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
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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)
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Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
4. FCD Model
(A. Rikimaru et al., 2002)
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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
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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
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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)
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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
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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]
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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
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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
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Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
15. Synthesis Model. Vegetation density %
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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 (%)
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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
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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
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Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
21. Dense Forestry Areas
FCD Map Google Earth
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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
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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
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Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com
24. Grassland with trees
FCD Map Google Earth
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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
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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
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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
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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
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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
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Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; avinue@gmv.com mggimenez@gmv.com