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Assessing the spatio-temporal ratesand patterns of land-use and land-cover changes in the Cerrados ofsoutheastern Mato Grosso, BrazilRosana Cristina Grecchi a , Q. Hugh J. Gwyn a , Goze Bertin Béniéa & Antônio Roberto Formaggio ba Centre d'Applications et de Recherche en Télédétection(CARTEL) , Université de Sherbrooke , Sherbrooke , Canadab Brazilian National Institute for Space Research (INPE) , Av. dosAstronautas , São José dos Campos , SP , BrazilPublished online: 20 Apr 2013.

To cite this article: Rosana Cristina Grecchi , Q. Hugh J. Gwyn , Goze Bertin Bénié & AntônioRoberto Formaggio (2013) Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso, Brazil, International Journal of RemoteSensing, 34:15, 5369-5392, DOI: 10.1080/01431161.2013.788798

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International Journal of Remote Sensing, 2013Vol. 34, No. 15, 5369–5392, http://dx.doi.org/10.1080/01431161.2013.788798

Assessing the spatio-temporal rates and patterns of land-useand land-cover changes in the Cerrados of southeastern

Mato Grosso, Brazil

Rosana Cristina Grecchia*, Q. Hugh J. Gwyna, Goze Bertin Béniéa, andAntônio Roberto Formaggiob

aCentre d’Applications et de Recherche en Télédétection (CARTEL), Université de Sherbrooke,Sherbrooke, Canada; bBrazilian National Institute for Space Research (INPE), Av. dos Astronautas,

São José dos Campos, SP, Brazil

(Received 9 April 2012; accepted 15 March 2013)

The Cerrados of central Brazil have undergone profound landscape transformation inrecent decades due to agricultural expansion, and this remains poorly assessed. Thepresent research investigates the spatial-temporal rates and patterns of land-use andland-cover (LULC) changes in one of the main areas of agricultural production in MatoGrosso State (Brazil), the region of Primavera do Leste. To quantify the different aspectsof LULC changes (e.g. rates, types, and spatial patterns) in this region, we applied apost-classification change detection method, complemented with landscape metrics, forthree dates (1985, 1995, and 2005). LULC maps were obtained from an object-basedclassification approach, using the nearest neighbour (NN) classifier and a multi-sourcedata set for image object classification (e.g. seasonal Thematic Mapper (TM) bands,digital elevation model (DEM), and a Moderate Resolution Imaging Spectroradiometer(MODIS)-derived index), strategically chosen to increase class separability. The resultsprovided an improved mapping of the Cerrados natural vegetation conversion intocrops and pasture once auxiliary data were incorporated into the classification data set.Moreover, image segmentation was crucial for LULC map quality, in particular becauseof crop size and shape. The changes detected point towards increasing loss and frag-mentation of natural vegetation and high rates of crop expansion. Between 1985 and2005, approximately 42% (6491 km2) of Cerrados in the study area were converted toagricultural land uses. In addition, it was verified that cultivated areas are encroach-ing into fragile environments such as wetlands, which indicates the intense pressure ofagricultural expansion on the environment.

1. Introduction

For thousands of years, landscapes have been transformed in order to supply humankindwith food, freshwater, fuel, and other essentials; however, the ongoing extents, rates,and magnitudes of land-use and land-cover (LULC) changes are unprecedented (Ellisand Pontius 2007). The Earth’s land surface has been dramatically modified in thepast 50 years, with results often indicating loss of environmental quality (MillenniumEcosystem Assessment 2005).

*Corresponding author. Email: rogrecchi@hotmail.com

© 2013 Taylor & Francis

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The southern border of the Amazon basin and the highlands of central Brazil are amongthe places that have experienced the greatest expansion of cultivated lands in the pastdecades around the world (Ramankutty, Foley, and Olejniczak 2002; Ramankutty et al.2006). Most agricultural land use in this region is located in the areas previously coveredby the Cerrado biome (Brazilian savannas). The Cerrados of Central Brazil comprise oneof the richest tropical savannas in the world, which are identified as one of the world’s bio-diversity hot spots (Myers et al. 2000). Until the 1970s, the Cerrado region was consideredunsuitable for agriculture due to poor soils (Bickel and Dros 2003); however, governmentprogrammes and technological and agronomic advances have made this an important pro-duction region (Santos 2005). The potential environmental impacts of these massive landchanges in the Cerrados are numerous but remain poorly quantified. They include habitatfragmentation, biodiversity loss, invasive species, soil erosion, water pollution, land degra-dation, changes in fire regime, and imbalances in the carbon cycle (Klink and Machado2005).

Understanding the environmental consequences of LULC changes is conditional onhaving accurate and detailed information about the different aspects of the changes, suchas rates, types, and spatial patterns (Loveland and Defries 2004). Remote-sensing technol-ogy has contributed to land change studies and has been increasingly used as a key toolfor providing cost-effective ways of detecting, characterizing, and monitoring changes atdifferent scales (Lunetta et al. 2002). Change detection has been defined as the processof identifying differences in the state of an object or phenomenon (e.g. nature and spatialextension), by observing it at different times (Singh 1989), and has been an active field ofresearch in remote sensing in the last decades due to the increasing need to monitor changesin the Earth‘s surface features (Lu et al. 2004).

Several articles have reviewed change detection techniques and their positive and neg-ative aspects for different applications (Singh 1989; Lunetta and Elvidge 1999; Coppinet al. 2004; Lu et al. 2004; Treitz and Rogan 2004). Lu et al. (2004) present a broadclassification of change detection methods into two types regarding the nature of change:(1) those detecting binary change/no-change information (e.g. image differencing, imageratioing, and PCA) and (2) those detecting detailed ‘from–to’ change information (e.g.post-classification comparison, CVA, and hybrid methods). The choice of the most appro-priate method of change detection depends mainly on the objectives of the research andthe study area characteristics. Agricultural land uses have the particularity of being verydynamic. For this reason, in agricultural lands, because of differences in the phenologi-cal stages of crops, techniques based on the determination of thresholds, such as imagealgebra, can lead to poor results due to difficulties in distinguishing positively changedareas from false-positively changed areas (Lu et al. 2004). These authors highlight howchange detection based on classification methods can avoid this kind of problem, despite theefforts made for its implementation. When methods based on individual classifications areemployed, great care is needed in the classification process in order to avoid multiplicationof the error in the final product.

