Technology development to support a national early warning system for environmental threats
PARTNERS: NASA Stennis Space Center; Oak Ridge National Laboratory; University of North Carolina Asheville's (UNCA) National Environmental Modeling and Analysis Center (NEMAC); USGS EROS Data Center; USDA Forest Service Western Wildland Environmental Threat Assessment Center, Forest Inventory and Analysis, Forest Health Monitoring, Forest Health Technology Enterprise Team, and Remote Sensing Applications Center
SUMMARY: The early warning system is an ongoing monitoring project that detects forest threats across the continental United States using remote sensing and geographic information systems. The system helps forest managers identify large-scale forest changes faster, allowing them to use traditional methods to confirm and determine the nature and severity of the forest threat.
EFETAC's ROLE: The Eastern Threat Center is contributing the design concept, development, sponsorship, production management, interpretation, and technology transfer.
PROGRESS: Scientists and collaborators have launched , the strategic research component of the national early warning system. ForWarn produces maps showing potential forest disturbance across the conterminous United States at 231-meter resolution every 8 days, based on images obtained over the preceding 24-day analysis window. Maps of potential disturbance result from comparisons of historical expectations of normal vegetation greenness with present greenness from a series of current satellite views. ForWarn's showcases recent national disturbance maps in spatial context. Although all but forest vegetation is usually masked out of disturbance maps, ForWarn detects and tracks disturbances in all vegetation, including potential disturbances in rangeland vegetation and agricultural crops.
The ForWarn development team has unveiled several new features, including:
- Rapid disturbance detection: A map series, called Early Detect, provides faster initial images of new potential forest disturbances. ForWarn users can view Early Detect maps first, to check for the earliest indications of new disturbances, before consulting the standard ForWarn maps and supporting information.
- Retroactive forest monitoring: ForWarn has been operating since 2010 based on satellite imagery dating back to 2000. Users can now go “back in time” to view pre-2010 maps of known disturbances to assist with recognition and identification of current (and future) forest disturbance events and causes.
- A new version of the Forest Change Assessment Viewer provides a mask-free view of all lands, and is enabled for portable computing devices.
Improved disturbance detections in years with early or late seasonal development: Two ForWarn map products show current phenology compared to “average” greenness levels for any given location and day of year. These maps can improve detections of potential forest disturbances in spite of year-to-year differences in the onset of spring and fall seasons.
Researchers continue to test, verify, and validate ForWarn products through a series of tasks, including: 1. completion of an initial mountain pine beetle (MPB) case study, which has been extended into a second MPB-impacted area, 2. adaptive length compositing to determine and utilize the best length of the satellite image compositing period for the current 24-day window, and 3. retrospective computing of standard ForWarn products for the entire MODIS satellite imagery period (year 2000-present). Successful development and implementation of these three tasks are expected to realize significant improvements in the performance of ForWarn. Researchers are also using ForWarn for several applications, including: national evergreen thrive/decline modeling, hemlock woolly adelgid modeling in the southern Appalachians, monitoring long term fire effects and successional predictions, and mapping seasonal variation in leaf phenology in the southern Appalachians.
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Bill Hargrove, Eastern Threat Center Ecologist, firstname.lastname@example.org or 828-257-4846
Updated June 2018