Land-use and vegetation-cover mapping of an indigenous land area in the

state of Mato Grosso (Brazil) based on spectral linear mixing model,

segmentation and region classification

Fernando Shinji Kawakubo*, Rubia Gomes Morato, Carmen Lucia Midaglia,Maria Lucia Cereda Gomide and Ailton Luchiari

Department of Geography, Universidade de Sao Paulo, Cidade Universitaria, Sao Paulo, Brazil

(Received 21 August 2008; final version received 18 September 2008)

A spectral linear-mixing model using Landsat ETMþ imagery was undertaken toestimate fraction images of green vegetation, soil and shade in an indigenous landarea in the state of Mato Grosso in the central-western region of Brazil. Thefraction images were used to classify different types of land use and vegetationcover. The fraction images were classified by the following two methods: (a)application of a segmentation based on the region-growing technique; and (b)grouping of the regions segmented using the per-region unsupervised classifiernamed ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters thatwere grouped into eight land-use and vegetation-cover classes. The mappingachieved an average accuracy of 83%, showing that the methodology is efficientin mapping areas of great land-use and vegetation-cover diversity, such as thatfound in the Brazilian cerrado (savanna).

Keywords: vegetation; cerrado; Landsat ETMþ; fraction images; segmentation;classification by region

1. Introduction

During the 1970s, the Brazilian government created POLOCENTRO, a develop-ment programme for the cerrado that had as its objective the stimulation of regionaldevelopment in the Brazilian central-western region with investments in infra-structure and technology. The incentives generated by the federal governmentmainly promoted cattle breeding and agricultural exports. However, in recent yearsthe expansion of the agricultural area has caused serious problems, such as illegaldeforestation and land conflicts between agro-industrial farmers, cattle ranchers,smallholders, mineral extractors and the indigenous population (Oliveira 1991).

The Brazilian central-western region consists of the states of Mato Grosso, MatoGrosso do Sul and Goias. The main biome of the central-western region is thesavannah, locally known as Brazilian cerrado. It occupies *23% of the Braziliannational territory and it is characterized by its xeromorphic vegetation with differentphysiognomies, varying from treeless grassland to dense woodland (Ratter 1992).

*Corresponding author. Email: fsk@usp.br

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Vol. 24, No. 2, April 2009, 165–175

ISSN 1010-6049 print/ISSN 1752-0762 online

� 2009 Taylor & Francis

DOI: 10.1080/10106040802488534

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This diversity is because of the high concentration of aluminium in the soil, whichincreases soil acidity and causes a deficiency of nutrients, the action of natural forestfires and the climate, which is characterized by distinct rainy and drought seasons(Ferri 1977, Ratter 1992).

Studies performed by Machado et al. (2004) point to dire prospects for the futureof the cerrado. Between 1985 and 2002, the cerrado diminished by an average of 2.2million hectares per year. This is mainly because of the increase in extensive cattleraising and soybean cultivation in the central-western region of Brazil. On thehypothesis that the rate of appropriation of uncultivated land remains the same, thestudy forecasts the disappearance of the cerrado by 2030.

According to the Brazilian Geography and Statistical Institute [InstitutoBrasileiro de Geografia e Estatıstica (IBGE 2004)], Mato Grosso State is solelyresponsible for about 30% of the national soybean production and 13% of the cattlebreeding. On the other hand, it is also the state responsible for most of thedeforestation in the central-western region. Despite its intense deforestation, merely5% of Mato Grosso State is included in conservation units (Borges and Iwagana2007).

In the state of Mato Grosso there are 73 indigenous lands, the majority of thempertaining to indigenous tribes of the cerrado. The indigenous lands play afundamental role in the preservation of the cerrado of Mato Grosso because they actas barriers to the advance of deforestation (Borges and Iwagana 2007). Many of themost characteristic areas of the cerrado remaining in Mato Grosso are located withinthe indigenous lands (Gomide and Kawakubo 2006).

