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Assessing the feasibility of sub-pixel burnedarea mapping in miombo woodlands of northernMozambique using MODIS imageryA. C. L. Sá a , J. M. C. Pereira a d , M. J. P. Vasconcelos d , J. M. N. Silva a , N. Ribeiro e &A. Awasse fa Department of Forestry , Instituto Superior de Agronomia , Tapada da Ajuda, Lisboa,1349-017, Portugal E-mail:b Department of Forestry , Instituto Superior de Agronomia , Tapada da Ajuda, Lisboa,1349-017, Portugal E-mail:c Department of Forestry , Instituto Superior de Agronomia , Tapada da Ajuda, Lisboa,1349-017, Portugal E-mail:d Tropical Research Institute , Travessa do Conde da Ribeira 9, Lisboa, 1300-142, PortugalE-mail:e Faculty of Agronomy and Forestry , Universidade Eduardo Mondlane , CampusUniversit@rio edificio 1, C.P.257, Maputo, Mozambique E-mail:f Servi@os Provinciais de Florestas e Fauna Bravia , C.P.36 Nampula, MozambiquePublished online: 26 Nov 2010.

To cite this article: A. C. L. Sá , J. M. C. Pereira , M. J. P. Vasconcelos , J. M. N. Silva , N. Ribeiro & A. Awasse (2003)Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of northern Mozambique using MODISimagery, International Journal of Remote Sensing, 24:8, 1783-1796, DOI: 10.1080/01431160210144750

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. . , 2003, . 24, . 8, 1783–1796

Assessing the feasibility of sub-pixel burned area mapping in miombowoodlands of northern Mozambique using MODIS imagery

A. C. L. SA1* , J. M. C. PEREIRA1, 2 , M. J. P. VASCONCELOS2 ,J. M. N. SILVA1 , N. RIBEIRO3 and A. AWASSE41Department of Forestry, Instituto Superior de Agronomia, Tapada da Ajuda,1349-017 Lisboa, Portugal; e-mail: anasa,jmcpereira, joaosilva@isa.utl.pt2Tropical Research Institute, Travessa do Conde da Ribeira 9, 1300-142 Lisboa,Portugal; e-mail: mperestrelo@clix.pt3Faculty of Agronomy and Forestry, Universidade Eduardo Mondlane, CampusUniversitario edificio 1, C.P.257, Maputo, Mozambique;e-mail: ldinis@topical.co.mz4Servicos Provinciais de Florestas e Fauna Bravia, C.P.36 Nampula,Mozambique

Abstract. The goal of this study was to evaluate the feasibility of sub-pixelburned area detection in the miombo woodlands of northern Mozambique,using imagery from the Moderate Resolution Imaging Spectroradiometer(MODIS). Multitemporal Landsat-7 ETM+ data were acquired to produce a highspatial resolution map of areas burned between mid-August and late September2000, and a field campaign was conducted in early November 2000 to gatherground truth data. Mapping of burned areas was performed with an ensemble ofclassification trees and yielded a kappa value of 0.896. This map was subsequentlydegraded to a spatial resolution of 500m, to produce an estimate of burned areafraction, at the MODIS pixel size. Correlation analysis between the sub-pixelburned area fraction map and the MODIS reflective channels 1–7 yielded low butstatistically significant correlations for all channels. The better correlations wereobtained for MODIS channels 2 (0.86mm), 5 (1.24mm) and 6 (1.64mm). A regressiontree was constructed to predict sub-pixel burned area fraction as a function of thoseMODIS channels. The resulting tree has nine terminal nodes and an overall rootmean square error of 0.252. The regression tree analysis confirmed that MODISchannels 2, 5, and 6 are the best predictors of burned area fraction. It may bepossible to improve these results considering, as an alternative to individual chan-nels, some appropriate spectral indices used to enhance the burnt scar signal, andby including MODIS thermal data in the analysis. It may also be possible toimprove the accuracy of sub-pixel burned area fraction using MODIS imagery byallowing the regression tree to automatically create linear combinations of indi-vidual channels, and by using ensembles of trees.

1. IntroductionBiomass burning represents a major perturbation of global atmospheric chem-

istry, comparable to that of fossil fuel burning (Andreae 1991, Levine 1991, Cicerone

*Corresponding author.This paper was presented at the 3rd International Workshop of the Special Interest Group

(SIG) on Forest Fires of the European Association of Remote Sensing Laboratories held inParis in May 2001.

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2003 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160210144750

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1994). Vegetation fires in Africa play a key role in tropical atmospheric chemistry.They account for an estimated 57% of all tropical belt biomass burning, of which49% are from savanna fires and 8% from deforestation burns (Lacaux et al. 1993).At the global scale, burning of African savannas accounts for almost one third ofannual gross emissions from biomass burning (Andreae 1991, 1997). The level ofuncertainty associated with these estimates is still quite high, as evidenced by Barbosaet al. (1999) who calculated that the mean annual biomass burned in Africa rangesfrom 704 to 2168 Tg.

The Southern Africa Fire-Atmosphere Research Initiative 2000 (SAFARI 2000,http://safari.gecp.virginia.edu/index.asp) is an ongoing international regional scienceinitiative, taking place in southern Africa. It addresses the study of linkages betweenland–atmosphere processes and the relationship of biogenic, pyrogenic or anthropo-genic emissions to the functioning of the biogeophysical and biogeochemical systemsof southern Africa. One of its main objectives is to reduce the levels of uncertaintiesassociated with the various factors affecting emissions from biomass burning. Levine(1996) considered the accurate assessment of area burned, including its timing andlocating, as the greatest single challenge to the biomass burning research community.The main objective of the present work is to evaluate the feasibility of accurate sub-pixel burned area estimation using satellite imagery from the Moderate ResolutionImaging Spectroradiometer (MODIS), one of the sensors onboard NASA’s EarthObserving System (EOS) TERRA satellite.

Mozambique is one of the southern African countries where fire incidence ishigher. Barbosa et al. (1999) estimates that between 18% and 38% of the total areaof the country is burned annually. From the standpoint of atmospheric emissionsthe miombo woodlands of northern Mozambique are very important due to theirhigh standing biomass and high net primary productivity. Our study area ( latitude14°15∞–14°55∞S, longitude 38°30∞–39°15∞E) is located in this region, in the Mecuburidistrict, Nampula province and includes the largest forest reserve in the country,containing miombo woodlands in various stages of preservation. The dominantvegetation types in the study area (White 1983), are forest patches of East Africancoastal mosaic and drier and wetter Zambezian miombo woodland, both dominatedby Brachystegia, Julbernardia and Isoberlinia species (figure 1). The area is a SAFARI2000 test site, namely for the validation of MODIS fire products (SAFARI 2000,http://safari.gecp.virginia.edu/index.asp).

2. Materials and methods2.1. Data and fieldwork

Two Landsat 7 ETM+ images, from 10 August and 27 September 2000 (path165, row 70), were used to map the area burned between these two dates. A MODISimage (MOD09GHK Surface Reflectance 500m product) from the same date as thesecond Landsat image (figure 2) was acquired to map burns at a coarser spatialresolution.

A field trip during 1–10 November 2000 served for generic reconnaissance of thestudy area, and to acquire ground data in support of satellite image classificationand accuracy assessment. Fieldwork was concentrated in the central part ( latitude14°15∞–14°55∞S, longitude 38°30∞–39°15∞E) of the area covered by the Landsat images,in an area of about 600 000 ha. According to the analyses of Barbosa et al. (1999)and Dwyer et al. (1999, 2000) the fire season in northern Mozambique lasts forapproximately 3 months, typically between August and October. Therefore, the

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Figure 1. Study area in Mozambique. Positioning (path 165, row 70) shown nearby Nampula.White’s (1983) main cover types inside the study area are also indicated.

present study did not cover the last month of the burning period. There was a gapof one month between the date of acquisition of the second Landsat image (and ofthe MODIS scene), and the date of fieldwork. During this gap, additional areasburned, and early season burns lost some of the evidence of fire occurrence, due toscattering of charcoal and ash by wind, and rapid regrowth of tropical grasses.

