int. j. remote sensing, 2000, vol. 21, no. 4, 673–687

Burned area mapping using logistic regression modeling of a singlepost-� re Landsat-5 Thematic Mapper image

N. KOUTSIAS† and M. KARTERIS

Laboratory of Forest Management and Remote Sensing, Department ofForestry and Natural Environment, Box 248, Aristotelian University,540 06 Thessaloniki, Greece

(Received 13 July 1998; in � nal form 10 March 1999)

Abstract. Logistic regression modeling was applied, as an alternative classi� ca-tion procedure, to a single post-� re Landsat-5 Thematic Mapper image for burnedland mapping. The nature of the classi� cation problem in this case allowed thestructure and application of logistic regression models, since the dependent vari-able could be expressed in a dichotomous way. The two logistic regression modelsconsisted of the TM 4, TM 7, TM 1 and TM 4, TM 7, TM 2 presented an overallaccuracy of 97.37% and 97.30%, respectively and proved to be the most wellperforming three-channel color composites. The discriminator ability in respectto burned area mapping of each one of the six spectral channels of ThematicMapper, which was achieved by applying six logistic regression models, agreedwith the results taken from the separability indices Je� ries-Matusita andTransformed Divergence.

1. IntroductionForest � res, especially in the Mediterranean Basin constitute one major ecological

process which in� uence, positively or negatively, the natural cycle of vegetationsuccession and the ecosystems’ structure and function. However, the tremendousamount of forest � res occurred each year and as a consequence the thousands ofhectares of burned areas, constitute a serious threat to natural ecosystems, contribut-ing further to their degradation and elimination. The protection of the areas a� ectedby wildland � res and their restoration, naturally or arti� cially, to the pre-� resituation, presupposes their accurate location and mapping.

For burned area mapping three major approaches can be distinguished dependingon the scale level of measurements. One which is referred to the micro-scale approachit is accomplished by on site human-made measurements. It is considered as timeconsuming, expensive but highly accurate method. The second is referred to meso-scale approach and it is accomplished by the use of black and white or color aerialphotography, in an approximate scale ranging from 1:10 000 to 1:25 000. The third,which is referred to macro-scale approach, is accomplished by the use of remotelysensed data acquired by various satellite systems in geometric resolution rangingfrom 10 m to 1100 m. Among the various operating satellite systems, the Landsat

†Corresponding author: e-mail: koutsias@for.auth.gr

Internationa l Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2000 Taylor & Francis Ltd

http://www.tandf.co.uk/journals/tf/01431161.html

N. Koutsias and M. Karteris674

and the NOAA series are widely used in burned area mapping. The involvement ofsatellite remote sensing in mapping of burned areas ensures for an inexpensive, quickand accurate enough method.

Satellite remote sensing data, multitemporal or single post-� re, acquired in vari-ous geometric, radiometric and spectral resolutions, have been used successfully tomap burned areas, species a� ected and damage severity levels, as well as to monitorthe vegetation regeneration status after the � re (Chuvieco and Congalton 1988,Lopez and Caselles 1991, Chuvieco 1994, Pereira et al. 1997, Koutsias and Karteris1998). On the other hand, various methods have been developed and applied inburned area detection and mapping. These range from simple univariate , such asvisual interpretation and single-channel density, to complex multivariate, such asregression and spectral mixture analysis (Pereira et al. 1997).

In this study, logistic regression models of single post-� re satellite data weredeveloped and applied to map the burned area of a large forest � re occurred inAthens, Greece. Moreover, another objective of this study was the estimation of thespectral information content of each spectral channel of Landsat-5 Thematic Mapperin respect to burned area discrimination. In a previous study concerning the samestudy area, made by Koutsias and Karteris (1998), logistic regression modeling wasperformed successfully for burned area mapping using multitemporal Landsat-5Thematic Mapper data.

