A spatial data infrastructure (SDI) is a framework of spatial data, metadata, tools, and a user community that are interactively connected so that spatial data can be used in an efficient and flexible way (Nebert, 2004). Spatial data and metadata are distributed, accessed, and exploited with software tools and services over computer networks. To achieve a well functioning SDI, it is necessary to define standards and to have good coordination between all actors; because an SDI is large (in size, cost, and number of interactors) and is usually government-related. An example of an existing SDI is the U.S. National Spatial Data Infrastructure (NSDI). Infrastructure for Spatial Information in the European Community (INSPIRE) is a European Commission initiative to build a European SDI beyond national boundaries, and the UN Spatial Data Infrastructure plans to create the same type of SDI for over 30 UN funds, programs, specialized agencies, and member countries (United Nations, 2008). INSPIRE sets out a framework and timetable that obliges public-sector organizations to publish key spatial datasets in ways that not only support the discovery of the data but provide access to them through visualization and downloading services. This chapter examines how an SDI can facilitate the collaboration of engineers, scientists, policy-makers, and community groups working across disciplines, on temporal and spatial scales, and in different types of geologic surfaces and subsurfaces.
Science agencies such as the U.S. Geological Survey (USGS) are tasked with providing reliable scientific information to support scientists, researchers, policy-makers, and the general public in making informed decisions on Earth science and natural resource issues facing the nation. As society demands action and
responsiveness to growing environmental issues, new process-based solutions grounded in scientific research are needed to generate the knowledge necessary to inform practical decisions. Addressing those challenges requires the synthesis of data and model projections that may routinely span length scales (from micro to global) and time scales (from a few tens of milliseconds to millennia). An SDI for Earth system science makes use of tools for data creation, curation, analysis, and archiving and leverages the Web as a platform for collection, analysis, reporting, and publication.
Understanding large-scale human-stressed environments over long time periods requires observing multiple variables on regional scales. This includes monitoring and measuring how the characteristics and functioning of environmental systems change and determining the cause and extent of such change. The resulting knowledge can lead to improved predictive models that inform decisions about more effective adaptive management policies and practices. Developing these predictive models requires not only establishing new environmental sensor networks, but also integrating data from existing sources and available sensors to provide high-resolution and integrated data. It requires a cyberinfrastructure capable of collecting, managing, and using integrated geospatial datasets. Having a robust data infrastructure would facilitate research investigations aimed at improving understanding of interacting environmental system processes.
The tools needed to conduct science and inform policy-making are changing. Meeting many of the challenges faced by the USGS Science Strategy (USGS, 2007) requires information from the basic sciences, but they also require new scientific approaches that focus on integrating physical, biogeochemical, engineering, and human processes. This cultural and organizational shift becomes more challenging as science becomes more computational and data-intensive. In this new research environment, scientific data are captured by instruments or generated by simulations and then processed by software into models that can be examined by scientists, policy-makers, and the public using the Web. As the Earth becomes increasingly instrumented with interconnected, low-cost, high-bandwidth sensors that are linked through the Web, scientists will be in a better position to sense the environment and predict possible environmental outcomes.
The USGS has multiple disciplinary data infrastructures, but recent efforts to develop a science SDI at the USGS have been conducted largely under the umbrella of The National Map (TNM). TNM is a collaborative effort with the USGS and federal, state, tribal, and local partners to create “a database of continuously maintained base geographic information for the United States and its territories that will serve as the Nation’s topographic map for the 21st century” (USGS, 2001). TNM provides a common set of base information for use by public and private stakeholders. The 2007 National Research Council report
A Research Agenda for Geographic Information Science at the United States Geological Survey recommended that the focus of the USGS Center of Excellence for Geographic Information Science be on TNM, with key extensions into information access and dissemination, integration of data from multiple sources, and data models and knowledge organization systems (NRC, 2007).
In creating and implementing TNM, the USGS is targeting improvements in data characteristics, such as currency, seamlessness, consistent classification and formatting, variable resolution, completeness, variable positional accuracy, spatial reference systems, standardized content, metadata, and temporal dimensions (USGS, 2001; Cramer and DeMulder, 2009). The data themes in TNM are orthoimagery, elevation, hydrography, geographic names, land cover, transportation, structures, boundaries of government units (such as states and counties), and publicly owned lands (such as national forests and state parks). These data themes were chosen to fulfill a gap and for use for the USGS topographic maps, therefore there are plans to retain data characteristics that are more useful to users of the USGS topographic maps, such as consistent feature identification and classification (NRC, 2007).
The USGS offers several methods for accessing data in TNM. For users seeking to view a data map, a map viewer is available (see http://viewer.nationalmap.gov/viewer) and provides basic Geographic Information System-type (GIS) query and analysis tools. Users can also retrieve national coverage data through interactive and preprocessed methods that can be accessed online, via the map viewer, or physical media. For users that create their own map viewer and that need access to tools and inventoried services, the USGS offers program applications and an online catalog of metadata entries that can be discovered and “harvested” into the Geospatial One-Stop portal. The USGS also has service-level agreements with agencies to provide more advanced Web-based access to national databases.
