A probabilistic A probabilistic model of fire model of fire ignition and ignition and spreading for the spreading for the Xingu HeadwatersXingu Headwaters

Rafaella Almeida Silvestrini – UFMG Britaldo Silveira Soares Filho – UFMGAne Alencar – IPAM, UFLHermann Oliveira Rodrigues - UFMGDaniel Curtis Nepstad – Moore Foundation/ IPAMPaulo Brando – IPAM, UFL

13/10/05Mauro Maciel

Motivation Why study fire models?

The frequency of fire have been increasing dramatically (Cochrane et al., 1999; Alencar et al., 2006);

Forest Fires are a major Brazilian contribution to global warming. (FEARNSIDE, 2002);

Reduce forest ability to retain water, exacerbating flooding, erosion and seasonal water shortages. As a consequence, it increases forest flammability (Cochrane et al., 2003);

Lead the Forest remnants to a cycle of self-destruction (Nepstad et al., 1998)

Aerosols released by forest fires disrupt normal hydrological processes and reduce rainfall;

Affects human health. So, fire modeling is important not only to understand fire

drivers in the Amazon but also to check what may happen in the future.

Structure of the Model

1. Simulate the occurrence of hot pixels along the Amazon forest fringe;

Hot pixels: pixels captured by NOOA-12 night.

2. Based on the hot pixels, simulate fire spreading at the Xingu Headwaters.

Most of these fires are understory fires.

3. Feedback of fire in the quantity of fuel

(biomass) and climate at the Xingu Headwaters.

CARLUC (CARbon and Land-Use Change Model)

Step 1: Modeling Hot Pixels

Part 1: Modeling the hot pixels

1. Spatial Modeling: anthropogenic and biophysical variables Spatial ProbabilityWeights of evidence – Annual Maps

2. Space-time Modeling: climatic variable – VPD (vapor pressure deficit ) Space-time ProbabilityLogistic Regression – Monthly maps

3. Combining the two previous maps A space-time probability map

Only hot pixels within < 5 km from the forests were analyzed

Modeling the hot pixels

Combining the two probability mapsFinal Probability map

=α (Anthropogenic Prob.) + (1- α) (Climatic Prob.)

where α is a constant that depends on the month of the year.

Given a high VPD value, hot pixels will concentrate in area of higher anthropogenic activity.

months αFebruary and March 0.2other months 0.4

Modeling the hot pixels

Example: August, 2004

0.4 * +0.6

*

=

Modeling the hot pixels

Simulating the hot pixelsThe simulation is done using 10 steps, each

one analyzing 10% of the cells, in each one:

1. There is a hot pixel if:{Rand(dist)}- γ < Final Prob < {rand(dist)} +

γ, where: rand(dist) = random number from the “dist”

probability; γ = constantFurthermore, x% of the cells are allocated as null

cells.

Parameter February - March May - SeptemberOctober - December,

January, April

γ 0.05 0.1 0.4x 90% 85% 90%

distBeta(0.985, 0.1) truncada em 0.5

Beta(0.985, 0.1) truncada em 0.5

Weibull(13,0.6)

Control the number of hot pixels Control the spatial distribution

2. The probabilty map is adjusted: Hot pixels neighboring cells have their probability increased by (1+ Moran Autocorrelation Index for month i)

Modeling the hot pixels

Observed hot pixels – August, 2004

Simulated hot pixels – August, 2004

Modeling the hot pixels

Validation (2002, 2004, 2005) 1. ROC Statistic:

above 0.85 in all months of all years.

2. Fuzzy Similarity: Match of 60~70% within a 10 km x 10 km resolution for the dry months.

3. Time series of observed and simulated hot pixels:

The number of simulated hot pixels was similar than the observed ones and the temporal distribution of the simulated events followed the seasonal tendency. Considering the whole year, the model predicted, in average, 15% more or less than the observed.