Recent burns, detected on the ground but not observable in the Landsat RGB7-4-3 colour composite were assumed to have occurred after 27 September 2000 andwere not taken into account for the collection of fire scar ground truth sites.

Fieldwork was affected by limited accessibility by road to most of the core studyarea, and the possible existence of land mines on the ground made it unsafe to walkaway from roads, into the bush. Therefore, it was impossible to pace and georeferencethe perimeter of burn scars with global positioning system (GPS) equipment. Evenwithout the accessibility constraints found, this would not have been feasible in thetime period available, due to the very large extent and complex spatial pattern ofthe burned area at the end of the dry season. All accessible roads in the core studyarea were travelled, and GPS-georeferenced ground truth sites corresponding toburned and to unburned patches, were located adjacent to or easily visible fromthose roads. The Landsat RGB 7-4-3 colour composite from 27 September 2000 wasused to select the areas to be visited for ground truth site selection. These areas werealso marked and labelled in the RGB print to extract training and validation areasfor the development of a Landsat-based burned area map.

Figure 3 displays a flowchart of the overall data collection and analysisprocedures used in this study.

2.2. Pre-processingA relative radiometric calibration with a band-to-band linear regression was

performed on the Landsat data (Olsson 1994). Due to stronger atmospheric con-tamination in the upper half of the latter date image, the regression coefficients were

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replaced by linear functions of the column and line positions, an implementation ofthe spatial expansion method of Casetti and Jones (1987). This approach accountsfor spatial drift in the relationship between independent and dependent variables,thus producing a model that is non-stationary in space and optimizes the calibrationslocally. The model, which accounts for spatial drift is defined as:

Bandpre-fire=a+bBandpos-fire (1)

where the coefficients a and b are replaced by the following expansion equations:

a=a0+a1C+a2L (2)

b=b0+b1C+b2L (3)

C and L are the variables representing the column and line position of each pixel inthe image. Replacing the right-hand sides of equations (2) and (3) into (1) results inthe following terminal model:

Bandpre-fire=a0+a1C+a2L+b0Bandpos-fire+b1CBandpos-fire+b2L Bandpos-fire(4)

2.3. L andsat burned area classification2.3.1. Data sample

Based on the results of previous research (Sa 1999), burned area mapping withLandsat images can be successfully performed using normalized difference spectralindices and a classification trees algorithm. The following indices (equations (5), (6)and (7)) were calculated for both Landsat dates available, and used as predictivevariables in the classification:

— The green Normalized Difference Vegetation Index (NDVI) (Gitelson et al. 1996):

(ch4−ch2)/(ch4+ch2) (5)

— The Normalized Difference Water Index (NDWI) (Gao 1996):

(ch4−ch7)/(ch4+ch7) (6)

— A NIR-thermal index (Sa 1999):

(ch4−ch6)/(ch4+ch6) (7)

Training areas for burned area classification were extracted from the spectralindex difference images, through the conventional procedure of on-screen digitizing,selecting observations from the areas visited in the field. Pre- and post-fire LandsatRGB 7-4-3 colour composite images were useful for visual checking of burned andunburned areas. A total of 9286 pixels were extracted, of which 6153 were used fortraining a classifier and 3133 for accuracy assessment of the classifier. Classificationwas performed using the classification and regression trees (CART) algorithm(Breiman et al. 1984) combined with a bootstrap aggregation approach (Breiman1996), as implemented in the Salford Systems CARTA software package (Steinbergand Colla 1997).