Perhaps the most fundamental change in TNM approach is the transition from reliance on internal USGS resources for collecting new data to reliance on partners for providing new data (NRC, 2007). These USGS partnerships involve a value-based exchange: In exchange for partners’ data, the USGS provides funding, expertise, data, data models, data-collection software tools, information technology, Web and other data-management services, access to contracts, and access to related management and quality-assurance processes.
TNM resides in a large environment that includes electronic mapping products and services provided by the government, academe, and private industry. In the USGS, there are multiple datasets (see Box 2.1) that could eventually be fed into a larger USGS SDI. In the private sector, the emergence of commercial products, such as Google Earth and Microsoft Bing Maps, has captured the interest of the public and professional users. To remain relevant, the 2007 NRC report A Research Agenda for Geographic Information Science at the United States Geological Survey states that the TNM “must be a trusted [emphasis added] geospatial information source for all of these constituencies,” and that “the mea-
Examples of USGS Spatial Datasets
The following are examples of the various types of spatial datasets maintained by the USGS. It should be noted that this is not a comprehensive list of datasets required to support the USGS Science Strategy, although many of the datasets listed are useful to several USGS Science Strategies.
National Land-Cover Dataset — A 21-class land-cover classification scheme that includes urban, agricultural, rangeland, forest, surface-water, wetlands, barren-lands, tundra, and perennial ice and snow classes.
National Orthoimagery Dataset — Data that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a planimetric map.
National Elevation Dataset — 10-meter and 30-meter digital elevation models and some higher of resolution derived from light detection and ranging (LIDAR) and Interferometric synthetic aperture radar (IFSAR).
National Transportation Dataset — Data on roads, ports, railroads, and other features associated with the transport of people or commerce.
National Boundaries Dataset — Data on major civil areas, including states, counties, federal, and Native American lands, and incorporated places, such as cities and towns.
National Structures Dataset — Data on selected structures, including locations and characteristics (such as physical form, function, name, location) of man-made structures.
Geographic Names Information System — Federally recognized names of physical and cultural geographic features (excluding roads and highways) in the United States and their locations by state, county, USGS topographic map, and geographic coordinates.
The National Hydrography Dataset — Data on surface waters of the United States, such as lakes, ponds, streams, rivers, canals, and oceans.
Watershed Boundary Dataset — Data on hydrologic units that establish a baseline drainage boundary framework, accounting for all land and surface areas in the United States.
sure of success for TNM will be the extent to which the diverse users embrace and depend on the product” (p. 36, NRC, 2007). However, as previously stated in another previous NRC report Weaving a National Map: A Review of the U.S. Geological Survey Concept of the National Map, it is impossible to be all things to all users at the outset (NRC, 2003). TNM can best serve its users by first focus-
Additional Attributes for The National Map
The 2007 National Research Council report A Research Agenda for Geographic Information Science at the United States Geological Survey provides a vision for the next generation of The National Map (TNM). In addition to including the existing features, TNM would consist of the following additional attributes:
• an authoritative geographic knowledge base of topographic features based on a geographic feature ontology,
• a comprehensive database of official geographic feature names, and local, regional, and historic variants in TNM gazetteer,
• an enhanced spatial-temporal integration framework for organizing and synchronizing with other USGS data collections,
• a geographic semantic reference system,
• multiple levels of spatial detail,
• feature histories (for spatial locations, attributes, and names),
• user-supported local validation,
• flexible product generation (for example, responding to fact queries, process-model data packages, maps on demand, and traditional topographic maps), and
• smart adjustment of maps or other visual display settings for different devices.
ing on high-impact research areas and by identifying what differentiates TNM in the crowded geospatial-product field.
As indicated by the 2007 National Research Council study, a successful TNM requires a high level of quality, accuracy, national coverage, standardization, and continuous updates (see Box 2.2 for additional attributes of TNM).
As the largest ongoing effort to develop a science SDI, TNM will likely play a key role in underpinning the future USGS SDI and the committee in no way envisions the TNM and the SDI to be mutually exclusive. TNM has the potential to be the go-to resource for the USGS and other federal, state, and local agencies across the nation. This report provides a roadmap to guide the USGS as it decides whether to expand, adapt, or subsume TNM in service of the agency-wide SDI (see Chapter 5).
The 2007 report Facing Tomorrow’s Challenges—U.S. Geological Survey Science in the Decade 2007–2017 (USGS, 2007) outlines a strategic scientific approach for the USGS SDI. It is a watershed report by the USGS that guides its direction in its Science Strategy. The USGS Science Strategy breaks away from the conventional approach of organizing strategically by discipline and instead
focuses on key future science-based challenges. A private-sector analogy would be a company organizing around markets and customers rather than according to products and technologies. The focus on the users of an organization’s products and services can be highly motivational and empowering for its employees. The USGS science-based challenges are categorized into six strategic areas: ecosystems, energy and minerals, climate and land-use change, environmental health, water, and natural hazards. The USGS Science Strategy report identifies the science needs that the USGS SDI will have to address, and the committee concurs with the recommendations provided in that report for addressing the needs.
