agriculture ͉ carbon ͉ land use change ͉ soybean T he ''arc of deforestation'' along the southern and eastern extent of the Brazilian Amazon is the most active land-use frontier in the world in terms of total forest loss (1) and intensity of fire activity (2). Historically, the dominant pattern of forest conversion has begun with small-scale exploration for timber or subsistence agriculture, followed by consolidation into largescale cattle ranching operations or abandonment to secondary forest (3-5). Recent expansion of large-scale mechanized agriculture at the forest frontier has introduced a potential new pathway for forest loss, generating debate over the contribution of cropland expansion to current deforestation dynamics (5-9). In the nine states of the Brazilian Legal Amazon, mechanized agriculture increased by 36,000 km 2 , † † and deforestation totaled 93,700 km 2 ‡ ‡ during [2001][2002][2003][2004]. Recent gains in the area under cultivation and the productivity of locally adapted crop varieties have made Brazil a leading worldwide producer of grains such as soybeans; the agribusiness sector now accounts for more than one-third of Brazil's gross national product (10).The state of Mato Grosso alone accounted for 87% of the increase in cropland area and 40% of new deforestation during this period. Whether cropland expansion contributes directly to deforestation activity or occurs only through the intensified use of previously deforested areas has important consequences for ecosystem services (11), such as carbon storage, and future deforestation dynamics.Amazon deforestation is Brazil's largest source of CO 2 emissions (12, 13). Carbon fluxes from deforestation are a function of the area of forest loss (14-16) and related forest disturbances, such as fire (17, 18) and logging (17,19), variations in forest biomass across the basin (20), and land use or abandonment after forest clearing (3,21). Land use after forest clearing remains a major source of uncertainty in the calculation of deforestation carbon fluxes because methods to assess deforestation trends in Amazonia have not followed individual clearings over time (4,5,(22)(23)(24)(25)(26)(27)(28). The relative contributions of smallholder agriculture and large-scale cattle ranching to annual forest loss have been inferred from the size of deforestation events (5, 28), but no direct measurements have been available. Rapid growth of large-scale agriculture in Amazonia challenges the historic relationship between land use and clearing size.We determine the fate of large deforestation events (Ͼ25 ha) during [2001][2002][2003][2004] in Mato Grosso State to provide satellitebased evidence for the relative contributions of cropland and pasture to increasing forest loss during this period (Fig. 1). Our approach combines satellite-derived deforestation data, vegetation phenology information from the Moderate Resolution Imaging Spectroradiometer (MODIS; ref. 29), and 2 years of field observations to establish the spatial and temporal patterns of land use after fo...

Abstract:The use of biofuels to mitigate global carbon emissions is highly dependent on direct and indirect land use changes (LUC). The direct LUC (dLUC) can be accurately evaluated using remote sensing images. In this work we evaluated the dLUC of about 4 million hectares of sugarcane expanded from 2005 to 2010 in the South-central region of Brazil. This region has a favorable climate for rain-fed sugarcane, a great potential for agriculture expansion without deforestation, and is currently responsible for almost 90% of Brazilian's sugarcane production. An available thematic map of sugarcane along with MODIS and Landast images, acquired from 2000 to 2009, were used to evaluate the land use prior to the conversion to sugarcane. A systematic sampling procedure was adopted and the land use identification prior to sugarcane, for each sample, was performed using a web tool developed to visualize both the MODIS time series and the multitemporal Landsat images. Considering 2000 as reference year, it was observed that sugarcane expanded: 69.7% on pasture land; 25.0% on annual crops; 0.6% on forest; while 3.4% was sugarcane land under crop rotation. The results clearly show that the dLUC of recent sugarcane expansion has occurred on more than 99% of either pasture or agriculture land.

