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.
Highlights
The growing demands for natural resources and the need to deal with climate change’s effects resulting from greenhouse gas emissions present significant challenges for our societies
To show the potential of the data sets and software products that we are producing, this paper presents the land use and cover maps created from the Earth observation (EO) data cubes using image time series analysis and machine learning methods
In the Brazil Data Cube (BDC) project, we are developing Web service specifications defined by the project team, such as Web Time Series Service (WTSS) and Web Land Trajectory Service (WLTS), and by third-party entities, such as the Tile Map Service (TMS) by THE Open Source Geospatial Foundation; and the Web Map Service (WMS), Web Feature Service (WFS), and Web Coverage Service (WCS) by the Open Geospatial Consortium (OGC) and ISO [43]
Summary
The growing demands for natural resources and the need to deal with climate change’s effects resulting from greenhouse gas emissions present significant challenges for our societies To understand how these changes are affecting the environment, one of our primary sources of information is land use and cover change data obtained by Earth observation satellites [1]. Big Earth observation data sets bring new challenges and opportunities, including the novel generation of technological solutions to store, process, disseminate, and analyze them [4] One of these opportunities is the use of time series analysis to extract landscape change information from many remote sensing images [2,5]. To show the potential of the data sets and software products that we are producing, this paper presents the land use and cover maps created from the EO data cubes using image time series analysis and machine learning methods
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