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On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types
 

On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types

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On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types...

On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types
Antonio Lanorte, Rosa Lasaponara - Institute of Methodologies for Environmental Analysis, National Research Council, Italy

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  • The characterization of land surface conditions and land surface variations can be efficiently approached by using satellite remotely sensed data mainly because they provide a wide spatial coverage and internal consistency of data sets.
  • Examples of very high resolution satellite sensors

On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types Presentation Transcript

  •   On the Use of Satellite Remote Sensing Data to Characterize and Map Fuel Types Antonio Lanorte & Rosa Lasaponara
  • Outline:
    • Fuel modelling and fuel mapping
    • Spatial scale and fuel purposes
    • Use of satellite data
    • Study cases
    • Conclusion
  • Pollino National Park – Fuel Characterization Prometheus system adapted for the study area
  • Pollino National Park – Fuel Characterization
  • Mappa delle tipologie vegetazionali (Basilicata)
  • Mappa dei tipi di combustibile Prometheus
  • Mappa dei modelli di combustibile (sistema NFFL)
  • nullo molto basso basso medio alto molto alto Mappa del potenziale pirologico dei combustibili
    • Fuel maps are essential to fire management at many spatial and temporal scales (Keane et al. (2001).
    • Coarse scale fuel maps (500 m–5 km)
    • Mid-scale fuel maps  (30–500 m)
    • Fine fuel maps (5–30 m)
  • Coarse scale fuel maps
    • Coarse scale fuel maps are at global, national down to “regional” fire danger assessment to :
    • more effectively plan, allocate, and mobilize suppression resources at weekly, monthly and yearly evaluation int ervals.
    • inputs for simulating regional carbon dynamics, smoke scenarios, and biogeochemical cycles
    • A Number of studies have been performed see for example : Deeming et al., 1972 [6], 1977 [7]; Werth et al. 1985 [8]; Chuvieco and Martin 1994 [9]; Simard 1996[10]; Burgan et al. 1998 [11]; Klaver et al. 1998 [12]; de Vasconcelos et al. 1998 [13]; Pausas and Vallejo, 1999 [14])
  • Mid-scale fuel map Mid-scale or regional-level digital fuel maps are important in (1) rating ecosystem health; (2) locating and rating fuel treatments; (3) evaluating fire hazard and risk for land management planning; (4) aiding in environmental assessments and fire danger programs Several studies see for example: Pala and Taylor 1989 [18]; Ottmar et al. 1994 [19]; Salas and Chuvieco 1994 [20]; Wilson et al. 1994 [21]; Hawkes et al. 1995 [22]; Cohen et al. 1996 [23] ; Sapsis et al. 1996 [24]; Chuvieco et al. 1997 [25]).
  • Fine scale fuel map
    • Fine scale or landscape-level fuel maps are essential for:
    • local fire management because they also describe:
      • fire potential for planning and
      • prioritizing specific burn projects
    • inputs to spatially explicit fire growth models to simulate planned and unplanned fires to more effectively manage or fight them
    • A number of studies see for example (Chuvieco and Congalton 1989 [26]; Pala et al. 1990 [27]; Maselli et al. 1996 [28) (Stow et al. 1993 [29]; Hardwick et al. 1996 [30]; Gouma and Chronopoulou-Sereli 1998 [31]; Grupe 1998 [32]; Keane et al. 1998a [33]).
  • Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir viewing angle (0-25 degrees). Coverage of sensor: 16.5-19km High revisit frequency of 1-3.5 days, depending on the latitudes. Bandwidth Panchromatic sensor: 450 – 900 nm; Multispectral sensor: 450-520 nm (blue); 520-600 nm (green); 630-690 nm (red); 760-900 nm (Near Infrared) Satellite time series available free of charge Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir viewing angle (0-25 degrees). Coverage of sensor: 16.5-19km High revisit frequency of 1-3.5 days, depending on the latitudes. Bandwidth Panchromatic sensor: 450 – 900 nm; Multispectral sensor: 450-520 nm (blue); 520-600 nm (green); 630-690 nm (red); 760-900 nm (Near Infrared) Satellite data Resolutions availability Multispectral NOAA/AVHRR Spatial resolutions 5 channels 1 km 1980th 630-690 nm (red) 760-900 nm (near IR) 2 Thermal channels 3700 nm Landsat /TM Spatial resolutions 7 channels 30 m 1970th 450-520 nm (blue) 520-600 nm (green) 630-690 nm (red) 760-900 nm (near IR) SPOT/ VEGETATION Spatial resolutions 4 channels 1 km 1998 450-520 nm (blue) 625-695 nm (red) 760-900 nm (near IR) nm (near IR) ATSR Spatial resolutions 1990th 4 channels - 1 km Red, NIR and thermal MODIS Spatial resolutions 2001 36 channels 1 km, 500m, 250m
  • Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir viewing angle (0-25 degrees). Coverage of sensor: 16.5-19km High revisit frequency of 1-3.5 days, depending on the latitudes. Bandwidth Panchromatic sensor: 450 – 900 nm; Multispectral sensor: 450-520 nm (blue); 520-600 nm (green); 630-690 nm (red); 760-900 nm (Near Infrared) VHR Satellite Sensors : panchromatic and multispectral sensors with resolutions of 61-72cm and 2.44-2.88m. Off-nadir viewing angle (0-25 degrees). Coverage of sensor: 16.5-19km High revisit frequency of 1-3.5 days, depending on the latitudes. Bandwidth Panchromatic sensor: 450 – 900 nm; Multispectral sensor: 450-520 nm (blue); 520-600 nm (green); 630-690 nm (red); 760-900 nm (Near Infrared) Satellite data Resolutions Panchromatic Multispectral IKONOS (1999) Spatial resolutions 1 mt 4 mt Spectral range 450-900 nm 445-516 nm (blue) 506-595 nm (green) 632-698 nm (red) 757-853 nm (near IR) QuickBird (2001) Spatial resolutions 0,61 mt 2,44 mt Spectral range 450-900 nm 450-520 nm (blue) 520-600 nm (green) 630-690 nm (red) 760-900 nm (near IR) GeoEye (2008) Spatial resolutions 0,41 mt 1,65 mt Spectral range 450-900 nm 450-520 nm (blue) 520-600 nm (green) 625-695 nm (red) 760-900 nm (near IR) WorldView1 (2007) Spatial resolutions 0,50 mt - Spectral range 450-900 nm - WolrldView-2 (2009) Spatial resolutions 0,46 mt 1,84 mt Spectral range 450-780 nm 400 - 450 nm (coastal) 450-520 nm (blue) 520-585 nm (green) 585 - 625 nm (yellow) 630-690 nm (red) 705 - 745 nm (red edge) 760-900 nm (near IR1) 860 - 1040 nm (near IR1)
  • Pollino National Park – Fuel Characterization MIVIS RGB picture from 13-7-1 spectral channels MIVIS ML classification Characteristic of the MIVIS spectral bands Confusion Matrix
  • Pollino National Park – Fuel Characterization ASTER RGB picture from 4-2-1 spectral channels ASTER ML classification Characteristic of the ASTER spectral bands Confusion Matrix
  • Pollino National Park – Fuel Characterization ASTER plot
  • Pollino National Park – Fuel Characterization Landsat TM picture from 3-2-1 spectral channels Landsat TM ML classification Spectral characteristic of the Landsat TM Confusion Matrix
  • Pollino National Park – Fuel Characterization Landsat TM subpixel classification using MTMF (Mixture Tuned Matching Filtering Confusion Matrix TM and MIVIS ML classification TM MTMF classification
  • San Giovanni in Fiore – Fuel Characterization Aster RGB (3,2,1) Aster maximum likelihood classification Confusion Matrix
  • San Giovanni in Fiore – Fuel Characterization QuickBird RGB (3,2,1) QuickBird maximum likelihood classification Confusion Matrix
  • San Giovanni in Fiore – Fuel Characterization Aster neural net classification Confusion Matrix
  • San Giovanni in Fiore – Fuel Characterization Aster K-Means Aster Mahalanobis Distance Aster Minimum distance Aster Spectral Angle Mapper Aster Maximum Likelihhod
  • San Giovanni in Fiore – Fuel Characterization Confusion Matrices
  • Fire susceptibility maps zoom Comune di Latronico 5 4 3 2 1 arato
  • Fuel maps as input to improve Fire severity map .
  • LiDAR –BASED FUEL TYPE MAP
    • LiDAR based classification
    • 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
    Shape identification: examples of customized models based on TerraScan software
  • Conclusion Satellite multispectral images provide valuable data to overcome the long and expensive field reconnaissance campaigns, which in the past were the only possible approach for fuel type mapping. New technologies, such LiDAR data offer the possibility to characterize the single tree Obviously, field surveys are still indispensable for fuel mapping to obtain (i) the basic source of data, (ii) to assess products generated at a lower level of detail, and (iii) to parameterise each fuel type. Field surveys are also recommended to create field reference datasets (i.e. ground-truth) and to validate maps created from remotely sensed data products.
    • GRAZIE