Abstract
Continental and global datasets based on earth observations or computational models challenge the existing map algebra approaches. The available datasets differ in their spatio-temporal extents and their spatio-temporal granularity, which makes it difficult to process them as time series data in map algebra expressions. To address this issue we introduce a new map algebra approach that is topology based. This topology based map algebra uses spatio-temporal topological operators (STTOP and STTCOP) to specify spatio-temporal operations between topological related map layers of different time-series data. We have implemented several topology based map algebra tools in the open source geoinformation system GRASS GIS and its open source cloud processing engine actinia. We demonstrate the application of our topology based map algebra by solving real world big data problems using a single algebraic expression. This included the massively parallel computation of the NDVI from a series of 100 Sentinel2A scenes organized as earth observation data cubes. The processing was performed and benchmarked on a many core computer setup and in a distributed container environment. The design of our topology based map algebra allows us to deploy it as a standardized service in the EU Horizon 2020 project openEO.
Highlights
Continental and global time series data from earth observation satellites [1,2,3] or computational simulations with arbitrary spatio-temporal granularities require very sophisticated tools for efficient analysis and processing
The syntax of the topology based map algebra was derived from the GRASS geographical information systems (GIS) map algebra module r.mapcalc
We extended the map algebra syntax by introducing a new spatio-temporal topological operator (STTOP) and a spatio-temporal comparison operator (STTCOP)
Summary
Continental and global time series data from earth observation satellites [1,2,3] or computational simulations with arbitrary spatio-temporal granularities require very sophisticated tools for efficient analysis and processing. In this research we develop a topology based map algebra to process large scale time series datasets with different spatio-temporal granularities and extents using algebraic expressions. Data 2019, 4, 86 how to apply topological algebraic expressions to Landsat, Sentinel2A and climate time series data to compute vegetation indices and hydro-thermal coefficients. We demonstrate the big data analysis capabilities of the topology based algebra by computing the NDVI from 100 Sentinel2A scenes using the tools that we developed on a many core computer system and a distributed docker container environment. Framework [6] to formulate algebraic expressions with spatio-temporal topological operators
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