Image classification can be performed according to two main ‘paradigms’: pixel-basedand object-based approaches, the latter representing a recent evolution in remote-sensingimage analysis, especially for dealing with high-resolution images, and has been calledobject-based image analysis – OBIA – or geographic object-based image analysis –GEOBIA (Hay and Castilla 2008; Blaschke 2010). In contrast to per-pixel classifications,in which the pixel is the elementary unit for analysis, in OBIA, before classification, imagesare segmented into meaningful homogeneous objects which are the ‘building blocks’ forimage analysis (Blaschke 2010). The main advantages reported for OBIA are due to

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the fact that segmentation provides meaningful objects that are more closely related toreal-world objects, the diversification of options for describing objects (e.g. distances,neighbourhoods, topologies, etc.), and the generation of information that can be more eas-ily integrated within geographical information systems – GISs (Platt and Rapoza 2008;Blaschke 2010). Change detection within the OBIA context has been termed object-basedchange detection – OBCD and is defined as ‘the process of identifying differences in geo-graphic objects at different moments using object-based image analysis’ (Chen et al. 2012).Comparing image objects rather than pixels poses other challenges and opportunities anddepends on specific research goals.

The principal sources of remotely-sensed data for change detection have been theLandsat Thematic Mapper (TM), Satellite Probatoire d’Observation de la Terre (SPOT),radar, and the Advanced Very High Resolution Radiometer (AVHRR) (Lu et al. 2004).Remote-sensing change detection techniques have also been broadly applied; some exam-ples include Petit, Scudder, and Lambin (2001); Yuan et al. (2005); Béland et al. (2006),and Kamusoko and Aniya (2009).

Despite the potential of these techniques for unveiling how this biome has been trans-formed over the years, there are only a limited number of Cerrado change detection studies,especially for agricultural landscapes, with scarce and controversial data on the extent,rates, and patterns of change through the biome (Brannstrom et al. 2008). Mapping theCerrado versus converted areas presents a series of challenges from a remote sensing pointof view. Among others, we can point to the spectral confusion between Cerrado physiog-nomies and converted lands (Ferreira et al. 2007), problems in distinguishing crops frompasture (Brannstrom et al. 2008; Maeda 2008), the strong seasonality of the natural vegeta-tion, and the intense spatial-temporal dynamics of agricultural land use (Sano et al. 2007).

Previous change detection studies in the Cerrados (e.g. Jepson 2005; Brannstromet al. 2008) mapped mainly the conversion of Cerrado vegetation to agro-pastoral areas,without discriminating crops from pasture which manage and protect the soil differ-ently. Moreover, historic changes in wetlands remain to be addressed. Maeda (2008) usedModerate Resolution Imaging Spectroradiometer (MODIS) images to discriminate cropsand pasture for the most recent date of a historical data set, but did not formally validatethe results. MMA-PROBIO (2007), in a single date study that mapped the entire biome for2002 using Landsat images, identified crops and pasture based on visual interpretation afterthe images had been segmented. SEPLAN-MT (2001) also relied on visual interpretation(supported by fieldwork and previous mappings) for mapping crops and pasture in MatoGrosso.

Mapping and monitoring of Cerrado LULC changes remain a challenge with regard tomany aspects and need more initiatives, especially for detailing the patterns and rates ofconversion in agricultural landscapes, which have been transformed under heavy anthropicpressure. Thus, the objective of this paper is to detail LULC changes (types, rates, and pat-terns) using a post-classification comparison approach in an area of interest (AOI) locatedin the southeastern portion of Mato Grosso, a region where the Cerrado has been intensivelyconverted into agricultural lands.

2. The study area

The AOI consists of approximately 15,550 km2 of the plateau drained by the MortesRiver in the Primavera do Leste region, southeasern Mato Grosso State (MT), in Brazil.It corresponds to a subset of Landsat TM scenes 225/70 and 225/71, with the followingcentral geographic coordinates: 53◦ 55′ 47.7′′ W and 15◦ 14′ 15.4′′ N (see Figure 1).

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Figure 1. Study area location in Mato Grosso State, Brazil.

The relief of this plateau region, pertaining to the ‘Planalto dos Guimarães’ geomor-phologic unit, is mostly flat to very gently undulating. The climate is identified as tropicalwet–dry or savanna (Aw) according to the Köppen climate classification (Moreno and Higa2005), and it is marked by a distinct dry season from May to September. The main soiltypes in the area are the Latossolos (SEPLAN-MT 2001) – equivalent to Haplustox in theUS Soil Taxonomy (Soil Survey Staff 2006), which occurs in 67% of the AOI – and theNeossolos Quartzarênicos (equivalent to the Quartzipsamment in the US Soil Taxonomy),which underlies 24% of the AOI.

The two major Cerrado formations mapped in the AOI (SEPLAN-MT 2001) aregrassland Cerrado (Savana Parque or Campo Cerrado) and woodland Cerrado (Savanaarborizada or Cerrado sensu stricto), with gallery forest along the rivers. Wooded Cerrado(Savana Florestada or Cerradão) also occurs. The AOI also encompasses wetlands (SavanaGramíneo-Lenhosa or Campos úmidos), which are areas of very flat relief, seasonallyor permanently flooded, known regionally or locally as covoais, campo de murundus,or veredas, among other names, depending on their particularities (Castro Júnior 2002).Although covering a small area compared with that of the non-wet savannas (França et al.

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2008), these wetlands have a special ecological significance because they provide habitatsand nutrients for many species and have a key role in supplying and maintaining waterresources (Maltby and Barker 2009). Some of these areas have recently been drained forcrops, and the consequences for the ecosystem are little known (Castro Júnior 2002).

