H32B-01
The National Ecological Observatory Network
The National Ecological Observatory Network (NEON) is a research platform designed to advance understanding of how ecosystems and organisms respond to variations in climate and changes in land use. NEON is the first long-term ecological observatory conceived as a continental-scale network; equipped with standardized sensors, cyberinfrastructure, and data-collection protocols across the network; and designed to simultaneously address a common set of research questions and support investigator-driven ecological research in all regions of the United States. The Observatory focuses on variations in climate and land use because they are primary drivers of the Nation's environmental challenges, as identified by the National Research Council--i.e., biodiversity, biogeochemical cycles, climate change, hydroecology, infectious disease, invasive species, and land use. At the broadest scale, NEON links the complexity of climate variation to the behavior of ecological systems, a core aspect of ecological complexity. At the same time, because of the complexity of the interactions among humans and ecosystems, the network design includes NEON sites in wild, managed and urban systems within climate domains. Observatory data will also be part of a national education program designed to advance ecological science literacy through new programs and activities that develop and promote scientific ways of thinking.
H32B-02
Building an End-to-end System for Long Term Soil Monitoring
We have developed and deployed an experimental soil monitoring system in an urban forest. Wireless sensor nodes collect data on soil temperature, soil moisture, air temperature, and light. Data are uploaded into a SQL Server database, where they are calibrated and reorganized into an OLAP data cube. The data are accessible on-line using a web services interface with various visual tools. Our prototype system of ten nodes has been live since Sep 2005, and in 5 months of operation over 6 million measurements have been collected. At a high level, our experiment was a success: we detected variations in soil condition corresponding to topography and external environmental parameters as expected. However, we encountered a number of challenging technical problems: need for low-level programming at multiple levels, calibration across space and time, and cross- reference of measurements with external sources. Based upon the experience with this system we are now deploying 200 mode nodes with close to a thousand sensors spread over multiple sites in the context of the Baltimore Ecosystem Study LTER. www
<a href='http://www.lifeunderyourfeet.org'>http://www.lifeunderyourfeet.org</a>
H32B-03
The Information Super Seaway: Networking the Seafloor for Interactive Scientific Discovery
Ship-based expeditionary research and satellite observations have provided basic descriptions of ocean processes and their interactions with terrestrial and atmospheric systems. Many critical processes, however, occur at temporal and spatial scales that cannot be effectively sampled or studied with these traditional techniques. Ship-based studies are particularly limited in their ability to investigate the onset and immediate aftermath of episodic events and non-linear processes. Enabled by technological advances and made timely by societal need, a wide range of ocean and earth observing systems are being planned, proposed, deployed and operated within the U.S. These systems will utilize real-time datasets for event detection and adaptive sampling, well-sampled spatial and temporal contexts for limited duration or process-study experiments, and sustained observations to observe long-term trends and capture rare episodic events. Recent developments in sensor technology, cyberinfrastructure, and modeling capabilities will enable scientists to consider an entirely new set of interdisciplinary science questions. In response to the need for long term in situ oceanographic data, the U.S. National Science Foundation has established the Ocean Research Interactive Observatory Networks (ORION) Program within which the Ocean Observatories Initiative (OOI) will provide the essential infrastructure to address high priority science questions outlined in the OOI Science Plan. This infrastructure will utilize electro-optical cables and moored buoys to enable real-time, high bandwidth transmissions of scientific data and images from key scientific sites in the coastal and open ocean. The OOI is an integrated observatory with three elements: 1) a regional cabled network consisting of interconnected sites on the seafloor spanning several geological and oceanographic features and processes, 2) relocatable deep-sea buoys that could also be deployed in harsh environments such as the Southern Ocean, and 3) new construction or enhancements to existing facilities leading to an expanded network of coastal observatories. The ORION Program will coordinate the science driving the construction of this research observing network as well as the operation and maintenance of the infrastructure; development of instrumentation and mobile platforms and their incorporation into the observatory network; and planning, coordination, and implementation of educational and public outreach activities. A critical integrating element of the seafloor observatory network will be a robust cyberinfrastructure system that can collect large volumes of heterogeneous data. This system is being developed to collect, manage, archive and distribute data; have mechanisms and protocols for rapid data transmission; have protocols for two-way communication with sensors and dynamic control of sensor networks; have access to remote computing resources for processing and visualization of data; have software tools for analysis of multidisciplinary, spatially extended, intermittent datasets; have knowledge representation software to ensure that these data are easily accessible and effortlessly shared across disciplines; have integrity between communications and control systems and data management and archiving systems; and have automated data quality control. The ORION Program will be the most complex initiative that ocean scientists have undertaken within the U.S. and will revolutionize the way that oceanographers study the sea.
