Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

Abstract

Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.

Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

“…We explored Cropland and Pasture samples over the Cerrado biome in Brazil using MODIS because of their high variability in different regions and years. An initiative called Brazil Data Cube [66] is producing Analysis-Ready Data (ARD) and multidimensional data cubes from medium-resolution satellite images for all Brazilian territory. Using these data cubes, we intend to apply the proposed method to 10-20 meters and five days Sentinel 2 image time series in order to separate all distinct types of land use and cover classes in Cerrado.…”

Accessing and Processing Brazilian Earth Observation Data Cubes With the Open Data Cube Platform

“…Recently, many institutions have been creating EO data cubes from remote sensing images to specific regions, such as Australian Data Cube (Lewis et al, 2017), Swiss Data Cube (Giuliani et al, 2017), Armenian Data Cube (Asmaryan et al, 2019), Africa Regional Data Cube (Killough, 2018), Colombian Data Cube (CDCol) (Ariza-Porras et al, 2017), and Brazil Data Cube (Ferreira et al, 2020). Except for the last one, the other initiatives use the framework Open Data Cube as the core technology to index and handle the EO data.…”

“…The Brazil Data Cube project has objectives to produce Analysis Ready Data structured in data cubes in medium resolution for the entire Brazilian territory. For this, the BDC project use and develop technologies necessary for the processing, analysis, and dissemination of these data products and to produce information on Land Use and Land Cover from the cubes using machine learning methods and image processing techniques (Ferreira et al, 2020).…”

“…Many institutions have been developing technologies that use these concepts and can be considered EODC (Giuliani et al, 2019b), such as Open Data Cube (ODC) (Open Data Cube, 2021a), Google Earth Engine (GEE) (Gorelick et al, 2017), JRC Earth Observation Data and Processing Platform (JEODPP) (Soille et al, 2018), Sentinel Hub (SH) (Sinergise, 2021) or Brazil Data Cube (BDC) Platform (Ferreira et al, 2020). These platforms adopt different data abstractions, standards, or technological solutions to provide their functionalities despite similar functionalities (Gomes et al, 2020).…”

“…In the Brazilian context, the National Institute for Space Research (INPE) leads the development of the Brazil Data Cube Platform for management and analyzing massive EO data. The main objectives of the BDC project are to produce cubes with Analysis Read Data in medium resolution for the Brazilian territory that allows the analysis of time series and the use and development of technologies for processing and storage of those data cubes, including cloud computing and distributed processing (Ferreira et al, 2020). This work presents the integration process between the data products of the BDC project and the ODC framework.…”