Matthew Handy

Interoperable Infrastructure for Flood Monitoring: SensorWeb, Grid and Cloud

The paper presents an international multi-disciplinary initiative, a Namibia SensorWeb Pilot Project, that was created as a testbed for evaluating and prototyping key technologies for rapid acquisition and distribution of data products for decision support systems to monitor floods. Those key technologies include SensorWebs, Grids and Computation Clouds. This pilot project aims at developing an operational trans-boundary flood management decision support system for the Southern African region to provide useful flood and water-borne disease forecasting tools for local decision makers. This effort integrates space-based and ground sensor data along with higher level geospatial data products to enable risk assessment and ultimately risk maps related to flood disaster management and water-related disease management. We present an overall architecture of the Pilot along with components and services being developed. Additionally, case-studies and results achieved so far are discussed. The presented work is being carried out within GEO 2009-2011 Work Plan as CEOS WGISS contribution.

Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot

The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of a variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with an open messaging architecture and web services. SensorWebs provide easier access to sensor data, automated data product production and rapid data product delivery. Disasters are the perfect arena to test SensorWeb functionality since emergency workers and managers need easy and rapid access to satellite, airborne and in-situ sensor data as decision support tools. The Namibia Early Flood Warning SensorWeb pilot project was established to experiment with various aspects of sensor interoperability and SensorWeb functionality. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team began to examine how to measure economic impact of SensorWeb technology infusion. This paper describes the architecture and software components that were developed along with performance improvements that were experienced. Also, problems and challenges that were encountered are described along with a vision for future enhancements to mitigate some of the problems.

The Namibia Early Flood Warning System, a CEOS pilot project

This paper describes a pilot project effort under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS) /Working Group on Information Systems and Services (WGISS) and originally moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during the past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to ongoing in 2012. The pilot effort has been fostered by CEOS to facilitate international efforts to promote satellite sensor data interoperability. In particular, the group has been making use of a technology effort call SensorWeb being developed at NASA which leverages Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to facilitate various satellite and ground sensor interoperability. The group has made use of such satellites such as Earth Observing 1, Terra/Aqua MODIS and the Canadian Space Agency (CSA) Radarsat together with various ground sensors such as river gauges in Namibia and models such as Global Disaster Alert and Coordination System (GDACS) from Joint Research Center (JRC) from the European Commission. Finally, the group has been experimenting with integrating a large Cloud Computing service provided by the Open Cloud Consortium (OCC) with the SensorWeb to provide management and distribution of the large data sets for emergency workers.

Hydrological Modeling and Capacity Building in the Republic of Namibia

AbstractThe Republic of Namibia, located along the arid and semiarid coast of southwest Africa, is highly dependent on reliable forecasts of surface and groundwater storage and fluxes. Since 2009, the University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA) have engaged in a series of exercises with the Namibian Ministry of Agriculture, Water, and Forestry to build the capacity to improve the water information available to local decision-makers. These activities have included the calibration and implementation of NASA and OU’s jointly developed Coupled Routing and Excess Storage (CREST) hydrological model as well as the Ensemble Framework for Flash Flood Forecasting (EF5). Hydrological model output is used to produce forecasts of river stage height, discharge, and soil moisture.To enable broad access to this suite of environmental decision support information, a website, the Namibia Flood Dashboard, hosted on the infrastructure of the Open Science Data Cloud, has been developed...

Towards a sensor web architecture for Disaster management: Insights from the Namibia flood pilot

The Group on Earth Observations, GEO, has identified the need to improve disaster risk management by providing timely information relevant to the full disaster management cycle of mitigation, preparedness/warning, response and recovery. The Committee on Earth Observing Satellites, CEOS, as the satellite arm of GEO, has recognized the important role that remote sensing contributes to all phases of the disaster management cycle. Activities to address the satellite information needs and gap analysis for disaster management systems are ongoing. This paper reports on results from two such activities, the southern Africa Flood and Health Pilot addressing annual floods in Namibia, and the GEOSS Architecture for Disasters analysis to enhance the use of satellite data. Direct interaction with Namibian hydrologists to experiment with satellite and in situ data products has helped inform the disasters architecture, providing lessons learned and best practices for the GEO societal benefit areas.

The Matsu Wheel: a reanalysis framework for Earth satellite imagery in data commons

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery and for detecting fires and floods to help support natural disaster detection and relief. We describe a framework for efficient analysis and reanalysis of large amounts of data called the Matsu “Wheel” and the analytics used to process hyperspectral data produced daily by NASA’s Earth Observing-1 (EO-1) satellite. The wheel is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most, of the data in a database or data repository. In contrast, standard queries typically process a relatively small percentage of the data. The wheel is a framework in which multiple scanning queries are grouped together and processed in turn, over chunks of data from the database or repository. Over time, the framework brings all data to each group of scanning queries. With this approach, contention and the overall time to process all scanning queries can be reduced. We describe our Wheel analytics, including an anomaly detector for rare spectral signatures or anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. The resultant products of the analytics are made accessible through an API for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.