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ArcFUEL Density Map based
                                               on the FCD Model.
                                       Case study: Sierra de las Nieves
                                                                (Spain)
                                     Forest Fires 2012 Conference
Session ArcFUEL: Advancing Forest Fuel Mapping techniques in Europe

                                           Arturo Vinué, Marta Gómez
               GMV | Isaac Newton 11 | 28760 Tres Cantos (Madrid), ES
          T: +34-918-072-100 | avinue@gmv.com mggimenez@gmv.com

    3rd International Conference on Modelling, Monitoring and Management of Forest Fires   1
    22 – 24 May, 2012, New Forest, UK
   Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Index
       FCD Model
       Input Data
       Data Harmonization
       Noise Reduction Process
       Indices Computation. Synthesis Model
       Integration Model
       Discussion
       References


                                                                                        2
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
FCD Model
 Developed during ITTO Project PD 32/93 Rev. 2 (F),
 “Rehabilitation of Logged-over Forests in Asia-Pacific
 Region, Sub-project III” (JOFCA 1991, 1993)
 Forest status assessed on the basis of canopy density
 FCD analysis utilizing data derived from four indices:
    Advanced Vegetation Index (AVI)
    Bare Soil Index (BI)
    Shadow Index or Scaled Shadow
 Index (SI, SSI)
    Thermal Index (TI)




                                                         (A. Rikimaru et al., 2002)
                                                                                        3
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
FCD Model




                                                         (A. Rikimaru et al., 2002)
                                                                                        4
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Input Data
 Landsat TM (Thematic Mapper) Data:
              LT52010352011257MPS00

                PRODUCT_TYPE                     "L1T"
                SPACECRAFT_ID                    "Landsat5"
                SENSOR_ID                        "TM"
                ACQUISITION_DATE                 2011-09-14
                WRS_PATH                         201
                STARTING_ROW                     35
                ENDING_ROW                       35




                                                                                        5
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
LT52010352011257MPS00
                                                                                      6
Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Input Data
 MUCVA10 (Andalusian Vegetation Cover and Use Map,
 2010)
 Hierarchical coding of land uses from 4 main types:
    Infrastructures and built surfaces
    Wetlands and water surfaces
    Agricultural lands
    Natural and forest areas
 112 cartographical classes




                                                                                        7
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Data Harmonization
 LANDSAT5 TM imagery converted from WGS84 UTM30 to
 ETRS89 LAEA
 MUCVA10 converted from ED50 UTM30 (official reference
 system in Spain until 2007). Conversion parameters as
 follows (IGN, 2005):
    ΔX (m) = -131.032
    ΔY (m) = -100.251
    ΔZ (m) = -163.354
    μ (ppm) = 9.39
    Ωx (arc seconds) = 1.2438
    Ωy (arc seconds) = 0.0195
    Ωx (arc seconds) = 1.1436




                                                                                        8
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
9
Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Noise Reduction Process
 Noise defined as an image component which interferes with
 the proper visual interpretation, such as, clouds, shadows,
 water bodies, etc.
 Three different masks carried out to accomplish further
 analysis out of the area of interest
    Water Bodies
    Clouds
    Cloud Shadows
 Water bodies masked out using an ENVI spectral module
 (LOC – Water)
 Clouds and Shadows masked out using training areas
 (parallelepiped and maximum likelihood supervised
 classifications)


                                                                                        10
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Input Landsat5
                                                                                  TM image


                                                                                  Building
                                                                                   masks


                                                                                 Landsat
                                                                               masked image


                                                                                 Pilot Area
                                                                                  location


                                                                                Sierra de las
                                                                               Nieves Natural
                                                                                    Park


                                                                                 MUCVA10




                                                                                             11
Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Range Normalization
 Linear stretching is applied from [min, max] to [0, 255]




                                                                                        12
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Advanced Vegetation Index
   The Advanced Vegetation
   Index is calculated with the
   following formula
   (Rikimaru et al. 2002):

B43 = B4 – B3
Case-a: B43 < 0 AVI= 0
Case-b: B43 > 0
AVI = ((B4 +1) x (256-B3) x B43)1/3




                                                            Avanced Vegetation Index


                                                                                           13
     Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Bare Soil Index
  The Bare Soil Index is
  calculated with the
  following formula
  (Rikimaru et al. 2002):

BI= [(B5+B3)-(B4+B1)] / [(B5+B3)
   + (B4+B1)] x 100 +100
[0 < BI <200]




                                                                Bare Soil Index


                                                                                          14
    Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Synthesis Model. Vegetation density %




                                                                                        15
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Synthesis Model. Vegetation density %
Variability components explained by
every component are:
611.1514 / (611.1514 + 88.6811) =
0.8733 ~ 87.3%
88.6811 / (611.1514 + 88.6811) =
0.1267 ~ 12.7%




