Northern Prairie Wildlife Research Center
Obtaining the data for a GIS is the major bottleneck in implementing a GIS (Aronoff 1989). The creation of an accurate and well-documented database is essential. Information generated from the GIS and resulting decisions made with that information can be accurate only if the initial data are accurate. Accuracy and reliability of all data layers should be documented. Documentation must include such information as date the information was collected, positional accuracy, classification accuracy, completeness, and procedures used to collect and encode the data. The accuracy of spatial databases is a complex issue and was the subject of a recent book (Goodchild and Gopal 1989).
The data to be entered into the GIS include the spatial data (location of the features) and the attribute information (data describing the features). Some data can be captured more readily in vector formats, whereas other data sets are more efficiently extracted from sources by using raster processing techniques.
Because of the expense of data for GISs, all data requirements must be documented before a GIS is initiated. Fortunately, the data required for a wildlife application may be the same data required for a land use planner, soils scientist, geographer, geologist, hydrologist, forester, or direct-marketing expert. Consequently, there is a growing source of existing data in digital format, and sharing or purchasing existing data is much cheaper than digitizing from existing maps or photos or extracting information from satellite data. It is critically important that all available sources of digital data are reviewed and evaluated before new data are acquired.
Various techniques are used to enter data into a GIS. Data can be entered manually from the computer keyboard or a digitizing table. Manual digitizing can be slow and expensive, but at times it may be the most efficient and accurate means of data entry. Scanning or scan digitizing is more automated than manual digitizing; Recent advances in scanning hardware and improvements in software for extracting information from scanned images are making scan digitizing a more attractive option to manual digitizing. For large areas, existing satellite technology and modem digital image-processing techniques can be a cost-effective means of capturing data for a GIS. The most effective and efficient method of capturing data for a GIS is to purchase existing digital data sources. Many federal agencies and a growing number of state and local agencies have digital data available. Many of these agencies are willing to share their data or will provide them at a minimum costs.
Often keyboard data entry is used in various digitizing techniques to enter attribute data for a specific feature. Location or the geographic component of features at times can be entered efficiently from the keyboard. This is particularly true for infrequent and widely distributed point data such as the location of cave entrances, nests, or animals that are radio-tracked. The location of points in the field now can be obtained with global positioning systems, termed GPSs (Fig. 7). Hand-held GPSs now can be obtained for <$3,000; they provide locational information in latitude and longitude, UTM, or other coordinates when used in the field. These field locations can be entered manually into the GIS with the computer keyboard, or the coordinates can be stored in the GPS and later downloaded to the GIS. For further information on GPSs, see Box 3.
|Fig. 7 -- A hand-held global positioning system can be used for obtaining accurate locations for features on the ground (photo provided by Trimble Navigation, Sunnyvale, Calif.).|
In manual-digitizing techniques, a map or aerial photograph is placed on a digitizing table (Fig. 8) and a pointing device (called a cursor, puck, or mouse) is used to record coordinates of features to be extracted from the map. The digitizing table electronically encodes the position of the cursor. Tracing the map features with the cursor can be time consuming and error prone.
|Fig. 8 -- A digitizing table can be used to record coordinates of features shown on maps or aerial photographs (photo by Altek Corp., Silver Spring, Md.).|
The attribute information about the feature also must be recorded. This frequently is done by labeling each feature with a unique number and building a list of attributes for each uniquely labeled feature. The efficiency of manual digitizing depends on the quality of the digitizing software, the skill of the operator, and the complexity of the map to be digitized. Editing the digitized data and assigning the feature labels or other attributes of the feature may take more time than initially digitizing the map.
Small digitizing tablets (0.3 X 0.3 m) can be purchased for <$100. Large digitizing tables (1.3 X 2 m) that can hold large maps range in cost from $3,000 to $20,000.
Recent advances in scanning hardware and software have made scanning a feasible alternative to manual digitizing for some applications. Continued advancements in this technology are coming and eventually it may replace manual digitizing.
