This letter uses satellite remote sensing to examine patterns of cropland expansion, cropland abandonment, and changing cropping frequency in Mato Grosso, Brazil from 2001 to 2011. During this period, Mato Grosso emerged as a globally important center of agricultural production. In 2001, 3.3 million hectares of mechanized agriculture were cultivated in Mato Grosso, of which 500 000 hectares had two commercial crops per growing season (double cropping). By 2011, Mato Grosso had 5.8 million hectares of mechanized agriculture, of which 2.9 million hectares were double cropped. We found these agricultural changes to be selective with respect to land attributes -significant differences (p < 0.001) existed between the land attributes of agriculture versus nonagriculture, single cropping versus double cropping, and expansion versus abandonment. Many of the land attributes (elevation, slope, maximum temperature, minimum temperature, initial soy transport costs, and soil) that were associated with an increased likelihood of expansion were associated with a decreased likelihood of abandonment (p < 0.001). While land similar to agriculture and double cropping in 2001 was much more likely to be developed for agriculture than all other land, new cropland shifted to hotter, drier, lower locations that were more isolated from agricultural infrastructure (p < 0.001). The scarcity of high quality remaining agricultural land available for agricultural expansion in Mato Grosso could be contributing to the slowdown in agricultural expansion observed there over 2006 to 2011. Land use policy analyses should control for land scarcity constraints on agricultural expansion.

Resumo -O objetivo deste trabalho foi avaliar o desempenho do índice de vegetação realçado (EVI) e do índice de vegetação da diferença normalizada (NDVI) -ambos do sensor "moderate resolution imaging spectroradiometer" (Modis) -, para discriminar áreas de soja das áreas de cana-de-açúcar, pastagem, cerrado e floresta, no Estado do Mato Grosso. Foram utilizadas imagens adquiridas em dois períodos: durante a entressafra e por ocasião do pleno desenvolvimento da cultura da soja. Para cada classe analisada, foram selecionadas 31 amostras de mapas de referência e avaliadas as diferenças nos valores de cada índice de vegetação, para a classe soja, foram avaliadas frente às demais classes, por meio do teste de Tukey-Kramer. Em seguida, foram avaliadas as diferenças entre os índices de vegetação, por meio do teste de Wilcoxon pareado. O NDVI apresentou melhor desempenho na discriminação das áreas de soja na entressafra, particularmente com uso das imagens do dia do ano (DA) 161 a 273, enquanto o EVI apresentou melhor desempenho no período de pleno desenvolvimento da cultura, especificamente com uso das imagens de DA 353 a 33. Portanto, o melhor resultado para classificação da soja, no Estado do Mato Grosso, via séries temporais do sensor Modis, pode ser obtida por meio do uso combinado do NDVI na entresssafra e do EVI no pleno desenvolvimento da soja.Termos para indexação: classificação multitemporal de imagens, dados Modis, estimativas de área agrícola, imagens de satélite, sensoriamento remoto. Modis vegetation indices applied to soybean area discriminationAbstract -The objective of this work was to evaluate the performance of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI) -both from the moderate resolution imaging spectroradiometer (Modis) sensor -to discriminate soybean cultivated areas from sugarcane, pasture, cerrado, and forest ones in the state of Mato Grosso, Brazil. Images acquired during two periods were used: off-season and maximum soybean crop development. For each analyzed class, 31 samples were selected from reference maps, and the differences in the values of each soybean vegetation index were evaluated against the other classes using the Tukey-Kramer test. Afterwards, the differences between the vegetation indices were assessed using the Wilcoxon paired test. NDVI performed best in discriminating soybean areas during the off-season period, particularly when using images acquired from day of year (DOY) 161 to 273, whereas EVI performed best during maximum crop development, particularly when using images from DOY 353 to 33. Therefore, best classification results for soybean in the state of Mato Grosso can be achieved by coupling Modis NDVI images acquired during off-season period and EVI images acquired during the maximum crop development period.Index terms: multi-temporal image classification, Modis data, crop area estimates, satellite images, remote sensing. IntroduçãoMonitorar sistemas dinâmicos, como a agricultura de ciclo anual, é um desafio que demanda recu...

Abstract:The Soy Moratorium is an initiative to reduce deforestation rates in the Amazon biome based on the hypothesis that soy is a deforestation driver. Soy planted in opened areas after July 24th, 2006 cannot be commercialized by the associated companies to the Brazilian Association of Vegetable Oil Industries (ABIOVE) and the National Association of Cereal Exporters (ANEC), which represent about 90% of the Brazilian soy market. The objective of this work is to present the evaluation of the fourth year of monitoring new soy plantations within the Soy Moratorium context. With the use of satellite images from the MODIS sensor, together with aerial survey, it was possible to identify 147 polygons with new soy plantations on 11,698 ha. This soy area represents 0.39% of the of the total deforested area during the moratorium, in the three soy producing states of the Amazon biome, and 0.6% of the cultivated soy area in the Amazon biome, indicating that soy is currently a minor deforestation driver. The quantitative geospatial information provided by an effective monitoring approach is paramount to the implementation of a governance OPEN ACCESSSustainability 2012, 4 1075 process required to establish an equitable balance between environmental protection and agricultural production.

This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts' knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a OPEN ACCESSRemote Sens. 2013, 5 6000 source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet.

ABSTRACT:A virtual globe to visualize time series of pixels from the MODIS sensor over the South American continent is available in the Internet and was developed at the Brazilian Institute for Space Research. The MODIS images acquired since the year 2000 were transformed to a vegetation index (EVI2, two-band Enhanced Vegetation Index) with pixel size of 250 m. This study aims to use these time series to identify land use changes (LUC) based on the temporal profile of EVI2 values of deforested polygons between 2007 and 2011 within the context of the Soy Moratorium. Deforested polygons were divided in two strata: with and without soy in crop year 2010/11. From the MODIS/EVI2 time series the following classes were identified: forest, degraded forest, total clearing of the area, regrowth of forest, regrowth with pasture, pasture, agriculture, and soy. For stratum 1, the dominant LUC trajectory was: forest -degradation -regrowth / regrowth with pasture. In the second stratum it was observed two main LUC trajectories: 1) forestdegraded forest -total clearing of the area -annual crop (rice) -soy; and 2) forest -total clearing of the area -annual crop (rice) -soy. For most samples of stratum 2 the LUC trajectory was agriculture (e.g., rice) between total clearing and soy cultivation. These patterns occurred on average over two harvests, which may be considered the necessary time for soil correction and total removal of above ground stumps and roots to enable mechanized soy harvesting. The fast evaluation of one hundred polygons during 11 years was only possible due to the virtual globe to visualize the MODIS time series that proved to be an important tool to improve the understanding of LUC dynamics in the Amazon region.