2. Materials and Methods2.1. Burned area mapping

In mapping of burned areas using Landsat-5 Thematic Mapper data, two broadapproaches are distinguished based on whether multitemporal or single post-� redata are used. In the multitemporal approach in which usually two satellite imagesare processed, one taken before the � re event and the other after, the methods forburned area mapping rely mainly on the di� erences in the spectral response of thesame objects between the pre- and post-� re satellite images. The removal of thevegetation layer caused by the � re modi� es a lot the spectral behavior of the ‘burnedcategory pixels’ compared to the pre-� re situation. Speci� cally, in the infrared channel(TM 4) of Landsat-5 Thematic Mapper, the raw digital values of the ‘burned categorypixels’ decrease signi� cantly from the pre-� re to post-� re satellite image (Pereiraet al. 1997, Koutsias and Karteris 1998). This reduction is due to the destruction ofthe leaves of the vegetation layer, which re� ects large amounts of the incident solarradiation in this spectral channel. In addition, in the mid-infrared channel (TM 7),the raw digital values of the ‘burned category pixels’ increase signi� cantly from thepre-� re to post-� re satellite image probably due to decrease of vegetation moisturecontent (Pereira et al. 1997, Koutsias and Karteris 1998). In single post-� re approachin which one satellite image is processed, taken after the � re event, the methods forburned area mapping rely on the di� erences in the spectral response of the objectsin the same satellite image. This fact modi� es a lot the methodological approachused to map burned areas.

Depending on the kind of the satellite information available, a wide diverse setof methods is applied to successfully detect and map the burned areas. In general,the spectral resolution of the sensor in� uences the types of methods applied, ratherthan the spatial or temporal resolution (Pereira et al. 1997). It has been proved thatmultitemporal satellite data are more advantageous than single post-� re satellitedata, since the multitemporal ones reduce the likelihood of confusion with permanent

L ogistic regression modeling for burned area mapping 675

land cover types (Pereira et al. 1997). However, single post-� re methods are superiorto multitemporal ones, because of the cost for data acquisition and the e� ort thatshould be put for registering and processing the multitemporal data set. Among themost critical issues in the multitemporal approach, is the radiometric and geometricmatching of the images used. Misregistration of both, radiometric and geometricdimension, may produce undesirable and unpredictable errors, which in turn mayresult in either under- or over-estimation of the burned areas.

Pereira et al. (1997), in an extended review paper concerning remote sensing ofburned areas, stated that due to the diverse and complex patterns of the spatial andtemporal variability of the spectral response of burned areas, their detection andmapping remains somehow problematic, although a large number of di� erent classi-� ers have been developed and used. These di� culties arise, to a large extent, fromthe classi� ers used and also from the burn age, as well as, from the local eco-climaticconditions. In the same work also, it is mentioned that land cover/use categorieswhich have been reported as highly confused are water bodies, urban areas andshadows.

2.2. L ogistic regression modelingVarious statistical multivariate techniques have been developed to predict a

dependent response variable from a set of independent explanatory measurementsor to classify individuals into a group of two or more categories. Discriminantanalysis and multiple regression are two of these statistical multivariate techniques,which can also be used to quantify the contribution of each independent variable inmaking the prediction. However, these techniques may not be appropriate when themultivariate normal model is not assumed or the set of independent measurementsis a mixture of categorical and continuous variables. In such cases, another multivari-ate technique, that of logistic regression, which is used actually for estimating theprobability of an event occurring, is applied. Logistic regression modeling is used,as an alternative method, for classi� cation, when neither multivariate normality isassumed nor the independent measurements consist only of continuous scalar vari-ables (A� � and Clark 1990, Norusis 1990).

The logistic model was developed and used in survival analysis in which thedependent response variable is dichotomous and is expressed as 1 or 0, dependingon whether the experimental question is true or false (Mendenhall and Sincich 1996).The logistic model which gives the quantitative relationship between the dependentresponse variable and the independent explanatory variables can be expressed as

E(y)=exp(b0 +b1 X 1 +b2 X 2 +b

kX

k)

1 +exp(b0 +b1 X 1 +b2 X 2 +...+bkX

k), (1)

(Mendenhall and Sincich 1996) or alternative

E(y)=1

1 +exp[Õ (b0 +b1 X 1 +b2 X 2 +...+bkX

k)]

, (2)

(A� � and Clark 1990) where

y= G1 if category A occurs

0 if category B occurs

N. Koutsias and M. Karteris676

E(y)= P(Category A occurs)= pX 1 , X 2 , . . . , X

kare quantitative or qualitative independent variables

b0 , b1 , ..., bk

are estimated coe� cients

Figure 1 presents graphically the curve of the logistic regression equation whichhas two asymptotes one minimum at 0 and one maximum at 1. Thus, the probabilityestimates of the logistic regression model for the dependent variable always rangebetween 0 and 1, which results to a realistic probability surface (Narumalani et al.1997). This behavior of the logistic regression model constitutes an advantage overother statistical techniques such as linear regression, where the estimates may rangeoutside the range 0–1.