Over the last ten years millions of gigabytes of MODIS (Moderate Resolution Imaging Spectroradiometer) data have been generated which is forcing the remote sensing users community to a new paradigm in data processing for image analysis and visualization of these time series. In this context this paper aims to present the development of a tool to integrate the 10 years time series of MODIS images into a virtual globe to support LULC change studies. Initially the development of a tool for instantaneous visualization of remote sensing time series within the concept of a virtual laboratory framework is described. The virtual laboratory is composed by a data set with more than 500 million EVI2 (Enhanced Vegetation Index 2) time series derived from MODIS 16-day composite data. The EVI2 time series were filtered with sensor ancillary data and Daubechies (Db8) orthogonal Discrete Wavelets Transform. Then EVI2 time series were integrated into the virtual globe using Google Maps and Google Visualization Application Programming Interface functionalities. The Land Use Land Cover changes for forestry and agricultural applications are presented using the proposed time series visualization tool. The tool demonstrated to be useful for rapid LULC change analysis, at the pixel level, over large regions. Next steps are to further develop the Virtual Laboratory of Remote Sensing Time Series Framework by extending this work for other geographical regions, incorporating new computational algorithms, testing data from other sensors and updating the MODIS time series.

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non-supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km 2 of land surface were burnt in Acre State. Of this, 3700 km 2 corresponded to the previously deforested areas and 2800 km 2 corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.

The use of biofuels to mitigate global carbon emissions is highly dependent on direct and indirect land use changes (LUC). The direct LUC (dLUC) can be accurately evaluated using remote sensing images. In this work we evaluated the dLUC of about 4 million hectares of sugarcane expanded from 2005 to 2010 in the South-central region of Brazil. This region has a favorable climate for rain-fed sugarcane, a great potential for agriculture expansion without deforestation, and is currently responsible for almost 90% of Brazilian's sugarcane production. An available thematic map of sugarcane along with MODIS and Landast images, acquired from 2000 to 2009, were used to evaluate the land use prior to the conversion to sugarcane. A systematic sampling procedure was adopted and the land use identification prior to sugarcane, for each sample, was performed using a web tool developed to visualize both the MODIS time series and the multitemporal Landsat images. Considering 2000 as reference year, it was observed that sugarcane expanded: 69.7% on pasture land; 25.0% on annual crops; 0.6% on forest; while 3.4% was sugarcane land under crop rotation. The results clearly show that the dLUC of recent sugarcane expansion has occurred on more than 99% of either pasture or agriculture land.

of total primary production (summarized in Melack and Forsberg 2001). Isotopic studies (Forsberg et al. 1993), however, have shown that the amount of fish carbon derived from the different plant groups is not proportional to their relative contribution to the floodplain production and that algal productivity can be important.The main challenge to further investigation of algal production at the scale of the Amazon basin is the size and complexity of the system subjected to oscillations of river discharge related to the annual cycle and interannual changes (Richey et al. 1989). Because the surface waters transport dissolved and particulate material that supports the biological system, variations of the river control the exchange of resources between the Amazon River and floodplain lakes (Forsberg et al. 1988;Richey et al. 1997). Moreover, inundation is also influenced by other water sources whose contribution to lakes depends on floodplain geomorphology (Mertes 1997), density of floodplain channels (Mertes et al. 1995), and the ratio of local drainage basin area to lake area (Forsberg et al. 1988;Lesack and Melack 1995).To address the wide range of spatial and temporal variability, we present here results from field and satellite analyses. The synoptic view, medium resolution (250 m × 250 m), and relatively high frequency of cloud-free overpasses provided by Terra Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance images make it possible to identify water color changes related to chlorophyll concentrations (Darecki and Stramski 2004). Recent applications of MODIS data to studies of aquatic systems include measurements of water quality in lakes (Koponen et al. 2004), suspended matter in coastal waters (Miller and McKee 2004), and chlorophyll concentration (Kwiatkowska and Fargion 2003).Most inland waters are classified as Case 2 waters (Morel and Prieur 1977;Kirk 1994), in which phytoplankton concentration is not tightly coupled to the amount of total seston and optical properties are determined by a mixture of components, including phytoplankton, inorganic particles, colloids, and dissolved organic matter (Mobley 1994). Much of the research on Case 2 water has been focused on Abstract To assess seasonal changes in phytoplanktonic chlorophyll distributions in Amazon floodplain lakes, a linear mixing model was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data acquired at four river stages: rising (April), high (June), decreasing (September), and low (November). The study area is located in a floodplain reach from Parintins (Amazonas) to near Almeirim (Pará). A three-end-member mixing model designed to uncouple three fractions [high suspended inorganic matter (ip), low inorganic suspended matter (w), and high chlorophyll a (Chl)] was tested in Lake Curuaí (1.5°S 55.43°W) based on field sampling done almost concurrently with satellite overpasses. During high water, phytoplankton patches are confined to lakes closer to terra firme under the influence of clear water inflow,...