LULC changes in this region started in the 1970s, initially with the conversion ofCerrado vegetation to pasture for cattle ranching, followed by rice crops (Portillo 2007).At that time, there were few cultivars adapted to Cerrado conditions and the yields werelow (Vieira 2002). In the 1980s soybean cultivation started to expand due to the imple-mentation of soil acidity correction practices and the availability of improved and adaptedvarieties. In the 1990s, maize was introduced in rotation with soybean, and since 1996,cotton has been cultivated in the area (Portillo 2007). Currently, land use is predominantlyagricultural: it is one of the principal production centres of grain and fibre in MT State,with highly mechanized cropping systems (Matsuoka, Mendes, and Loureiro 2003). Themain crops cultivated in the area are soybean (∼80%), cotton (∼12%), and maize (∼8%)(IBGE 2010). The majority of crops are rainfed, although irrigation increased significantlyfrom 1995 to 2005. Millet, sorghum, and corn are used as second crops in rotation withthe main crops. A secondary part of the agricultural areas is covered by planted pasture inareas with more sloping terrain.

3. Materials and methods

The methodological approach used for quantifying LULC changes consisted of a post-classification change detection method because this allowed us to obtain detailed changeinformation and to handle the intense intra-annual dynamics of agricultural land use in thearea. This method was complemented by landscape metrics and focused on the identifi-cation of agricultural expansion, vegetation and wetland loss, and habitat fragmentation.A detailed methodological flow diagram is presented in Figure 2.

3.1. Data sets

3.1.1. Remote-sensing data

The main data set used in this research consisted of multi-temporal Landsat TM images (seeTable 1) for the three years of interest (1985, 1995, and 2005), covering the main periodof agricultural expansion in this region. We selected the images based on (1) availabilityof cloud-free images, (2) availability of anniversary date images (or images from the sameperiod of the year), and (3) ability to distinguish the targets of interest. Because of the per-sistent cloud cover during the rainy season in this region, images from the dry period (Juneto September) were used as base images for the segmentation process. However, becauseof the intense dynamics of agricultural land use in the area, these images showed somelimitation in identifying the targets of interest during the classification process. Therefore,additional images from different dates or sensors were used according to their pertinenceand availability. This included the use of MODIS images.

3.1.2. Ancillary data

Ancillary data sets included a digital elevation model (DEM) from the Shuttle RadarTopography Mission (SRTM) (Rabus et al. 2003), a land use and vegetation map elabo-rated by the Mato Grosso planning agency – SEPLAN (SEPLAN-MT 2001), and a LandsatGeoCover data set provided by the Global Land Cover Facility (GLCF 2010).

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Figure 2. Methodological flow diagram of present research.

3.1.3. Fieldwork data

Fieldwork was carried out in two campaigns, January 2007 and March 2008, to acquireground reference data for classification calibration and evaluation of the most recent land-cover mapping (2005). Although the field data were acquired later than the image to beevaluated (2005), this did not represent a problem because of the few changes that hadtaken place during this time period. Field observation points were located using one globalpositioning system (GPS) (about 15 m precision) connected to a GIS displaying the satelliteimage. As a result, considerable data were collected on land-cover types along the roadssurveyed (771 in situ observations). Detailed descriptions and photo-documentation weremade at selected locations. Sampling design was focused on covering the whole AOI andthe main land-cover types (e.g. annual crops, pasture, and natural vegetation), although dueto problems related to road conditions, difficulties in getting authorization to access theSangradouro-Volta Grande Indigenous Reserve, and inaccessible private properties, accesswas prevented to some parts of the study area. From our in situ data set, two subsets wererandomly selected to constitute the training and evaluation data sets for the classification.As it was not possible to access the Sangradouro-Volta Grande Indigenous Reserve (the

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Table 1. Remote sensing data sets used in this research.

Remote sensing system Path Row Acquisition dates Resolution

Main temporal data setLandsat 5 TM (winter

images)225 70/71 10 June 1985 30 m

9 August 1995 30 m20 August 2005 30 m

Supplementary data setsTerra MODIS* V10 H12 14 September 2005

17 January 200622 March 2006

250 m

Landsat 5 TM(summer images)

225 70/71 18 February 1985 30 m13 January 1995 30 m

Landsat 5 TM**(spring image)

225 70/71 17 November 1985 30 m

Notes: *MOD13Q1 product – 16 days Vegetation Indices (NDVI and EVI) at 250 m spatial resolution.** Used for extracting wetlands.

largest continuous tract of Cerrado vegetation remaining in the AOI), reference data withinthis area were acquired from SEPLAN mapping (SEPLAN-MT 2001).

3.2. Pre-processing

Prior to image processing, the Landsat temporal series was geometrically corrected in orderto remove spatial distortions before superimposition. The images were registered in animage-to-image procedure using the Landsat GeoCover data set as reference. The time-series was geometrically corrected using 30 control points per image, nearest-neighbourresampling, and a second-order polynomial warp function. The root mean square error(RMSE) for each image was ≤0.5 pixel. As indicated in the literature (Jensen 2005), theimages were not corrected for atmospheric differences because they were classified inde-pendently (for each date) and training data were collected from each of the images to beclassified (same relative scale).

3.3. Image classification

The land-cover maps were produced independently for each year selected, using asupervised, object-based classification approach that involved three main steps: segmen-tation, feature selection/classification, and accuracy assessment. Definiens Professional 5(eCognition) software (Definiens 2006) was used to carry out the classification.

3.3.1. Classification scheme

The selected classification scheme (see Table 2) was based on the objective of this research,which was to quantify the conversion of natural vegetation to agricultural lands (cropsand pasture), and on a priori knowledge (fieldwork and previously published mapping) ofland-cover types occurring in the study area. Our final map legend included the identifica-tion of annual crops and pasture, which represents potential differences in soil protectionand management and is important for future applications such as soil loss modelling.A more generalized level was also used to report the final results. Moreover, wetlands

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Table 2. Classification scheme.