Three types of scanners are available. Flat-bed scanners have a flat scanning surface on which a map or a photograph is placed. Small flat-bed scanners (20 X 30 cm.) cost <$2,000 and have scanning resolutions of 100-150 dots per centimeter (DPC). The flat-bed scanner that scans a 25 X 25-cm map at 100 DPC will produce a raster data file of 6,250,000 cells (a matrix of 2,500 lines by 2,500 elements). The scanned cells can contain intensity values ranging from 0 for a black object to 255 for a white object (When scanning is done in panchromatic mode with 8-bit data). When scanning is done in color mode, each cell contains the intensity of red, green, and blue light being reflected from the map. These intensities usually are measured in a range from 0 to 255.
Normally when resolutions of >150 DPC are required, or large maps are used, drum scanners are required. The map is mounted on a cylindrical drum, which spins as a detector is moved horizontally across the drum. Black and white intensities are recorded in panchromatic mode, or red, green, and blue intensities are recorded in color mode. The area viewed by the detector is termed the spot size (Aronoff 1989) and can be as small as 20 microns. Scanning a large map at 20 microns will create a large raster file.
For some applications, a video scanner can be used. A video camera is mounted on a copy stand and the map is placed beneath the video camera, which is raised or lowered to include a larger or smaller portion of the map. Video scanning typically produces a raster file with <512 elements and 512 lines. The spatial resolution of the cell depends upon the scale of the map and the distance between the map and the video camera.
Scanning by flat-bed, drum, or video scanners produces raster files. Maps that have been especially prepared for scanning show only the lines between features, and coordinates for these features are extracted readily. Extracted coordinates for features from scanned maps or aerial photographs may be complex and will rely on sophisticated, line-following algorithms or feature classification and extraction algorithms to obtain the desired information from the map or photo. As in manual digitizing, much time will be spent editing scanned maps.
GISs are more than simply a warehouse for map information or storage for maps. However, many GISs can effectively store images of maps and aerial photographs obtained from flat-bed or drum scanners. Features on these images are not identified. These high-resolution images often are stored on CD-ROMs. Any of these images can be retrieved by the GIS and viewed on, a color monitor. Feature information from these scanned images can be extracted with feature classification and extraction algorithms, or information (such as the distance between two points or areas) can be calculated with available software of the GIS. Many GISs in the future will support scanned-image libraries.
Advances in remote sensing and GIS technology have followed the advances in computer capabilities since the late 1960s. In many situations, remote sensing techniques that use satellite data are the only feasible means for collecting data for GIS applications over large regions. Remote sensing can be defined as any technique by which we gather data about an object without directly touching the object. Remotely sensed data for GIS applications are obtained from satellites or aircraft.
The most effective techniques of remote sensing used for GIS applications are those that provide digital data for the study area. These digital raster data sets can be obtained by satellite sensors, by digital sensors mounted in aircraft, or from scanned aerial photographs.
Remote sensing can employ active or passive systems. Satellite systems, such as Landsat and SPOT, use passive sensors, which measure the intensity of natural radiation. Active systems, such as radar and laser systems, transmit energy to the ground, then measure the energy returned from the ground to the sensor. Photographic cameras, video cameras, and multi-spectral sensors in aircraft or satellite are examples of passive systems. Some satellite and aircraft remote sensing systems use active sensors such as radar. Numerous passive and active remote sensing systems mounted on satellites or aircraft are currently available for acquiring data for GIS applications, and many additional remote sensing systems will become available to GIS users in the near future.
Landsat, the U.S. land remote sensing satellite system, began as an experimental program conducted by the National Aeronautics and Space Administration (NASA). Landsat 1, launched on 23 July 1972, was expected to function for about 1 year and finally ceased operating in 1978 after nearly 5 years of continuous operation. During that time, it returned digital data for some 300,000 images of the earth's surface. The Landsat system was declared an operational system in 1993 and turned over to the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce. In 1984, the Land Remote Sensing Commercialization Act (Landsat Act) was established to transfer the commercial operation of the Landsat program to the private sector. Earth Observation Satellite Company (EOSAT) was selected as the commercial operator for the Landsat program.