Various applications can be found in the literature concerning the use of logisticregression modeling (Pereira and Itami 1991, Boxall and McFarlane 1995, Bian andWest 1997, Narumalani et al. 1997, van Deventer et al. 1997). Especially, in the � eldof remote sensing, GIS and wildland � res, logistic regression modeling has beenapplied to:

1. Predict the daily people-caused � re occurrence (Martell et al. 1987 ).2. Predict post � re mortality in certain species (Ryan and Reinhardt 1988).3. Examine the relationship between National Fire Danger Rating System

(NFDRS) indices and � re occurrence (Loftsgaarden and Andrews 1992).4. Model the distribution of � re occurrence probability for estimating � re danger

(Chou et al. 1990, Chou 1992) and5. Map the burned areas using a multitemporal Landsat-5 Thematic Mapper

data set (Koutsias and Karteris 1998).

2.3. Study area and satellite dataIn September 1992 a large � re occurred in northern part of Attica in Greece

(� gure 2), one of the most a� ected areas by forest � res. It burned about 5500 hectares.

Figure 1. The curve of the logistic regression equation has two asymptotes. One minimumat the value 0 and one maximum at the value 1.

L ogistic regression modeling for burned area mapping 677

Figure 2. Geographical location of the study area.

This was the study area for the development and application of the logistic regressionmodels in burned area mapping. For this purpose, a Landsat-5 Thematic Mapperimage (� gure 3), taken on 11 September, just a few days after the � re, was acquiredand constituted the basic source of information.

The study area belongs to Mediterranean type climate, while the vegetation inthe area is composed mainly of conifers and shrubs. In detail, the bioclimate ischaracterized as semi-arid, with high temperatures and low relative humidity duringthe � re season. As a result, the forested land is composed by pines (Pinus halepensis,Pinus brutia), and various Mediterranean shrubs (Maquis), that are well adapted insuch climatic conditions.

2.4. MethodologyThe nature of the problem of burned area mapping allows the structure and the

application of logistic regression models, since the dependent variable can beexpressed in a dichotomous way. That is a pixel in the satellite image may be burnedor unburned. Thus, to represent the burned area mapping in a dichotomous way, adummy or indicator variable E is created, which takes the value 1 if the respondentis burned or 0 if the respondent is unburned. The set of the independent explanatoryvariables consists of the spectral channels of the post-� re satellite image and con-sequently the independent measurements are actually the radiometric values of thepixels in each spectral channel. The logistic regression equation, actually, estimatesthe probability based on which a pixel of the satellite image belongs to the burnedarea and consequently can be classi� ed as burned or unburned. The criterion usedto classify an individual pixel into one of the two groups depends on the value of

N. Koutsias and M. Karteris678

Figure 3. Landsat-5 Thematic Mapper satellite image acquired on 11 September just afterthe � re. The spectral channels TM 7, TM 4, and TM 1 displayed in Red, Green, andBlue color planes enhance the discrimination between the burned area and the otherland cover categories. The burned area appears in dark red, while vegetative areas ingreen, the sea in dark blue and the bare land, clouds and urban areas in white.

E(y). When E(y)> 0.5 then the individual pixel is classi� ed as burned, otherwiseunburned.

Before continuing with the sampling process, radiometric restoration whichincludes the haze removal due to atmospheric e� ects was applied to satellite data.Actually, the minimum value of each spectral channel was subtracted from eachpixel brightness in that channel, in order to remove the shifting in histogram dataplots occurred by the existence of the atmosphere (Sabins 1986).

For the sampling process, a certain set of prerequisites should be ful� lled toensure the validity of the structure and application of the logistic models. These arethe following (Koutsias and Karteris 1998):

1. The sampling areas for the burned and the unburned cases should be accuratelydetermined on the satellite image.

L ogistic regression modeling for burned area mapping 679

2. The sampling size regarding both cases should be about the same to avoidbias in the sampling process.

3. For both cases a satisfactory absolute sample size should be obtained and4. The samples should represent all the variability occurring on the satellite

image within and outside the burned area.

