03.forest fires 2012_gmv_170523_v2

Arturo Vinué, Marta Gómez; GMV; T:+34 918 072 100; [email protected] [email protected]

3rd International Conference on Modelling, Monitoring and Management of Forest Fires22 – 24 May, 2012, New Forest, UK

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ArcFUEL Density Map based on the FCD Model.

Case study: Sierra de las Nieves (Spain)

Forest Fires 2012 ConferenceSession ArcFUEL: Advancing Forest Fuel Mapping techniques in Europe

Arturo Vinué, Marta GómezGMV | Isaac Newton 11 | 28760 Tres Cantos (Madrid), ES

T: +34-918-072-100 | [email protected] [email protected]


IndexFCD ModelInput DataData HarmonizationNoise Reduction ProcessIndices Computation. Synthesis ModelIntegration ModelDiscussionReferences


FCD ModelDeveloped 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 densityFCD 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)


FCD Model

(A. Rikimaru et al., 2002)


Input DataLandsat 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


LT52010352011257MPS00


Input DataMUCVA10 (Andalusian Vegetation Cover and Use Map, 2010)Hierarchical coding of land uses from 4 main types:

Infrastructures and built surfacesWetlands and water surfacesAgricultural landsNatural and forest areas

112 cartographical classes


Data HarmonizationLANDSAT5 TM imagery converted from WGS84 UTM30 to ETRS89 LAEAMUCVA10 converted from ED50 UTM30 (official reference system in Spain until 2007). Conversion parameters as follows (IGN, 2005):

ΔX (m) = -131.032ΔY (m) = -100.251ΔZ (m) = -163.354μ (ppm) = 9.39Ωx (arc seconds) = 1.2438Ωy (arc seconds) = 0.0195Ωx (arc seconds) = 1.1436


Noise Reduction ProcessNoise 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 BodiesCloudsCloud 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)


Input Landsat5 TM image

Building masks

Landsat masked image

Pilot Area location

Sierra de las Nieves Natural

Park

MUCVA10


Range NormalizationLinear stretching is applied from [min, max] to [0, 255]


Advanced Vegetation Index The Advanced Vegetation Index is calculated with the following formula (Rikimaru et al. 2002):

B43 = B4 – B3Case-a: B43 < 0 AVI= 0Case-b: B43 > 0AVI = ((B4 +1) x (256-B3) x B43)1/3

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Avanced Vegetation Index


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]

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Bare Soil Index


Synthesis Model. Vegetation density %

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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%

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PCA1

Synthesis Model. Vegetation density %


Vegetation Density (%)

Synthesis Model. Vegetation Density %Vegetation Density is extracted after rescaling PCA1 as indicated in the figure below. Method used is a linear conversion


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

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Scaled Shadow Index


Forest Canopy Density

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)


Dense Forestry Areas

FCD Map Google Earth


Dense Shrublands with trees



Sparse Shrublands with trees



Grassland with trees



Dense shrubland without trees



Sparse Shrubland without trees



Grasslands



Open areas bare or barely vegetated



DiscussionQualitative assessment producing good resultsQuantitative assessment to be done. JRC Tree Cover map use to be investigatedNon-fuel masks (urban areas) to be applied to avoid miss-detectionsCorrelations 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


DiscussionShrublands vs Forest based on SSI to be investigatedDigital Elevation Models to be included in the process to mask shadowsDEM to produce altitudinal profiles in order to characterize shrublands vs forestry


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/Landsat_DN_to_Kelvin.pdf) /Rikimaru, A., Roy, P.S., Miyatake, S.,2002. Tropical forest cover density mapping. Tropical Ecology 43(1): 39-47Rikimaru, A. and Tateishi, R., 2003. Development of Forest Cover Density Mapping Methodology. Proceedings CEReS International Symposium Remote Sensing, 41-49

03.forest fires 2012_gmv_170523_v2

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