The purpose of this work consists of mapping the land use and vegetationcover (LUVC) in the indigenous land around Sao Marcos in the southeast ofMato Grosso. Sao Marcos is an indigenous land of the Xavante people thatcovers an area of 188,478 ha and accommodates *1800 inhabitants (FUNAI2007). Being located in an area of expansion of mechanized agriculture, the SaoMarcos indigenous territory has been suffering great pressure, mainly from thelarge soybean producers, who are eager to expand soybean cultivation within theborders of the Xavante lands. Another problem is related to cattle-raisingactivities. It is very common for ranchers to release their herds into indigenousterritory in search of food. As such, land-use and vegetation-cover mapping is ofextreme importance in helping to establish measures aimed at the protection ofthis land.

The methodology adopted in this work is an adaptation of the Amazonia Projectof Deforestation Estimation – PRODES (Camara et al. 2006), which has beensuccessfully used in several other applications (Shimabukuro et al. 1998, Rodriguezet al. 2000, Shimabukuro and Almeida 2002, Kawakubo et al. 2004). The approachincludes: the generation of fraction images based on the spectral linear mixing model(SLMM), fraction image segmentation and a per-region unsupervised classification.These algorithms are available in the SPRING GIS (Camara et al. 1996) that can befreely downloaded through the Internet: http://www.dpi.inpe.br/spring/english/.

2. Study area

The area selected for study is delimited by the upper right coordinates 148 370S–528250W and lower left coordinates 158 270S–528 590W. This area takes in the SaoMarcos indigenous territory and its surroundings. LUVC mapping of this area is

166 F.S. Kawakubo et al.

important for evaluating the context in which the indigenous territory exists and formonitoring deforestation resulting from conflicts between permitted and ‘illegal’land uses.

The main river crossing the region is called ‘Rio das Mortes’. As can be observedon the vegetation map (Figure 1) produced by Radambrasil (1981), it is possible toidentify at least three physiognomic classes of vegetation in the study area:

. (Sd) Savana arborea densa presenting continuous canopy of dense woodedcerrado, regionally known as cerradao;

. (Saf) Savana arborea aberta consisting of wooded cerrado and shrub cerradowith the predominance of herbaceous plants, both presenting gallery forest;

. (Sas) Small patches of savana arborea aberta not presenting gallery forest.

Other classes such as alluvial forest, dense submontana forest and farming/pasture occupy the surroundings of the selected region. Currently, with the advanceof agribusinesses in the central-western region of Brazil, the cerrado underwentsubstantial modifications. Extensive areas of native vegetation were destroyed bythese new undertakings and replaced by pastures and soybean cultivation.

Figure 1. Simplified vegetation map according to Radambrasil (1981). Sd, Savana arboreadensa; Saf, Savana arborea aberta with gallery forest; Sas, savana arborea aberta not presentinggallery forest; Fa, alluvial forest; Fs, sub-montana forest and Ap, farming and pasture.

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The cerrado region has a tropical climate characterized by very-well-defined rainyand dry seasons. The rainy season occurs between October and March (spring andsummer); the dry season from April to September (autumn and winter). Accordingto Nimer and Brandao (1989), the rainy season in the region results from the actionof the tropical instability zones originating from west–north intertropical conver-gence systems. The long periods of drought in the region are mainly caused by theweakening of these two systems, especially in winter, and by the action of the south-polar fronts. According to measurements collected in meteorological stations locatednext to the Sao Marcos indigenous land, the annual average temperatures oscillatearound 228C and the rainfall reaches a total of 1550 mm.

3. Methods and procedures

In this study, we used Landsat ETMþ images of bands 1–5 and 7 (with 30-mnominal resolution) from path/rows 224/71 taken on 23 June 2001 (corresponding tolocal dry season). These images were acquired by the National Institute for SpaceResearch (Instituto Nacional de Pesquisas Espaciais – INPE). Images from 2001were chosen because at that time a project for mobilization of the indigenouscommunities for the management of the Rio das Mortes basin was in development(Wara 2005).

