This is the seventh quarterly report discussing progress made on the EPA-supported REMAP project being conducted by the Rangeland Resources Department at Utah State University. The report highlights project activities during the time period of January 1 to March 31, 2000. Our emphasis in this report is upon how the protocol utilizes and integrates with GIS. First, we discuss three other activity areas during the quarter:
Presentations at meetings Preparation for summer 2000 field work Utah watershed information on the WWW
PRESENTATIONS AT MEETINGS
Several presentations related to USU's REMAP project were made at the Society for Range Management annual meeting held in Boise, Idaho, February 12th to 18th. Allen Rasmussen presented A Conceptual Approach to Integrating Upland, Riparian, and Stream Monitoring at an Intermediate Sized Watershed Scale (Figure 1). Co-authors for the poster were Craig Goodwin and Jim Dobrowolski. The poster's abstract as well as a slide-show version are available on line. The poster provides an introductory overview of the proposed protocol. We received some very good comments on the presentation. In particular, several attendees were excited about a truly watershed-based protocol that gave significance to upland areas. They felt that existing efforts place too much emphasis upon analyzing streams to the detriment of hillslope areas.
Figure 1. Poster presented by Rasmussen et al. at SRM 2000 annual meeting. A slide-show version of this poster is available.
Allen Rasmussen also made a presentation for the Cooperative Riparian Restoration Symposium at the SRM meeting. The presentation entitled Using Monitoring to Understand Trend: Life Form Transects can be viewed on line.
Another SRM presentation that provided some initial study findings was
Foliar
and Ground Cover Relationships on Selected Communities in the Great Basin.
Chandra Heaton and Allen Rasmussen authored this poster presentation. At
four of the Utah study watersheds, vegetation transect data were collected
using a methodology to gather both first foliar hit and ground cover data.
The relationship between cover and foliar data was examined to ascertain
whether or not simplified monitoring methods could be developed.
PREPARATION FOR SUMMER 2000 FIELD WORK
Another task begun during this quarter was identifying Phase II test watersheds at which data will be collected during the summer of 2000. At present, we have identified candidate watersheds in Arizona, Wyoming, and Idaho (Figure 2). In general, these watersheds are associated with previous U.S.D.A. Agricultural Research Service experimental watersheds, such as the Walnut Gulch, Arizona and Reynolds Creek, Idaho research watersheds.
Figure 2. Location of Utah watersheds and out-of-state waterheds being proposed for Phase II data collection.
UTAH WATERSHED INFORMATION ON THE WWW
A data and file structure has been adopted for storing the GIS-based
information collected during the course of the project. Although the original
data sets will be unavailable until project completion, graphical depictions
of these data sets are becoming available on line. Go to the Watersheds
page to access these data.
IMPLEMENTING THE PROTOCOL WITHIN A GIS FRAMEWORK
Because of the spatial nature of watershed data, a geographic information system (GIS) provides a logical tool for mapping and presenting watershed monitoring information. Frequently, people tend to focus only upon GIS’s automated mapping capabilities. The real benefits of GIS come from its data storage, analysis, and manipulation capabilities (Somers, 2000). We recognize that GIS is a tool capable of substantially more than just map making. A GIS can provide an integrated framework for efficiently conducting the many tasks required for implementing a monitoring protocol. Within the protocol, a GIS is utilized for the following five tasks:
- Establishing and organizing a spatial working environment
- Locating sampling sites
- Determining parameters of watershed elements
- Storing, analyzing, and presenting information
- Modeling
Establishing and Organizing a Spatial Working Environment
An inherent advantage of employing a GIS in a monitoring protocol is that it places an up-front emphasis upon the importance of establishing a spatial context for the monitoring program’s data collection effort. Necessarily, a geographic reference system must be selected that can provide the geographic basis for locating all monitoring sites. In the field, a global positioning system (GPS) is used to locate a point within the geographic reference system. Within the GIS, data sets are geographically stored and mapped using the reference system. Geographic reference systems include latitude-longitude and various mapping projections such as the Universal Transverse Mercator (UTM) and State Plane coordinate systems. GIS software often allows conversion between coordinate systems, even to the point of allowing on-the-fly conversion for simultaneously displaying data sets stored in different coordinate systems. In our analyses, we utilize the UTM coordinate system as our geographic reference system. Use of the UTM system is advantageous in that it provides a uniform metric grid backdrop for spatially locating monitoring sites. More importantly, three fundamental data sets used in the monitoring protocol are available in digital format that is georeferenced by UTM coordinates: 1) digital raster graphics (DRG), 2) digital orthophoto quarter-quads (DOQQ), and 3) digital elevation models (DEM).