Throughout the USGS Science Strategy report, references are made to the need for geospatial data and, by extension, the need for an effective SDI. An effective SDI will be essential to the success in each of the six science directions. Many of the characteristics of an SDI are already outlined in the USGS Science Strategy report, such as providing a framework for interactively connecting data users, open standards, and the ability to integrate data from environmental sensor networks and land imaging with spatial modeling capabilities (USGS, 2007). This committee’s report attempts to further outline an SDI roadmap that cuts across the six strategic science directions to show the need for and value of spatial data, and to discuss how a well-designed SDI can benefit each strategy.
A key element of the USGS Science Strategy is “Understanding Ecosystems and Predicting Ecosystem Change” (USGS, 2007). The plan recognizes that ecosystems are multi-scalar in space and time, and that information-analysis tools are needed that can accommodate analysis of ecosystems on multiple scales and incorporate modeling tools. Analysis of ecosystems is thematically considered in various ways, from a geographic point of view as a unit of study and in the context of ecosystem services that are related to the structure and function of ecosystems on diverse space and time scales. A fundamental framework is needed for the Survey to measure, map, understand, monitor, predict, and engage in relevant issues, and a geospatial framework for the analysis of ecosystems will be important to advance the Science Strategy. The geospatial framework can be used to integrate various sets of information for informing ecosystem models and for analysis of interactions between biophysical and societal effects on ecosystems.
The Science Strategy calls for a coordinated effort to produce a scientifically rigorous map of national ecosystems on scales that have meaning for land managers and for understanding interactions between, biophysical, anthropogenic, and biological processes that can be used in the prediction of ecosystem change. The USGS and organizations such as the Environmental Protection Agency (EPA) and the National Ecological Observatory Network (NEON) have already developed versions of ecosystem maps and these can provide a foundation for developing a suite of ecosystem layers within the SDI. A geospatial platform will directly
aid in the study of effects of land-use changes on ecosystem dynamics and in the prediction of ecosystem change that may affect ecosystem services.
Energy and Minerals
Science and information on energy and mineral resources underpin private and public decisions that determine resource availability, costs, and conditions for producers and consumers. Scientific research and information collection and dissemination benefit society through four federal roles in energy and mineral resources: (1) unbiased national source of science and information, (2) basic research, (3) advisory functions, and (4) international compilation. The emergence of a global economy affects the demand for all resources, and the use of natural resources is occurring on a scale that may modify terrestrial, marine, and atmospheric environments. The use of and competition for natural resources on a global scale and the natural threats to these resources have the potential to harm the nation’s ability to sustain its economy, national security, quality of life, and natural environment.
Under the USGS Science Strategy, the energy and minerals direction is sufficiently broad to deal with resource availability and related land, water, and environmental concerns. Linking the USGS energy and minerals mission area to the USGS ecosystems, water, and climate and land-use change mission areas provides a scientific foundation for resource security, environmental health, economic vitality, and resource management.
The major challenge with respect to energy and minerals is to provide a scientific foundation for resource security, environmental health, economic vitality, and stewardship of the nation’s lands. This challenge is complicated by the fact that the appropriate response will need to address evolving U.S. domestic and global priorities and a broad, diverse user base. Responding to national priorities and global trends requires a strategy that builds on existing USGS strengths and partnerships and that advances unprecedented innovations that will be made possible only if they are effective in integrating USGS digital data resources, data viewing, and hypothesis testing. An important challenge is to create user interfaces that do more than view single datasets, instead providing overlaid comparative datasets and map features that support analysis that can lead to new scientific interpretations.
The National Geologic Map Database (NGMDB)1 effectively locates USGS publications that pertain to energy and minerals, including those on geology (bedrock and surficial), geophysics (magnetic, gravity, and radiometric), and resources (metals, nonmetals, petroleum, and coal). However, the value of this search capability could be enhanced with search capabilities that span a broad sequence of steps involved in scientific investigation and data integration. Inte-
grating the NGMDB with an SDI offers much potential, but much work remains to develop and implement the new infrastructure before it can take on additional capabilities to serve as a much needed multidisciplinary 3-D GIS database.
Climate and Land-Use Change
With regard to climate variability and change, the USGS brings to bear an impressive array of capabilities for research, monitoring, modeling, and assessment on various relevant topics, including land and ecosystem dynamics, hydroclimatology, coastal processes, and biogeochemical cycles. The USGS climate and land-use change strategy underscores the key role that the USGS can play in helping the nation to “understand and prepare for climate change and its effects” (USGS, 2007). It identifies three critical subjects for focused efforts: monitoring, research, and assessment.