Logistic regression models were structured, � rst to access the discriminator abilityof each spectral channel and thus to estimate their spectral information content forburned area mapping and second to predict and classify successfully the pixels ofthe satellite image. For that purpose six logistic models were structured each oneusing as independent variable one of the six available spectral channels (exceptthermal). Finally, a logistic model which uses as independent variables the most wellperforming spectral channels was developed and applied on the satellite data.

3. Results and discussion3.1. Spectral information content assessment

Apart from the structure and evaluation of the � nal logistic model to predict theresponse variable and consequently to map the burned areas, another objective ofthis study was the estimation of the spectral information content of each spectralchannel.

In logistic regression, as well as in multiple regression, it is di� cult to estimatethe contribution of an individual variable in predicting the response variable, becausethe behavior of each variable depends and in� uenced by the other variables presentedin the model. This problem is enhanced when the independent variables are highlycorrelated (Norusis 1990) as in the case of Thematic Mapper data. To overcomethese limitations regarding the interpretation of the coe� cients of the logistic model,six di� erent models including one of the six spectral channels of Thematic Mapperwere structured and evaluated.

Table 1 summarizes the results acquired from the construction of these six logisticmodels. In this table, the percentage of correct classi� ed observations (burned,unburned and overall ) in each logistic model indicates the performance of the modeland consequently determines the contribution of each spectral channel on the discrim-ination and mapping of burned areas. So based on the overall accuracies presentedin table 1, it is evident that the most well performing channel for burned areadiscrimination is the infrared channel (TM 4). It provides an overall accuracy of67.32% consisting of 62.98% of correct classi� ed unburned cases and 72.53% ofcorrect classi� ed burned cases. It is important to mention that both sub-accuracies,

Table 1. Classi� cation results of the logistic regression models, which include only onespectral channel, based on the comparisons of observed versus predicted observations.

Percentage of correct classi� ed observations Statistics of performance

Unburned, % Burned, % Overall, % Õ 2 LL Goodness of � t

TM 1 80.12 0.18 43.78 16 692 12 136TM 2 58.57 27.35 44.38 16 428 12 003TM 3 60.08 1.54 33.47 16 651 12 131TM 4 62.98 72.53 67.32 14 978 11 658TM 5 100 0 54.55 16 916 12 275TM 7 70.53 50.29 61.33 15 829 16 290

N. Koutsias and M. Karteris680

those of burned and unburned observations, should contribute the same to the � naloverall accuracy. According to Koutsias and Karteris (1998), TM 4 proved to bethe most sensitive regarding alterations in spectral response of the burned categorypixels. Although that study was focused on the contribution of the spectral informa-tion provided by a multitemporal data set, this conclusion is also veri� ed in thecurrent study. In this case, spectral channel TM 4 provides the highest discriminatorability among the six available Thematic Mapper channels.

Similarly, the second spectral channel which provides the highest contributionto burned area discrimination is TM 7, for which the overall percentage of correctclassi� ed observations is 61.33%, consisting of 70.53% of correct classi� ed unburnedcases and 50.29% of correct classi� ed burned cases. Regarding the visible part ofelectromagnetic spectrum, TM 2 seems to be the most valuable, since the overallaccuracy of the model is 44.38%. The performance of both, TM 1 and TM 3, islimited since the percentage of correct classi� ed burned observations is only 0.18%and 1.54%, respectively. Finally, for the spectral channel TM 5 although its overallaccuracy is 54.55%, it is considered as the last well performing channel, because thisvalue is estimated based on the weighted mean of both sub-accuracies, which is100% for the unburned observations and 0% for the burned ones.

3.2. Spectral behavior of burned areasIn the previous section a clear view of the discriminator ability of each spectral

channel to predict burned versus unburned category pixels was achieved. Howeverthe contribution to the total discrimination of each land cover/use type included inthe unburned category it was not justi� ed. For that purpose a comparison of thespectral signatures between the burned areas and the other land cover/use categorieswas accomplished in order to achieve a better understanding of their spectralbehavior and potential discriminator ability. Simple statistics such as means, standarddeviations, covariances etc. were calculated for certain land cover/use categories inthe post-� re satellite image. Four major groups, that of vegetation, water bodies,bare/low vegetated land and shadows/urban areas were identi� ed on the satelliteimage and their spectral signatures were compared against the spectral signature ofburned areas. These comparisons were accomplished, � rst based on a graphicalevaluation of the spectral signature plots and second based on the estimation of theseparability indices Transformed Divergence and Je� ries-Matusita.