Landsat 1 through 3 satellites had two sensors. The Return-Beam Vidicom (RBV) sensor, which is similar to the television camera, recorded red, green, and infrared energy reflected from the surface of the earth. The Multi-Spectral Scanner (MSS) was the main instrument carried on these satellites and is still operating in Landsat 4 and 5 satellites. The MSS sensor collects data by scanning the earth from west to east with an oscillating mirror. Radiation from four different spectral bands (green, red, and two in the near infrared) is recorded. The radiation is transferred by fiber optics to filters that permit only certain wavelengths of radiation to strike the sensor's detectors. The picture element (pixel) sampled by the MSS is about 79 X 56 m (the size of a football field in the U.S.). Landsat satellites 2 and 3 ceased operating in 1983. Landsat 1, 2, and 3 satellites orbited the earth at 900 km and provided repeat coverage for any location on earth every 18 days.
Landsat 4 and 5 satellites were launched in 1982 and 1984, respectively. Landsat 4 is used sparingly because of an electrical problem that developed shortly after its launch. As of July 1993, Landsat 5 was still operating. Landsat 4 and 5 satellites circle the earth every 98.9 minutes in a near polar orbit of 705 km. Each satellite provides repeat coverage for any area every 16 days, at,the same local time of day. Landsat 4 and 5 satellites weigh nearly 2,000 kg each and carry the MSS sensor and the Thematic Mapper (TM) sensor. The TM sensor has excellent capabilities for meeting the data needs of many GIS applications for large regions. Along each orbital path, the TM and MSS sensors can continually scan a swath 185 km wide. The scanned data are systematically divided into an area termed a "Landsat Scene," which encompasses approximately 185 X 170 km. Each scene covers approximately 3.2 million ha. Users of Landsat data can purchase data from an existing archive maintained by EOSAT or can schedule the collection of data for any site. The images from the TM sensor on Landsats 4 and 5 satellites have significantly better geometric quality than images from sensors on earlier Landsat missions due to engineering enhancements to the spacecraft. This has facilitated geodetic rectifications of the images to the accuracy standards for 1:24,000-scale map products (Welch et al. 1985).
The TM sensor provides significant improvements in spatial, spectral, and radiometric resolution compared to the MSS. The instantaneous field of view (IFOV) of the TM is square and results in a ground-resolution cell and image pixel of approximately 30 m on a side. The TM measures the intensity of reflected radiation in six spectral bands--three in the visible wavelengths, blue (0.45-0.52 µm), green (0.52-0.60 µm), red (0.63-0.69 µm); one in the near infrared (0.76-0.90 µm); and two in the shortwave infrared (1.55-1.75, 2.08-2.35). The TM also measures emitted thermal radiation (10.4-12.5 µm), although the IFOV for this spectral band is 120 m on a side. The greater radiometric resolution is achieved by the analog-to-digital conversion of the electrical signal to 8 bits or 256 gray levels compared to the 127 gray levels of the MSS on the first three Landsat satellites. Figures 9-12 provide examples of the raster data collected by the Landsat TM sensor and types of information that can be extracted for use in GISs. Landsat 6 was launched on 5 October 1993; however, communication with the satellite was not established.
|Fig. 9 -- (top left) A TM image for central North Dakota. Fig. 10 -- (top right) Landsat TM data for the Pearl Lake, North Dakota, map sheet. This is a classified file containig >240 spectral classes. The color assigned to the spectral classes is based upon the mean values from bands 3, 4, and 5. The color table resembles the colors obtained from color-infared photography. Fig. 11 -- (bottom left) Digital image-processing techniques were used to extract wetland information from a Landsat TM scene for the Pearl Lake, North Dakota, map sheet. Fig. 12 -- (bottom right) GIS processing functions can be used to label each wetland basin identified on the Landsat TM classified file and to ascertain the acreage of each wetland type in the basin. The acreages of the various wetland types are shown for the wetland types are shown for the wetland basin labeled number 178.|
The first SPOT (Systeme Pour I'Observation de la Terre) satellite was launched by France in 1986. The SPOT program was designed to be a long-term, operationally commercial program, whereas the Landsat program was designed initially as an experimental system. The SPOT program was established by the French government in 1981 under the French space agency, CNES. France, several European banks, and industries from Belgium and Sweden have invested in this commercial entity. SPOT Images, S.A., which is partly owned by the French government, operates the SPOT system. SPOT Image Corporation was formed to market SPOT data in the U.S., and Radarsat, Inc., markets the data in Canada.