Monitoring activities build on the USGS’s long and distinguished record of environmental observations in key arenas, such as land-use and land-cover change, species distribution, hydrology, glaciology, and geochemistry. Proposed strategic initiatives include development of a national phenology (plant and animal cycles) network, a baseline soils database, and a collection of altitudinal transects to study population dynamics and adaptation of life cycles in vulnerable environments across the United States. In the research arena, the USGS brings to bear strong capabilities in analysis and modeling of the terrestrial carbon cycle, climate–land use–ecosystem interactions, hydrologic effects of climate variability and change, and climatic effects on ecosystem health. Monitoring and research together form a key basis for assessment, which encompasses the development of simulation models and predictive tools that can support understanding and decision-making about climate changes and their effects on spatial and temporal scales. The USGS climate strategy recognizes the imperative to integrate multi-disciplinary research in developing complex models and assessing feedbacks and linkages between land, water, biological, ecological, and human systems.
The strategic actions related to climate variability and change proposed by the USGS depend on the development and use of geospatial data and models, which would benefit from an enhanced SDI. For example, modernizing existing USGS observing networks and developing new capabilities integrated with those operated by other federal agencies will require the implementation of state-of-the-art geospatial technologies, interoperability standards, and data-management processes and procedures. The ability to identify, evaluate, assess, and predict environmental and natural resource responses to climate change would be enhanced by ready access to harmonized geospatial data (i.e. the combination of common data components) from different disciplines and observational networks that combine historical and baseline data with current observations and by model simulations and forecasts. Analysis and reporting of climate trends and effects— and effective support of decision-making—require the ability to aggregate and
disaggregate data on different spatial scales and to provide geospatial visualizations geared to the needs of decision-makers and other users.
In the face of climate variability and change, the USGS Science Strategy appropriately recognizes the important role of the USGS in working with the Bureau of Reclamation, the National Park Service, the Bureau of Land Management, and other agencies of the Department of the Interior to directly manage about one-fifth of the U.S. land resources. Land-related data and models are needed to demonstrate the impact of climate change on land areas. Integration and interoperability of land-related data and models would directly benefit land and resource managers and contribute to the overall adaptation and resilience of the United States to future climate change.
Similarly, close cooperation with other federal agencies, such as the Department of Energy and the U.S. Department of Agriculture, is needed in areas such as carbon capture and storage and soil management. The USGS also needs to coordinate with the international scientific community, as in the case of its research and observational efforts related to biogeochemical cycles, paleoclimate, and cryospheric processes and the USGS’s contribution to the Intergovernmental Panel on Climate Change process. All those efforts would benefit from the ability to build on and connect the USGS SDI to the broader NSDI and the emerging global SDI.
The USGS Science Strategy includes the goal of understanding the role of environment and wildlife in human health. Most USGS efforts in this arena have been on health problems caused by environmental contamination and emerging infectious diseases, and research has focused on public health threats that result from the relationship between people and the physical, chemical, and natural environments. The USGS specializes in research in vector-borne and zoonotic diseases, water contamination, airborne contaminants, bioaccumulative contaminants in the food chain, and environmental threats to public health. It does not, however, focus on health effects that result from the built environment, although the aforementioned research certainly involves these parts of the environment in addition to the natural environment.
The USGS efforts in environmental health can best be described as investigator-driven science, so research is dispersed throughout the agency. For the most part, this research is excellent and is published in top scientific journals, such as work on Lyme disease (Ginsberg et al., 1998, 2005) and methylmercury in ocean fish (Sunderland et al., 2007, 2009). One of the benefits of USGS’s research on human health is project longevity which often allows longitudinal health studies, compared with university-based health research studies that are typically 2-5 years for the project duration.
Even though high-quality research on the role of environment and wildlife
in human health is being conducted at the USGS, the decentralized structure creates substantial challenges. Funding for individual studies does not come through sources directly targeted to human health, so consistent funding has been a challenge for USGS researchers. A well-functioning SDI could assist USGS health researchers in this struggle by centralizing data access and pairing data with other relevant datasets. The lack of a centrally organized health research agenda makes it difficult to establish systems for efficiently supporting the sharing of spatial data within the USGS, and it is even more difficult to share with other federal agencies or researchers outside the federal government. Investigators do not have much incentive to make their datasets available, let alone discoverable (see discussion later in this chapter on “Incentives for Scientists”).
Organizing USGS research efforts around the Science Strategy on the role of environment and wildlife in human health will help to alleviate some of those challenges. In the last few years, the USGS has organized meetings to bring the research community together on human health issues as they are related to the environment and wildlife. The USGS convened meetings titled “Natural Science and Public Health: Prescription for a Better Environment” (USGS, 2003) and “The Second National Conference on USGS Health-Related Research” (Buxton et al., 2007); each had more than 60 paper presentations on the aforementioned USGS health-research topics that underscored the importance of spatial data. The purposes of the conferences were to enhance communication among federal agencies; to identify common science interests for leveraging science research, results, and resources; and to establish joint science investigations and cooperative partnerships to increase the use of USGS information by the public health community. The goals of expanding access to existing data and strengthening partnerships and collaborations are consistent with the principles underlying the USGS Science Strategy.