Land-cover types in the AOI Final map legend Generalized levelWetlandmapping

Varied land covers – annual crops Annual crops Agricultural landsGrasslands Planted pasturePrimavera do Leste Urban areas Urban areasSeasonal forestGallery forestShrublands/Woodlands

Savanna formations(‘Cerrado’)

Natural vegetation Natural vegetationGrasslands/Shrublands

}⎫⎪⎪⎬⎪⎪⎭

Wet grasslands WetlandsRivers and lakes Waterbodies Waterbodies

were discriminated (in a separate mapping) because they are of particular ecologicalsignificance.

The natural vegetation in the AOI encompasses different Cerrado physiognomies,although it was presented (in the final map) and validated as a single class since greaterdetail was not necessary to meet the research objectives. The same applies to annual crops,which we sought to map as a single category; nevertheless, from a remote sensing point ofview, these correspond to a variety of land cover resulting from the dynamics of this landuse (e.g. shifts in planting time) and different crop types (e.g. differences in canopy struc-ture or row spacing) that had to be handled in intermediate steps. Planted pasture presentssimilar spectral characteristics to some crops at certain times of the year, although showsdifferent seasonal profiles (Toledo 2008). Moreover, this particular land cover occurs pre-dominantly in areas of more sloping terrain, as the areas of flat to gently undulating relief(favourable to mechanization) are occupied by crops. For this reason, additional featureswere used for improving pasture–crop classification. Waterbodies in the area consist ofrivers and small lakes. Most rivers in this area are less than 30 m wide, and for this reasoncannot be extracted at the resolution used in this work. The same applies to with roads, mostof which are unpaved farm roads, and generally these cannot be extracted at the resolutionused.

Wetlands occurring in the AOI consist of poorly drained depressions/floodplains, areseasonally or permanently flooded, and are predominantly covered by graminaceous vege-tation. They were targets for mapping in order to quantify the historical impacts of land-usechanges in these fragile environments. These areas had been partially mapped in differentSEPLAN thematic maps (e.g. humid depressions in the geomorphologic map, swamp orterrain susceptible to flooding in the hydrological map, and humid fields or campos úmidosin the vegetation map); however, previous mapping did not capture all wetlands occurringin the AOI, and they had been manually extracted. Because of spectral similarities with thenon-wet grass savannas, they were extracted separately from other uses. Urban areas in theAOI correspond to minimal areas (a few polygons). For this reason and to avoid spectralconfusion with other LULC classes, they were manually identified (after segmentation) andmasked out from the classification.

3.3.2. Classification method

3.3.2.1. Image segmentation. The first stage in the classification process consisted ofsegmenting the image into discrete objects. For this task, we used the multi-resolution

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segmentation algorithm of eCognition (Benz et al. 2004), which is a bottom-up segmenta-tion algorithm based on a pairwise region merging technique that iteratively merges pixelsto larger units until an upper threshold of homogeneity is reached locally (Definiens 2008).The homogeneity criterion results from the combination of colour (spectral) and shapeproperties. In addition the resulting segmentation is controlled by a scale parameter thatcontrols the size of resultant objects. The software also allows weighting of the bands tobe used in the segmentation process, which adds further flexibility in determining the finalsegmentation results.

We segmented the images by testing different combinations of algorithm parameter inorder to identify homogeneous objects that represented real-world landscape elements (e.g.crop fields, vegetation patches, and pasture fields), compatible with the scale and legendadopted in this study until a satisfactory result was obtained. The need to test different seg-mentation parameters to obtain the ‘optimal’ result for a given task has been highlighted inthe literature (Karsenty, Antunes, and Centeno 2007; Platt and Rapoza 2008). A summaryof segmentation parameters used is presented in Table 3.

3.3.2.2. Object-based classification. To classify image objects, we used the nearest neigh-bour (NN) classifier of eCognition. Since NN is a non-parametric algorithm, it does notrequire that remote-sensing data follow a normal distribution and does not assume that theforms of the probability densities are known (Jensen 2005). The algorithm classifies imageobjects within a given feature space, based on recorded training samples, and searches theclosest sample in the feature space for each object. Several iterations of sample selectionand classification are recommended for improving the final results (Definiens 2006). Themain advantages reported for using the NN classifier within eConition are (1) it operatesin a multi-dimensional feature space, which increases separability in cases of overlappingclasses (Ivits and Koch 2002); (2) it is simple to adapt it to other areas and can be appliedto any number of classes using any original, composite, transformed, or customized bands(Myint et al. 2008); and (3) it is less time consuming compared with that of decision treeapproaches (Laliberte et al. 2006).

The classification process involved definition of class hierarchy, selection of trainingsamples, selection of suitable variables for composing the feature space, and revisionof results. Training samples (spectrally homogeneous polygons) were selected for allland-cover types, guided by fieldwork samples and available thematic data. Class cate-gories were hierarchically subdivided into subclasses in order to account for all spectralclasses resulting from the dynamics of agricultural land use and different vegetationtypes, later being combined to produce the final thematic maps with the classes ofinterest.

Table 3. Summary of segmentation parameters.

Image dates

Segmentation parameters 20 August 2005 9 August 1995 10 June 1985

Bands used Bands 2,3,4,5,7 Bands 2,3,4,5,7 Bands 2,3,4,5,7Scale 50 50 50Compactness/Smoothness

factor0.5/0.5 0.5/0.5 0.5/0.5

Shape/colour factor 0.2/0.8 0.2/0.8 0.2/0.8

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Table 4. Summary of features used for classification of the temporal data set.