3.2.1. Graphical evaluationFigure 4(a) presents the spectral signature plot of agricultural crops and forested

areas in comparison to burned areas. It is evident that the discriminator abilityo� ered by the infrared spectral channels is much higher than the visible. Amongthem spectral channel TM 4 and TM 7 provides the highest discrimination betweencrops and burned areas, while spectral channel TM 5 and TM 7 provides the highestdiscrimination between forested and burned areas. In the spectral channel TM 4 theraw digital values of the burned areas are lower than the vegetated, while in thespectral channel TM 7 burned areas are depicted with higher values than thevegetated. Regarding the visible channels, TM 3 provides the highest discriminatorability.

Figure 4(b) presents the spectral signature plot of water bodies, that of sea andlake, compared against burned areas. In this case, the spectral channel TM 5 providesthe highest discrimination between the water bodies and burned areas, followed by

L ogistic regression modeling for burned area mapping 681

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N. Koutsias and M. Karteris682

the spectral channel TM 7. Also, spectral channels TM 4 and TM 3 contribute tothe total discrimination, while channels TM 1 and TM 2 do not provide any extravaluable spectral information.

Figure 4(c) presents the spectral signature plot of shadows and urban areas incomparison to burned areas. Spectral channel TM 5 provides the highest discrimin-ator ability between burned areas and shadows, followed by TM 7 and TM 4. Also,in the visible channels there is an evident discrimination between these two categories.As expected shadows are depicted with very low digital values, unlike the burnedareas which are depicted with relatively high. In addition, urban areas have higherdigital values than those of burned areas especially in the visible channels whichprovide the highest discriminator ability.

Finally, � gure 4(d ) presents the spectral signature plot of bare soil and lowvegetated areas compared against burned areas. In addition to the previous threecases in which there was a clear discrimination in some spectral channels, in thiscase the spectral response of the burned areas is similar with the spectral responseof bare soil and low vegetated areas. As a consequence a confusion among thesecategories is very possible to happen, since the potential discriminator ability o� eredby each spectral channel is not very high.

3.2.2. Separability indicesSeparability indices provide a prior quantitative measurement of the discrimina-

tion of the spectral categories o� ered by each spectral channel. In this studyTransformed Divergence and Je� ries-Matusita (Swain and Davis 1978) were estim-ated for each spectral channel. Both indices provide a separability measure for acertain pair of classes. Thus in order to acquire a complete view of the totaldiscrimination, all possible combinations of the classes by two should be considered.Moreover, an average value can be estimated to provide a single index whichrepresents the total discrimination.

Tables 2 and 3 summarizes the estimated values of Je� ries-Matusita andTransformed Divergence between burned areas and other land cover/use categoriesin each spectral channel. First of all, it should be noticed that a general agreementis observed with the results acquired by the graphical evaluation presented in the

Table 2. Je� ries-Matusita separability index between burned areas and other land cover/usecategories in each spectral channel.

Burned areas

TM 1 TM 2 TM 3 TM 4 TM 5 TM 7 Mean Min Max

Urban 1404 1370 1246 924 497 534 996 497 1404Low vegetated 921 986 888 794 902 773 877 773 986Crops 465 412 801 1407 666 1128 813 412 1407Bare land 630 752 789 792 762 987 785 630 987Lake 528 380 940 984 1378 1385 933 380 1385Sea 481 890 1274 1394 1399 1389 1138 481 1399Forested 944 819 1032 721 838 1177 922 721 1177Clouds 1400 1411 1410 1393 1292 1121 1338 1121 1411Mean 847 878 1048 1051 967 1062Minimum 465 380 789 721 497 534Maximum 1404 1411 1410 1407 1399 1389

L ogistic regression modeling for burned area mapping 683

Table 3. Transformed Divergence separability index between burned areas and other landcover/use categories in each spectral channel.

