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.

The availability of freshwater is becoming a global concern. Because agricultural consumption has been increasing steadily, the mapping of irrigated areas is key for supporting the monitoring of land use and better management of available water resources. In this paper, we propose a method to automatically detect and map center pivot irrigation systems using U-Net, an image segmentation convolutional neural network architecture, applied to a constellation of PlanetScope images from the Cerrado biome of Brazil. Our objective is to provide a fast and accurate alternative to map center pivot irrigation systems with very high spatial and temporal resolution imagery. We implemented a modified U-Net architecture using the TensorFlow library and trained it on the Google cloud platform with a dataset built from more than 42,000 very high spatial resolution PlanetScope images acquired between August 2017 and November 2018. The U-Net implementation achieved a precision of 99% and a recall of 88% to detect and map center pivot irrigation systems in our study area. This method, proposed to detect and map center pivot irrigation systems, has the potential to be scaled to larger areas and improve the monitoring of freshwater use by agricultural activities.

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O presente artigo tem como objetivo analisar o fator topográfico do modelo USLE calculado com base nos dados SRTM, comparando-o com o fator topográfico calculado a partir de dados cartográficos nas escalas de 1:10.000 e 1:50.000. Para tanto, foram gerados modelos digitais de elevação (MDE) a partir dos dados da missão SRTM e MDE gerados a partir dos dados das cartas topográficas, servindo como insumo para o cálculo do fator topográfico. Buscou-se, também, avaliar a utilização de diferentes algoritmos de distribuição do fluxo e ainda avaliar a influência do espaçamento da grade na modelagem do fator topográfico. Foi então possível verificar que o fator topográfico modelado a partir dos dados SRTM possibilitou obter resultados semelhantes aos obtidos por meio das cartas topográficas nas escalas 1:10.000 (R = 0,84) e 1:50.000 (R = 0,65). Desta forma, concluiu-se que a estimativa do fator topográfico a partir dos dados SRTM é adequada e pode beneficiar a modelagem da erosão em áreas que carecem de dados cartográficos com escalas adequadas ao cálculo do fator topográfico da USLE.

This paper aim to describe the land-cover and the morfometric characteristics of the Ribeirão Grande watershed. The Permanent Preservation Areas (APP) created by Brazilian Forest Law 4771/65 on the study area were mapped and the land-cover in these areas was investigated. The land-cover was classified using a TM/Landsat-5 image and the morfometrics characteristics were based on SRTM digital elevation model. The results indicated high erosion and flood risks.