The HOTSAT multiplatform system for the analysis of infrared data from satellites provides a framework that allows the detection of volcanic hotspots and an output of their associated radiative power. This multiplatform system can operate on both Moderate Resolution Imaging Spectroradiometer and Spinning Enhanced Visible and Infrared Imager data. The new version of the system is now implemented on graphics processing units and its interface is available on the internet under restricted access conditions. Combining the estimation of time-varying discharge rates using HOTSAT with the MAGFLOW physics-based model to simulate lava flow paths resulted in the first operational system in which satellite observations drive the modelling of lava flow emplacement. This allows the timely definition of the parameters and maps essential for hazard assessment, including the propagation time of lava flows and the maximum run-out distance. The system was first used in an operational context during the paroxysmal episode at Mt Etna on 12–13 January 2011, when we produced real-time predictions of the areas likely to be inundated by lava flows while the eruption was still ongoing. This allowed key at-risk areas to be rapidly and appropriately identified.

HOTSAT: a multiplatform system for the thermal monitoring of volcanic activity using satellite data

With the fast development of remote sensing techniques, the volume of acquired data grows exponentially. This brings a big challenge to process massive remote sensing data. In the paper, an in-memory computing framework is proposed to address this problem. Here, Spark is an open-source distributed computing platform with Hadoop YARN as resource scheduler and HDFS as cloud storage system. On the Spark-based platform, data loaded into memory in the first iteration can be reused in the subsequent iterations. This mechanism makes Spark much suitable for running multi-iteration algorithms compared to MapReduce which has to load data in each iteration. The experiments are carried out on massive remote sensing data using multi-iteration singular value decomposition SVD algorithm. The results show that Spark-based SVD can obtain significantly faster computation timethan that by MapReduce, usually by one order of magnitude.

A Spark-Based Big Data Platform for Massive Remote Sensing Data Processing

RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each presentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive eruption onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.

Conclusion: recommendations and findings of the RED SEED working group

Image enhancement and edge-preserving denoising are relevant steps before classification or other postprocessing techniques for remote sensing images. However, multisensor array systems are able to simultaneously capture several low-resolution images from the same area on different wavelengths, forming a high spatial/spectral resolution image and raising a series of new challenges. In this paper, an open computing language based parallel implementation approach is presented for near real-time enhancement based on Bayesian maximum entropy (BME), as well as an edge-preserving denoising algorithm for remote sensing imagery, which uses the local linear Stein's unbiased risk estimate (LLSURE). BME was selected for its results on synthetic aperture radar image enhancement, whereas LLSURE has shown better noise removal properties than other commonly used methods. Within this context, image processing methods are algorithmically adapted via parallel computing techniques and efficiently implemented using CPUs and commodity graphics processing units (GPUs). Experimental results demonstrate the reduction of computational load of real-world image processing for near real-time GPU adapted implementation.

Enhancement and Edge-Preserving Denoising: An OpenCL-Based Approach for Remote Sensing Imagery

Gosat-2 tanso-cai-2 l2雲フラグ算出処理におけるgpu利用可能性の検討

Satellite image processing algorithms often offer a very high degree of parallelism (e.g., pixel-by-pixel processing) that make them optimal candidates for execution on high-performance parallel computing hardware such as modern graphic processing units (GPUs) and multicore CPUs with vector processing capabilities. By using the OpenCL computing standard, a single implementation of a parallel algorithm can be deployed on a wide range of hardware platforms. However, achieving the best performance on each individual platform may still require a custom implementation. We show some possible approaches to the optimization of satellite image processing algorithms on a range of different platforms, discussing the implementation in OpenCL of the classic Brightness Temperature Difference ash-cloud detection algorithm.

Ricardo Zanmar Sanchez

Papers

Optimizing Satellite Monitoring of Volcanic Areas Through GPUs and Multi-Core CPUs Image Processing: An OpenCL Case Study