SPOT-1 carries two identical, high-resolution visible (HRV), pushbroom scanners. Each scanner can operate in one of two modes. In the panchromatic mode, 10-m resolution data can be obtained. This single band records visible energy ranging from 0.51 µm to 0.73 µm. In the multi-spectral mode, three bands are recorded at 20-m spatial resolution: green (0.5 µm-0.59 µm), red (0.61 µm-0.73 µm), and near infrared (0.79 µm-0.89 µm).
SPOT orbits at 832 km and repeats the orbit every 26 days. Each of the two sensors images a 60-km-wide swath. Pointed vertically, the two sensors can record a 117-km-wide swath. The sensors can be pointed, which provides two major advantages. First, a particular site can be imaged not only from the path directly over the site but also from adjacent satellite paths. This allows the potential for acquiring data for a site more frequently than every 26 days. Secondly, stereo images can be produced by acquiring scenes for the same area from two widely separated locations.
SPOT's panchromatic band has nine times more spatial detail than Landsat TM data. Combining Landsat's spectral data with the spatial advantages of SPOT's panchromatic data can produce spectacular images. A short-wave infrared spectral band is planned for the fourth satellite of the SPOT series.
Coastal Zone Color Scanner
The Coastal Zone Color Scanner (CZCS) was launched on the Nimbus-7 satellite by the U.S. Government in 1978 and operated until June 1986. The CZCS measures ocean color and temperature with six spectral bands, including four bands measuring narrow portions of the visible spectrum, a near-infrared band, and a thermal-infrared band. This sensor provides spatial resolution of 0.825 km2 at nadir and a scan width of 1,600 km. CZCS data have been used successfully to map suspended sediments and phytoplankton in coastal regions (Clark and Maynard 1986, Tassan and Sturn 1986) and in the detection of acid-waste pollution (Elrod 1988).
In 1979 the NOAA-6 satellite and all subsequent satellites in the NOAA series carried the Advanced Very High Resolution Radiometer (AVHRR) sensor. The spatial resolution of the AVHRR varies from 1.1 km2 at nadir to 12.6 km2 at the end of the scan line. The sensor scans throuah 110.8° as it examines the earth, producing a scanline of 2.925 km. This wide scan angle, + 54° of nadir, permits daily views of the earth. The AVHRR measures reflected radiation in the red and near infrared wavelengths and emitted thermal radiation in three spectral bands. Two data formats; are available from NOAA--local area coverage (LAC) at full spatial resolution and reduced resolution global area coverage (GAC) with a spatial resolution of 4 km2 at nadir. Originally designed to provide improved determination of hydrologic, oceanographic, and meteorological parameters, AVHRR data, because of their high temporal frequency, also have proven useful for study of the phenology and productivity of terrestrial ecosystems on continental and global scales (Justice et al. 1985).
The digital data and photographic images are used in a variety of time-critical applications over large areas. AVHRR data have been used by the U.S. Fish and Wildlife Service to monitor snow cover in the arctic region of Canada for use in forecasting production of arctic nesting geese. LAC data were used to monitor water distribution for waterfowl wintering in the Central Valley of California (L. Strong, U.S. Fish Wildl. Serv., unpubl. data). Other uses of AVHRR data were described by Lillesand and Kiefer (1987) and Aronoff (1989).
Geostationary Operational Environmental Satellites (GOES) orbit the earth at an altitude of 36,000 km in the same direction as the earth's rotation. In this orbit, they maintain a stationary position relative to the earth (geostationary orbit). Two GOES satellites are operated by the U.S. and cover the western and eastern parts of North America. Europe and Japan operate additional GOESs. GOESs provide continuous monitoring of temperature, humidity, and cloud cover for weather forecasting. GOES data have been used for some GIS applications over huge regions (Meisner and Arkin 1984).
GOES collects two bands of data, a visible band (0.55-0.75 µm) and a thermal infrared band (10.2-12.5 µm). NOAA can provide data from the visible band at 1-, 2-, 4-, and 8-km resolution and thermal-infrared imagery at 8-km to 14-km resolutions.