The first procedure consisted of transformation of the digital data from the originalbands into exoatmospheric reflectance values according to the procedure proposed byMarkham and Barker (1987). The minimum and maximum values of spectral radiance(LMAX and LMIN) and the average spectral irradiance of the Sun at the top of theatmosphere (ESUN) necessary for the conversion to physical values were extracted fromIrish (2000).

The geometric rectification of the Landsat ETMþ images to the UniversalTransverse Mercator coordinates system was carried out by means of 40 identifiedcommon control points in the image and the topographical maps of scale 1:100,000from IBGE (1977) and DSG (1978). From these control points a polynomialadjustment of the second order was generated, reaching a value of root-mean squareerror accuracy of the order of 1.1 size of pixel. Subsequently, the images wereresampled by the nearest-neighbour interpolation, containing an area with 3083 linesand 2042 columns by the end of this process.

To reduce the dimensionality data in the feature space, fraction imagesrepresenting green vegetation (called only vegetation), soil and shade were generated(Aguiar et al. 1999). The data-dimensionality reduction is important to reduceimage-processing times. The generation of fraction images derived from the SLMMhas shown itself to be an excellent alternative technique that allows for reductions inthe number of data while at the same time enhancing targets of interest (Adams et al.1995, Shimabukuro and Almeida 2002, Souza et al. 2003, Lu et al. 2004).

SLMM consists of a technique that estimates the proportions of different types ofmaterials (denominated components) in each pixel of the image. The problem withthe pixel mixture is related to the instantaneous field of view of the sensor, whichregisters in each unit the integrated radiances of all the components present on thesurface. When two or more components are registered, the resulting pixel is amixture of these components plus the atmospheric effect.

SLMM considers the mixture to be the result of the linear combination inrespective proportions of the components present in the pixel under examination.

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When the spectral responses of the pure components are known (endmember spectralsignatures) it is possible to estimate the proportions in the mixture of each pixel bymeans of fraction images obtained from multispectral images. Estimates of themixture are subject to the following restrictions: the sum of proportions within eachpixel must be equal to one and the values of the proportions must be non-negative.

The endmember spectral signatures necessary to estimate these fraction imageswere collected directly from Landsat ETMþ data (image endmembers) by means ofan interactive process that evaluates the pixel distribution on the red band (band 3)and the near infrared band (band 4) in a two-dimensional scatter plot. The adoptedprocedure accepts that the ‘pure pixels’ of vegetation, soil and shade are distributedin the extremities of this graph (Bajjouk et al. 1998).

The SPRING GIS has three methods to solve the linear-mixture model based onleast-squares criteria: the constrained least-squares method (CLS), the weightedleast-squares method (WLS) and a third method that combines a principal-components transform with a least-squares resolution method.

The current work used the CLS method for mixture estimation to generatefraction images of vegetation, soil and shade. According to Shimabukuro and Smith(1991), CLS has the advantage of being faster computationally than WLS andproduces similar results. More details of the algorithm can be obtained Shimabukuroand Smith (1991) and Aguiar et al. (1999).

The classification of the fraction images (vegetation, soil and shade) was made bymeans of a hybrid procedure that starts with: (a) a spatial filtering stage; (b) asequence of segmentation tests; followed by (c) the selection of a threshold ofacceptance for unsupervised classification; and (d) the identification of generatedclusters.

In the first stage, a low-pass median filtering (Gonzalez and Wood 1992) wasapplied with the intention of reducing textural variance and keeping boundaryinformation at the same time – very important aspects in the images for a goodsegmentation (Bins et al. 1992, Nascimento and Almeida 1996). This procedureimproves the performance of the segmentation because it increases the spectralseparability among the classes to be mapped (Hill and Food 1994, Lobo 1997, Hill1999).