The USGS Science Strategy also includes a partial listing of USGS environmental health–related databases that could be incorporated into a science SDI. The USGS Science Strategy report specifically calls for developing an “online data atlas of potential environmental health threats” to improve the ability of the United States to respond quickly to health threats and for developing a national “environmental health information system” that integrates basic USGS scientific data with GIS decision-support tools (USGS, 2007). Administrative structures, incentives, and funding are some challenges that make it difficult to make the aforementioned goals a reality.
A goal of the USGS Science Strategy is to develop a National Water Census that includes quantifying, forecasting, and securing freshwater for America’s future. Equal emphasis is given to water quantity and water quality as related to water availability and to meeting both human and ecological needs in the context
of land-use change, climate change, and changing demands for water resources. Previous USGS studies and monitoring programs focused on human needs; the added dimension of quantifying ecological needs is new. The USGS is uniquely qualified to develop a National Water Census because of its unique capabilities with its diverse scientific staff and technicians (diverse geographically and in expertise) and its state-of-the-art groundwater models. The goal of the National Water Census is to inform the public on: (1) the status of and trends in freshwater resources, (2) water use for human, environmental, and wildlife needs, (3) relationship of freshwater availability to storage and transport in natural and engineered systems, water use, and related transfer, (4) identification of uncommon water sources, and (5) forecasts of effects of changes in land use and land cover, natural and engineered infrastructure, water use, and climate on water availability, including water quality, and aquatic ecosystem health (USGS, 2007).
The National Water Census will need to pull together several disparate data sources at multiple scales. The StreamStats application will provide water availability information on precipitation, runoff, baseflow, and trends. The USGS National Water Quality Assessment (NAWQA) Program will be used to assess the role of water quality in water availability. The detailed field studies throughout the nation conducted as part of the NAWQA Program will provide spatial data on natural and anthropogenic contaminants in aquifers. Relationships to geology and land use will be used to assess sources and mobilization mechanisms of contaminants. The intensification of the hydrologic cycle will likely result in longer-term droughts and more intense floods (USGCRP, 2009), which could further burden already stressed water systems. Quantifying the role of groundwater in water availability will require spatial and temporal information on recharge, groundwater yields, changes in storage, saltwater intrusion, trends in groundwater indexes, groundwater demand, and groundwater–surface water interactions. The National Water Census will rely primarily on the Regional Groundwater Availability Studies for much of that information. Data on water withdrawals will be obtained from the National Water Use Information program. Satellite data on evapotranspiration could also be used to estimate water demand in irrigated regions. Information on the geohydrologic framework of aquifers will be obtained from the National Cooperative Geologic Mapping Program.
Because water is inherently multidisciplinary, a well-designed SDI is essential for the success of the water census, connecting climate forcing with detailed measurements and monitoring of surface and subsurface hydrology. Linking satellite data, including evapotranspiration maps and water storage changes from the Gravity Recovery and Climate Experiment with ground-based measurements of water storage and demand, will be important for ground referencing satellite-based estimates. Because the water census will rely heavily on datasets produced by many state programs and will also rely upon water infrastructure data from the U.S. Environmental Protection Agency, an open framework SDI, one that allows individual components to be easily added or replaced, will be helpful for data-
sharing. An SDI that allows discovery and provides detailed metadata information will be valuable because water is of general interest to the public in addition to providing essential research data for various studies. Because of the potentially large number of groups providing data to the census, common standards and tools will be essential for the success of the National Water Census.
The USGS is involved in every part of the disaster cycle from scientific study to disaster response. It is charged with identifying, understanding, monitoring, and mapping hazards. It is an active participant in preparation for, response to, and recovery from disasters that occur when humans and cultural artifacts are exposed to a hazardous event. USGS scientists study the geophysical phenomena that lead to volcanic eruptions, earthquakes, landslides, tsunamis, floods, coastal inundation, droughts, and other natural disasters. The USGS is also responsible for the International Charter on Space and Major Disasters for the United States and in the event of a disaster can activate the International Charter to direct assets to the disaster.
The USGS operates and participates in various monitoring networks, such as seismic and stream monitoring networks, which are fundamental to hazards-related work. Data produced by those networks are necessary for both early warnings and pre- and post-disaster assessments, and they are often the primary data used in fundamental and applied research. The data are used operationally for mapping hazard zones, which are used by (1) communities for planning, (2) responders for identifying emergency routes and the potential severity of an event, (3) industry for siting facilities and infrastructure, (4) insurance companies for determining risk, and (5) private individuals for housing and other decisions. Information generated from the monitoring networks are used directly in the science and disaster management communities, where they are combined with additional data, information, and models to advance knowledge and provide valuable, usually critical information to those who need it.
