Personal Informations


Name: Diêgo Pereira Costa;

Origin Country: Brazil;

Origin Instituition: Universidade Federal da Bahia (Federal University of Bahia);

Origin Department: Programa de Pós-Graduação em Energia e Ambiente (Graduate Program in Energy and Environment);

Age: 28; Birth: 10/28/1993.

ORCID: https://orcid.org/0000-0002-5117-898X

Academic Informations

PhD in Progress in Energy and Environment

Institution: Federal University of Bahia

Theme: Time series analysis of satellite images for the study of climate change and land degradation in the Caatinga biome

Master's Degree Completed in Modeling in Earth and Environmental Sciences

Institution: State University of Feira de Santana

Theme: Deforestation hotspots as subsidy to desertification studies in the Caatinga biome

Graduated in Geography

Institution: State University of Feira de Santana


Related and Recent Projects

Land Use and Land Cover Mapping in the Caatinga Biome - See Complete Features Here

Burn Scars Monitoriong, using Deep Learning, Cloud Computing and Remote Sensing, in the Caantinga Biome - See Complete Features Here

Draylands Deforestation Alerts System - See Complete Features Here

Desertification and Degradation Monitoring in Arid and Semi-Arid Regions of Brazil - See Complete Features Here

Water Resources Monitoring in the Caatinga Biome - https://plataforma.agua.mapbiomas.org

Recent Publications

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth EngineBrazil 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.
Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep LearningFire 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.

Recent Jobs

GeoDatin Intelligence in Data and Geoinformation

Chief Executive Officer - Project Coordinator

2018 - Actual


Federal University of Bahia

Researcher - Energy, Environment and Remote Sensing Themes

2019- Actual



State University of Feira de Santana

Researcher - Modeling in Earth and Environmental Sciences

2011- 2019


Related Skills

Advanced User in Google Earth Engine - JavaScript Language;


Intermadiate User in R Language;


Advanced User in GIS Softwares;


Advanced User In Remote Sensing Tecniques;


Experience in Agile Development Methodologies;


Experience in Project Management;


Experience with Group Work and People Management.

e-mail: costapdiego@gmail.com; costapdiego@ufba.br Linkedin: https://www.linkedin.com/in/costapdiego/