IESA - Instituto de Estudos Socioambientais

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http://200.137.215.59//handle/ri/814
O IESA - Instituto de Estudos Socioambientais, da Universidade Federal de Goiás, oferece Graduação em: Bacharelado em Geografia; Licenciatura em Geografia; e, Ciências Ambientais. Além de Especialização (Lato Sensu) em: Educação Ambiental; e, Turismo.

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Navegando IESA - Instituto de Estudos Socioambientais por Autor "Alencar, Ane Auxiliadora Costa"

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    Long-term landsat-based monthly burned area dataset for the brazilian biomes using deep learning
    (2022) Alencar, Ane Auxiliadora Costa; Arruda, Vera Laisa da Silva; Silva, Wallace Vieira da; Costa, Dhemerson Estevão Conciani da; Costa, Diêgo Pereira; Crusco, Natalia; Duverger, Soltan Galano; Ferreira, Nilson Clementino; Rocha, Washington de Jesus Sant'Anna da Franca; Hasenack, Heinrich; Martenexen, Luiz Felipe Morais; Piontekowski, Valderli Jorge; Ribeiro, Noely Vicente; Rosa, Eduardo Reis; Rosa, Marcos Reis; Santos, Sarah Moura Batista dos; Shimbo, Julia Z; Martin, Eduardo Vélez
    Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.
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    Reconstructing three decades of land use and land cover changes in brazilian biomes with Landsat Archive and Earth Engine
    (2020) Souza Junior, Carlos M; Shimbo, Júlia Zanin; Rosa, Marcos Reis; Parente, Leandro Leal; Alencar, Ane Auxiliadora Costa; Rudorff, Bernardo Friedrich Theodor; Hasenack, Heinrich; Matsumoto, Marcelo; Ferreira Junior, Laerte Guimaraes; Souza Filho, Pedro Walfir Martins e; Oliveira, Sergio W. de; Rocha, Washington de Jesus Sant'Anna da Franca; Fonseca, Antônio V; Marques, Camila Balzani; Diniz, Cesar Guerreiro; Costa, Diêgo Pereira; Monteiro, Dyeden; Rosa, Eduardo R; Martin, Eduardo Vélez; Weber, Eliseu José; Lenti, Felipe Eduardo Brandão; Paternost, Fernando F; Pareyn, Frans Germain Corneel; Siqueira, João Victor Nascimento; Viera, José L; Ferreira Neto, Luiz Cortinhas; Saraiva, Marciano Machado; Sales, Marcio H; Salgado, Moises P. G; Vasconcelos, Rodrigo Nogueira de; Duverger, Soltan Galano; Mesquita, Vinicius Vieira; Azevedo, Tasso
    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.
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    Woody aboveground biomass mapping of the brazilian savanna with a multi-sensor and machine learning approach
    (2020) Bispo, Polyanna da Conceição; Rodríguez-Veiga, Pedro; Zimbres, Bárbara de Queiroz Carvalho; Miranda, Sabrina do Couto de; Cezare, Cassio Henrique Giusti; Fleming, Sam; Baldacchino, Francesca; Louis, Valentin; Rains, Dominik; Garcia, Mariano; Espírito-Santo, Fernando Del Bon; Roitman, Iris; Pacheco-Pascagaza, Ana María; Gou, Yaqing; Roberts, John; Barrett, Kirsten; Ferreira Junior, Laerte Guimaraes; Shimbo, Júlia Zanin; Alencar, Ane Auxiliadora Costa; Bustamante, Mercedes Maria da Cunha; Woodhouse, Iain Hector; Sano, Edson Eyji; Ometto, Jean Pierre Henry Balbaud; Tansey, Kevin; Balzter, Heiko
    The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.