Abstract
Inquiry using data from remote Earth-observing platforms often confronts a straightforward but particularly thorny problem: huge amounts of data, in ever-replenishing supplies, are available to support inquiry, but scientists’ agility in converting data into actionable information often struggles to keep pace with rapidly incoming streams of data that amass in expanding archival silos. Abstraction of those data is a convenient response, and many studies informed purely by remotely sensed data are by necessity limited to a small study area with a relatively few scenes of imagery, or they rely on larger mosaics of images at low resolution. As a result, it is often challenging to thread explanations across scales from the local to the global, even though doing so is often critical to the science under pursuit. Here, a solution is proposed, by exploiting Apache Spark, to implement parallel, in-memory image processing with ability to rapidly classify large volumes of multiscale remotely sensed images and to perform necessary analysis to detect changes on the time series. It shows that processing on three different scales of Landsat 8 data (up to ~107.4 GB, five-scene, time series image sets) can be accomplished in 1018 seconds on local cloud environment. Applying the same framework with slight parameter adjustments, it processed same coverage MODIS data in 54 seconds on commercial cloud platform. Theoretically, the proposed scheme can handle all forms of remote sensing imagery commonly used in the Earth and environmental sciences, requiring only minor adjustments in parameterization of the computing jobs to adjust to the data. The authors suggest that the “Spark sensing” approach could provide the flexibility, extensibility, and accessibility necessary to keep inquiry in the Earth and environmental sciences at pace with developments in data provision.
Highlights
Data provided by remote sensing have long presented as a critical resource in monitoring, measuring, and explaining natural and physical phenomena
For the advances in the sensing capabilities of remote, Earth-observing platforms have continued to produce more and more data, with increasing observational breadth and finesse of detail. These developments carry a dual benefit and problem: analysis and inquiry in the environmental and Earth sciences are routinely awash with data and often struggle to match pace in building empirical knowledge from those data because the data are incoming with such haste and heft
The first experiment is performed on a 20-node yet another resource negotiator (YARN) computing cluster
Summary
Data provided by remote sensing have long presented as a critical resource in monitoring, measuring, and explaining natural and physical phenomena. For the advances in the sensing capabilities of remote, Earth-observing platforms have continued to produce more and more data, with increasing observational breadth and finesse of detail. These developments carry a dual benefit and problem: analysis and inquiry in the environmental and Earth sciences are routinely awash with data and often struggle to match pace in building empirical knowledge from those data because the data are incoming with such haste and heft. Studies with pure remotely sensed data involved only a few scenes of data in a limited study area, or they rely on Journal of Sensors low-resolution remotely sensed images in large-area experiments [5]. Processing the massive volume of remotely sensed data is not the only problem: the intrinsic complexity of those data is an important issue that must be considered
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