DRGs are scanned versions of U.S. Geological Survey (USGS) topographic maps. As with normal topographic maps, DRGs provide fundamental information regarding the physical and cultural features of an area, albeit generalized and simplified by cartographic interpretation. For the scale of problems being evaluated by the monitoring protocol, DRGs derived from 1:24,000 scale topographic maps (7½ minute quads) are most appropriate. A DRG based upon a 7½ minute quad has a ground pixel resolution of about 2.4 m. DRGs maintain the positional accuracy of their source maps, which is about 12 m for 90 percent of points, based upon National Map Accuracy Standard (NMAS) production requirements for these maps. The utility of DRGs for the monitoring program is the same as that of the paper maps they replace: they provide a readily understandable visual display of an area.
DOQQs are digital files of scanned (rasterized), rectified, and georeferenced aerial photographs with a resolution (1 m) that allows landscape details to approximately 2 m to be assessed. DOQQs are created from National Aerial Photography Program (NAPP) aerial imagery that is being retaken nationally on an approximately 5-year cycle. NAPP photographs are acquired at 6,100 m (20,000 feet) above average terrain elevation and may be either black-and-white or color-infrared images. Standard DOQQs derived from NAPP imagery cover one-quarter of a 7 ½ minute quadrangle and have similar accuracy standards to DRGs as described above. Complete DOQQ coverage of the U.S. is expected by 2004, with a 10-year update thereafter for most areas and a 5-year update in urban areas. The anticipated updating of DOQQs makes them an essential element in the monitoring protocol.
DEMs are grids of elevation points used for determining land slopes,
aspects, elevations, and more complex topographic features such as watersheds.
Most typically, DEMs are generated from digitized contours, but they may
also be derived by surveying or photogrammetric methods. In the U.S., DEMs
are produced by the USGS in 7½-minute by 7½-minute blocks
providing the same coverage areas as 7½ minute DRGs and topographic
maps. These DEMs have a 30 m by 30 m data point spacing with horizontal
position located using UTM coordinates and vertical position presented
as elevation above sea level. Data quality of DEMs is dependent upon a
number of factors including original data source quality and method of
extracting the DEM from the source. Within the protocol, the DEM data set
is used for delineating watershed elements and for deriving several landscape
parameters.
Locating Sampling Sites
A GIS allows ready implementation of random, systematic, or stratified sample site selection procedures. For the RWM protocol, we implement several levels of randomization and stratification in site selection. All site selection is undertaken in the office prior to any field data collection. Maps and air photos with plotted sample sites and lists of sample site georeference coordinates are produced to help locate sampling sites in the field. As described earlier, the monitoring protocol is based upon a watershed approach for geographically selecting sample sites, with sample sites located within a river basin, watershed, subwatershed, hillslope, OFE, and/or the drainage network.
For the protocol, we use a GIS-based approach to extract drainage networks and create watersheds from DEMs. Methods for extracting drainage features from DEMs have been devised by Jenson and Domingue (1988), Tarboton et al. (1991), and others and are available in many GIS software packages. Using this GIS-based approach, all second-order watersheds within a river basin are identified. Depending upon specific monitoring program goals, one or more of the second order watersheds can be selected either randomly or for meeting specific project requirements. Each selected second-order watershed is subdivided into its constituent elements: a drainage network, subwatersheds, hillslopes, and OFEs. The drainage network and subwatersheds are delineated using automated GIS procedures similar to the procedures for delineation of second-order watersheds. At present, automated procedures have not been developed to delineate hillslopes or OFEs. However, "heads-up" delineation of these features is simplified by interactive use of the GIS environment. Figure 3 illustrates the delineation of a second-order watershed into OFEs. Depending upon watershed size and character, there will typically be 50 or more OFEs per second-order watershed.
Figure 3. Division of a second-order watershed into OFEs (numbered polygons). Top: OFEs are categorized into clusters (polygon coloring) based upon soils, vegetation, and slope. Random points (blue dots) within randomly selected OFEs are located for collecting point data within OFEs. Bottom: Same watershed with sample points overlain on a DOQQ.