                                                                     PCA1


                                                                                          16
    Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Synthesis Model. Vegetation Density %
 Vegetation Density is
 extracted after rescaling
 PCA1 as indicated in the
 figure below. Method used is
 a linear conversion




                                                          Vegetation Density (%)


                                                                                        17
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Shadow Index (Scaled Shadow Index)
   The Shadow Index is
   calculated with the following
   formula (Rikimaru et al.
   2002):

SI= ((256-B1) x (256-B2) x (256-B3))


   SSI is obtained by linear
   transformation of SI



                                                             Scaled Shadow Index


                                                                                          18
    Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Integration Model (FCD Map)
   Integration of VD and SSI
   means transformation for
   forest canopy density value

FCD = (VD x SSI + 1)1/2 – 1 (Rikimaru
   et al. 2002)




                                                             Forest Canopy Density


                                                                                           19
     Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
20
Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Dense Forestry Areas




                 FCD Map                                   Google Earth



                                                                                        21
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Dense Shrublands with trees




                 FCD Map                                   Google Earth



                                                                                        22
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Sparse Shrublands with trees




                 FCD Map                                   Google Earth



                                                                                        23
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Grassland with trees




                 FCD Map                                   Google Earth



                                                                                        24
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Dense shrubland without trees




                 FCD Map                                   Google Earth



                                                                                        25
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Sparse Shrubland without trees




                 FCD Map                                   Google Earth



                                                                                        26
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Grasslands




                 FCD Map                                   Google Earth



                                                                                        27
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Open areas bare or barely
vegetated




                 FCD Map                                   Google Earth



                                                                                        28
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Discussion
       Qualitative assessment producing good results
       Quantitative assessment to be done. JRC Tree
       Cover map use to be investigated
       Non-fuel masks (urban areas) to be applied to
       avoid miss-detections
       Correlations between TI and SSI to be analyzed
       in order to include temperature information in the
       process (Black Soil Detection step)
       More detailed vegetation information to be used
       for validation




                                                                                        29
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
Discussion
       Shrublands vs Forest based on SSI to be
       investigated
       Digital Elevation Models to be included in the
       process to mask shadows
       DEM to produce altitudinal profiles in order to
       characterize shrublands vs forestry




                                                                                        30
  Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
References
        Center for Earth Observation , University of Yale,
        2012. Converting Landsat TM and ETM+ thermal
        bands to temperature. Available on:
        (http://www.yale.edu/ceo/Documentation/Lands
        at_DN_to_Kelvin.pdf) /
        Rikimaru, A., Roy, P.S., Miyatake, S.,2002.
        Tropical forest cover density mapping. Tropical
        Ecology 43(1): 39-47
        Rikimaru, A. and Tateishi, R., 2003. Development
        of Forest Cover Density Mapping Methodology.
        Proceedings CEReS International Symposium
        Remote Sensing, 41-49



