Context Information for Understanding Forest Fire Using Evolutionary Computation

7/27/2019 Context Information for Understanding Forest Fire Using Evolutionary Computation

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Context Information for Understanding ForestFire Using Evolutionary Computation

L. Usero3 , 4 , A. Arroyo 2, and J. Calvo 1

1 Dpto. de Organizacion y Estructura de la informaci on,Universidad Politecnica de Madrid, Spain

2 Dpto. de Sistemas Inteligentes AplicadosUniversidad Politecnica de Madrid, Spain

3 Dpto. Ciencias de la Computaci onUniversidad de Alcal a, Spain

4 Center for Spatial Technologies and Remote Sensing, U. California. One ShieldsAve. 95616-8617 Davis, CA. USA

[email protected], [email protected], [email protected]

Abstract. One of the major forces for understanding forest re riskand behavior is the re fuel. Fire risk and behavior depend on the fuelproperties such as moisture content. Context information on vegetationwater content is vital for understanding the processes involved in initi-ation and propagation of forest res. In that sense, a novel method wastested to estimate vegetation canopy water content (CWC) from simu-lated MODIS satellite data. An inversion of a radiative transfer modelcalled Forest Light Interaction-Model (FLIM) from performed using evo-lutionary computation. CWC is critical, among other applications, inwildre risk assessment since a decrease in CWC causes higher proba-bility to have wildre occurrence. Simulations were carried out with theFLIM model for a wide range of forest canopy characteristics and CWCvalues. A 50 subsample of the simulations was used for the training pro-cess and 50 for the validation providing a RMSE=0.74 and r2=0.62.Further research is needed to apply this method on real MODIS images.

Keywords: Genetic Programing, Vegetation Water Content, Forest FireUnderstanding.

1 Introduction

Detecting the water content (Cw) is useful to monitor vegetation stress evenforest re. Context information gathered by remote sensing is vital to understandthe forest re risk and behavior. So it is signicant to use of remote sensing to

measure spectral properties of leaves can provide an indirect structural canopyvariables estimation in order to obtain a comprehensive spatial and temporaldistribution.

Vegetation canopy water content (CWC) is the weight of the water per leaf area unit and per ground area unit. CWC retrieval results critical for severalenvironmental applications including wildre risk [2]. Fires front advances when

J. Mira and J.R. Alvarez (Eds.): IWINAC 2007, Part II, LNCS 4528, pp. 271 276, 2007.c Springer-Verlag Berlin Heidelberg 2007
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272 L. Usero, A. Arroyo, and J. Calvo

the CWC is dried out. Empirical methods have been commonly applied to deriveCWC from satellite data based on the response to changes in reectance in thenear infrared and shortwave infrared part of the spectrum [7]. They work wellfor a specic location, but they need to be calibrated from site to site. Radiative

transfer models model the response in reectance of the vegetation canopy, toaccount for a wide range of biophysical conditions [3]. Therefore, these modelscan be applied to derive CWC for different sites of diverse ecosystem conditions.The simplest radiative transfer models assume that the vegetation forms a con-tinuous canopy of leaf layers. More sophisticated models consider the effect of the tree shadows in the reectance response, assuming trees are homogeneouslydistributed and equal in size. Further complete models take into account het-erogeneous canopies with tree of different sizes and even understorey layer [6].Simpler models make more assumptions, so they could be far from reality, but

they are easier to parameterize, with the less number of input variables.One limitation of the radiative transfer models is that inversion to deriveCWC is computationally very expensive. In order to reduce this limitation neuralnetworks and genetic algorithms have been tested [ 5]. Forest Light Interaction-Model (FLIM) assumes a homogeneous forest canopy, accounting for the treeshadows [4]. This paper uses FLIM to generate CWC from simulated MODISsatellite data. The model was selected since it is fairly complicated, but simplerto parameterize than models that account for the heterogeneity in the tree dis-tribution. Evolutionary computation was applied to test the sensibility of several

vegetation indexes to obtain CWC and to provide a robust model to predict thisvariable from the reectance response.In the last years, several new intelligent approaches emerge to obtain the

content and spatial distribution of vegetation biochemical information over lo-cal to regional and eventually global scales through remote sensing data. Thesenew approaches are related to soft computing techniques close to the computa-tional intelligence. In [ 8], EWT and DM on dry samples estimations with neuralnets were as good as other methods tested on the same dataset, such as inver-sion of radiative transfer models. DM estimations on fresh samples using ANN