The USGS provides geospatial information in response to domestic disasters and provides assistance whenever possible in response to international disasters (see Box 2.3). For example, in anticipation of Hurricane Katrina, the Survey was on site with hard-copy maps, digital data, analytical tools, and computer hardware 3 days before the hurricane made landfall. In its continuing effort to ensure maximum access to needed data and information, USGS scientists and technicians work with the National Geospatial-Intelligence Agency (NGA) to ensure that software is prepared to facilitate the integration of NGA data with data from USGS and other civilian agencies. In the international arena, USGS is the lead agency for the International Charter to provide remote sensing data to nations that request them during times of disaster. It was a major asset in response to the May 12, 2008, earthquake in Sichuan, China, when USGS worked on a team
Prompt Assessment of Global Earthquakes for Response
Prompt Assessment of Global Earthquakes for Response (PAGER) is an automated system that quickly estimates the number of cities and people worldwide exposed to severe shaking after significant earthquakes. PAGER began operation on March 28, 2005.
While PAGER was still in the design and testing phase, a major aftershock of the 2004 Sumatra–Andaman earthquake occurred about 300 kilometers to the southeast of the original quake. Massive devastation took place on the island of Nias, major damage on the island of Simuelue, and damage on other islands. A 4-meter tsunami hit parts of the coast of Sumatra. Information from the then experimental PAGER program was used to identify where the most affected populations would be. The earthquake occurred at 11:09 p.m., and rescue workers worked through the night to prepare flight plans for rescue helicopters. The PAGER data were transmitted to the flight planners so that pilots could reach the most at-risk populations. The rapid and effective transmission of the PAGER data allowed the launch of an efficient rescue mission, a much more successful one than would otherwise have been possible. PAGER has since become an important operational component in the global disaster-response toolbox.
Source: Kelmelis et al., 2006.
Table 2-1 Major USGS Geophysical Monitoring Networks Central to Data Collection, Hazard Monitoring, and Event Warning
|Monitoring Network||Hazard Type|
Advanced National Seismic System
National Volcano Early Warning System
Stream gage (stable-core network)
Flood and drought
Marsh Surface Elevation Table Network
Light detection and ranging (LIDAR)
Land-cover change and coastal inundation
to broker access to government and commercial satellite data to assist with the response and recovery effort and worked with Chinese scientists to develop a new understanding of the fault zones and other geologic conditions in the earthquake area. Those types of situations require information that combines geographic data with a variety of geophysical data.
As one of its major future investments, the USGS Science Strategy describes the modernizing of major geophysical monitoring networks (see Table 2-1). It also discusses the importance of USGS’s taking advantage of new and emerging technologies for network communication, characterizing and assessing hazards, providing forecasts based on understanding of physical processes, and develop-
ing partnerships that will advance the state of the art of decision support systems and intelligent access to data. An important responsibility for the emerging SDI will be to support those advances while ensuring the continuation of traditional natural-hazard and disaster-management functions until the advances are online.
Creating a functioning SDI presents numerous technical challenges. As previously stated, SDIs are inherently complex because they are large and have multiple administrative and technical components—such as software and hardware, standards definition and adoption, and institutional agreements—on a wide variety of scales. The task of integrating and visualizing data generated on different spatial scales remains one of the grand technical challenges in spatial science (Goodchild, 2008). Adding the dimension of time to spatial datasets to enable spatial–temporal correlations remains a painstaking process because spatial data collected decades apart use different standards and equipment. Organizational challenges include training and developing partnerships across different disciplines in and outside the Survey. This section discusses the technical and organizational challenges in the context of a USGS SDI.
The challenges faced by the nation and identified in the USGS Science Strategy call for support of geospatial information on multiple spatial scales in a timely manner to address widely varied issues: national hazards and risks, human health, climate variability, ecosystem dynamics, water quality and quantity, and energy and mineral resources. Advances in geospatial computing and information technology are beginning to provide tools that can integrate information across multiple scales of space and time (such as www.geo.data.gov). The framework to work across multiple scales must provide consistency from one level to another and provide the ability to “telescope” down or up in scale depending on the need of the analysis. That is far from trivial and requires planning and coordination to work across two or more spatial datasets.
The ability to provide geospatial support across multiple spatial, elevational, and temporal scales (4-D analysis) will give researchers, decision-makers, and managers the ability to assess hazards and risks as they occur, to evaluate ecosystem and hydrologic changes across scales useful to wildlife managers and resource managers dealing with regional drought conditions, to track health risks due to mobile animal populations or more stationary water quality changes that result from mining effects, and to respond to storm surges. A robust and integrated SDI will provide a basis for those kinds of analyses to be conducted in a timely manner that is appropriate for researchers, decision-makers, and managers.
A major challenge for the USGS will be to garner spatial–temporal correlations for understanding and predicting ecosystem changes over space and time and to use such interdisciplinary information to inform decision-making. As documented in the USGS Science Strategy (USGS, 2007), existing databases and related maps in USGS programs are not functionally integrated, and this severely limits the use of science and spatial–temporal correlations for making informed decisions. Until scientific geospatial analysis protocols are established in the USGS and requisite application software tools are widely implemented, decision-making based on spatial–temporal correlations will remain tentative or will at best be successful on a limited scale.