The first Japanese remote sensing satellite, the Marine Observation Satellite 1 (MOS-1), was launched in February 1987. It has three sensors: a multi-spectral, self-scanning radiometer (similar to the Landsat MSS), a visible and thermal-infrared radiometer (similar to NOAA AVHRR), and a microwave scanning radiometer. No data tape recorders are on MOS-1, consequently data can be collected only when the satellite is in view of a ground receiving station. The U.S. has no such stations, but data are available from two Canadian receiving stations.
Japan launched JERS-1 (Japanese Earth Remote Sensing Satellite) on 11 February 1992. The three sensors of JERS-1 are an L-band (horizontal polarization synthetic aperture radar system), a visible and near-infrared radiometer, and a short-wave infrared radiometer. All provide 18-m resolution data.
The European Space Agency's first remote sensing satellite, ERS-1, was launched in 1991. ERS-1 carries a C-band, vertical polarization synthetic aperture radar instrument. Both high-resolution (25-35 m) and low-resolution (100 m) data are available in digital and photographic forms. Canada is planning to collect and distribute ERS-1 data for much of North America.
Canada is developing RADARSAT, a radar remote sensing system to be deployed in 1995, the first Canadian remote sensing satellite. RADARSAT will assume a sun-synchronous orbit at approximately 800 km. The repeat cycle will be every 24 days, but with a change in the look angle, data can be collected for a specific site every 3 days. RADARSAT's synthetic aperture radar (SAR), a C-band with horizontal polarization, is designed to operate in several modes to provide numerous options in terms of swath widths, spatial resolutions, and angles of incidence. The standard beam mode will provide coverage with approximately 100-km-wide swath with a spatial resolution of 28 m. The wide swath beam will collect 28-m data over a 150-km swath. In the fine-resolution beam mode, 10-m data for a 50-km swath will be collected.
In addition to the SAR instrument, RADARSAT will include a scatterometer and two optical instruments. The scatterometer is a microwave sensor that collects data on wind speed and direction for a 600-km swath. One of the optical instruments is a multilinear array sensor, which records four spectral bands at 30-m resolution for a 400km swath. The other optical instrument is an AVHRR sensor capable of collecting five spectral bands at 1,300m resolution over a 3,000-km swath.
The radar data collected by RADARSAT could be valuable for mapping and monitoring wetlands, because radar is an active sensor creating its own illumination source, and data can be collected for areas covered with clouds or even at night. Place (1985) reported that the accuracy of mapping forested wetlands was improved by 85%, when radar images collected from SEASAT were used to complement conventional aerial photography used by photo-interpreters for mapping wetlands (SEASAT was launched in 1978, but failed only 99 days after launch).
Satellite-based sensors have many advantages for meeting GIS data needs. Satellite data have low cost per hectare of coverage, a geometric fidelity that facilitates registration of images to various maps projections, and freedom from mission planning. However, for some applications, the spatial resolution's of satellite-based sensors may be too gross, and the temporal frequency or clouds prevent data acquisition during optimum times. The time of data acquisition can be critically important for the successful use of the data. For example, temporary wetlands may be inundated for only a few weeks. Acquiring satellite data when the temporary or seasonal wetlands are dry makes detection and identification of temporary or seasonal wetlands difficult. Aerial photography similarly acquired when the wetland basins are dry will not provide acceptable delineation of wetlands.
Aircraft sensors offer great flexibility of spatial resolution, timing, and wavelengths of spectral data. Aircraft sensor data can be scheduled to be collected at optimum time for extracting information from desired features. Spatial resolution can be as fine as 1 m or as coarse as 50 m and is dependent on the aircraft altitude, the optical system, and the size of the sensor's detector elements. However, when compared to satellite data, aircraft sensors provide a relatively narrow swath width. A major problem with aircraft scanner data is the poor geometry of the data. These data are adversely affected by variations in aircraft attitude (roll, pitch, and yaw) and deviations from the flight line. Digital elevation models (DEMs) and GPS can be used to suppress the geometric problems inherent in aircraft multi-spectral data. Lee (1991) provided an excellent review of applications of aircraft multi-spectral data for classifying and mapping wetlands.
