The FTSC at URI participating in the development of Natural Resource Condition Assessments (NRCA) for Acadia National Park (ACAD), Fire Island National Seashore (FIIS), Sagamore Hill National Historic Site (SAHI), and Gateway National Recreation Area (GATE). The goal of the Natural Reource Condition Assessment program is to determine the status of park resources, to identify knowledge gaps, and to make recommendations for futher studies that address threats to these resources. Development of NRCAs for ACAD, FIIS, SAHI, and GATE is ongoing collaborative effort involving researchers and GIS professionals from the University of Maine, Stony Brook University, the Univeristy of Rhode Island, and NPS staff from the New England Region.

Personnel from the FTSC have participated in numerous site visits and meetings, offering technical perspective on upcoming research and highlighting the need for quality data and metadata. We assessed the current status of park GIS databases and identified possible gaps in the available data.

To equip researchers at the parks, we have developed GIS databases for each park. Data for these databases were obtained from existing park databases as well as from the Inventory and Monitoring Program. Once existing data were assembled, data were mined from external databases, including the New York GIS Clearinghouse and other sources. The database structure used for ACAD was determined to be the most useful structure, and we have used it to store data for all four parks.

Many legacy datasets predate FGDC metadata standards. When possible, metadata were imported into the datasets. Datasets without metadata were given a template dataset, warning the user of possible problems and providing the user with basic information about the dataset, such as the projection and the contact for the Data Steward of the relevant park. 

The Python Script Creator for Batch Processing Version 1.0 is a prototype of what can be done with just one of the ArcGIS Geoprocessing commands using Python Scripts.  The example script converts multiple image files (.tif, or .img) to multiple GRIDS.  The original idea was based on the need to batch process many of the functions in ArcGIS.  However, Model Builder currently lacks batch geoprocessing, and coding in Python can be a significant hurdle for some.  We tested a prototype Python Script in ArcGIS 9.1 with Python 2.1 installed, and it continues to work.  So feel free to pass it along.  With a bit more coding, there are hundreds of others that could be added or similar pages could be developed. To use this Python script creator in ArcGIS 9.2 (click here). For additional Python script creators, see FTSC Projects page for Fiscal Year 2006-2007.

During the spring of 2006, Nigel Shaw and Michael Bradley led a group of University of Rhode Island students in the collection of GPS data at Saratoga National Historical Park (SARA).  The GPS Swat Team trip was conducted as part of the course, Application of GeoSpatial Tools (NRS-524), which was co-taught by Mike and Nigel.   Coursework for NRS-524 included GPS theory and tutorials as well as hands on training with Trimble GPS units.  Data collected at SARA included roads, culverts, wetlands, paths, picnic areas, monuments, overlooks, gravestones, trees, and other historical features specific to Saratoga.  Knowledgeable NPS staff assisted with field identification of historical features.  Data were post-processed, quality checked, and migrated to GIS format.  All data have FGDC-compliant metadata.

The Global Positioning System Swat Team is sponsored annually by the NER GIS Program to map features in a national park. The SWAT Team collects locational and attribute data on everything from trails to rare lichens to archaeological sites and have even, on one occasion, mapped a kitchen sink location!  The essence of the GPS Swat Team is that we spend a short time in the park, collect data as specified by the park staff, and provide completed digital map layers ready for use in GIS.  While we prioritize for data collection projects that contribute to a specific project or base dataset, we will, in truth, map just about anything we can find in the field.

The National Park Service (NPS) has developed a standard for modeling buildings located in National Park Service lands within a Geographic Information System (GIS).  Historically coverages and shapefiles have been used to represent the locations of building footprints; however, these legacy formats have limitations when dealing with large or robust datasets.  The new NPS Buildings data standard uses a geodatabase format to address these limitations.  The NPS Buildings data standard facilitates meaningful building geometry storage and flexible feature attributes, integrates with external NPS facility and structure management systems, and interchanges with national efforts.  The geodatabase structure allows for multiple relationships to be established between tables and features.  Buildings can be represented as both points and polygons for more advanced cartographic analysis and organization potential across the NPS.  Parks can create their building footprints and assign unique ID values, independent of any particular external databases.  These unique identifiers allow parks to add their building footprint data to federal databases or share them with other parks without encountering conflicts between features. 

This geodatabase structure can be thought of as having two components: a tabular component, and a spatial component.  The spatial component is divided into two feature classes: polygons and points.  Each of the feature classes can be divided into multiple subtypes.  For example, a single building may be defined by the centroid of the building footprint or by the entrance point.  Either or both of these points can be used within the data model.  In addition, the building may also be represented by a building footprint (Perimeter_Polygon), or by an area that extends beyond the building footprint (Buffer_Polygon).  The tabular component is centered on an NPS_Building table that relates the spatial data with the tabular data.  This table structure allows for more rapid data entry and better error checking than previous data models.  It is believed that this standardized approach to a data format will serve as a model for the future development of geo-spatial data standards within NPS.  A more detailed explanation of the data model can be found here:  http://science.nature.nps.gov/nrdata/metadata.cfm?ID=38714

Our NER FTSC office edited documents that detail the process of converting legacy GIS data into this Geodatabase standard.  This conversion is a multi-step process requiring the use of both ArcCatalog and ArcMap.  The NER FTSC continues to participate in meetings and provide comments as the building model continues to be developed.  As part of this effort, we were invited to participate as a member of the Building Standards Committee in the Buildings Standards and Implementation Workshop, Sept. 25-27, 2006 in Lakewood, CO, with NPS GIS technical experts. 

As a part of the National Wild and Scenic Rivers System, the first 73.4 miles of the Delaware River are protected as the Upper Delaware Scenic and Recreational River (UPSRR), under the management of the National Park Service (NPS).  UDSRR is visited by approximately 225,000 people per year primarily for recreational purposes to enjoy the pristine river and scenic vistas the river valley provides.  In conjunction with providing outstanding family recreation opportunities the river provides exceptional habitat for a great variety of fish, endangered species such as Bald Eagles and dwarf wedge mussels, as well as drinking water for over 17 million people.  The US Route 97 Scenic Byway also follows the river corridor.

We created a TIN of Bar Harbor, ME for Acadia National Park. This work involved writing Python scripts, writing FGDC-compliant metadata, and processing large volumes of data to process ASCII data files delivered by a contractor for Bar Harbor, ME.

Traveled to two parks, Martin Van Buren National Historic Site (MAVA) and the Roosevelt-Vanderbilt National Historic Sites (ROVA), to interview park staff and determine their needs in the contxt of NPS enterprise and internet technology. These parks were selected to represent a small and a large park within the Northeast Region. At MAVA, some applicaitons of GIS at the park include future work on a General Management Plan, maps of regular maintenance activities, comparisons of historical and present-day conditions at the park, and monitoring of sensitive archaeological resources. At ROVA, some applicaitons of GIS include monitoring of invasive plant species, creation of maps for public meetings and local organizations, organization of special events on the property, and future work on a General Management Plan.