After this, the technique of segmentation by region growing (Bins et al. 1996) wasused to partition the fraction images in homogeneous regions. The segmentationalgorithm extracts the spectral/tonal, geometric and contextual attributes of eachregion. The segmentation is controlled by threshold of similarity (value thatexpresses the Euclidean distance between the average digital numbers of two regions)and the size of the area (represented in minimum number in pixel of a region) definedby the user. The threshold choice is made by interactive and visual forms, testingdifferent values with the aim of reaching an adequate result; that is, a segmentationthat is not too dense but adequate to be used to separate efficiently the necessarydetails required for the mapping.

The segmented fraction images were classified using an unsupervised classifier byregions named ISOSEG (Bins et al. 1992). ISOSEG is a clustering algorithm thatcalculates the centre of each class using the covariance matrix and the average vectorof the regions. For its application, an image or an image group is initially selected toform the feature space. Then, an acceptance threshold is defined (expressed as apercentage) that corresponds to the maximum Mahalanobis distance. First, theISOSEG algorithm creates a list of regions ordered by size. The first region from a

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decreasing order list in terms of size will be used as initial statistics parameters for theclass. Given an acceptance threshold, it then removes the list of all the other regionswith distance inferior to the Mahalanobis threshold defined.

The following stage of the classification consists of identifying clusters generatedby ISOSEG in order to represent with higher integrity the different types of LUVC inthe area (Hill 1999). The identification is done visually, on the computer’s screen,associating each cluster to a previously defined legend. This identification was madewith the aid of Landsat ETMþ colour composite of bands 5 (Red), 4 (Green), 3(Blue).

Classification accuracy was evaluated by collecting about 50 independentsamples for each class of LUVC in the error matrix, as suggested by Congalton(1991). These samples were collected in locations representative of each category(Forghani et al. 2007). Not all points were checked in the field because of the largenumber of points and accessibility problems. The validation of these ‘uncheckedpoints’ was based on visual interpretation of the images.

4. Results

The darkest and clearest areas in the fraction images (see Figure 2b–d) representminor and greater proportion of vegetation, shade and soil, respectively. As can beobserved in Figure 2(e), distinct classes of LUVC present different proportions ofmixture.

In the vegetation fraction image, areas with vegetation present appear in lightertones (light grey) and areas with little or no vegetation in darker tones (dark grey).The vegetation fraction image is very important because it allows the separation ofdifferent classes of vegetation in regeneration and decline, called capoeira. The soilfraction image mainly highlights bare soil (light grey) and allows the separationbetween different classes of capoeira (dark grey), cerrado (medium grey) and pasture(light grey slightly darker than bare soil). The shade fraction image reveals excellentcontrast between areas of cerradao (light grey) and other types of vegetation such ascapoeira (dark grey) and cerrado (mid grey). Cerradao had the highest shade fractionbecause canopy roughness produces self shadowing. Capoeira and cerrado wascharacterized by smoother canopies and had a lower amount of shadow. Also, theshade fraction image provides excellent contrast between vegetation and water.Adams et al. (1995) and Shimabukuro et al. (1998) have shown the potential of shadefraction images for this type of study.

After several segmentation tests, the thresholds that presented the best resultswere 6 for similarity and 20 for area, together with the selection of the soil and shadefraction images. Visual analysis showed that the inclusion of vegetation fractionimage increased the processing time considerably without bringing up greatimprovements in the segmentation. For this reason, it was excluded from thesegmentation process.

From ISOSEG classification, three fraction images were selected (vegetation,shade and soil). To minimize misclassification, a low acceptance thresholdcorresponding to 75% was adopted. This approach reduces confusing errors buttends to increase considerably the number of clusters (e.g. threshold 90 and 99.9%generate respectively, 36 and 17 clusters). The 44 clusters were grouped into eightclasses: water, cerradao, cerrado-1, cerrado-2, capoeira-1, capoeira-2, pasture andbare soil.

