7th Workshop on Forest Fire Management - EARSeL, European ...

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98 I - PRE-FIRE PLANNING AND MANAGEMENT Figure 3 - Time series for Bosco of Orsara affected by fire: from top to down: (i) NDVI, (ii) (NDVI-NDVIm), and (iii) NDVIdn trend. Red arrows show time of fire occurrence (2001, and 2005). 3 - Final remark Results obtained suggest that the satellite MODIS time series provides reliable information for the characterization of vegetation variations at different temporal scale: (i) short term to monitor fire susceptibility, (ii) medium and long term to assess post fire vegetation recovery. References Telesca L., Lasaponara R. and Lanorte A., Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION, Time Series, Ecological Complexity, 5, 151-156, 2008.
REMOTE SENSING-BASED MAPPING OF FUEL TYPES USING MULTISENSOR, MULTISCALE AND MULTITEMPORAL DATA SET Abstract: The characterization of fuel types is very important for computing spatial fire hazard and risk, for simulating fire growth and intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up. The advent of sensors with increased spatial and spectral resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from Quickbird, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat Temathic Mapper, and MODIS. 1 - Introduction R. Lasaponara, R. Coluzzi, A. Lanorte CNR-IMAA, Tito Scalo (PZ), Italy a.lanorte@imaa.cnr.it In the context of fire management, fuel maps are essential information requested at many spatial and temporal scales for managing wild-land fire hazard and risk and for understanding ecological relationships between wildland fire and landscape structure. Remote sensing data provide valuable information for the characterization and mapping of fuel types and vegetation properties at different temporal and spatial scales including the global, regional, landscape levels, down to a local scale level. This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, different approaches have been adopted for fuel type mapping: the well-established classification techniques performed at the pixel level and spectral mixture analysis (SMA) which allows a sub-pixel analysis. Among the classifications performed at the pixel level, two different techniques, parametric and non parametric were considered in this study. The K NN (nearest neighbor) was applied to obtain a non parametric classifications of fuel types; whereas the maximum likelihood classification (MLC) was adopted for performing a parametric analysis. The K NN and MLC were adopted because both of them are “conventional supervised classifiers” widely used for remote sensed imagery. 99
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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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Page 59 and 60: CLASSIFICATION OF SITE AND STAND CH
Page 61 and 62: Classification of site and stand ch
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Page 67 and 68: 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
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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: Cyberpark project: Multitemporal sa
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
Page 121: Relationships between combustion pr
Page 129 and 130: OPERATIONAL USE OF REMOTE SENSING I
Page 131 and 132: Operational use of remote sensing i
Page 133: Operational use of remote sensing i
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Page 143 and 144: Global monitoring of the environmen
Page 145: Global monitoring of the environmen
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148 II - VALIDATION OF RS PRODUCTS
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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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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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