Two categories of information are extracted from each watershed: that representing hillslope conditions and that representing conditions along the flow path network. A subset of OFEs is selected for assessing and monitoring hillslope conditions within the watershed. OFE’s are first grouped into one of several classes in each watershed, with classes derived using cluster analysis statistical procedures. These classes are created based upon similarity of OFE slope, vegetation, and soils. For each OFE class, one or more OFE’s are randomly selected, with economics and program goals determining the number of required OFEs. Point or site information required from within OFEs is randomly selected using a spatial random grid point generator available with the GIS (Figure 3). These points can be printed out overlaying the DRG map or DOQQ aerial photograph so they may be located in the field. Additionally, a table of geographic coordinates can be generated which can be used in conjunction with a GPS to locate these points in the field.
The second type of information required by the protocol is that collected
along the flow path network within a watershed. Data are collected at points
or reaches along the network at locations where the sampled OFE’s drain
into the channel network and at the watershed outlet. This stationing assures
that sites selected to represent network conditions have the best potential
for linking to hillslope conditions. Additional random points can be selected
if necessary for other requirements. As with the upland points, these points
can be printed onto maps aerial photograph and tabulated into GPS coordinate
lists.
Determining Parameters of Watershed Elements
Many required watershed parameters can be derived from the three geographic data sets used in establishing and organizing a monitoring program’s spatial working environment. The most important data set is the DEM. Various GIS procedures can use the DEM to 1) spatially define the watershed, subwatersheds, hillslopes, OFEs, and the flow path network and 2) derive parameters representing various aspects of these elements. For each grid cell (pixel), some of the parameters that can be derived include:
Depending upon the requirements of specific analyses, these parameters may be aggregated up from the pixel level to average values (or distributions) for the various watershed elements. Although many of these data could be derived using non-GIS methods, GIS allows more rapid and perhaps more accurate determination of these parameters. Examples of these GIS-derived data are available for the Utah watersheds on the Watersheds page.Elevation Slope Aspect Watershed area Maximum flow path length to cell Total of flow path lengths to cell
Storing, Analyzing, and Presenting Information
In any long-term monitoring program, handling and maintaining the tremendous volume of data that are collected can be overwhelming (Pickett, 1991). Because a GIS is a database, its database functionality can be used for organizing and storing data. The special advantage of a GIS database is that information may be selected and accessed using spatial criteria. If a GIS utilizes a standard database format for storage of non-spatial data, then these data may be accessed and manipulated with non-GIS software, although the capability for selecting information using spatial criteria may not be available.
Three general categories of information are associated with the protocol. These categories are:
- Spatial/Temporal Information. Data of this type has a spatial location within the watershed and varies over time. An example of this type of data set is vegetation cover information collected along established transects.
- Non-Spatial/Temporal Information. Some types of information vary temporally but will not have a spatial component to them. For example, annual precipitation depth might be assumed to be spatially uniform over a watershed but be variable year to year. This information would not be associated with any specific watershed location.
- Spatial/Non-Temporal Information. Many of the landscape parameters derived using GIS methods (described in the previous section) are time invariant. Average OFE slope, for example, is not expected to vary over the short-term time scales of human observation.
Modeling
Perhaps the most underutilized use of GIS is for modeling. At present, there are two endpoint means for linking GIS and modeling, which are referred to as loose coupling and tight coupling.
To date, most efforts to link GIS and modeling have followed the loose coupling approach, as this approach does not require rewriting existing models. We are taking this approach to link the WEPP model to the GIS. For flow path modeling, we are in the process of incorporating TOPMODEL concepts and sediment redistribution directly into a GIS.Loosely coupled models. Loosely coupled models use existing models with no changes made to model structure, input, or output. However, the GIS database and its associated programs are written to extract information and write it out into the input file structure(s) required of the model being used. After a model simulation is run, its output may be input into the GIS for spatial analysis of results. Tightly coupled models. In tightly-coupled models, the science behind the model is completely incorporated into the GIS. No additional software modeling programs are required, for the model is completely contained in the GIS.
References
Jensen, S. K., and Domingue, J. O., 1988, Extracting topographic structure from digital elevation data for geographic information system analysis: Photogrametric Engineering and Remote Sensing, v. 54, p. 1593-1600.
Pickett, S. T. A., 1991, Long-term studies: past experience and recommendations for the future, in Risser, P. G., ed., Long-term ecological research - an international perspective: Chichester, John Wiley and Sons, p. 71-88.
Somers, R., 2000, GIS stalled? Getting past the mapping phase: GeoInfo Systems, v 10., no. 3, p. 20-22.
