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

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72 I - PRE-FIRE PLANNING AND MANAGEMENT 4 - Object-oriented classification The basic processing units of object-oriented image analysis are image objects or segments and not single pixels. The object-oriented image analysis procedure involved two steps: image segmentation and image classification. The bottom-up segmentation approach was used, and the multiresolution segmentation algorithm was employed. Two different levels of objects were created. For the image classification the nearest neighbor method was used to the spectral layer mean values of the image and the NDVI. The same methodology was applied to Attica, after minor adjustments. Figure 2 shows the results of the two fuel classifications. Figure 2 - a) Fuel type map of Chalkidiki, b) Fuel type map of Attica. 5 - Results and discussion Accuracy assessment in both cases was achieved by comparing the map with field data collected from the study areas. Furthermore, in the area of Attica the accuracy of the maps was checked by fire managers, and the map is already used in pre-fire planning. An approximate overall accuracy of 85% and 80% was reached for Chalkidiki and Attica respectively. The problem was between “sparse broadleaves with dense regenaration” and “very dense and tall shrublands”. 6 - Conclusions Object-oriented image analysis was applied to enhanced SPOT5 imagery in order to map fuel types in Chalkidiki. The methodology was transferred and applied to Attica. The accuracy of the final fuel type maps shows that the methodology is transferable. Further assessment is needed before the model can be used operationaly the fire managers.
References Fuel type mapping using SPOT-5 imagery and object based image analysis 73 Giakoumakis, N.M., Gitas, I.Z., San-Miguel, J., 2002. Object-oriented classification modeling for fuel type mapping in the Mediterranean, using LANDSAT TM and IKONOS imagery-preliminary results. In: Viegas (Eds.), Forest Fires Research & Wildland Fire Safety, Millpress, Rotterdam. Gitas, I.Z., Mitri, G.H., Kazakis, G., Ghosn, D., Xanthopoulos, G., 2006. Fuel type mapping in Anapolis, Crete by employing QuickBird imagery and object-based classification. Forest Ecology and Management 234 (S1), S228. doi:10.1016/j.foreco. 2005.08.255. Lasaponara, R., Lanorte, A., 2007. On the capability of satellite VHR QuickBird data for fuel type mapping characterization in fragment landscape. Ecological Modelling 204, pp. 79-84. Merril, D.F., Alexander, M.E., 1987, Glossary of Forest Fire Management Terms, fourth edn. National Research Council of Canada, Canadian Committee on Forest Fire Management, Ottawa, Ontario. Pausas, J.G., Vallejo, V.R., 1999. The role of fire in European Mediterranean ecosystems. In: Chuvieco, E. (Ed), Remote Sensing of Large Wildfires. Springer, Berlin, pp. 3-16. Riaño, D., Chuvieco, E., Salas, F.J., Palacios-Orueta, A., Bastarrica, A., 2002. Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems. Canadian Journal of Forest Research 32, 1301-1315.
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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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18 I - PRE-FIRE PLANNING AND MANAGE
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
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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 65 and 66: FUEL MOISTURE CONTENT ESTIMATION: A
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
Page 81 and 82: FIRST STEPS TOWARDS A LONG TERM FOR
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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
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Page 117 and 118: RELATIONSHIPS BETWEEN COMBUSTION PR
Page 119 and 120: Relationships between combustion pr
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122 II - VALIDATION OF RS PRODUCTS
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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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