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.
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