Burned areas

TM 1 TM 2 TM 3 TM 4 TM 5 TM 7 Mean Min Max

Urban 1982 1877 1553 865 306 358 1157 306 1982Low vegetated 987 1492 1242 1000 1336 1057 1186 987 1492Crops 267 202 830 1993 700 1918 985 202 1993Bare land 545 951 1101 1107 974 1757 1073 545 1757Lake 279 154 902 1362 2000 2000 1116 154 2000Sea 265 942 1868 2000 2000 2000 1513 265 2000Forested 895 694 1172 657 731 1643 965 657 1643Clouds 2000 2000 2000 1997 1686 1345 1838 1345 2000Mean 903 1039 1334 1373 1217 1510Minimum 265 154 830 657 306 358Maximum 2000 2000 2000 2000 2000 2000

previous section. A conclusion drawn from the comparisons of the mean values ofboth separability indices in each spectral channel, is that spectral channels TM 7,TM 4 and TM 3 constitute the best three channels, which can be used in burnedarea mapping. According to both separability indices (tables 2 and 3), TM 7 providesthe highest mean discriminator ability (1062, 1510) between burned areas with otherland cover/use categories, followed by TM 4 (1051, 1373) and TM 3 (1048, 1334).In addition, according to the minimum values of both indices, it is concluded thatTM 3 (789, 830) is the best followed by TM 4 (721, 657) and TM 7 (534, 358).However, according to both, the mean and the minimum values, these three channelsare the best among the six available.

According to the maximum values of both indices (right column in tables 2and 3) between the burned areas and the other land cover/use categories, it isconcluded that a very possible confusion may happen with the classes of lowvegetated areas and bare land. This conclusion is also veri� ed from the graphicalevaluation made in the previous section.

Finally, both indices were estimated for some color composites, which includedthe most well performing channels (see table 4). According to the estimated valuesfor both indices in this table, it is concluded that TM 247 is the best three-channelcomposite, although the values for both indices were not much di� erent from theother composites. This � nding agrees with the conclusion drawn in logistic regressionmodeling in which TM 4, TM 7 and TM 2 proved to be the most well performingchannels, since they provided the highest percentage of correct classi� ed observations(table 1).

3.3. Development of logistic regression modelsHaving acquired a good knowledge about the spectral behavior of burned areas

and having also determined the discriminator ability of each spectral channel forburned area mapping, six logistic regression models were structured. Four of them,as depicted in table 5, included the most well performing channels, that is TM 4,TM 7 and one of the visible, while the rest two consisted of the Normalized Di� erenceVegetation Index (NDVI), that is (TM 4 Õ TM 3)/(TM 4 +TM 3) and the NormalizedDi� erence between TM 4 and TM 7, that is (TM 7 Õ TM 4)/(TM 7 +TM 4).

N. Koutsias and M. Karteris684

Table 4. Je� ries-Matusita and Transformed Divergence separability indices for some compos-ites between burned areas and other land cover/use categories present in the study area.

Burned areas

Je� ries-Matusita Transformed divergence

Composite 47 147 247 347 47 147 247 347

Urban Areas 1361 1414 1413 1408 1897 2000 1999 1992Low vegetated Areas 913 1088 1121 1025 1317 1524 1678 1506Agricultural Crops 1414 1414 1414 1414 2000 2000 2000 2000Bare land 1058 1076 1087 1085 1800 1819 1838 1827Lake 1407 1408 1410 1409 2000 2000 2000 2000Sea 1401 1409 1409 1403 2000 2000 2000 2000Forest 1404 1405 1405 1407 2000 2000 2000 2000Clouds 1410 1413 1413 1413 2000 2000 2000 2000Average 1296 1328 1334 1321 1877 1918 1939 1916

Table 5. Classi� cation results of the logistic regression models based on the comparisons ofobserved versus predicted observations.