Modelagem dos focos de calor

Part 2:Modeling fire spreading

Data

Fire Scars in Xingu region, mapped by Ane Alencar. Most of them are not detected by satellite – understory fires. Is there a relation between these scars and the Hot Pixels?

Relation between hot pixels and understory fire Are the Hot Pixels and fire scars spatially

dependent?Ripley’s K12 Function

Once the observed function is above the confidence envelope (dashed lines), there are evidences (with 99% of confidence) that hot pixels and forest fires are positively spatially dependent.

Probability of spreading

1. Probability based on: Distance to a hot Pixel Altitude Water bodies Forest / non forest

(<=4Km from the forest)

2. Probability based on the inner VPD For fires outside the forest this Probability is set to 1

Modeling the hot pixels

Cost Map = fire effort to spread

Weihts of Evidence Probability Map

Fuzzy Function

Monthly Maps

The Spreading Model

Cost Prob. Map

For each month

Inner VPD

Fuzzy

Functi

on

Multiply

Hot Pixels

Spreading (30)

Cellular Automata

Wind Map

Adjust the Probability

Spread Map

(initial map)

Calculate the number of neighbors in fire

Spread Map

Neighborhood

(initia

l map)

June, July, August

Example: June, July and August 2005

ValidationNumber of Simulated Scars = 1.023 Observed Scars

Considering the Fuzzy similarity we have achieved a match of 50% in a window of 9 km.

Part 3:Coupling CARLUC and the Fire model

Foto: Antonio Ruano.

CARLUC Items Based on NPP, decomposition and

mortality functions. Disturbances: Deforestation, Logging

and Fire.

NPP

Wood

Leaves

Roots

Charcoal

Wood DebrisWood Products

Atmosphere

Fine Litter

Humus

Mortality rates

Conclusions Satisfactory validation results concerning the hot pixels model,

considering both spatial and temporal distributions. The probability map (hot pixels) is well fitted, so it can be useful

on monitoring the occurrence of these events at the Amazon. Anthropogenic variables, such as distance to town centers,

roads, protected areas, etc – play an important role when talking about fire in the Amazon, where fire is not only a natural event.

Spreading model also showed good results once its spatial and temporal distribution were satisfactory.

Besides obtaining the extension of the areas that can be burned, the spreading model is important because given a hot pixel it is possible to be attentive to the areas that are more prone to the spreading of the hot pixels, mainly the understory fires, that the monitoring satellites can not easily capture.

Next step: finish CARLUC calibration so that the coupled model (fire, CARLUC) can give an estimative of the forest feedback on fire on the next 50 years.

Obrigada/Thank you

Rafaella Almeida Silvestrini – UFMG

rafaufmg@yahoo.com.br

UNIVERSIDADE FEDERAL DE MINAS GERAIS

References

Cochrane, M.A. 2003. Fire science for rainforests. Nature 42: 913-919. Marengo, J. et al. The drought of Amazonia in 2005. Journal of Climate,

2006 (Submetido). FEARNSIDE, Philip M.. Fogo e emissão de gases de efeito estufa dos

ecossistemas florestais da Amazônia brasileira. Estud. av.,  São Paulo,  v. 16,  n. 44,  2002.  Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-40142002000100007&lng=en&nrm=iso>. Acesso em: 08  June  2007.  Pré-publicação.

SISMANOGLU, R.A.; SETZER, A.W. Risco de fogo da vegetação na América do Sul: comparação de três versões na estiagem de 2004. Anais XII Simpósio Brasileiro de Sensoriamento Remoto [online], Goiânia, GO, p. 3349-3355, 16-21/Abr/2005. [acesso em 24 de maio de 2007]. Disponível em: http://www.cptec.inpe.br/queimadas/documentos/pub_queimadas.pdf

Soares Filho, B.S. 2006. Dinamica project. Disponível em <http://www.csr.ufmg.br/dinamica> Acesso em 3 set. 2006.

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