Airborne videography recently has been used successfully for assessing wetland and riparian habitats in North Dakota (Cowardin et al. 1988a) and for evaluating rangeland and other vegetation in Texas (Driscoll 1990). Sidle and Ziewitz (1990) described the use of aerial videography for wildlife studies. Lee (1991) described many of the advantages of videography: (1) imagery can be captured by microcomputer for immediate use; (2) in-flight error-proofing can be done; (3) narrow-band filters for fine spectral resolution can be used; (4) data can be acquired in a wide range of atmospheric conditions; (5) data can be acquired any time; 6) cost of videography systems is low; and (7) standard digital image-processing techniques, which are typically used on satellite data, can be used to analyze the video data. Disadvantages of videography include: (1) images provide coverage of only small areas; (2) resolving power is much less than that of aerial photography; (3) geometric distortion from motion in the plane is difficult to correct; (4) spectral resolution of solid-state detectors is limited to visible and near infrared wavelengths; (5) multi-spectral data collection is difficult because single cameras have problems with focus for different wavelengths, and multiple cameras require accurate bore sighting and large camera ports on the aircraft; (6) calibration of video data is difficult because of automatic gain control; and (7) images are vignetted.
Various video systems have been developed (Mausel et al. 1992), including single-band panchromatic systems, single-band color systems, and multi-spectral systems, some of which include near-infrared capabilities. Everitt and Escobar (1989) described many of the available systems. In GIS applications, the best use of videography may be to update existing information layers.
Because of the expense of acquiring digital data layers for GIS applications by digitizing existing maps or by remote sensing techniques, GIS users always should search for existing digital data sets to meet their data needs, before capturing the data themselves. Sources of existing databases include third-party vendors, federal, government agencies, and state and local government agencies.
Before searching for existing databases, one must have a clear vision of what kind of information is required and exactly how the information is to be used. Knowing the exact data requirements is critical to identifying good potential information sources.
The available digital data sets were produced to satisfy a wide range of users. Consequently, the data are not always suitable for a specific GIS application. The cost, accuracy, and currency of the data vary greatly with existing sources. By the time the data have been collected, reviewed, digitized, edited, and distributed, they may be out of date for some applications. Dulaney (1987) reviewed many of the problems associated with existing databases. Descriptions for some of the more widely used databases follow. The GIS World Source Book (Parker 1991), which is published yearly, is an excellent source of information on existing data available for GIS applications.
Land Use and Land Cover and Assosiated Maps
The Land Use and Land Cover (LULC) and associated data files are available from the U.S. Geological Survey (USGS) and provide information on five data layers: (1) land use and land cover, (2) political units, (3) hydrologic units, (4) census county subdivisions, and (5) federal land ownership. These files are derived from maps at scales of 1:250,000 and 1:100,000.
Land use and land cover areas are classified into nine major classes: urban or built-up land, agricultural land, rangeland, forestland, water, wetland, barren land, tundra, and perennial snow or ice. Each major class is composed of several minor classes (e.g., forestlands are further classified as deciduous, evergreen, or mixed). This classification system (Anderson et al. 1976) was reviewed by a committee of representatives from the USGS, NASA, Soil Conservation Service (SCS), the Association of American Geographers, and the International Geographical Union. The classification system (Table 2) was designed to be used with data obtained from remote sensors on aircraft and satellites.
The minimum mapping area (smallest area mapped) for all urban areas, bodies of water, surface mines, quarries, gravel pits, and certain agricultural areas is 4 ha. The minimum mapping area for all other categories is 16 ha. Thus, a residential area <4 ha would not be recorded in these files, nor would an area of cropland or pastureland <16 ha. Aerial photographs and satellite data serve as the primary sources used in compiling the LULC maps. Some areas on each map are field checked for accuracy.
The four associated maps are prepared at the same scale as the LULC files. The political units file contains county and state boundaries as shown on USGS maps. The hydrologic-units file was digitized from the 1:500,000-scale state maps delineating hydrologic units, which were compiled by the Water Resources Council and published by USGS's Water Resources Division. The census county subdivisions file shows minor unit divisions or equivalent areas. Census tracts within Standard Metropolitan Statistical Areas (SMSA) are represented in this file. The federal land ownership file delineates surface ownership for all areas >16 ha. Federal subsurface ownerships are not delineated.