2.3.2. Bagged classification treesThe process of constructing classification trees requires the specification of various

parameters, including selection of a node impurity criterion to guide the tree splitting

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(a)

(b)

(c)

Figure 2. MODIS RGB colour composite (7, 2, 1) corresponding to the same geographicalarea as the Landsat scene, with the marked Landsat sub-scene study area (a); Pre-fireRGB (7, 4, 3) colour composite of the Landsat sub-scene, from 10 August 2000 (b);Post-fire RGB (7, 4, 3) colour composite of the Landsat sub-scene, from 27 September2000 (c).

process, specification of prior probabilities and classification error costs for eachclass of the dependent variable, selection of a minimum number of observations forterminal tree nodes, specification of the value of a complexity parameter that penalizeslarge trees and determines optimal tree size, and selection of an accuracy assessmentprocedure. Results are presented in the form of an inverted tree, which can beinterpreted as a set of IF-THEN rules, where each rule is a single variable or a linear

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Figure 3. Flowchart of the study.

combination of variables. The contribution of each variable to the classification treeis determined through an importance score. A detailed description of classificationand regression trees is beyond the scope of this paper, and can be found in Breimanet al. (1984), Safavian and Landgrebe (1991), Clark and Pregibon (1992), and Zhangand Singer (1999).

Recent research has shown that ensembles of classifiers often produce substan-tially higher classification accuracies, in comparison to a single classifier (Breiman1996). Various approaches have been proposed to combine classifiers (Bauer andKohavi 1999, Dietterich 2000). Opitz and Maclin (1999) demonstrated that one ofthese ensemble approaches, known as bagging (acronym for bootstrap aggregating)consistently produces a classifier that is more accurate than a standard classifier.

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Bagging is a method for generating multiple versions of a classifier and to aggregatethem into an ensemble (Breiman 1996). The multiple versions of a classifier areobtained by repeatedly sampling with replacement (bootstrap resampling) from thetraining data set and using the bootstrap samples as new learning sets. The resultingclassifiers are aggregated by majority voting of the class allocation decision for eachobservation (pixel, in the case of image classification). We generated a set of 10classification trees, which produce 10 burned area maps representing the set ofclassification rules induced by CART from each bootstrap sample. A final burnedarea map (the ensemble map) results from the combination of those 10 maps, settingas burned those pixels classified as such by at least five of the ensemble trees.

In our CART modelling of the Landsat image data, we selected the Gini indexcriterion of impurity for node splitting (Breiman et al. 1984), assumed equal classprior probabilities and equal classification error costs for the burned and unburnedclasses, and required all terminal nodes to contain at least 10 cases. The tree selectedas optimal for each of the ten bootstrap samples was the smallest tree within a singlestandard error of the minimal cost tree, which was considered a good compromisebetween tree accuracy and complexity (defined as the number of terminal nodes).Accuracy assessment was performed on an independent subset of the training data,for each of the 10 tree classifiers, producing a set of matrices for misclassificationerror rates. An overall misclassification matrix and an estimate of the kappa indexof agreement (Cohen 1960, Jensen 1995) were also calculated.

2.4. Sub-pixel burned area detection with MODIS data2.4.1. Correlation between each MODIS channel and burned area fraction

In order to analyse the detectability of sub-pixel burns in MODIS imagery, theLandsat-based burned area map was degraded to a 500m pixel size using a meanfilter with a kernel of 17 by 17 pixels. This produced a burned area map whereeach pixel value corresponds to a burned area fraction. The MODIS image wasco-registered with the original Landsat data (i.e. at 30m resolution) using nearestneighbour resampling with an overall accuracy of 0.212 of the MODIS pixel.

Areas covered by clouds/shadows were eliminated from the MODIS image usinga mask obtained from a classification tree developed with CART. This mask wasbuilt considering equal class prior probabilities, and equal misclassification costs.Linear combinations of MODIS channels were also allowed at each node split.

Correlation coefficients between the cloud/shadow-free areas in the MODISimage and the Landsat-derived burned area fraction were calculated for each of the1–7 reflective MODIS channels. All pixels with a proportion of burned area under0.01 (estimated from the spatially degraded Landsat classification) were excludedfrom the analysis. Use of such a low exclusion threshold imposes a very stringenttest on the sensitivity of MODIS channels to the presence of sub-pixel burning.

