Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

This study’s overarching aim is to establish the areal extent and characteristics of the rapid sugarcane expansion and land use change in São Paulo state (Brazil) as a result of an increase in the demand for ethanol, using Landsat type remotely sensed data. In 2003 flex fuel automobiles started to enter the Brazilian consumer market causing a dramatic expansion of sugarcane areas from 2.57 million ha in 2003 to 4.45 million ha in 2008. Almost all the land use change, for the sugarcane expansion of crop year 2008/09, occurred on pasture and annual crop land, being equally distributed on each. It was also observed that during the 2008 harvest season, the burned sugarcane area was reduced to 50% of the total harvested area in response to a protocol that aims to cease sugarcane straw burning practice by 2014 for mechanized areas. This study indicates that remote sensing images have efficiently evaluated important characteristics of the sugarcane cultivation dynamic providing quantitative results that are relevant to the debate of sustainable ethanol production from sugarcane in Brazil.

The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the MOD13Q1 product and TM/Landsat-5 images followed by aerial survey and field inspection. In the 2009/2010 crop year, 6.3 thousand ha of soybean (0.25% of the total deforestation) were identified in areas deforested during the moratorium period. The use of remote sensing satellite images reduced by almost 80% the need for aerial survey to identify soybean planting and allowed monitoring of all deforested areas greater than 25 ha. It is still premature to attribute the recent low deforestation rates in the Amazon biome to the Soy Moratorium, but the initiative has certainly exerted an inhibitory effect on the soybean frontier expansion in this biome.

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.

Traditional manual sugarcane harvesting requires the pre-harvest burning practice which should be gradually banned by 2021 for most of São Paulo State, Brazil, on cultivated sugarcane land (terrain slope ≤12%) according to State Law number 11241. To forward the end of this practice to 2014, a “Green Ethanol” Protocol was established in 2007. The present work aims at analyzing five years of continuous sugarcane harvest monitoring, based on remote sensing images, to evaluate the effectiveness of the Protocol, thus helping decision makers to establish public policies to meet the Protocol’s expected goals. During the last five crop years, sugarcane acreage expanded by 1.5 million ha, which was compensated by a correspondent increase in the green harvested land. However, no significant reduction was observed in the amount of pre-harvest burned land over the same period. Based on the current trend, this goal is likely to be achieved one or two years later (2015–2016), which will be five or six years ahead of 2021 as the goal in the State Law number 11241 states. We thus conclude that the“Green Ethanol” Protocol has been effective with a positive impact on the increase of GH, especially on recently expanded sugarcane fields.

Coffee is the second most valuable traded commodity worldwide. Brazil is the world’s largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield.