Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics

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 \u3c 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 \u3c 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 \u3c 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

Bayesian networks for raster data (BayNeRD): plausible reasoning from observations

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 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. \ an

BayNeRD: inferência baseada em observações

Understanding the behaviour and the interaction among the complex phenomena in the field of Earth sciences is an active challenge to the predictive science. Interactions of probabilities are pointed out as the most promising basis to allow a computer to have a plausible reasoning. When the number of variables increases or even when the complexity of the relationships among the variables and also between the variables and the phenomenon rises, the Bayesian Network (BN) is a representation suited to model and handle the task. Thus, the objectives of this paper are to develop and implement a computer aided BN method that is able to incorporate experts knowledge handling with raster data: Bayesian Networks for Raster Data (BayNeRD). A case study under the context of soy identification and mapping in Mato Grosso State, Brazil, was used to test the methodology and the implemented algorithms. BayNeRD algorithm was implemented on R software and allow the understanding of complex phenomena through plausible reasoning based on data observation. Based on observation of a vegetation index, terrain slope, road and water body distances and soil aptitude, BayNeRD was able to compute the probability of soy occurrence. Results showed that BayNeRD can be used in several applications.Pages: 2369-237

Mapeamento da área de cana-de-açúcar em Porto Xavier-RS por meio de imagens Landsat

Brazil leads sugarcane world production and it makes properly information on sugarcane acreage be particularly important for government, traders, and farmers. The aim of this paper was to use multitemporal images acquired by Thematic Mapper (TM) sensor onboard Landsat-5 spacecraft to identify and map sugarcane crop area in Porto Xavier municipality, Rio Grande do Sul (RS) State, Brazil. In order to define the most appropriate period to identify sugarcane fields we analyzed six images from October to June during crop year of 2008/2009. Sugarcane fields were mapped through a digital classification over the image acquired in early April followed by a careful process of visual interpretation using all six images. To map sugarcane fields in crop years 2006/2007 and 2007/2008, we used two images acquired in two key periods during sugarcane growing season (from mid January to late April) for each crop year. Results showed that multitemporal TM images are suitable to identify and map sugarcane fields in Porto Xavier-RS provided that at least two cloud free images are available within two key periods (between mid January and late February and early March to early May, respectively), and at least a two months interval from each other, when those fields are better discriminated over the images. By applying the methodology we could find a sugarcane crop area of 911, 966, and 747 hectares for crop years from 2006/2007 to 2008/2009, respectively.Pages: 299-30

Mapeamento e estimativa prévia das áreas de soja no Mato Grosso a partir de imagens EVI

In Brazil, soybean plantations overcome 25 million hectares, and Mato Grosso State accounts for 27% of this area, so that earlier information about soybean mapping and acreage is very important for traders and farmers as well as for supporting studies that require up-to-dated land mappings over crop seasons. In this paper we assessed the Crop Enhanced Index-Preview Estimate (CEI-PE) methodology which intends to provide three soybean mapping and acreage up to 45 days prior to the original CEI. We produce soybean maps over 11 crop seasons (2000/2001 to 2010/2011) using MODIS images of the Enhanced Vegetation Index (EVI). CEI in Mato Grosso uses EVI images in two key periods during soybean crop season, the first (MinEVI) is composed from Day of Year (DOY) 161 to 224 (off-season). The second (MaxEVI) is composed using EVI Images acquired during soybean peak canopy development. To provide CEI-PE we computed EVI images over three different periods, from DOY 321 to 48, 32 and 16, called MaxEVI-49, -33 and -17, respectively. Then, we compared soybean acreage and mapping agreement between CEI-PE and the original CEI. The CEI-PE decreased mapping accuracy from CEI-49 to -33 and to -17, with average spatial agreement of 91.6, 82.8 and 73.9%, respectively. On acreage estimation, CEI-PE showed average difference around 2.8, 3.6 and -7.8%, for CE-49, -33 and -17, respectively. Moreover, soybean acreage was generally overestimated in CEI-49 and -33 and underestimation in CEI-17 compared to the original CEI.Pages: 356-36

Soja em Áreas Prioritárias para Conservação da Biodiversidade no Mato Grosso

In order to fulfill the Brazilian Policy for Biodiversity, in 2007 the Ministry of the Environment gathered researchers from diverse ecosystems with the aim of updating the Priority Areas for Conservation (PAC's) of Biological Diversity. However, since the new areas are not legally protected they are under pressure to be occupied by agriculture use. The province of Mato Grosso is the biggest producer of soybean in Brazil and 56% of its area match to priority areas for conservation. In this context, this study aimed to assess the distribution and expansion of soybean areas mapped by MODIS imagery, among 2002 and 2009 years, on the PAC's in the province of Mato Grosso. We assessed the relationships between the increasing area of soybean, deforestation and PAC's. The outcomes have showed that soybean is gradually increasing its area in the province, mostly on PAC's in the Amazon biome. Even when the deforestation decreased, the expansion of the soybean areas over the assessed period was five times higher than in other biomes, showing that Amazon biome is under higher pressure than the other ones.Pages: 7353-736

A soja e o desflorestamento no Mato Grosso: safras 2001/02 a 2004/05

Recent expansion of large-scale mechanized soybean production at the agricultural frontier in Mato Grosso State (MT), Brazil, motivated by food consumption increase, has provoked debates about the contribution of cropland expansion to current Amazon deforestation dynamics. The study aims to make a quantitative diagnosis of soybean occupation degree and expansion between 2001/02 and 2004/05 crop years in MT, focusing the analysis over deforested sites. The Crop Enhanced Index (CEI), based on MODIS vegetation indices products, was used to produce the soybean thematic maps for 2001/02 to 2004/05 crop years. The MT deforested sites were obtained from PRODES/INPE maps. To evaluate the soybean occupation over deforestations, the soybean thematic maps were overlaid to PRODES sites at two moments: soybean over old deforestation (accumulated from 1988 to 2000) and soybean over recent deforestation (yearly deforestation maps from 2001 to 2004). Results show that, until 2001/02 crop year, only 15% (0.59 Mha) of the MT soybean area was distributed over deforested sites. From 2001/02 to 2004/05 this area increased by 1.02 Mha. Moreover in the same period the deforestation in MT increased by more than 3.8 Mha. In 2005 only 9% (0.36 Mha) of the recent deforestation sites (from 2001 to 2004) were cultivated with soybean. Most of the soybean expansion over deforested areas (66%) was over old deforestation (from 1988 to 2000), suggesting that the soybean expansion observed during recent years was, predominantly, over other land uses.Pages: 323-33

Modis vegetation indices applied to soybean area discrimination

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.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