet canopy reflectance data - application to TM and AVIRIS sensors. Remote Sensing of Environment 1995, 52:163-172.
77. Jacquemoud S., Ustin S.L., Verdebout J., Schmuck G., Andreoli G., Hosgood B. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment 1996, 56:194-202.
78. Jolly W.M. Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture. International Journal of Wildland Fire 2007, 16:503-509.
79. Jurdao S., Chuvieco E., Arevalillo J.M. Modelling fire ignition probability from satellite estimates of live fuel moisture content. The Journal of the Association for Fire Ecology 2012, 8:77-97.
80. Jurdao S., Yebra M., Guerschman J.P., Chuvieco E. Regional estimation of woodland moisture content by inverting Radiative Transfer Models. Remote Sensing of Environment 2013, 132:59-70.
81. Justice C.O., Giglio L., Korontzi S., Owens J., Morisette J.T., Roy D., et al. The MODIS fire products. Remote Sensing of Environment 2002, 83:244-262.
82. Keeley J.E. Lessons from the October 2003 wildfires in Southern California. Journal of Forestry 2004, 102:26-31.
83. Keeley J.E., Safford H., Fotheringham C.J., Franklin J., Moritz M. The 2007 southern California wildfires: Lessons in complexity. Journal of Forestry 2009, 107:287-296.
84. Keetch J., Byram G. A Drought Index for forest fire control. Research paper. SE-38 1968, USDA Forest Service, Southeastern Forest Experiment Station, Asheville, NC.
85. Knipling E.B. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1970, 1:155-159.
86. Koetz B., Baret F., Poilvé H., Hill J. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sensing of Environment 2005, 95:115-124.
87. Kogan F., Gitelson A., Zakarin E., Spivak L., Lebed L. AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: Calibration and validation. Photogrammetric Engineering and Remote Sensing 2003, 69:899-906.
88. Kozlowski T.T., Kramer P.J., Pallardy S. The physiological ecology of woody plants 1991, Academic Press, San Diego.
89. Kustas W., Anderson M. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology 2009, 149:2071-2081.
90. Kuusk A. A two-layer canopy reflectance model. Journal of Quantitative Spectroscopy and Radiative Transfer 2001, 71:1-9.
91. Lawson B.D., Hawkes B.C. Field evaluation of moisture content model for medium-sized logging slash. Proceedings of the 10th conference on fire and forest meteorology, Ottawa, ON, Canada 1989, 247-257. Atmospheric Environment Service, Downsview, ON. D.C. MacIver, H. Auld, R. Whitewood (Eds.).
92. Leblon B., Kasischke E., Alexander M., Doyle M., Abbott M. Fire danger monitoring using ERS-1 SAR images in the case of northern boreal forests. Natural Hazards 2002, 27:231-255.
93. Lee T.E., Miller S.D., Turk F.J., Schueler C., Julian R., Deyo S., et al. The NPOESS VIIRS Day/night visible sensor. Bulletin of the American Meteorological Society 2006, 87:191-199.
94. Li L., Cheng Y.B., Ustin S., Hu X.T., Riano D. Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression. Advances in Space Research 2008, 41:1755-1763.
95. Linn R., Reisner J., Colman J.J., Winterkamp J. Studying wildfire behavior using FIRETEC. International Journal of Wildland Fire 2002, 11:233-246.
96. Maki M., Ishiahra M., Tamura M. Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment 2004, 90:441-450.
97. McArthur A.G. Control burning in eucalypt forest 1962, 31. Commonwealth of Australia Forest and Timber Bureau, Canberra, Australian Capital Territory.
98. McArthur A.G. Fire behaviour in eucalypt forests 1967, 25. Commonwealth of Australia Forest and Timber Bureau, Canberra, Australian Capital Territory.
99. Mell W., Jenkins M.A., Gould J., Cheney P. A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 2007, 16:1-22.
100. Moran M.S., Clarke T.R., Inoue Y., Vidal A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment 1994, 49:246-263.
101. Morvan D., Dupuy J.L. Modelling the propagation of a wildfire through a Mediterranean shrub using a multiphase formation. Combustion and Flame 2004, 138:199-210.
102. Nagler P.L., Daughtry C.S.T., Goward S.N. Plant litter and soil reflectance. Remote Sensing of Environment 2000, 71:207-215.
103. Nelson R.M. Water relations of forest fuels. Forest fires: Behavior and ecological effects 2001, 79-149. Academic Press, San Diego, Calif. E.A. Johnson, K. Miyanishi (Eds.).
104. Newnham G.J., Verbesselt J., Grant I.F., Anderson S.A.J. Relative Greenness Index for assessing curing of grassland fuel. Remote Sensing of Environment 2011, 115:1456-1463.