Gaining scientific understanding, mapping, monitoring, modeling, and advising the Department of the Interior by using a systems approach to temporal and spatial change are central issues. A geospatial framework requires the integration of various sets of information for informing ecosystem models and for analyzing interactions between biophysical, anthropogenic, and biological processes to projecting ecosystem change. A scientific foundation is critical for effectively managing the use of energy and mineral resources, the environment, and lands. The geospatial platform will be an essential tool for gauging the effects of land-use changes on ecosystem dynamics, for monitoring the effects of climate change, and for predicting ecosystem change over time. Nation-wide datasets with consistent definitions are required to meet the challenges in spatial–temporal correlation. Topographic map coverage in raster, elevation, and land cover constitutes a starting point. Key datasets to add include multi-temporal (time-series) data for hydrography and data on invasive species for ecosystems analysis. Until geospatial science is fully integrated into all the USGS strategic directions, it will be difficult to address broader questions related to sustainability and how Earth’s surface will evolve in the Anthropocene—a new geologic period in which human influence dominates the Earth system (NRC, 2009).
High-resolution systematic data need to be made available in a large, seamless archive to support activities that detect changes over space and time. Emergency responders rely on a variety of spatial–temporal correlations for information, including hazards information based on detailed time-series analysis (such as coastline changes with severe storms). Also needed is rapid access to real-time and archived geospatially referenced data, including access to satellite imagery over time, airborne and ground-based light detection and ranging (LIDAR) land-surface images, airborne imagery photos, and forward-looking infrared (FLIR) thermal imaging camera data.
Historical and Baseline Data
Another function of a well-designed SDI will be to provide a coherent framework for collecting, organizing, accessing, and analyzing vast amounts of historical USGS data that are vital for understanding baseline conditions and for modeling and projecting future environmental behavior. Long-time series data are essential inputs into efforts to improve prediction of earthquakes, volcanoes, floods, and other hazards; to understand and predict climate variability and change; and to manage land, water, and ecological resources. Unique data collections, such as the Landsat and Corona data archives at the USGS EROS Data Center, provide invaluable information about land-cover and land-use changes over the last 50 years. Historical data are often essential for calibrating new instruments and monitoring activities and for developing reference or baseline datasets used to assess the effects of future trends or policy changes.
Historical data have often been stored in separate, disconnected databases and datasets and have often been collected and stored with different methods, standards, and tools. That has led to spatial and temporal discontinuities in data, unnecessary barriers to data integration, reduced data quality, and duplication of effort. Another important challenge is the large amount of historical data and documentation that has not yet been digitized and may be at risk of deterioration because it remains on physical media (such as paper, film, and microfiche).
An SDI would serve as a common foundation for historical and baseline data that would enable more flexible integration of different data types and facilitate discovery, access, and use of historical data in conjunction with new observational and monitoring efforts. It could also facilitate priority-setting among efforts to rescue datasets at risk by providing a synoptic view of the most important data gaps and needs across the USGS and partner agencies.
Multidisciplinary and Collaborative Activities
Environmental challenges today arise from complex interactions between multiple disciplines including human activities, ecosystems, and biophysical features, such as climate, land-use change, hydrologic dynamics, and disturbance regimes. Integration of information and analysis across those activities is necessary to ascertain emergent properties of environmental changes related to challenges being met in the USGS Science Strategy. For instance, evaluation of avian influenza needs to account not only for instances of outbreaks but for migration of bird populations, wetland locations, and settlement patterns if it is to accurately predict the emergence and spread of avian influenza. Similarly, issues related to hazards and risks rely not only on environmental factors but on patterns of transportation, settlements, waterways, topography, weather, and other factors that have particular spatial patterns that need to be integrated in assessment of risks posed by environmental conditions.
A well-known important challenge in moving an SDI forward is the cross-disciplinary collaborative ethos necessary in the USGS to develop and maintain an SDI that was previously lacking. In evidence and testimony provided to the committee, stovepipes and silos were terms used to describe USGS structure and operation although the recent USGS reorganization is a substantial step toward removing many of the silos. The USGS has an amalgam of scientific disciplines and areas of expertise. The data, processes, and cultures in the USGS are also diverse, and it is governed by agency-specific and domain-specific goals. Part of that discreteness could be explained by the presence of several factors: legitimate discipline differences, reluctance of scientists to engage outside their specialties, the competitive nature of science funding, and a natural tendency toward precision in one’s own field. Implementing an SDI will require effective collaboration among multiple disciplines and will require agreement on issues such as data standards, sharing, and maintenance. In addition, a well-implemented TNM would replace the focus from one-off base data acquisition with a comprehensive, nationally-consistent dataset, just as desired in an SDI. The USGS will also need to ensure that key datasets are discoverable and accessible over the long term.