2005 1995 1985

(Mean) winter TM Band 2 (Mean) winter TM Band 3 (Mean) winter TM Band 2(Mean) winter TM Band 4 (Mean) winter TM Band 4 (Mean) winter TM Band 4(Mean) winter TM Band 7 (Mean) winter TM Band 7 (Mean) winter TM Band 7(Mean) CEI (Mean) Slope (Mean) spring(Mean) Slope (Mean) summer TM Band 4 (Mean) summer TM Band 4

(Mean) spring TM Band 5

The main data sets used for the classification of objects were Landsat images from thedry (winter) period (10 June 1985, 9 August 1995, and 20 August 2005), from which asubset of three bands was selected by assessing bidimensional plots (Jensen 2005) in orderto avoid redundant spectral information. These data sets were complemented by additionalfeatures (bands from other dates and indices) for increasing class separability, especiallyfor crops and pasture (see Table 4). The input features used for object classification wereselected according to a priori knowledge about AOI land-cover characteristics and dataavailability, taking into account some of AOI specificities directly influencing land-covermapping: (1) crop dynamics/calendar and (2) geographical stratification of some LULCclasses. We searched for additional features for highlighting crops and also relief differ-ences, as crops and pasture are mostly stratified according to relief characteristics. Duringfeatures selection, we also used the feature view tool in eCognition, which allows thecomparison of image attributes in order to evaluate suitable features for class separation(Definiens 2006).

Because of the area’s crop calendar, and the fact that crops and pasture have dif-ferent seasonal profiles (Toledo 2008), images obtained in strategic periods, such as atplanting or full canopy development, can contribute favourably to distinguish these twoland categories. However, Landsat images from these periods are seldom available with-out cloud cover. Images from the MODIS sensor, for example, because of their almostdaily temporal resolution, allow the generation of composite images (e.g. 16 days), whichsignificantly increases the chances of obtaining cloud-free images during these strategicperiods (Rizzi et al. 2009). Thus, for 2005, an index obtained from MODIS images (CropEnhancement Index – CEI) was calculated and incorporated into the data set to be clas-sified. This follows the methodology proposed by Rizzi et al. (2009), also applied byMaeda (2008) in Mato Grosso, which allows for the identification of annual crops, ifthe crop calendar is known, based on MODIS enhanced vegetation index (EVI) for peri-ods of maximum and minimum EVI values during the growing season as follows (seeEquation (1)):

CEI = EVIMax − EVIMin

EVIMax + EVIMin, (1)

where EVIMax is the EVI image for the crop full canopy development period and EVIMin isthe EVI image for the planting period.

Because CEI is calculated using EVI images strategically chosen according to the area’scrop calendar, and considering that the other land uses (e.g. natural vegetation and pas-ture) have a more ‘constant’ seasonal profile when compared with crops, in the resultantimage, crop areas correspond to the highest CEI values. In addition to CEI, slope gradients

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were added to the classification data set. Both data sets (CEI and SLOPE) were resam-pled to conform to 30 m Landsat resolution. The fact that mean CEI and SLOPE valueswere considered for each polygon, rather than on a pixel basis, helped to accommodate theresolution differences.

For 1995, the classification was guided by the previous year’s experience and theSEPLAN LULC map (SEPLAN-MT 2001). TM band 4 from January 1995 (crop ‘fullcanopy development’ period) and slope gradients were selected as additional features. TheLandsat TM band 4 from this period had some cloud cover, though not sufficient to preventits utilization and utility. For 1985, the classification was guided by both previous years’experience and 1985 multi-seasonal images, as there was no other reference to guide theclassification or means to validate it. TM band 4 from February 1985 (crop ‘full canopydevelopment’ period) and slope were used as additional features for the classification.Moreover, the TM band 5 of a November 1985 image, available for this year, was alsoincorporated because it highlighted exposed soil for agriculture, helping to discriminatethis category.

3.3.3. Accuracy assessment

The classification accuracies were assessed in an error matrix (see Tables 6 and 7), the mostfrequently used method to assess classification accuracy obtained from remote-sensing data(Congalton 1991). We evaluated the main classes of interest (crops, natural vegetation, andpasture), which account for more than 99% of the AOI.

The 2005 classification was evaluated using an independent data set comprising270 samples (90 per main class), selected from in situ observations (2007/2008). To eval-uate the 1995 classification, we used 270 samples (90 per main class) randomly generatedfrom the SEPLAN LULC map, which is the most authoritative data set available and wasprepared based on the interpretation of 1994/1995 Landsat images. No quantitative accu-racy assessment could be performed for the 1985 classification due to the lack of a referencedata set. However, visual inspection of the results was carried out based on images from dif-ferent dates and seasonal growth stages for that year (1985). The sample size used to fill inthe error matrix was based on Congalton (1991), who suggests a minimum of 75–100 sam-ples per category in cases of especially large areas (i.e. more than 1 million acres), as in thepresent case.

3.3.4. Wetlands extraction

Wetlands were extracted from a Landsat TM image of 17 November 1985 (beginning ofthe wet season), which showed better discrimination of these areas – at other periods theywere spectrally similar to non-wet grass Cerrado. The 1985 image was used to serve as abaseline, because these areas have been modified over the following decades. The classifica-tion followed the same steps as for other LULC maps, following which a layer containingonly wetlands was created from this preliminary classification. Considering the difficul-ties in accessing these areas during fieldwork and some spectral confusion with non-wetCerrado, all thematic data available from the geomorphologic, hydrologic, and vegetationmaps (SEPLAN-MT 2001) were assembled to guide and edit the preliminary classification.In order to assess the resultant map, 50 random points were selected on the classified layer(only wetlands) and compared with high-resolution images from Google EarthTM. Wetlandswere mapped for 1985 and, for subsequent years, land-cover changes within these areaswere assessed.

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Table 5. Landscape metrics used to provide information about fragmentation in the AOI.

Metrics Description

Total (class) area (CA) A measure of landscape composition; specifically, how much ofthe landscape is comprised of a particular patch type.

Number of patches (NP) The number of patches of a particular patch type.Mean patch size (MPS) The sum of the area of all patches of a particular patch type,

divided by the number of patches of the same class.Largest patch index (LPI) Quantifies the percentage of total landscape area comprised by the

largest patch.Landscape shape index (LSI) Provides a simple measure of class aggregation or clumpiness

(LSI increases as the patch type becomes more disaggregated).

