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112 II - VALIDATION OF RS PRODUCTS FOR FIRE MANAGEMENT a b R 2 Bias RMSE RMSES RMSEU MAD MADP GEMI 0.12±0.18 -0.04±0.05 0.66 -0.004 0.06 0.010 0.06 0.04 14% EVI (MOD 09) 0.88±0.19 0.04±0.06 0.51 -0.0019 0.07 0.010 0.07 0.05 16% SAVI 1.5±0.2 -0.13±0.07 0.74 0.002 0.06 0.04 0.003 0.05 17% EVI (MOD 13) 0.93±0.17 0.01±0.05 0.61 -0.008 0.06 0.012 0.06 0.05 16% Table 1 - Statistics of the validation for the three indices. 5 - Conclusions In this work we present a comparative study of spectral vegetation indices with the aim of obtaining a simple empirical model for the estimation of fire risk in the Galicia region. Eight different indices have been selected. The variations suffered by each one, in a two weeks period, are used as the entry parameter for the model. Three of the indices are shown to fit a linear regression with the fire probability: EVI, GEMI and SAVI. From the validation results we may conclude that the most appropriate indices for the fire risk estimation are GEMI and EVI, with a relative error of about 15%. In future works we will apply this model to other regions and include the surface temperature as an additional input of the model. Acknowledgements This work has been financed by the Science and Innovation Spanish Ministry (Projects CGL2007-64666/CLI, CGL2008-03668/CLI, and Juan de la Cierva research contract of J.M. Sánchez). References Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S., 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment, 82, 188-197. Gao, B.C., 1996. NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257-266. Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76-87. Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., & Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the
Assessment of spectral indices derived from modis data as fire risk indicators in Galicia 113 MODIS vegetation indices. Remote Sensing of Environment, 83, 195-213. Hunt, E.R., & Rock, B.N., 1989. Detection of changes in leaf water-content using near-infrared and middle-infrared reflectances. Remote Sensing of Environment, 30, 43-54. Pinty, B. y Verstraete, M.M., 1992. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101, 15-20. Rouse, J.W., Hass, R.H., Schell, J.A. and Deering, D.W., 1974. Monitoring vegetation systems in the Grat Plains with ERTS. Proceedings, third Earth Resources Technology Satellite-1 Symposium, Greenbelt, NASA SP-351, pp. 309-317. Sánchez, J.M., Caselles, V., Bisquert, M.M., Paz Andrade, M.I., Legido, J.L., 2009. Fire risk estimation from MODIS Enhanced Vegetation Index data. Application to Galicia region (northwest Spain). International Journal of Wildland Fires (under review).
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Proceedings of the VII Internationa
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SCIENTIFIC COMMITTEE Olivier Arino
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Analysis of human-caused wildfire o
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Monitoring post-fire vegetation reg
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10 Agency, Italian National Forestr
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I PRE-FIRE PLANNING AND MANAGEMENT
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16 I - PRE-FIRE PLANNING AND MANAGE
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USING SPATIAL ANALYSIS TO CHARACTER
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Using spatial analysis to character
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Abstract: Fire is the major stand-r
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Fire and climate change in Boreal F
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PROJECTING FUTURE BURNT AREA IN THE
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Projecting future burnt area in the
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Projecting future burnt area in the
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MULTISCALE CHARACTERIZATION OF SPAT
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Multiscale characterization of spat
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Multiscale characterization of spat
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CLASSIFICATION OF SITE AND STAND CH
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Classification of site and stand ch
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Page 65 and 66: FUEL MOISTURE CONTENT ESTIMATION: A
Page 67 and 68: Fuel moisture content estimation: a
Page 69: Fuel moisture content estimation: a
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Page 77 and 78: FUEL MODEL MAPPING USING IKONOS IMA
Page 79 and 80: Fuel model mapping using ikonos ima
Page 81 and 82: FIRST STEPS TOWARDS A LONG TERM FOR
Page 83 and 84: First steps towards a long term for
Page 85: 3 - Conclusion and further research
Page 93 and 94: FUEL MOISTURE CONTENT ESTIMATION BA
Page 95 and 96: 3 - Results Fuel moisture content e
Page 97 and 98: CYBERPARK PROJECT: MULTITEMPORAL SA
Page 99 and 100: Cyberpark project: Multitemporal sa
Page 101 and 102: REMOTE SENSING-BASED MAPPING OF FUE
Page 103 and 104: Remote Sensing-based Mapping of fue
Page 105 and 106: ASSESSING CRITICAL FUEL PARAMETERS
Page 107 and 108: Assessing critical fuel parameters
Page 109: II VALIDATION OF RS PRODUCTS FOR FI
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Page 117 and 118: RELATIONSHIPS BETWEEN COMBUSTION PR
Page 119 and 120: Relationships between combustion pr
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Page 129 and 130: OPERATIONAL USE OF REMOTE SENSING I
Page 131 and 132: Operational use of remote sensing i
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Page 141 and 142: GLOBAL MONITORING OF THE ENVIRONMEN
Page 143 and 144: Global monitoring of the environmen
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Page 153 and 154: DAILY MONITORING OF PRE-FIRE VEGETA
Page 155 and 156: Daily monitoring of pre-fire vegeta
Page 157 and 158: THE GLOBAL MODIS BURNED AREA PRODUC
Page 159 and 160: The global MODIS burned area produc
Page 161: III FIRE DETECTION AND FIRE MONITOR
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162 III - FIRE DETECTION AND FIRE M
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REAL-TIME MONITORING OF THE TRANSMI
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Real-time monitoring of the transmi
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NOAA’S OPERATIONAL FIRE AND SMOKE
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Noaa’s operational fire and smoke
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SYSTEM FOR EARLY FOREST FIRE DETECT
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3 - Result in Germany System for ea
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References System for early forest
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ADVANCING THE USE OF MULTI-RESOLUTI
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Advancing the use of multi-resoluti
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Advancing the use of multi-resoluti
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IV BURNED LAND MAPPING, FIRE SEVERI
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204 IV - BURNED LAND MAPPING, FIRE
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Abstract: The aim of this paper is
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Burnt area index using MODIA and AS
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4 - Conclusions Burnt area index us
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SATELLITE DERIVED MULTI-YEAR BURNED
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Satellite derived multi-year burned
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Satellite derived multi-year burned
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MODIS REFLECTIVE AND ACTIVE FIRE DA
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3 - Methodology and results MODIS r
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MODIS reflective and active fire da
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SOME NOTES ON SPECTRAL PROPERTIES O
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Some notes on spectral properties o
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MONITORING POST-FIRE VEGETATION REG
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Abstract: SAR data from a test site
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Backscatter properties of x- and c-
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6 - Conclusions Backscatter propert
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CORRECTION OF TOPOGRAPHIC EFFECTS I
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Correction of topographic effects i
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Correction of topographic effects i
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BURNED AREAS MAPPING BY MULTISPECTR
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Burned areas mapping by multispectr
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Burned areas mapping by multispectr
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4 - Conclusions Burned areas mappin
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Abstract: In this paper NDVI VEGETA
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Post Fire vegetation recovery estim
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Post Fire vegetation recovery estim
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292 FORTUNATO G. pag. 191 FRENCH N.
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Finito di stampare nel mese di agos
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