                                                                                         31
   Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com

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  • 1. ArcFUEL Density Map based on the FCD Model. Case study: Sierra de las Nieves (Spain) Forest Fires 2012 Conference Session ArcFUEL: Advancing Forest Fuel Mapping techniques in Europe Arturo Vinué, Marta Gómez GMV | Isaac Newton 11 | 28760 Tres Cantos (Madrid), ES T: +34-918-072-100 | avinue@gmv.com mggimenez@gmv.com 3rd International Conference on Modelling, Monitoring and Management of Forest Fires 1 22 – 24 May, 2012, New Forest, UK Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 2. Index FCD Model Input Data Data Harmonization Noise Reduction Process Indices Computation. Synthesis Model Integration Model Discussion References 2 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 3. FCD Model Developed during ITTO Project PD 32/93 Rev. 2 (F), “Rehabilitation of Logged-over Forests in Asia-Pacific Region, Sub-project III” (JOFCA 1991, 1993) Forest status assessed on the basis of canopy density FCD analysis utilizing data derived from four indices: Advanced Vegetation Index (AVI) Bare Soil Index (BI) Shadow Index or Scaled Shadow Index (SI, SSI) Thermal Index (TI) (A. Rikimaru et al., 2002) 3 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 4. FCD Model (A. Rikimaru et al., 2002) 4 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 5. Input Data Landsat TM (Thematic Mapper) Data: LT52010352011257MPS00 PRODUCT_TYPE "L1T" SPACECRAFT_ID "Landsat5" SENSOR_ID "TM" ACQUISITION_DATE 2011-09-14 WRS_PATH 201 STARTING_ROW 35 ENDING_ROW 35 5 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 6. LT52010352011257MPS00 6 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 7. Input Data MUCVA10 (Andalusian Vegetation Cover and Use Map, 2010) Hierarchical coding of land uses from 4 main types: Infrastructures and built surfaces Wetlands and water surfaces Agricultural lands Natural and forest areas 112 cartographical classes 7 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 8. Data Harmonization LANDSAT5 TM imagery converted from WGS84 UTM30 to ETRS89 LAEA MUCVA10 converted from ED50 UTM30 (official reference system in Spain until 2007). Conversion parameters as follows (IGN, 2005): ΔX (m) = -131.032 ΔY (m) = -100.251 ΔZ (m) = -163.354 μ (ppm) = 9.39 Ωx (arc seconds) = 1.2438 Ωy (arc seconds) = 0.0195 Ωx (arc seconds) = 1.1436 8 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 9. 9 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 10. Noise Reduction Process Noise defined as an image component which interferes with the proper visual interpretation, such as, clouds, shadows, water bodies, etc. Three different masks carried out to accomplish further analysis out of the area of interest Water Bodies Clouds Cloud Shadows Water bodies masked out using an ENVI spectral module (LOC – Water) Clouds and Shadows masked out using training areas (parallelepiped and maximum likelihood supervised classifications) 10 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 11. Input Landsat5 TM image Building masks Landsat masked image Pilot Area location Sierra de las Nieves Natural Park MUCVA10 11 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 12. Range Normalization Linear stretching is applied from [min, max] to [0, 255] 12 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 13. Advanced Vegetation Index The Advanced Vegetation Index is calculated with the following formula (Rikimaru et al. 2002): B43 = B4 – B3 Case-a: B43 < 0 AVI= 0 Case-b: B43 > 0 AVI = ((B4 +1) x (256-B3) x B43)1/3 Avanced Vegetation Index 13 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 14. Bare Soil Index The Bare Soil Index is calculated with the following formula (Rikimaru et al. 2002): BI= [(B5+B3)-(B4+B1)] / [(B5+B3) + (B4+B1)] x 100 +100 [0 < BI <200] Bare Soil Index 14 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 15. Synthesis Model. Vegetation density % 15 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 16. Synthesis Model. Vegetation density % Variability components explained by every component are: 611.1514 / (611.1514 + 88.6811) = 0.8733 ~ 87.3% 88.6811 / (611.1514 + 88.6811) = 0.1267 ~ 12.7% PCA1 16 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 17. Synthesis Model. Vegetation Density % Vegetation Density is extracted after rescaling PCA1 as indicated in the figure below. Method used is a linear conversion Vegetation Density (%) 17 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 18. Shadow Index (Scaled Shadow Index) The Shadow Index is calculated with the following formula (Rikimaru et al. 2002): SI= ((256-B1) x (256-B2) x (256-B3)) SSI is obtained by linear transformation of SI Scaled Shadow Index 18 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 19. Integration Model (FCD Map) Integration of VD and SSI means transformation for forest canopy density value FCD = (VD x SSI + 1)1/2 – 1 (Rikimaru et al. 2002) Forest Canopy Density 19 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 20. 20 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 21. Dense Forestry Areas FCD Map Google Earth 21 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 22. Dense Shrublands with trees FCD Map Google Earth 22 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 23. Sparse Shrublands with trees FCD Map Google Earth 23 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 24. Grassland with trees FCD Map Google Earth 24 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 25. Dense shrubland without trees FCD Map Google Earth 25 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 26. Sparse Shrubland without trees FCD Map Google Earth 26 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 27. Grasslands FCD Map Google Earth 27 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 28. Open areas bare or barely vegetated FCD Map Google Earth 28 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 29. Discussion Qualitative assessment producing good results Quantitative assessment to be done. JRC Tree Cover map use to be investigated Non-fuel masks (urban areas) to be applied to avoid miss-detections Correlations between TI and SSI to be analyzed in order to include temperature information in the process (Black Soil Detection step) More detailed vegetation information to be used for validation 29 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 30. Discussion Shrublands vs Forest based on SSI to be investigated Digital Elevation Models to be included in the process to mask shadows DEM to produce altitudinal profiles in order to characterize shrublands vs forestry 30 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
  • 31. References Center for Earth Observation , University of Yale, 2012. Converting Landsat TM and ETM+ thermal bands to temperature. Available on: (http://www.yale.edu/ceo/Documentation/Lands at_DN_to_Kelvin.pdf) / Rikimaru, A., Roy, P.S., Miyatake, S.,2002. Tropical forest cover density mapping. Tropical Ecology 43(1): 39-47 Rikimaru, A. and Tateishi, R., 2003. Development of Forest Cover Density Mapping Methodology. Proceedings CEReS International Symposium Remote Sensing, 41-49 31 Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com