H32B-04
Identification of Atmospheric Events Using Data Mining and Observations From an Atmospheric Monitoring Cyberinfrastructure
As part of the National Science Foundation's Collaborative Large-scale Engineering Analysis Network for Environmental Research (CLEANER) initiative researchers at Arizona State University in connection with the Arizona Department of Environmental Quality have established an atmospheric monitoring cyberinfrastructure. One of the cyberinfrastructure's valuable constituents is the use of data mining algorithms to identify atmospheric events. Results show that the algorithms have the ability to accurately identify events of interest using routine observations. Before the data mining algorithms are used to identify specific atmospheric events they must be trained and tested. They are first trained using historical data. Events are carefully identified within historical data and used by the algorithms in learning to recognize data patterns. For atmospheric events the patterns include changes in wind speed, wind direction, temperature, barometric pressure, and relative humidity. Training is conducted for each instrument being used. Once training of the algorithm is completed it is tested against more recent observations to check its accuracy. In the present case, the atmospheric events being analyzed are evening transition fronts -- mixing events that initiate katabatic flow down gradual mountain slopes. Observations received via the cyberinfrastructure are scanned for evidence of the event using the data mining algorithms and then labeled accordingly. A good agreement is shown to exist between actual event occurrence and identification of events through data mining. The labeled data are assisting researchers in identifying the events, thus reducing the time required for data analysis. Eventually, this will allow events with complicated signatures to be automatically identified. Data mining as a means of identifying atmospheric phenomena will one day assist weather prediction models in the automation of severe weather warning systems as well as lead to the design of more intelligent sensors.
H32B-05 INVITED
CUAHSI's Hydrologic Measurement Facility: Putting Advanced Tools in Scientists' Hands
Like related environmental sciences, the hydrologic sciences community has been defining environmental observatories and the support components necessary for their successful implementation, such as informatics (cyberinfrastructure) and instrumentation. Unlike programs, such as NEON and OOI, that have been pursuing large-scale capital funding through the Major Research Equipment program of the National Science Foundation, CUAHSI has been pursuing incremental development of observatories that has allowed us to pilot different parts of these support functions, namely Hydrologic Information Systems and a Hydrologic Measurement Facility (HMF), the subject of this paper. The approach has allowed us to gain greater specificity of the requirements for these facilities and their operational challenges. The HMF is developing the foundation to support innovative research across the breadth of the Hydrologic Community, including classic PI-driven projects as well as over 20 grass-roots observatories that have been developing over the past 2 years. HMF is organized around three basic areas: water cycle instrumentation, biogeochemistry and geophysics. Committees have been meeting to determined the most effective manner to deliver instrumentation, whether by special instrumentation packages proposed by host institutions; collaborative agreements with federal agencies; and contributions from industrial partners. These efforts are guided by the results of a community wide survey conducted in Nov-Dec 2005, and a series of ongoing workshops. The survey helped identify the types of equipment that will advance hydrological sciences and are often beyond the capabilities of individual PI's. Respondents to the survey indicated they were keen for HMF to focus on providing supported equipment such as atmospheric profilers like LIDAR, geophysical instrumentation ranging from airborne sensors to ground-penetrating radar, and field-deployed mass spectrophotometers. A recently signed agreement with the USGS will for the first time provide university researchers with rental access to hydrological equipment ranging from data loggers to advanced acoustic doppler current meters through USGS's Hydrologic Instrument Facility.
H32B-06
Integrated Hydrologic Science and Environmental Engineering Observatory: CLEANER's Vision for the WATERS Network
With increasing population and urban development, societies grow more and more concerned over balancing the need to maintain adequate water supplies with that of ensuring the quality of surface and groundwater resources. For example, multiple stressors such as overfishing, runoff of nutrients from agricultural fields and confined animal feeding lots, and pathogens in urban stormwater can often overwhelm a single water body. Mitigating just one of these problems often depends on understanding how it relates to others and how stressors can vary in temporal and spatial scales. Researchers are now in a position to answer questions about multiscale, spatiotemporally distributed hydrologic and environmental phenomena through the use of remote and embedded networked sensing technologies. It is now possible for data streaming from sensor networks to be integrated by a rich cyberinfrastructure encompassing the innovative computing, visualization, and information archiving strategies needed to cope with the anticipated onslaught of data, and to turn that data around in the form of real-time water quantity and quality forecasting. Recognizing this potential, NSF awarded \$2 million to a coalition of 12 institutions in July 2005 to establish the CLEANER Project Office (Collaborative Large-Scale Engineering Analysis Network for Environmental Research; http://cleaner.ncsa.uiuc.edu). Over the next two years the project office, in coordination with CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.; http://www.cuahsi.org), will work together to develop a plan for a WATer and Environmental Research Systems Network (WATERS Network), which is envisioned to be a collaborative scientific exploration and engineering analysis network, using high performance tools and infrastructure, to transform our scientific understanding of how water quantity, quality, and related earth system processes are affected by natural and human-induced changes to the environment. This presentation will give an overview of the draft CLEANER program plans for the WATERS Network and next steps.
H32B-07
Fine-Scale Environmental Sensor Networks for Water Quality Monitoring
Application of fine-scale environmental sensor networks for water quality provides data at the level necessary for analysis of cause and effect relationships between land use change and water quality. This study was conducted to develop technologies to allow intelligent data collection of soil moisture and water quality data at multiple locations throughout a stream network. Small networked computers were interfaced with a variety of sensors and data collection rates were intelligently varied by the computers based on environmental conditions. Two case studies of environmental sensor networks will be discussed. The first application used soil moisture sensors to monitor the change in soil moisture during rain events in an attempt to identify ephemeral stream channels in a forested environment. The second case study will describe the deployment of an environmental sensor network to monitor stream turbidity.