The LULC and associated data files are available in vector and raster formats on 9-track tapes or CD-ROMs. The raster format uses a cell size of 4 ha. More information on these files can be obtained from the regional USGS Earth Science Information Centers (ESIC) offices (see Appendix I for the addresses and telephone numbers for USGS ESIC offices).
|Table 2. Land use and land cover categories used by the U.S. Geological Survey (Anderson et al. 1976).|
Digital Line Graphs
Digital line graphs (DLGs) are the digital representation of the planimetric information (line map data) shown on a map. DLGs have been compiled by USGS from 1:2,000,000-scale maps, some 1:250,000- to 1:100,000-scale maps, and some of the 1:24,000- and 1:62,500-scale maps.
DLGs compiled from 1:2,000,000-scale maps are available for three categories: (1) boundaries, which include state and county boundaries and federally administered lands; (2) transportation, which includes roads, railroads, and airports; and (3) hydrography, which includes streams and water boundaries. A CD-ROM that contains data for all 50 states organized into 21 geographic regions can be purchased for only $32 from the ESIC in Reston, Virginia.
The DLGs compiled from 7.5- and 15-minute topographic quadrangles include nine thematic categories: (1) boundaries; (2) transportation; (3) hydrography; (4) U.S. Public Land Survey System (PLSS) (including township, range, and section information); (5) hypsography, including contours and supplemental spot elevation; (6) vegetative surface cover, including woods, scrubs, orchards, vineyards, and marshes and swamps; (7) nonvegetative features including lava, sand, and gravel; (8) survey and control markers, including horizontal and vertical positions of benchmarks; and (9) hurnamnade features, including cultural features not collected in other major data categories, such as buildings. Any feature shown on a 7.5 or 15-minute topographic map will be delineated on the DLGs. These data are not available currently for many locations. DLG data do not carry quantified accuracy statements. However, the data are inspected for attribute accuracy and topological fidelity.
Digital Elevation Data
Elevation, slope, and aspect can be important information for a variety of wildlife applications of GISs. Digital elevation data, frequently termed digital elevation model (DEM) or digital terrain model (DTM), provide elevation information along a contour or at regularly spaced sample points. Aronoff (1989) described four basic formats for capturing and storing elevation data. These data can be used to derive information about the morphology of the landscape such as slope and aspect, which are important to solar insolation and microclimate. Algorithms have been developed to extract the drainage network from DEMs and to partition the landscape into watersheds, subcatchments, and hillslopes for hydrologic modeling (Jenson and Domingue 1988, Band 1989).
The U.S. Defense Mapping Agency produced the first DEM for the entire U.S. by scanning the contour overlays for all 1:250,000-scale topographic maps. From the scanned contour lines, elevations were sampled every 3 arc-seconds of latitude and longitude (approximately every 90 m). The elevation accuracy of these data ranges from 15-m RMSE (Root Mean Square Error) to 60-m RMSE, depending on the terrain. RMSEs are usually lower in flat terrain and increase in steep terrain. These DEM data are sold by USGS in sections 1 X 1 degree in size.
USGS is compiling elevation data from the 7.5-minute topographic maps. From these maps, elevation is sampled every 30 m. Vertical accuracy varies from 7 m to 15 m. DEMs derived from the 7.5-minute topographic maps are available for about 50% of the U.S. as of 1993. Errors in the elevation data can introduce significant errors into calculations of slope and aspect. Errors tend to occur in areas of rapid change in slope and exposure such as along ridges and ravines (Davis and Dozier 1990).
National Wetlands Inventory
More than 30,000 detailed wetland maps have been produced by the National Wetland Inventory Program (NWI). NWI maps cover nearly 70% of conterminous U.S., 21 % of Alaska, and all of Hawaii. Most of the maps cover the same area as covered by the 7.5-minute, 1:24,000-scale topographic maps distributed by USGS, but some NWI maps have been produced at scales as small as 1:100,000 (Gravatt 1991).
The maps have excellent consistency, because one classification system (Cowardin et al. 1979), one set of photo-interpretation conventions, and one set of cartographic conventions were used. In April 1991, more than 1,100,000 copies of NWI maps had been distributed (Gravatt 1991). The USFWS is on schedule to complete the mapping of the conterminous U.S. by 1998 as required by the Emergency Wetland Resource Act of 1986. Mapping in Alaska should be completed by 2000.