105. North P.R.J. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing 1996, 34:946-956.
106. Okin G.S. Relative spectral mixture analysis - A multitemporal index of total vegetation cover. Remote Sensing of Environment 2007, 106:467-479.
107. Okin G.S., Clarke K.D., Lewis M.M. Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance data. Remote Sensing of Environment 2013, 130:266-279.
108. Oliveras I., Gracia M., Moré G., Retana J. Factors influencing the pattern of fire severities in a large wildfire under extreme meteorological conditions in the Mediterranean basin. International Journal of Wildland Fire 2009, 18:755-764.
109. Paltridge G.W., Barber J. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment 1988, 25:381-394.
110. Pellizzaro G., Duce P., Ventura A., Zara P. Seasonal variations of live moisture content and ignitability in shrubs of the Mediterranean Basin 2007, 633-641.
111. Peñuelas J.I.F., Biel C., Serrano L., Savé R. The reflectance at the 950-970nm region as an indicator of plant water status. International Journal of Remote Sensing 1993, 14:1887-1905.
112. Peñuelas J., Piñol J., Ogaya R., Filella I. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing 1997, 18:2869-2875.
113. Peterson S., Roberts D.A., Dennison P.E. Mapping live fuel moisture with MODIS data: A multiple regression approach. Remote Sensing of Environment 2008, 112:4272-4284.
114. Pingheng L., Quan W. Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating Ill-posed problems. Geoscience and Remote Sensing, IEEE Transactions on 2011, 49:2499-2506.
115. Plucinski M.P., Anderson W.R., Bradstock R.A., Gill A.M. The initiation of fire spread in shrubland fuels recreated in the laboratory. International Journal of Wildland Fire 2010, 19:512-520.
116. Qi Y., Dennison P.E., Spencer J., Riaño D. Monitoring live fuel moisture using soil moisture and remote sensing proxies. The Journal of the Association for Fire Ecology 2012, 8:71-87.
117. Rahimzadeh-Bajgiran P., Omasa K., Shimizu Y. Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing 2012, 68:1-12.
118. Riaño D., Vaughan P., Chuvieco E., Zarco-Tejada P., Ustin S.L. Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: Analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing 2005, 43:819-826.
119. Roberts D.A., Gardner M., Church R., Ustin S., Scheer G., Green R.O. Mapping chaparral in the Santa Monica Mountains using multiple endmembers spectral mixture models. Remote Sensing of Environment 1998, 65:267-279.
120. Roberts D.A., Peterson S., Dennison P.E., Sweeney S., Rechel J. Evaluation of Aibone 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 2006, 111:GO4S02. 10.1029/2005JG000113.
121. Romero A., Aguado I., Yebra M. Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion. International Journal of Remote Sensing 2012, 33:396-414.
122. Rothermel R.C. A mathematical model for predicting fire spread in wildland fuels 1972, USDA, Forest Service, Ogden, Utah.
123. Rouse J.W., Haas R.W., Schell J.A., Deering D.H., Harlan J.C. Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation. Type III final report 1974, NASA/GSFC, Greenbelt, MD, USA.
124. Schoenberg F.P., Peng R., Huang Z., Rundel P. Detection of non-linearities in the dependence of burn area on fuel age and climatic variables. International Journal of Wildland Fire 2003, 12:1-6.
125. Serbin G., Hunt E.R., Daughtry C.S.T., McCarty G.W., Doraiswamy P.C. An improved ASTER Index for remote sensing of crop residue. Remote Sensing 2009, 1:971-991.
126. Shimabukuro Y.E., Smith J.A. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Transactions on Geosciences and Remote Sensing 1991, 29:16-20.
127. Stow D., Madhura N., Kaiser J. Time series of chaparral live fuel moisture maps derived from MODIS satellite data. International Journal of Wildland Fire 2006, 15:347-360.
128. Stow D., Niphadkar M. Stability, normalization and accuracy of MODIS-derived estimates of live fuel moisture for southern California chaparral. International Journal of Remote Sensing 2007, 28:5175-5182.
129. Stow D., Niphadkar M., Kaiser J. MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. International Journal of Remote Sensing 2005, 26:3867-3873.
130. Thomas J.R., Namken L.N., Oerther G.F., Brown R.G. Estimating leaf water content by reflectance measurements. Agronomy Journal 1971, 63:845-847.
131. Trombetti M., Riano D., Rubio M.A., Cheng Y.B., Ustin S.L. Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA. Remote Sensing of Environment 2008, 112:203-215.
132. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 1979, 8:127-150.
133. Tucker C.J. Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment 1980, 10:23-32.
134. Ustin S.L., Riaño D., Hunt E.R. Estimating canopy water content from spectroscopy. Israel Journal Of Plant Sciences 2012, 60:9-23.
