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

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104 I - PRE-FIRE PLANNING AND MANAGEMENT 2 - Methods There are two different types of ALS: (i) conventional scanners based on discrete echo and (ii) full-waveform scanners. The conventional or discrete echo scanners detect a representative trigger signal for each laser beam, whereas the full-waveform (FW) laser scanning systems permit one to digitize the complete waveform of each backscattered pulse. FW ALS data allows us to have more control in the interpretation process of the physical measurements, this enables the extraction of additional information about the structure and the physical backscattering characteristics of the illuminated surfaces. The FW ALS sensor can effectively penetrate vegetation canopies and therefore it allows us to identify and better classify the understory. 3 - Data analysis The ALS survey was operated by GEOCART on the 26 th April 2007 and carried out using a RIEGL Airborne Laser Scanner LMS-Q560 mounted on an helicopter. The average point density value of the dataset is about 30 points/m2. The accuracy is 25 cm in xy and 10 cm in z. Usually, laser scanning products can be classified by: (i) height; (ii) intensity; (iii) RGB colours if an ortophoto is available; (iv) echo width. Herein, we will focus on the elaboration we performed using both height, obtained from the 3D point clouds, and ortophoto acquired at the same time as ALS survey. Due to its ability to pass between plants or tree branches, ALS is suited to assess both canopy and understory, but it is necessary to process the ALS point cloud using appropriate numerical filters. For the case study the elaborations were performed using a commercial software TerraScan (Terrasolid, www.terrasolid.fi), which represents a high standard for the laser data processing. TerraScan classification is based on a parametric approach and develops according to an orderly sequence of stages decided by an operator. In this case study, the classification of laser data was performed using a strategy based on a set of “filtrations of the filtrate”. Appropriate criteria for the classification and filtering were set to gradually refine the intermediate results in order to obtain fuel height identification and the discrimination between canopy and understory. The workflow can be summarized as follows: - Classify low point; - Classify ground; - Classify points below surface; - Classify points by class; - Classify points by height from ground for different heights; - Classify isolated points; - Shape identification and load computation.
Assessing critical fuel parameters using airborne full waveform LIDAR: the case study of Bosco dell’Incoronata 105 The critical aspect of this classification is the appropriate choice of the threshold values. Figure 1 - (left) Study area Location and (right) orthophoto of the study area acquire at the same time as lidar data set. Figure 2 - LIDAR classification by vegetation height: (upper) a section and (lower) a spatial mapping.
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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 65 and 66: FUEL MOISTURE CONTENT ESTIMATION: A
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: 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
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Page 141 and 142: GLOBAL MONITORING OF THE ENVIRONMEN
Page 143 and 144: Global monitoring of the environmen
Page 145: Global monitoring of the environmen
Page 153 and 154: 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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