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262 IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT Sensor TerraSAR-X Sensor ASAR Polarization HH HV HH&HV Polarization VV HH HV HH&HV Scene data 12.1908 Scene data 09.12.08 01.08.09 03.19.09 All pseudo-plots 0.529 0.085 0.546 0.101 0.176 0.539 0.542 Pseudo-plots grouped by local incidence angle 26°-30° 0.833 0.291 0.916 6°-10° 0.359 0.768 0.700 0.843 31°-35° 0.707 0.234 0.861 10°-15° 0.338 0.555 0.733 0.758 36°-40° 0.732 0.050 0.857 16°-20° 0.361 0.449 0.609 0.648 41°-45° 0.666 0.291 0.815 21°-25° 0.186 0.354 0.693 0.752 46°-50° 0.621 0.402 0.792 26°-30° 0.001 0.198 0.699 0.699 51°-55° 0.612 0.520 0.824 31°-35° 0.007 0.154 0.767 0.767 56°-60° 0.584 0.464 0.833 36°-40° 0.001 0.174 0.715 0.715 61-65 0.476 0.582 0.843 41-45 0.050 0.109 0.744 0.744 Table 1 - Determination coefficients explaining the agreement between dNBR and radar backscatter at X- and C-band. 5 - Discussion Both sensors showed similar trends of the backscatter with respect to dNBR for co- and cross-polarized beams. Figures 1 and 2 show that for co-polarized beams the backscattering coefficient increased with burn severity. For X-band HH-polarization, the backscattering coefficient increased (Figure 2) from low to medium burn severity reaching saturation for high burn severities (i.e. dNBR 800). The HV-backscatter decreased with the increase of burn severity at both frequencies (Figures 1 and 2). This is explained by the larger amount of gaps in the canopy and the increased transmissivity with the increase of burn severity. Figure 3 shows the strong effects of the local incidence angle on the backscatter at all SAR frequencies and polarizations. For increasing local incidence angle decreasing trends were registered for co-polarized beams. This decrease is the result of a decreasing proportion of direct scatter from the ground to the total forest backscatter. Cross-polarized beams showed increase of the backscatter coefficient with increasing local incidence angle explained by the longer path traveled by the radar wave within the canopy. The strength of association (i.e. R 2 ) for co-polarized beams decreased with increasing incidence angle while the error increased (Table 1). The simultaneous use of cross and co-polarized channels (HH and HV) provided the highest R 2 coefficients and the smallest errors.
6 - Conclusions Backscatter properties of x- and c-band sar in a mediterranean pine forest affected by fire 263 This work was a first step towards the understanding of the properties of SAR backscatter of burned areas. Aim of this study is to develop new methods for burn severity mapping. The study indicates that dual polarization data has good potential for burn severity mapping in the Mediterranean environments as long as the local incidence angle is accounted for. Acknowledgments The work has been financed by the Spanish Ministry of Science and Education and the European Social Fund. ASAR and TerraSAR-X data were provided by the ESA and DLR. References Key, C.H. and Benson, N.C., 2006. Landscape assessment (LA). In Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland S. & Gangi, L.J., (Eds.), FIREMON: Fire effects monitoring and inventory system. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Gen. Tech. Rep. RMRS-GTR-164-CD: pp. 1-55. Tanase, M., Pérez-Cabello, F., Riva, J.d.l. and Santoro, M., 2009. TerraSAR- X data for burn severity evaluation in Mediterranean forests on sloped terrain. IEEE Trans. Geosci. Remote Sensing, accepted for publication. Ulander, L.M.H., 1996. Radiometric slope correction of synthetic-aperture radar images. IEEE Trans. Geosci. Remote Sensing, 34: 1115-1122.
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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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Classification of site and stand ch
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FUEL MOISTURE CONTENT ESTIMATION: A
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Fuel moisture content estimation: a
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Fuel moisture content estimation: a
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FUEL MODEL MAPPING USING IKONOS IMA
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Fuel model mapping using ikonos ima
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FIRST STEPS TOWARDS A LONG TERM FOR
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First steps towards a long term for
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3 - Conclusion and further research
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FUEL MOISTURE CONTENT ESTIMATION BA
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3 - Results Fuel moisture content e
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CYBERPARK PROJECT: MULTITEMPORAL SA
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Cyberpark project: Multitemporal sa
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REMOTE SENSING-BASED MAPPING OF FUE
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Remote Sensing-based Mapping of fue
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ASSESSING CRITICAL FUEL PARAMETERS
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Assessing critical fuel parameters
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II VALIDATION OF RS PRODUCTS FOR FI
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110 II - VALIDATION OF RS PRODUCTS
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RELATIONSHIPS BETWEEN COMBUSTION PR
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Relationships between combustion pr
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Relationships between combustion pr
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OPERATIONAL USE OF REMOTE SENSING I
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Operational use of remote sensing i
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Operational use of remote sensing i
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GLOBAL MONITORING OF THE ENVIRONMEN
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Global monitoring of the environmen
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Global monitoring of the environmen
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DAILY MONITORING OF PRE-FIRE VEGETA
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Daily monitoring of pre-fire vegeta
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THE GLOBAL MODIS BURNED AREA PRODUC
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The global MODIS burned area produc
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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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206 IV - BURNED LAND MAPPING, FIRE
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Abstract: The aim of this paper is
Page 213 and 214: Burnt area index using MODIA and AS
Page 215 and 216: 4 - Conclusions Burnt area index us
Page 217 and 218: SATELLITE DERIVED MULTI-YEAR BURNED
Page 219 and 220: Satellite derived multi-year burned
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Page 229 and 230: MODIS REFLECTIVE AND ACTIVE FIRE DA
Page 231 and 232: 3 - Methodology and results MODIS r
Page 233: MODIS reflective and active fire da
Page 245 and 246: SOME NOTES ON SPECTRAL PROPERTIES O
Page 247 and 248: Some notes on spectral properties o
Page 249 and 250: MONITORING POST-FIRE VEGETATION REG
Page 251 and 252: Monitoring post-fire vegetation reg
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Page 261 and 262: Abstract: SAR data from a test site
Page 263: Backscatter properties of x- and c-
Page 273 and 274: CORRECTION OF TOPOGRAPHIC EFFECTS I
Page 275 and 276: Correction of topographic effects i
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Page 279 and 280: BURNED AREAS MAPPING BY MULTISPECTR
Page 281 and 282: Burned areas mapping by multispectr
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Page 285 and 286: 4 - Conclusions Burned areas mappin
Page 287 and 288: Abstract: In this paper NDVI VEGETA
Page 289 and 290: Post Fire vegetation recovery estim
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Page 294 and 295: 292 FORTUNATO G. pag. 191 FRENCH N.
Page 296: Finito di stampare nel mese di agos
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