2.4.2. Burned area fraction prediction using MODIS dataSpectral mixture analysis (SMA) is the most widely used approach for sub-pixel

satellite image analysis (Adams et al. 1986, Smith et al. 1990, Caetano et al. 1996).It has received limited application in burned area mapping, with some notableexceptions (Caetano et al. 1996, Vazquez et al. 2001). Regression tree modelling forsub-pixel mapping was employed by Rosenthal and Dozier (1996) to estimatefractional snow-covered area at subpixel resolution from the Landsat TM data.

We believe that regression tree analysis has some advantages over SMA, especially

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during the earlier exploratory phases of a research project. In terms of operationalmapping, regression tree algorithms perform faster than spectral unmixing becausethe data are classified by a relatively small set of splitting rules, rather than throughthe computation of constrained least square solutions. This set of fully explicit rulesis organized in a hierarchical classifier, which is easily interpreted and applied toclassification of the data. Results from the application of the regression tree algorithmalso include a measure of each predictor variable importance, which constitute aform of feature selection procedure. Accuracy assessment can be performed inmultiple ways, using independent datasets or cross-validation procedures.

Evaluation of the detectability of sub-pixel burned area using a combination ofthe seven reflective MODIS channels was performed using a regression tree (Breimanet al. 1984), in which the mean value observed in each terminal node is taken asits predicted value. Accuracy of the optimal regression tree was assessed with anindependent data set, which is ‘dropped down the tree’. A root mean square error(RMSE) at each terminal node is calculated from the difference between the predictedvalue and the actual node-specific values for the target variable. The mean of thenode-specific RMSE values for all the tree nodes is the overall RMSE. Althoughbagging might have improved model accuracy, at this stage it was decided not touse it, in order to facilitate the interpretation of the regression model.

Data to develop the regression tree were sampled from the MODIS image usinga systematic point grid, where all pixels with a proportion of burned area below0.01 were again excluded from the analysis. A total of 6951 pixels were extracted,from which 5234 were used to develop the regression tree and 1717 to assess itsaccuracy.

3. ResultsThe burned area maps for the Landsat scene and sub-scene are shown in figure 4,

with burned areas represented in black.Results of misclassification for each class as well as an overall measure of

classification accuracy for the ensemble of the 10 bagged classification trees areshown in table 1. A kappa value of 0.896 indicates a very good performance of the

Figure 4. Burned area classification map of the Landsat scene and sub-scene.

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Forest fire management—new methods and sensors 1791

classifier. The main error component is omission from the burned class, andcorresponding commission in the unburned class.

Figure 5 shows the Landsat burned area map of the sub-scene, filtered with a17×17 mean square filter and degraded to the MODIS pixel size of 500m. Darkerpixels represent higher sub-pixel burned area fractions.

Table 2 shows results of the correlation analysis between the map in figure 5 andeach one of the MODIS channels studied. Correlation coefficients are low, butstatistically significant. The higher correlation coefficients were obtained for MODISchannels 2, 5 and 6, corresponding to the near-infrared and middle-infraredspectral regions.

The regression tree induced to model burned area fraction as a function ofMODIS data is shown in figure 6.

The optimal tree has nine terminal nodes and a proportion of explained varianceof 0.779. Prediction accuracy for each terminal node is shown in table 3. Purerterminal nodes (high or low burned area fraction) are easier to classify and yieldsmaller RMSE values. This is the case of node 1, for a high burned area fraction,and of nodes 6, 8, and 9, for low burned area fractions. Higher errors are obtained

Table 1. Error matrix used for accuracy assessment of the sub-scene burned area map.Overall accuracy of the classifier is 0.948, where the classification accuracy in theburned and unburned class is 0.937 and 0.959, respectively. The kappa statistic forthis matrix has a value of 0.896 (units are number of pixels).

