Governmental agencies provide a large and open set of satellite imagery that can be used to track changes in geographic features over time. The current available analysis methods are complex and they are very demanding in terms of computing capabilities. Hence, scientist cannot reproduce analytic results because of lack of computing infrastructure. Therefore, we propose a combination of streaming and map-reduce for analysis of time series data. We tested our proposal by applying the break detection algorithm BFAST to MODIS imagery. Then, we evaluated computing performance and requirements quality attributes. Our results revealed that the combination between Hadoop and R can handle complex analysis of remote sensing time series.
The answers to planetary problems could be hidden in gigabytes of satellite imagery from the last 40 years. Unfortunately, scientists lack the means for processing such amount of data as they are used to work over small quantities of satellite images. To amend this issue, we propose the use of web services from Big Earth data platforms along collaborative analysis environments. Both Web services and collaborative analysis environments fit the hypothesis-test workflow followed by researchers while writing analysis routines. Besides, the early use of Big Earth data structures eases the subsequent process of scaling analysis up to larger extensions. To test our proposal, we use our own Big Earth observation data platform, on which decades of satellite images are arranged into data cubes. By using our Web services platform, we integrate those data cubes into our collaborative analysis environment (a Jupyter notebook). Since our analysis routines consume the same data structure of the whole data sets, it is easier to scale up the analysis.
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