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A probabilistic model of fire ignition and spreading for the Xingu Headwaters Rafaella Almeida Silvestrini – UFMG Britaldo Silveira Soares Filho – UFMG.

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Presentation on theme: "A probabilistic model of fire ignition and spreading for the Xingu Headwaters Rafaella Almeida Silvestrini – UFMG Britaldo Silveira Soares Filho – UFMG."— Presentation transcript:

1 A probabilistic model of fire ignition and spreading for the Xingu Headwaters Rafaella Almeida Silvestrini – UFMG Britaldo Silveira Soares Filho – UFMG Ane Alencar – IPAM, UFL Hermann Oliveira Rodrigues - UFMG Daniel Curtis Nepstad – Moore Foundation/ IPAM Paulo Brando – IPAM, UFL 13/10/05 Mauro Maciel

2 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.

3 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)

4 Step 1: Modeling Hot Pixels

5 Part 1: Modeling the hot pixels 1. Spatial Modeling: anthropogenic and biophysical variables Spatial Probability Weights of evidence – Annual Maps 2. Space-time Modeling: climatic variable – VPD (vapor pressure deficit ) Space-time Probability Logistic 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

6 Combining the two probability maps Final 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. Modeling the hot pixels

7 Example: August, * +0.6 * = Modeling the hot pixels

8 Simulating the hot pixels The 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 = constant Furthermore, x% of the cells are allocated as null cells. 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

9 Observed hot pixels – August, 2004Simulated hot pixels – August, 2004 Modeling the hot pixels

10 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

11 Part 2: Modeling fire spreading

12 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?

13 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.

14 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

15 The Spreading Model Cost Prob. Map For each month Inner VPD Fuzzy Function 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 Neighb orhood (initial map)

16 June, July, August

17 Example: June, July and August 2005

18 Validation Number of Simulated Scars = Observed Scars Considering the Fuzzy similarity we have achieved a match of 50% in a window of 9 km.

19 Part 3: Coupling CARLUC and the Fire model Foto: Antonio Ruano.

20 CARLUC Items Based on NPP, decomposition and mortality functions. Disturbances: Deforestation, Logging and Fire. NPP Wood Leaves Roots Charcoal Wood Debris Wood Products Atmosphere Fine Litter Humus

21 Mortality rates

22 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.

23 Obrigada/Thank you Rafaella Almeida Silvestrini – UFMG UNIVERSIDADE FEDERAL DE MINAS GERAIS

24 References Cochrane, M.A Fire science for rainforests. Nature 42: Marengo, J. et al. The drought of Amazonia in 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, Disponível em:. Acesso em: 08 June Pré-publicação. SISMANOGLU, R.A.; SETZER, A.W. Risco de fogo da vegetação na América do Sul: comparação de três versões na estiagem de Anais XII Simpósio Brasileiro de Sensoriamento Remoto [online], Goiânia, GO, p , 16-21/Abr/2005. [acesso em 24 de maio de 2007]. Disponível em: Soares Filho, B.S Dinamica project. Disponível em Acesso em 3 set Bonham-Carter, G., 1994: Geographic information systems for geoscientists: modelling with GIS. Pergamon, 398 pp.


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