Resumo -O objetivo deste trabalho foi avaliar dados multitemporais, obtidos pelo sensor "moderate resolution imaging spectroradiometer" (MODIS), para o estudo da dinâmica espaço-temporal de duas sub-regiões do bioma Pantanal. Foram utilizadas 139 imagens "enhanced vegetation index" (EVI), do produto MOD13 "vegetation index", dados de altimetria oriundos do "shuttle radar topography mission" (SRTM) e dados de precipitação do "tropical rainfall measuring mission" (TRMM). Para a redução da dimensionalidade dos dados, as imagens MODIS-EVI foram amostradas com base nas curvas de nível espaçadas em 10 m. Foram aplicadas as técnicas de análise de autocorrelação e análise de agrupamentos aos dados das amostras, e a análise de componentes principais na área total da imagem. Houve dependência tanto temporal quanto espacial da resposta espectral com a precipitação. A análise de agrupamentos apontou a presença de dois grupos, o que indicou a necessidade da análise completa da área. A análise de componentes principais permitiu diferenciar quatro comportamentos distintos: as áreas permanentemente alagadas; as áreas não inundáveis, compostas por vegetação; as áreas inundáveis com maior resposta de vegetação; e áreas com vegetação ripária.Termos para indexação: sensoriamento remoto, série temporal, análise de agrupamento. Spatial-temporal analysis of MODIS image applied to dynamic of Pantanal biomeAbstract -The objective of this work was to evaluate multitemporal data, obtained by moderate resolution imaging spectroradiometer (MODIS) sensor, for the study of spatial-temporal dynamics in two subregions of the Pantanal biome. One hundred and thirty nine enhanced vegetation index (EVI) images, from MOD13 vegetation index product, altimetry data from shuttle radar topography mission (SRTM) and tropical rainfall measuring mission (TRMM) precipitation data were used. In order to reduce data dimensionality, MODIS EVI images were sampled based on contour lines spacing of 10 m. The autocorrelation and cluster analysis were used for spatial and temporal evaluation of the samples; and the principal components analysis was applied to all dataset for spatial and temporal analysis. Results showed a spatial and temporal dependence between spectral response and precipitation. The cluster analysis indicated two spatial groups, suggesting the need for the analysis of the entire study area. The principal components analysis allowed to distinguish four behaviors: the areas permanently fl ooded; nonfl ooded areas composed by vegetation; fl ooded areas with higher spectral vegetation response; and riparian vegetation areas.

This work presents a methodology that uses digital fraction images derived from Linear Spectral Mixture Model and wavelets transform from MODIS satellite sensor time-series for land cover change analysis. Our approach uses MODIS surface reflectance images acquired from 2000 to 2006 time period. For this study, a test site was selected in the Mato Grosso State, Brazilian Amazonia. This site has shown high deforestation rates in the last years. The samples of land cover classes were collected during four field campaigns (2003, 2004, 2005 and 2006) to be used as ground truth. The linear spectral mixture model was applied to the MODIS surface reflectance images of red surface reflectance band (620-670 nm bandwidth), near infrared surface reflectance band (NIR, 841-876 nm bandwidth) and medium infrared surface reflectance band (MIR, 2105-2155 nm bandwidth). This model generated the vegetation, shade, and soil fraction images. In the next step, the Meyer orthogonal Discrete Wavelets Transform was used for filtering the time-series of MODIS fraction images. The filtered signal was reconstructed excluding high frequencies for each pixel in the fraction images (soil, vegetation, and shade) of the time-series. This computational procedure allows to observe the original signal without clouds and other noises. The results show that wavelets transform can provide a gain in multitemporal analysis and visualization on inter-annual fraction images variability patterns.