3.4. Change detection

In order to quantify changes, the final LULC maps were compared in pairs, pixel-by-pixel,using a cross-classification/tabulation GIS operation that calculated a complete ‘from–to’change matrix. Annual rates were also calculated using the compound interest rate (seeEquation (2)) used by Sivrikaya et al. (2007):

P = 100

t2 − t1ln

A2

A1, (2)

where P is percentage of ‘vegetation loss’ per year, and A1 and A2 are the amounts of ‘veg-etation cover’ at time t1 and t2, respectively. Moreover, changes in wetlands were quantifiedby comparison with LULC maps. In addition, landscape metrics were employed in order toquantify patterns of LULC change, as described in Section 3.5.

3.5. Landscape metrics

Landscape metrics are measures used to quantify landscape spatial patterns that can pro-vide important information on landscape status and for the understanding of interactionsbetween spatial patterns and ecological processes (Cardille and Turner 2002).

The present study focuses particularly on the metrics describing habitat loss andlandscape fragmentation, because these have been identified as the predominant changetrajectory in a number of anthropogenic landscapes worldwide (McGarigal et al. 2002).Despite the large number of metrics used to measure fragmentation, many are stronglycorrelated (Fahrig 2003). For this reason, and considering the objectives of this researchand a review of the metrics used as ‘fragmentation indices’ (McGarigal et al. 2002; Fahrig2003; Kamusoko and Aniya 2007; Sivrikaya et al. 2007; Carvalho, De Marco Júnior, andFerreira 2009), five metrics were selected that provided key information on fragmenta-tion in the study area (see Table 5). The metrics selected were calculated at the class level,for each land-cover type, using FRAGSTATS 3.3 (http://www.umass.edu/landeco/research/fragstats/fragstats.html; McGarigal et al. 2002). Details about the equations for each metriccan be found in McGarigal et al. (2002).

4. Results and discussion

4.1. Image classification

The classification results for the three years selected are presented in Figure 3, and the accu-racy statistics and error matrices in Tables 6 and 7. Because urban areas and waterbodiestogether represent less than 1% of the total area, they are not shown in the graphics.

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Figure 3. Classification results for 1985, 1995, and 2005.

Table 6. Error matrix and accuracy statistics – 2005 classification.

Reference data

Classified dataAnnualcrops

Naturalvegetation

Plantedpasture Total

User’saccuracy

Annual crops 88 6 21 115 77%Natural vegetation 1 80 5 86 93%Planted pasture 1 4 64 69 93%Total 90 90 90 270

Producer’saccuracy

98% 89% 71%

Notes: Overall accuracy = 86%; Overall kappa statistics = 0.79.

Table 7. Error matrix and accuracy statistics – 1995 classification.

Reference data

Classified dataAnnualcrops

Naturalvegetation

Plantedpasture Total

User’saccuracy

Annual crops 68 3 4 75 91%Natural vegetation 13 82 21 116 71%Planted pasture 9 5 65 79 82%Total 90 90 90 270

Producer’saccuracy

76% 91% 72%

Notes: Overall accuracy = 80%; Overall kappa statistics = 0.69.

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The overall accuracy obtained for the 2005 classification was 86% (kappa 0.79) forannual crops and pasture assessed separately, increasing to 93% (kappa 0.87) when pastureand crops were combined as ‘agricultural use’. The error matrix and accuracy statisticsfor the 2005 classification (see Table 6) show that ‘Natural vegetation’ was classified withapproximately 90% accuracy. For annual crops, the producer’s accuracy was very high,inferring that the majority of croplands identified in the field were classified as ‘crops’.In contrast, the commission error for this class was high due to the pasture samples beingmisclassified as crops. For the same reason, the omission error for ‘pasture’ was relativelyhigh (29%) but commission error was much lower (7%). The misclassifications of pas-ture samples (reference data) can be partially explained by the fact that pasture areas inthe field were identified systematically, wherever they occurred. However, in addition tothe areas of predominance of this land use (more sloping terrains), pasture also occursas a minor land use within crop fields, and in this situation is generally not mappable atthe scale used in this research, and was thus classified as crops. Thus, the error couldbe assumed to be lower when the scale of the mapping is taken into account (estimatedat 1:250,000). Furthermore, areas with a predominance of pasture were more difficult toaccess in the field and thus validation samples for this class were not as well distributed asthose for crops, and this may also have influenced the accuracy assessment. Figure 4 illus-trates the 2005 classification results for crops and pasture, together with field points for bothcategories.

Figure 4. 2005 classification results and field observations for crops and pastures draped over ashaded relief image.

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For 1995, the overall accuracy was 80% (kappa 0.69), increasing to 86% (kappa0.71) when pasture and crops were aggregated. The error matrix and accuracy statisticsfor the 1995 classification are depicted in Table 7. Before assessing the error matrix for the1995 classification, it is important to underline that the methodology used to produce thereference data set was different. We chose to compare our result with the SEPLAN mapbecause this included the only authoritative reference data available and had been basedon satellite images from similar dates (1995/1994). However, since SEPLAN mapped onlylarger patches, thus missing considerable detail, especially regarding gallery forests, theaccuracy assessment was severely affected. Planted pasture was the class with the highestomission error, due, in great part, to the differences in mapping methodologies. Moreover,in SEPLAN mapping, LULC information was extracted by visual interpretation and mayshow some lack of precision in regard to unit boundary lines. Nevertheless, they showedgood correspondence in terms of proportion of land categories and their distribution in thelandscape, despite the difference in the details of the two maps, which is reflected in theaccuracy statistics.

The wetlands classification results are presented in Figure 5. From the 50 samplesused for accuracy assessment, 92% were correctly classified. Because the wetlands wereextracted and validated independently and the samples for validation were generated on theclassified layer (and compared with high-resolution Google Earth images), the omissionerror was not assessed.

Figure 5. Wetlands classification results (1985).

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4.2. Land-cover changes

In 1985, 80% of the study area was still covered by natural vegetation; the Cerrado vegeta-tion was subsequently intensively converted. By 2005, the percentage of natural vegetationwas reduced to 38%, found mainly in the Sangradouro-Volta Grande Indigenous Reserve,along drainage lines, and in steeper terrains (see Figure 6). The spatial distribution of themain LULC changes is illustrated in Figure 6. The remaining natural vegetation in 2005 isdepicted in green, areas converted from natural vegetation to agricultural use (crops or pas-ture) in red, and areas that were already crop/pasture in 1985 in light yellow. Agriculturalland use (crops/pasture) occupied 20% of the study area in 1985, increasing to 50% in1995, and to 62% in 2005, thereby becoming the predominant element in the landscape.