Observed/estimated Unburned Burned Total

Unburned 2943 126 3069Burned 193 2891 3084Total 3136 3017 6153

Errors committed in each classOmission error 0.041 0.063Commission error 0.062 0.042

Figure 5. Spatially degraded (500 m) Landsat sub-scene burned area map.

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Table 2. Correlation coefficients between each MODIS channel and the proportion of burnedarea given by the Landsat-7 image (spatially degraded to 500 m pixel size). All valuesare statistically significant (p=0.01).

MODIS channel Bandwidth (nm) Correlation coefficient

1 620–670 −0.1662 841–876 −0.3983 459–479 0.0804 545–565 −0.1205 1230–1250 −0.3936 1628–1652 −0.3317 2105–2155 −0.071

Node 1MOD2 <=0.212

Avg = 0.336

Node 2MOD5 <=0.240

Avg = 0.411

Node 4MOD4 <=0.770

Avg = 0.373

Node 5MOD7 <=0.225

Avg = 0.461

Node 8MOD2 <=0.334

Avg = 0.193

Terminal Node 9

Avg = 0.148N=967

Terminal Node 8

Avg = 0.244N=836

Terminal Node 1

Avg = 0.716N=186

Terminal Node 2

Avg = 0.560N=352

Terminal Node 3

Avg = 0.407N=503

Terminal Node 4

Avg = 0.567N=253

Terminal Node 5

Avg = 0.440N=189

Terminal Node 6

Avg = 0.321N=1830

Terminal Node 7

Avg = 0.521N=118

Node 3MOD2 <=0.152

Avg = 0.614

Node 7MOD2 <=0.168

Avg = 0.332

Node 6MOD7 <=0.264

Avg = 0.343

Figure 6. Optimal regression tree inducted MODIS data to predict burned area fraction.Each terminal node represents an estimation of an average burned area proportion.

predicting proportion of area burned for more mixed pixels, where the spectraldiscrimination between burned and unburned is not so clear. These are areas wherethe burned signal is not so intensive due to vegetation regrowth, removal from windand/or ploughing.

Figure 7 shows a regression scatterplot between the regression tree predictedresponse and the median observed response columns of table 3. The fit betweenpredicted burned area fractions and median values of the observed burned areafractions is very good, but the internal heterogeneity of tree terminal nodes tends tobe quite high, especially for the nodes representing more evenly mixed pixels, aspreviously mentioned.

The variable importance score produced by CART (figure 8) confirms the resultsof the single-channel correlation analysis between MODIS reflectance and burnedarea fraction. Channels 2, 5, and 6 were also considered as the most effectivepredictors of fire extent at the sub-pixel level.

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Table 3. Root mean square error (RMSE) for each terminal node of the optimal regressiontree. Mean values of overall predicted and observed response, and overall RMSE are0.345, 0.337 and 0.252, respectively.

MedianPredicted Mean observed observed

Terminal node response response response RMSE

1 0.72 0.70 0.77 0.2642 0.56 0.55 0.62 0.3093 0.41 0.39 0.32 0.2724 0.57 0.56 0.57 0.2735 0.44 0.40 0.33 0.3136 0.32 0.34 0.27 0.2627 0.52 0.41 0.37 0.3128 0.24 0.23 0.13 0.2239 0.15 0.14 0.07 0.169

Figure 7. Box plot diagram for each terminal node of the optimal regression tree, representingthe relationship between the average predicted response and the observed response.The median and the interquartile range measures of dispersion of the data are alsoindicated for each terminal node.

Figure 8. MODIS channels score (0–100) of importance in the optimal regression tree nodesplitting.