(r2=0.86) improved signicantly the results using inversion of radiative transfermodels (r2=0.38). Applications of the genetic algorithms (GA) to a variety of optimization problems in remote sensing have been successfully demonstrated[9]. In [9] estimated LAI by integrating a canopy RT model and the GA opti-mization technique. This method was used to retrieve LAI from eld measuredreectance as well as from atmospherically corrected Landsat ETM+ data. Fourdifferent ETM+ band combinations were tested to evaluate their effectiveness.The impacts of using the number of the genes were also examined.

The major aim of this work is to assess the accuracy of estimating LAI in-

formation by means of evolutionary computation. Following section, we depictour evolutionary computational method. Finally, the authors present severalsuccessfully experimentations and conclusions.
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Context Information for Understanding Forest Fire 273

2 Experimental Results

The objective of this experiment is to nd an index that is able to correlate as faras possible with CwLAI value. This index will be formed by a data combination

obtained beyond the rst seven bands of the MODIS sensor.Indexes those are normally used in remote sensing (e.g. NDVI) are not usefulfor this purpose. These indexes are lack of correlation with the searched CwLAIvalues. (All of them have coefficient determination inferior than 0.1).

Table 1.

Variations in Cross and Reproduction OperatorsPopulation size 1000 charactersLikelihood cross 0.9Likelihood reproduction 0.1Kind of Selection tournamentTournament size 10Elitism among 1 and 5 charactersFinal Nodes Seven bands MODIS (M1 .. M7)

Fig. 1. Correlation between CwLAI values and the ones which have been obtained withthe index that is pointed out in the graph


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274 L. Usero, A. Arroyo, and J. Calvo


In order to nd this index inside the set of possible ones those are formed bymeans of data combination we have decided to use Evolutive Computation Tech-niques, and concretely, Genetic Algorithm (GA). Data combinations are obtainedfrom MODIS sensor together with allowed operations for creating the index.

In order to achieve the test with Genetic Algorithm, we have used a systemof investigation in Evolutive Computation based in Java (EJC) [10] developedby Evolutionary Computation Laboratory (ECLab) George Mason University.

For the whole test system we have used a set of 1000 samples obtained bymeans of FLIM model. From this set, 500 samples have been spent in trainingphase, the other 500 ones have been used for evaluating obtained solutions intraining phase (see Table 1).

Tests have also been achieved bearing in mind typical indexes in remote sens-ing like nal nodes. Modications in aptitude function have been carried out in

order to incorporate RMSE values in optimization process.From Figure 1 to Figure 3, we can observe indexes that present the greatestcorrelation among all the possibilities in the different executions from the chosenevolutive schema are depicted. For each one of these indexes, a tested graph isshowed. Testing has been achieved with training data set and test data set.We can observe RMSE values, degree Pearson correlation (r) and coefficient
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determination (r2). Each gure is divided into four graphics. Each row representsthe same expression but one is using a training data set and the other is workingwith a test data set.

Training data sets are, obviously, offering better results than test data set.

Training set spend a certain period of time in learning how to improve thesolution.We can appreciate twelve different results for the proposed index. Six with

training data set (right side in the gures) and six with test data set (left sidein the gures) as we have explained before. Ideally, the best result would bethe closest to 1, despite of the obtained indexes are close to 0.7 (acceptablecorrelation degree), it is very important to remark that we have tried not toexecute complicated expressions in the genetic algorithm in order to obtain usefulindexes. So that, results are easy to manage by a real teledetection system.

3 Conclusions and Future Work

We have shown how Genetic Algorithm improves the estimation of vegetationwater content. This context information is vital to assess forest re risk and


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Context Information for Understanding Forest Fire Using Evolutionary Computation