Crops expanded at an average annual rate of 9.3% between 1985 and 1995. From1995 to 2005, crops continued to expand but at a lower rate of 4.3%. Pasture areas alsoexpanded at a high annual rate from 1985 to 1995 (8.6%), but decreased from 1995 to2005. A summary of LULC changes (areas and annual rates) is presented in Table 8.

Figure 6. Change map – LULC changes from 1985 to 2005.

Table 8. Land-cover ‘status’ for 1985, 1995, and 2005 and calculated annual rates of change.

Land coverChanges annual

rate (%)

1985 1995 2005 1985–1995 1995–2005

Area (km2) % Area (km2) % Area (km2) %Annual crops 1945 12 4952 32 7623 49 9.3 4.3Planted pasture 1194 8 2817 18 2011 13 8.6 −3.4Natural vegetation 12,390 80 7776 50 5899 38 −4.7 −2.8

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Table 9. Change matrices (1985–1995; 1995–2005).

‘To’ 1995 (% area)

Annual cropsNatural

vegetationPlantedpasture

Total‘From’ 1985

Annual crops 11 0 1 12

Natural vegetation

17 49 14 80

Planted pasture

4 1 3 8

Total 32 50 18 100

‘To’ 2005 (% area)

Annual cropsNatural

vegetationPlanted pasture

Total‘From’ 1995

Annual crops 30 1 1 32

Natural vegetation

10 35 5 50

Planted pasture

9 2 7 18

Total 49 38 13 100

No – change

Notes:

From natural vegetation to pastureFrom natural vegetation to crop From pasture to cropMinor changes

Change detection matrices for the periods 1985–1995 and 1995–2005 are presentedin Table 9. Assessing the conversion trajectories for the two time windows selected, it ispossible to verify that for 1985–1995, 17% of natural vegetation was converted to cropsand 14% was converted to pasture. Moreover, 4% of the pasture was converted to crops inthis period. In the following decade (1995–2005), 10% of the natural vegetation was con-verted to crops and 5% was converted to pasture. In addition, 9% of the pasture areaswas converted to crops. Globally, approximately 42% (6491 km2) of the natural vege-tation was converted to agricultural use between 1985 and 2005. A closer assessmentreveals that the majority of this conversion took place between 1985 and 1995, withapproximately 4614 km2 (about 30%) of the Cerrado converted in this period. In thesame period, there was an expansion of both crops and pasture. In the following decade(1995–2005), crops continued to expand, but pasture areas were reduced as the percentageof vegetation converted to pasture in this period was lower than that of pasture converted tocrops.

Part of the natural vegetation loss corresponds to wetlands loss (assessed separately):from 1985 to 2005, more than 20% of the wetlands in the AOI were converted to crop-lands. The important changes occurred from 1995 to 2005, with ∼17,000 ha of wetlandsconverted to crops in this period. Although not very extensive in area (approximately 6%of the AOI), wetlands have many important functions in the landscape, such as water sup-ply, water purification, climate regulation, and flood regulation (Millennium EcosystemAssessment 2005).

To summarize the foregoing, a timeline of selected historical aspects of land-usechanges in the AOI is presented in Figure 7. It is based on the results obtained in this

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Figure 7. Historical LULC changes in the AOI.

research and complemented by those from Portillo (2007), who studied an adjacent area inthe same context as the present AOI.

This summary of the main events and milestones of LULC changes in the study areaindicates a progressive expansion and intensification of agricultural land use. Assuming thatconversion of the Cerrado began approximately in 1975, and that in 1985, 20% had alreadybeen converted, the annual rate of natural vegetation conversion from 1975 to 1985 wouldhave been about 2.3%, considerably lower than from 1985 to 1995 (4.7%). If we assumethat the changes started in the early 1970s, the annual rate of change between 1970 and1985 would have been in the order of 1.5%. Clearly the main changes occurred from 1985 to1995 and are marked by the expansion of soybean as well as the introduction of maize inrotation with soybean. In 1995, the conversion of the natural vegetation in this area hadreached 50%. Changes progressed during the next decade (1995–2005), with increasingareas of cotton and the encroachment of crops into more fragile environments such as thewetlands.

4.3. Landscape metrics

Important changes have occurred in vegetation composition/configuration in the 20-yearperiod assessed (see Figure 8). The results are interpreted as showing a progressive lossand fragmentation of natural vegetation, as indicated by decrease in the vegetation classarea (CA), increase in the number of vegetation patches (NP), decrease in mean patch size(MPS), decline in largest patch index (LPI), and increase in the segregation of patches(LSI).

From 1985 to 1995, as vegetation loss progressed, the changes in other metrics wereaccentuated, especially for MPS and NP. From 1995 to 2005, NP and the MPS more orless stabilized but LPI continued to decrease and LSI continued to increase. Examinationof ‘annual crops’ in 1985 reveals that they were mainly patches within the matrix of theCerrado, which dominated the landscape. As annual crops expanded, agricultural fieldsamalgamated into large areas and thus became the dominant element of the landscape –this is indicated for example by increase in LPI, decrease in the number of patches, andincrease in the percentage of area occupied by crops.

Planted pasture shows a different pattern compared with annual crops. This increasedsomewhat in the area they occupied (CA) from 1985 to 1995 and decreased in the following

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Figure 8. Landscape metrics results (1985, 1995, and 2005).

decade (1995–2005). In fact, it showed a much more stable profile in the 20-year studyperiod when compared with the marked changes in ‘natural vegetation’ and ‘annual crops’.MPS and LPI also remained very stable. The LSI metric for pasture followed the samepattern as CA, increasing slightly from 1985 to 1995 and decreasing from 1995 to 2005.A significant change with respect to pasture was seen in the number of patches, whichdecreased markedly from 1995 to 2005. In fact, the number of planted pasture patches for1985 and 1995 is much higher than for natural vegetation or annual crops, possibly dueto the fact that this land use occurs more in areas of higher slopes (when compared withcrops), where the landscape elements are smaller, and also includes newly opened areashaving a more fragmented pattern.