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4. Discussion and conclusionsThe goal of assessing the feasibility of sub-pixel burned area mapping in miombo

woodlands using MODIS data was attained. A highly accurate map of burned areasat high spatial resolution was produced using multitemporal Landsat ETM+ imagery,and it was validated with ground truth data. The map was spatially degraded tomatch the MODIS pixel size. Correlation of MODIS reflective data with the burnedarea fraction map revealed that MODIS channels 2 (0.86mm), 5 (1.24mm), and 6(1.64mm) are the better single predictors of sub-pixel burned area extent. However,the low correlation coefficient values obtained indicate that no single channel canaccurately predict sub-pixel burned area fraction. A regression tree model developedto predict burned area fraction as a function of seven MODIS reflective channelsconfirmed those results. The importance of a 1.6mm channel in tropical burned areasdiscrimination had already been demonstrated by Eva and Lambin (1998) using theAlong Track Scanning Radiometer (ATSR). However, the better predictive ability ofthe 1.6mm channel (MODIS6) relative to the 2.1mm channel (MODIS7) contrastswith previous findings of burned area mapping studies in temperate zones (Koutsiaset al. 1999, Pereira et al. 1999, Sa 1999, Silva and Pereira 1999). Trigg and Flasse(2000) characterized the spectral-temporal response of burned savannas in northeasternNamibia. One hour after the fire, the sharpest decrease in reflectance, relative to thepre-fire values, was observed in the spectral region of MODIS channels 5, 2, and 6,in agreement with our observations, albeit in a different ecosystem. However, 13 daysafter the fire MODIS channel 7 exhibited the strongest spectral contrast relative tothe unburned savanna, followed by MODIS channels 5 and 2, while the pre-burnversus post-burn reflectance difference in MODIS channel 6 had become statisticallynon-significant. Therefore, relative discriminant ability of the various spectral bandsis not constant, varying with time since fire. This finding needs to be taken intoaccount in the analysis of burned areas in African savannas. MODIS channel 5(1.24mm), which corresponds to a spectral region previously unavailable in EarthObservation Satellites (EOS), proved to be very sensitive to the presence of burnedareas in the land cover types of the study area. It remains to be seen whether thisperformance holds for other ecosystems, but it may become very useful for burnedarea studies. The results obtained from the regression tree demonstrate that it oughtto be feasible to improve on the currently predominant approach of producing dicho-tomous burned/unburned area maps, and to replace them with sub-pixel burned areafraction, or continuous field maps. A great benefit of employing regression trees foran exploratory study such as this one lies in the inherent interpretability of the rulesautomatically induced by CART, and in the series of model performance diagnosticsgenerated. However, at a more advanced stage of this research, it may be worthwhileto sacrifice some model interpretability for the sake of increased predictive accuracy.This may be obtained, for example, by using spectral indices instead of individualchannels by allowing the automatic generation of linear combinations of predictorvariables, or using ensembles of predictors, as done to create the Landsat-based burnedareas map. Use of thermal data may also contribute to obtain better results, especiallywhen analysing recent burns (Pereira et al. 1999, Trigg and Flasse 2000). A topic forfurther research is a comparative study of different algorithms for sub-pixel analysisapplied to burned area mapping.

AcknowledgmentsWe are very grateful to several people who contributed to make this study

possible. Chris Justice, Department of Geography, University of Maryland facilitated

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our participation in SAFARI 2000 in multiple ways. David Roy, Department ofGeography, University of Maryland developed the fieldwork protocol, pre-processedsatellite imagery, and produced image prints for field use. We also thank EduardoMansur and Patrick Mushove, of FAO Project GCP/MOZ/056/NETC.P., andMichel Gregoire, of the Swiss Agency for Development and Cooperation, inMozambique for logistic support in the field. Maria Vasconcelos, Ana Sa, Joao Silvaand Natasha Ribeiro received financial support from the Institute for InternationalScientific and Technological Co-operation (ICCTI) of Portugal. Ana Sa and JoaoSilva were additionally funded by the Foundation for Science and Technology (FCT),Portugal, through scholarships SFRH/BD/891/2000 and SFRH/BD/1026/2000.This research is conducted under project POCTi/33582/CTA/2000 with the title‘Reduction of uncertainties of estimates of atmospheric emissions from fires insouthern Africa’ also funded by FCT.

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