135. Vidal A., Pinglo F., Durand H., Devaux-Ros C., Maillet A. Evaluation of a temporal fire risk index in Mediterranean forest from NOAA thermal IR. Remote Sensing of Environment 1994, 49:296-303.
136. Viegas D.X., Piñol J., Viegas M.T., Ogaya R. Estimating live fine fuels moisture content using meteorologically-based indices. International Journal of Wildland Fire 2001, 10:223-240.
137. Viegas D.X., Soares J., Almeida M. Combustibility of a mixture of live and dead fuel components. International Journal of Wildland Fire 2013, (WF12031 Accepted 15 February 2013).
138. Viegas D.X., Viegas T.P., Ferreira A.D. Moisture content of fine forest fuels and fire occurrence in central Portugal. International Journal of Wildland Fire 1992, 2:69-85.
139. Viney N.R. A review of fine fuel moisture modelling. International Journal of Wildland Fire 1991, 1:215-234.
140. Wang L., Hunt J.E.R., Qu J.J., Hao X., Daughtry C.S.T. Towards estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sensing of Environment 2011, 115:836-840.
141. Wang L., Hunt E.R., Qu J.J., Hao X., Daughtry C.S.T. Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices. Remote Sensing of Environment 2013, 129:103-110.
142. Way J.B., Paris J., Dobson M.C., McDonald K., Ulaby F.T., Weber J.A., et al. Diurnal change in trees as observed by optical and microwave sensors: The EOS synergism study. IEEE Transactions on Geoscience and Remote Sensing 1991, GE-29:807-821.
143. Weise D.R., Hartford R.A., Mahaffey L. Assessing live fuel moisture for fire management applications. Fire in ecosystem management: Shifting the paradigm from suppression to prescription. Proceedings from the 20th Florida Tall Timbers Fire Ecology Conference, 7-10 May 1996, Boise, ID 1998, 49-55. Tall Timbers Research Station, Tallahassee, FL. T.L. Pruden, L.A. Brennan (Eds.).
144. Weise D.R., Zhou X.Y., Sun L.L., Mahalingam S. Fire spread in chaparral - 'go or no-go?'. International Journal of Wildland Fire 2005, 14:99-106.
145. Weiss M., Baret F., Myneni R.B., Pragnère A., Knyazikhin Y. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 2000, 20:3-22.
146. Xanthopoulos G., Maheras G., Gouma V., Gouvas M. Is the Keetch-Byram drought index (KBDI) directly related to plant water stress?. V international conference on forest fire research Figuera da Foz, Portugal 2006, Elsevier BV, Amsterdam. D.X. Viegas (Ed.).
147. Xanthopoulos G., Wakimoto R.H. A time to ignition-temperature-moisture relationship for branches of three western conifers. Canadian Journal of Forest Research 1993, 23:253-258.
148. Yebra M. Estimación del contenido de humedad de vegetación mediterránea a partir de imágenes MODIS. Departamento de Geografía 2008, 202. Universidad de Alcala, Alcala de Henares.
149. Yebra M., Chuvieco E. Generation of a species-specific look-up table for fuel moisture content assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2009, 2:21-26.
150. Yebra M., Chuvieco E. Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem. Remote Sensing of Environment 2009, 113:2403-2411.
151. Yebra M., Chuvieco E., Aguado I. Comparación entre modelos empíricos y de transferencia radiativa para estimar contenido de humedad en pastizales: Poder de generalización. Revista de Teledetección 2008, 29:73-90.
152. Yebra M., Chuvieco E., Riaño D. Estimation of live Fuel Moisture Content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology 2008, 148:523-536.
153. Yilmaz M.T., Hunt E.R., Goins L.D., Ustin S.L., Vanderbilt V.C., Jackson T.J. Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery. Remote Sensing of Environment 2008, 112:350-362.
154. Zarco-Tejada P.J., Rueda C.A., Ustin S.L. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment 2003, 85:109-124.
155. Zahn S.M., Henson C. A synthesis of fuel moisture collection methods and equipment: A desk guide. 1151 1806P 2011, U.S. Department of Agriculture, Forest Service, San Dimas Technology and Development Center, San Dimas, CA.
156. Zhang J., Wu J.J., Zhou L. Deriving vegetation leaf water content from spectrophotometric data with orthogonal signal correction-partial least square regression. International Journal of Remote Sensing 2011, 32:7557-7574.
157. Zylstra P. Forest flammability: Modelling and managing a complex system. School of physical, environmental and mathematical sciences 2011, 435. University of New South Wales and Australian Defence Force Academy.
158. Zylstra P. Rethinking the fuel-fire relationship. Bushfire CRC & AFAC 2011, Sydney Convention Centre, Darling Harbour. R.P. Thornton (Ed.).