Externally, the USGS has an extensive array of stakeholders to engage and collaborate with, including federal, state, tribal, local, international, academic, and public entities (see Box 2.4). It will be difficult for the USGS to achieve its strategic goals alone; it will need to partner with others to achieve them. Surveys of external users show that they use the spatial data that the USGS makes available (e.g. orthophotography, elevation, transportation) and would like data that is currently more difficult to find and access, such as the location of wells and springs (Sugarbaker et al, 2009).
Program vs. Project
System studies are often anchored by place-based studies in which greater detail can be extracted on a wide array of processes and components of a system, such as tower-flux studies, acid-deposition studies, and watershed studies. Those placed-based studies are often associated in a network of sites so that various environmental factors can be evaluated to understand how controlling variables affect, for instance, water quality, plant productivity, or carbon emissions. These typically one-off studies also can provide process understanding of system interactions when linked to other studies in a region or linked to similar studies in other locations or conducted at different times. However, the information collected in and shared between USGS programs and individual projects can vary considerably. For instance, satellites to assess land cover in the United States provide consistent, almost seamless, coverage of land cover across the nation. But a number of specialized land-cover databases have been developed by various groups or projects for special purposes to elucidate processes that feature specific characteristic or spatial patterns that are not captured by the national efforts.
External Users of USGS Spatial Data
The USGS has a wide variety of stakeholders that use spatial data. The examples below illustrate the needs of representative user groups.
Climate Modelers — The effort to downscale climate projections to the regional level requires models of regional land change that, in turn, require authoritative data on land use and cover, much of this derived from satellite imagery. These data are often combined with socio-economic data to project the impoacvt of policy decisions on land cover changes.
Coastal County Managers — Coastal managers are currently using many of the core data layers provided by the USGS, such as elevation and land use and cover. One of the greatest challenges to coastal managers, particularly in small and medium sized counties, is the lack of knowledge about what spatial data are available to them.
Natural Hazards — The combination of orthoimagery, transportation network, and elevation data provide a powerful tool for mitigating and responding to natural hazard events. However, these data are only useful together if they are truly integrated by sharing a common georeference framework and are updated regularly.
Sources: NACo, 2008; Sugarbaker et al, 2009.
For example, boundaries of seasonally inundated areas or wetland areas may be established in detail for a specific project, whereas national datasets on the same phenomena are more generalized.
To resolve the program–project discontinuity, criteria for developing harmonization of program-level data in a national geospatial database would be useful. The scheme would need to provide a basis for regional cross-comparison analysis and for future data-product improvements. The information for the harmonization effort might also include enough information to reconcile data from the program-level analysis and the national database. This effort in cross-linkage between various geospatial products will greatly enhance the transparency of derived products relative to the base heritage geospatial information sets.
For program- and project-level datasets, the spatial data infrastructure will need to allow for easy tagging of information in a common data format. Tools to facilitate the tagging of geospatial and temporal information from program and project analyses will enhance the integration and synthesis of studies across programs and agencies. The ability to conduct synthetic analysis across project and program data will greatly improve USGS’s ability to further understand how
critical ecosystem and natural resources dynamics are affected by human and environmental changes.
Incentives for Scientists
Staff incentives for sharing data are an important component of successful implementation and management of an SDI. The USGS workforce consists primarily of research scientists—there are nearly 2,000 full-time research-grade scientists. Like their academic counterparts, USGS scientists are evaluated through an annual research graded evaluation (RGE) process and rewarded on the basis of their scholarly productivity. Productivity is typically measured by scientific outputs such as publication in peer-reviewed journal articles and original research. Salary increases for and career advancement of scientists typically require prolific publication and high-impact research. Because research scientists are rewarded through publication and research, there are few compelling reasons for scientists to invest time and effort in sharing data unless these include scientific collaborations that directly involve co-authorship of scholarly output. As Nobel laureate and current US Secretary of Energy Steven Chu has remarked, “We seek solutions. We don’t seek—dare I say this—just scientific papers any more” (Del Vecchio, 2007).
The RGE process does not consider data-sharing as an output category, and USGS budgets do not sufficiently allocate for resources that are needed to share data efficiently. As a result, scientists are often reluctant to make their data available until they have been interpreted and published. A potential solution is to modify the RGE process in such a way that USGS scientists are evaluated on the data they shared. Another possible solution for this “publish or perish” problem could be a requirement that USGS-funded projects have part of their budget devoted to data-sharing and that proposals include plans for data-sharing. One example is the funding model in the National Aeronautics and Space Administration, which requires free and open access to data in an SDI; such an arrangement would probably result in greater utility of USGS data and improved relevance of the USGS itself. The USGS will also need to devise career incentives for scientists to provide open access to data and to partner with non-USGS researchers and organizations. That would require the support of skilled employees whose responsibility would be to facilitate data-sharing among USGS divisions. An SDI could be the integration tool for data-sharing inasmuch as most Survey data are inherently spatial. The diversity of data and of technological and scientific approaches makes it necessary for scientists to partner and to leverage expertise and knowledge.
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