In 1991, digital data files were available for more than 6,200 maps representing 10.5% of the continental U.S. An index map that shows the current availability of digital NWI data can be obtained from NWI in St. Petersburg, Florida. Digital data are available for sale from the USGS's ESIC offices for $25/map; they are available on magnetic tape in MOSS export, DLG3 optional, or GRASS formats (Gravatt 1991).
Digital Soils Data
The SCS has the responsibility for the National Cooperative Soil Survey (NCSS), which includes collecting, storing, maintaining, and distributing soils information for privately owned lands in the U.S. (Nielsen 1991).
The SCS has established three digital geographic databases for soil. Each consists of a spatial component that describes the location of the named soil unit and an attribute component that describes characteristics of the soil unit in detail. These digital data help facilitate the storage, retrieval, analysis, and display of soil data in a highly efficient manner. These data can be integrated readily with other spatial and demographic data in GISs. A soils data layer may be one of the most important GIS data layers for wildlife applications.
The Soil Survey Geographic database (SSURGO) is a vector database describing soil delineation boundaries. The boundaries of the soil units are delineated from aerial photographs ranging in scale from 1:15,840 to 1:31,680 combined with extensive fieldwork. The delineated soil boundaries are transferred to 7.5-minute orthophotoquads or topographic maps before the digitizing proceeds.
State Soil Geographic database (STATSGO) was digitized from 1:250,000-scale topographic maps, on which a generalization of the detailed soil surveys was mapped. For areas for which detailed soil survey maps were not available, the generalized soils information was compiled from existing data on geology, topography, vegetation, and climate. STATSGO data are distributed as complete coverage for a state.
The National Soil Geographic database (NATSGO) was derived from general soil maps for each state. NATSGO data were digitized from a map covering all of the U.S. at a scale of 1:7,500.
The Soil Interpretations Record (SIR) database provides attribute data describing the characteristics for each map-unit component and interpretative data for numerous uses. The accuracy of these maps is not determined. Data standardization between field surveys has been a problem, and as a result soil types and properties at the boundaries of adjacent maps often disagree (Burke et al. 1991). GIS technology not only will revolutionize the way the data are analyzed and displayed, but also the way data are collected. SSURGO, STATSGO, and NATSGO data files and the associated attribute files are available from SCS. NATSGO costs $500 for the entire U.S., STATSGO costs $500/state, and SSRUGO costs $500/county. More information on the availability and distribution of these databases can be obtained from USDA's National Cartographic Center, Fort Worth, TX 76115.
The U.S. Bureau of the Census created a spatial data set describing street networks, street addresses, political boundaries, and major hydrographic features for approximately 350 major cities and suburbs in the U.S. These files were created with the Dual Independent Map Encoding (DIME) system to automate the processing of the 1970 and 1980 U.S. censuses. DIME files have limited application as a digital map base. For example, streets are represented by straight lines connecting adjacent intersections. Even a curved street is represented by a straight line.
To overcome the limitations of the DIME files and to prepare for the 1990 census, the U.S. Bureau of the Census developed the TIGER (Topologically Integrated Geographic Encoding and Referencing) system. The TIGER files provide vector data for hydrography, transportation, political, and statistical areas (such as county, incorporated area, census tract, and census block). Data collected from the 1980 and 1990 censuses, such as population, number of housing units, income, occupation, and housing values, serve as attribute data for these files. Nearly all commercially available GIS software systems have procedures for importing TIGER data. Various companies have developed inexpensive GIS systems strictly for the use of TIGER files and the associated census data. These companies sell hardware, software, TIGER, and census data as a complete package. TIGER data also can be purchased directly from the Bureau of the Census, Washington, DC 20233. With the release of the TIGER files, the Census Bureau no longer supports or sells the DIME files.
The TIGER files comprise one of the most detailed computerized digital map databases ever developed for the U.S. More than 7 years and $200 million dollars were required to complete the TIGER files. The complete TIGER files for the entire U.S. contain nearly 40,000,000 line segments and require more than 15,000 megabytes of storage for the vectors alone (Anonymous 1989).
Appendix II provides the addresses and telephone numbers for many sources of digital data in the U.S. and Canada.