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

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62 I - PRE-FIRE PLANNING AND MANAGEMENT only along for the strata of the forest community but along a temporal gradient as well. Fuel accumulation should then be possible to be simulated with the forest model PICUS. From that perspective it would be possible to test forest management activities for its consequences from the fire perspective during succession simulations also. References Gossow et al., 2008; Gossow, H., Hafellner, R., Arndt, N.: More forest fires in the Austrian Alps - a real coming danger?, in: Borsdorf, A. et al., Managing Alpine Future, Proceedings Han et al., 2003; Han G.J., Keun H.R., Kwang H.C., Yeon K.Y, Statistics based Predictive Geo Spatial data mining: Forest Fire Hazardous Area mapping Killian, Müller, Starlinger 93; Killian W., Müller F., Starlinger F., Die Forstlichen Wuchsgebiete Österreichs, Eine Naturraumgliederung nach waldökologischen Gesichtspunkten; BWF - Berichte Schriftenreihe der Bundesforschungs - und Ausbildungszentrums für Wald, Naturgefahren und Landschaft, Wien, 2006; Nr. 82; 60 pp Lexer & Hönninger, 1998; M.J. Lexer, K. Hönninger; A modified 3D patch model for spatially explicit simulation of vegetation composition in heterogeneous landscapes; For. Ecol. Manage. 144 (2001) pp. 43 – 65 Lundquist JE; Beatty JS., 2002. A method for characterizing and mimicking forest canopy gaps caused by different disturbances. Forest Science. 48: 582-594. ÖWI, 2002, Gabler Schadauer; Gabler K., Schadauer K.; Methoden der Österreichischen Waldinventur 2000/02 - Grundlagen der Entwicklung, Design, Daten, Modelle, Auswertung und Fehlerrechnung; BWF- Berichte Schriftenreihe der Bundesforschungs- und Ausbildungszentrums für Wald, Naturgefahren und Landschaft, Wien, 2006; Nr. 135; 132 pp. Rothermel, 1972; Rothermel R.C., A mathematical model for predicting fire spread in wildland fuels; GTR-INT-115; Odgen, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station. Seidl et al., 2005. Seidl R., Lexer M.J., Jäger D., Hönninger K., 2005. Evaluating the accuracy and generality of a hybrid patch model; Tree physiology 25 939-951. Ulanova, 2000. Ulanova N.G., The effects of windthrow on forests at different spatial scales: a review, For. Ecol. Manage. 135 (2000), pp. 155- 167. Volney and Fleming, 2000. W.J.A. Volney and R.A. Fleming, Climate change and impacts of boreal forest insects, Agric. Ecosyst. Environ. 82 (2000), pp. 283-294. Allgower Bachmann, 2001, Allgoewer B., Bachmann A., A consistent wildland fire risk terminology is needed!; Fire Management Today; 64; pp. 4.
FUEL MOISTURE CONTENT ESTIMATION: A LAND-SURFACE MODELLING APPROACH APPLIED TO AFRICAN SAVANNAS Abstract: The aim of this study is to reduce the uncertainty associated with modelling the fire regime in Africa, by providing a more robust estimation of fuel moisture content (FMC) through model simulation. By constraining model predicted FMC through the assimilation of remotely sensed land-surface temperature (LST) and Normalised Difference Vegetation Index (NDVI) data into a leading land-surface model, the findings presented here show that a combination of satellite data and biophysical modelling provides a viable option to adequately predict FMC over continental scales at high temporal resolution. 1 - Introduction D. Ghent 1 , A. Spessa 2 1 Department of Geography, University of Leicester, Leicester, UK 2 Walker Institute for Climate System Research, Department of Meteorology, University of Reading, UK Despite the importance of fire to the global climate system, in terms of emissions from biomass burning, ecosystem structure and function, and changes to surface albedo, current land-surface models do not adequately estimate key variables affecting fire ignition and propagation. FMC is considered one of the most important of these variables (Chuvieco et al., 2004). However, the complexity of plant-water interactions, and the variability associated with short-term climate changes, means it is one of the most difficult fire variables to quantify and predict. Previous studies (Sandholt et al., 2002; Snyder et al., 2006) have represented FMC as a surface dryness index, expressed as the ratio between NDVI and LST; with the argument that this ratio displays a statistically stronger correlation to FMC than either of the variables considered separately. This is the approach taken in this study. Previous modelling studies of fire activity in Africa savannas, such as Lehsten et al., (2009), have reported significant levels of uncertainty associated with the simulations. This uncertainty is important because African savannas are among some of the most frequently burnt ecosystems and are a major source of greenhouse trace gases and aerosol emissions (Scholes et 63
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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
Page 15: I PRE-FIRE PLANNING AND MANAGEMENT
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Page 23 and 24: USING SPATIAL ANALYSIS TO CHARACTER
Page 25 and 26: Using spatial analysis to character
Page 27 and 28: Abstract: Fire is the major stand-r
Page 29 and 30: Fire and climate change in Boreal F
Page 35 and 36: PROJECTING FUTURE BURNT AREA IN THE
Page 37 and 38: Projecting future burnt area in the
Page 39 and 40: Projecting future burnt area in the
Page 41 and 42: MULTISCALE CHARACTERIZATION OF SPAT
Page 43 and 44: Multiscale characterization of spat
Page 45: Multiscale characterization of spat
Page 59 and 60: CLASSIFICATION OF SITE AND STAND CH
Page 61 and 62: Classification of site and stand ch
Page 63: Classification of site and stand ch
Page 67 and 68: Fuel moisture content estimation: a
Page 69: Fuel moisture content estimation: a
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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112 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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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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