170 F.S. Kawakubo et al.

Comparing the classification with the vegetation map of Radambrasil (Figure 1),the cerradao includes vegetation dominated by trees that includes the savana arboreadensa (Sd) as well as the gallery forests (Fa). Cerrado-1 has a predominance of smalltrees intercalated with shrub and herbaceous plants known as savana arborea aberta(Saf). Cerrado-2 corresponds to grass land with scattered shrubs. The classes withdirect human interferences are represented by capoeira-1 (vegetation in regenera-tion), capoeira-2 (vegetation in decline conditions), pasture (Ap) and bare soil.

Figure 2. (a) Colour composite of fraction images: soil (red), vegetation (green) and shade(blue). In detail: (b) vegetation fraction, (c) soil fraction, (d) shade fraction and (e) colourcomposite of bands 5 (red), 4 (green) and 3 (blue) identifying the different types of land-useand vegetation-cover.

Table 1. Quantitative evaluation of accuracy for the LUVC map.

LUVC Accuracy (%) Omission (%) Commission (%)

Water 94 6 0Cerradao 93 7 3Cerrado-1 98 2 0Cerrado-2 95 5 5Capoeira-1 83 17 23Capoeira-2 88 12 22Pasture 78 22 49Bare soil 38 62 12

Total 83 16 14

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Analysis of classification errors (Table 1) shows that the results have an averageaccuracy of 83%. There was practically no error in classifying water, cerradao,cerrado-1 and cerrado-2. The least accuracy was found in the bare-soil class with only38%. This class was mainly confused with pasture, resulting in the high omissionerror. The bare-soil and pasture classes are slightly differentiated in the bare-soilfraction image, but this difference apparently was not sufficient to separate them inthe classification. The classes capoeira-1 and capoeira-2 had high omission andcommission errors. This could be minimized by including the vegetation fractionimage in the segmentation stage, because this image provides important informationregarding the stage of regeneration of the vegetation (Lu et al. 2004).

Figure 3. Land-use and vegetation-cover map of the indigenous land of Sao Marcos.

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The final map (Figure 3) shows great changes in relation to the map of 1981(Figure 1). Currently, within the indigenous territory of Sao Marcos, cerrado-1 andcerrado-2 predominate, making a contrast with its surroundings, which is dominatedby pasture. To the southwest there is an area of pasture inside the limits of theindigenous land. In the northwestern area, a mixture of fragments of cerradao,cerrado-2, capoeira-1 and capoeira-2 can be found. In the near future they willprobably disappear and give way to pasture areas. At the east side of the indigenousland one can see a great patch of bare soil that represents areas that are beingprepared for the soybean culture.

5. Conclusions

The results show that the employed methodology using fraction images derived fromthe SLMM followed by segmentation and unsupervised classification by regions(ISOSEG) is efficient in the mapping of the LUVC in the areas of the Braziliancerrado biome. Fraction images of vegetation, soil and shade were associated todifferent types of LUVC. The shade fraction image provided excellent contrastbetween cerradao and other types of vegetation such as capoeira and cerrado. Thesoil fraction image was important for differentiating between capoeira, cerrado andpasture. The vegetation fraction image is important in separating classes ofvegetation in regeneration and decline (capoeira 1 and capoeira 2). Different fromthe conventional approaches that are based only on spectral attributes of the images(e.g. maximum likelihood), the adopted classification takes into account spectral,geometric and contextual attributes. The resulting errors of misclassification arecontrolled by adopting a low threshold of acceptance in the ISOSEG classifier.Nevertheless, this brings out the disadvantage of considerably increasing the numberof clusters. The results of the mapping reveal an average accuracy of 83%.Confusions that arose between bare-soil and pasture classes occurred as a result oflow contrast between the two classes in the fraction images. Confusions betweenclasses capoeira 1 and capoeira 2 could be minimised by exploring the vegetationfraction in more depth.

Acknowledgements

The authors acknowledge the anonymous reviewers who contributed much to improve themanuscript.

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