4.4. Discussion

The approach employed in the present research differs from previous mapping studies inthe Cerrados; in that, it combined image segmentation and a multi-source data set forclassification, which allowed us to distinguish between crops and pasture. In addition,

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change detection was observed for a period of three years (most studies used only oneor two dates), and the changes in wetlands were quantified.

Image segmentation was the crucial aspect of OBIA application in this study. Thesegmentation of satellite images into objects prior to classification made a significantdifference because of crop field sizes (much larger than Landsat resolution), their rect-angular form, and the internal heterogeneity of fields. Pixel-based approaches (tested in thepreliminary stages) led to poor results regarding real-world features, because contextualinformation was not taken into account. The fact that the classification was performed forobjects rather than pixels resulted in a spatially meaningful result, which also allowed land-scape metrics calculation for assessment of changes in landscape structure. In addition,classification based on objects greatly facilitated the integration of auxiliary information(CEI and DEM) into the classification process; by considering mean values for each poly-gon, it helped to accommodate the differences in the original resolution of these data sets.Although the OBIA approach increases the possibilities for classification of objects inaddition to spectral information (e.g. texture, distances, size, and form) and also allowsmore sophisticated rule-based approaches, for our purpose of identifying broader classeswhile dealing with dynamic land uses, the NN algorithm with a feature space composed ofstrategically chosen features emerged as a very suitable approach, after several tests. Theapproach used for feature selection, although not automated, was based on the key knowl-edge of AOI characteristics that can be adapted to other areas, especially those experiencingsimilar processes within the Cerrados. The negative aspect is that this approach is depen-dent in part on data availability, in particular the availability of images at crop maximumgrowing peak.

The overall accuracy of the 2005 classification result was 93% (kappa 0.87) for cropsand pasture combined, which is considerably higher than the 72% (kappa 0.56) or 84%(kappa 0.75) reported by Brannstrom et al. (2008) for two areas in the Cerrado (easternMato Grosso and Bahia State, respectively). They mapped combined crops and pasture(agro-pastoral class) because of difficulties in separating them spectrally. Jepson (2005)reported an overall accuracy of 96.47% for his 1999 classification (the only year validatedof the three years), discriminating Cerrado, Forests, and Agro-pastoral. Moreover, he foundthat considerable levels of regeneration of Cerrado vegetation had occurred. In contrast, inthe present research, no significant change ‘from’ pasture or crops ‘to’ natural vegetationwas observed. Planted pasture and annual crops are rarely separated, due to the intenseannual dynamics and spectral confusion of these two land-cover types. They have beendifferentiated in previous mappings based on visual interpretation (SEPLAN-MT 2001;MMA – PROBIO 2007), which is problematic primarily because it is difficult to reproduceand it is time consuming.

This study has revealed that important changes took place in the area studied and alsothat the rates and patterns of change differed for the two periods assessed. From 1985 to1995, the changes were more accentuated in comparison with the second interval, 1995 to2005, when the changes continued to progress along the same pathway (loss of natural veg-etation and increase in agricultural areas), but at a lower rate. Moreover, the details providedby the quantification of wetlands’ loss and the independent analysis of crops and pastureallowed us to demonstrate that (1) annual crops dominated the changes and are currentlythe main land cover in the AOI; (2) although natural vegetation conversion progressed at aslower pace from 1995 to 2005, crops encroached into more fragile lands; and (3) pastureareas reduced from 1995 to 2005 in the AOI as more pasture areas were converted to cropsthan natural vegetation was to pasture. These differences between the rates and patterns of

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changes for crops and pasture had not yet been assessed; neither had the pattern of cropsencroaching into fragile lands been quantified.

The extensive and rapid landscape transformation observed in this region has beenthe result of several interacting drivers such as public policies, technological advances,land prices, and increasing demands. The topographical characteristics of these landscapeshave also played a key role, as the flat/smooth reliefs are very favourable for agriculturalmechanization. Nevertheless, it has been demonstrated by our study results that changesprogressed fast and transgressed the limits of this ‘favourable physiographic condition’,encroaching into more fragile lands (wetlands) at the risk of degradation of land and waterbodies.

5. Conclusions

Remote sensing is a reliable tool for understanding the historical transformations that havetaken place in recent decades in the AOI, especially for unveiling the ‘long-term’ pattern,rates, and trends of LULC changes in an area that is representative of many other agricul-tural landscapes undergoing heavy changes within this very important biome. The changesdetected point towards increasing loss and fragmentation of natural vegetation. Between1985 and 2005, 6491 km2 of Cerrados were converted to agricultural land uses (from atotal area of 15,555 km2), which indicates the intense pressure of agricultural expansion onthe natural vegetation resource. The trends of changes were ‘from’ Cerrado (natural vege-tation) ‘to’ annual crops and planted pasture and ‘from’ planted pasture ‘to’ annual crops.Conversion ‘from’ annual crops ‘to’ other classes was not significant. High annual ratesof crop expansion predominated, especially from 1985 to 1995. From 1995 to 2005, therate was moderated somewhat; however, during this latter period, agriculture advanced intomore fragile lands. Thus, our study provides the foundation for further analysis towardslinking these massive LULC changes to other environmental changes, as for example,changes in erosion risks, water quality, and biological diversity, among others.

AcknowledgementsThe authors thank the Brazilian Institute of Space Research (INPE) for the use of the satellite dataand for the interest and advice of the personnel during this project. The International DevelopmentResearch Centre (IDRC), through the Canada–Latin America and the Caribbean Research ExchangeGrants (LACREG) programme, provided funding to the senior author for fieldwork in Brazil.We thank the anonymous reviewers for their pertinent suggestions and recommendations in revisingthe manuscript.

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