Percentage of correct classi� edobservations Statistics of performance

Unburned, % Burned, % Overall, % Õ 2 LL Goodness of � t

TM 4, TM 7 98.36 94.75 96.72 2769.25 34 661.48TM 4, TM 7, TM 1 98.51 96.00 97.37 2136.46 28 906.47TM 4, TM 7, TM 2 98.49 95.88 97.30 2232.18 49 018.33TM 4, TM 7, TM 3 98.48 95.50 97.09 2376.66 80 801.64NDVI 69.04 45.72 58.44 15 799.42 12 076.14(TM 7 Õ TM 4)/ 91.11 98.37 91.68 5268.64 10 953.30(TM 7+TM 4)

Between the two indices the one which utilizes the spectral information of channelsTM 4 and TM 7 has been proved to be superior for burned area mapping. A resultvery similar to what has been found by Lopez and Caselles (1991), who concludedthat this index is suitable to evaluate the areas a� ected by forest � res and also tomonitor the vegetation regeneration in burned areas. Based on the results acquiredfrom the logistic regression modeling (table 5) this index showed a superior perform-ance over the NDVI. This is very reasonable since, as it has been found (Koutsiasand Karteris 1998), signi� cant di� erences of the spectral signatures of burned areasoccurred in spectral channels TM 4 and TM 7 rather in TM 4 and TM 3 which areused in NDVI. The values of this index for a healthy vegetated or forested area arelower than those for a burned area. Forested areas are presented with negative valuesunlike those of burned which are presented with positive. In addition, the values ofNDVI for a healthy vegetated or forested area are higher than those for the burned.However both, vegetated and burned areas, are presented with positive values.Figure 5 depicts the spatial distribution of the NDVI (left image) and the vegetationindex (TM 7 Õ TM 4)/(TM 7 +TM 4) (right image). It is quite evident that thespectral discrimination between the burned areas and the other land cover/usecategories is much higher in the latter vegetation index.

L ogistic regression modeling for burned area mapping 685

Figure 5. Graphical presentation of the Normalized Di� erence Vegetation Index ( left image)and the Vegetation Index (TM 7 Õ TM 4)/(TM 7+TM 4) (right image).

The four logistic models which included the three most well performing channels(TM 4, TM 7 and one of the TM 1, TM 2 or TM 3) according to their overallpercentage of correct classi� ed observations, presented a similar performance(table 5). However, for further analysis a x2 -test was applied to test statistically theresults acquired from these four logistic regression models. From this analysis(table 6) it is concluded that signi� cant di� erent results were acquired only betweenTM 47—TM 471 and TM 47—TM 472 models. All the three-channel compositesemployed in the analysis presented a similar performance, since their results werenot signi� cantly di� erent.

A conclusion drawn from the structure and evaluation of these logistic regressionmodels is that TM 4, TM 7 and one of the visible channels, preferably TM 1 orTM 2, constitute the best three-channel color composite for burned area mapping.This � nding was also veri� ed in the spectral information content assessment, whichhas been accomplished by various techniques.

4. ConclusionsThe main classi� cation target in this study was to examine whether a candidate

pixel in the satellite image belonged or not to the burned category pixels. Thisclassi� cation question was processed by logistic regression modeling, a statisticalmultivariate analysis which has been developed and used in survival analysis wherethe dependent response variable is a dichotomous variable.

The models which included the spectral channels TM 7, TM 4 and one of the

Table 6. The results acquired from the x2 -test applied to the logistic regression models.

Colour composites

Colour composites TM 47 TM 471 TM 472

TM 471 23.903* –TM 472 18.813* 0.239 –TM 473 7.512 4.027 2.251x2

3 , 0 . 0 5 = 7.81473 * Signi� cantly di� erent

N. Koutsias and M. Karteris686

TM 1 and TM 2 of a single post-� re Landsat-5 Thematic Mapper image presentedan overall accuracy of 97.37% and 97.30%, respectively. Among the original spectralchannels, TM 4 proved to be the most well performing in burned area mappingfollowed by TM 7. Concerning the visible channels, the green (TM 2) presented asuperior performance over the blue (TM 1) and the red (TM 3) region of thespectrum. Moreover, the study concluded that the normalized di� erence index(TM 7 Õ TM 4)/(TM 7 +TM 4) presents a superior performance over the traditionalNormalized Di� erence Vegetation Index (TM 4 Õ TM 3)/(TM 4 +TM 3), for burnedland mapping.

Finally, the study demonstrated the usefulness of logistic regression modeling inspectral information content assessment. The discriminator ability of each spectralchannel in respect to burned area discrimination was achieved by logistic regressionmodeling for each spectral channel of Landsat-5 Thematic Mapper image.

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