BASE MAPPING. Technical Approachs

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Gertrude Patience Sanders
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BASE MAPPING Technical Approachs STATEMENT OF THE PROBLEM A major stumbling block to the integration of remotely sensed data into existing GIS data base structures is the issue of positional accuracy

1 BASE MAPPING Technical Approachs STATEMENT OF THE PROBLEM A major stumbling block to the integration of remotely sensed data into existing GIS data base structures is the issue of positional accuracy of the existing line-work within the vector database. This inaccuracy manifests itself when overlain to more positional consistent imagery data. Figure 1 shows a typical situation occurring between the existing GIS data layer (parcels) and the imagery base. Figure 1: Vector overlay on IKONOS PRESENT SITUATION In this case the parcel map had a variable accuracy of up to 40 ft plus or minus once the various tiles were combined. This is the result of data being built by hand historically remaining unedgematched between tiles within a mylar mapping system. The investment to convert this (the only base map widely used) was made and the sheets were scanned and vectorized by the private sector, which very accurately reproduced the inherent errors of this mapping approach. With the incorporation of GPS and the associated problems of edgematching the tiles into a seamless database the consortium was stymied. This lead to the development of an image based reference for these data layers from the existing DOQQs (1995 vintage) (Figure 2).

Figure 2: 1995 Digital Orthophotography POTENTIAL BENEFITS Through the creation of a single base image map the respective users and developers of the numerous data sets being created and compiled

2 Figure 2: 1995 Digital Orthophotography POTENTIAL BENEFITS Through the creation of a single base image map the respective users and developers of the numerous data sets being created and compiled within the county, city, and private sector, could be spatially integrated and the investments being made across within the region maximized. TECHNICAL APPROACH TAKEN A process was developed that assisted the county in creating a seamless database that could be used by any local government or public works group as an accurate and reliable base map from which all other vector data could be migrated while maintaining the integrity of the relativepositional accuracy of the vector line-work. The process involved the original acquisition of the DOQQ data from the Missouri Spatial Data Information Service (MSDIS). The DOQQ data was delivered to MSDIS on CD-ROM. The DOQQ data for Boone County came in the form of the original unprocessed DOQQ data. These were the DOQQs that were used for the base map portion of the BOCOMO project. Processed DOQQs were also delivered to MSDIS on CD- ROM. These processed DOQQs had been linear stretched and appeared visually unappealing when they were placed together in a seamless database. The differences in the contrast from one DOQQ to the next DOQQ were unappealing. Therefore, it was decided to use the unprocessed DOQQs for this project. The original DOQQs had to be processed using an AML created to read the USGS data format and convert the DOQQs into a readable image file. The AML created Band Interleaved by Pixel (BIP) formats. The next step involved converting the BIP DOQQs into Arc/Info grids.

3 This was done using the imagegrid command in ARC. Imagegrid converts images into grids. These grids were then projected from their original format (Projection UTM, Zone 15, Datum NAD83, Units Meters) to their new projection (Projection Stateplane, Zone 4426, Datum NAD83, Units Feet). This was done because Boone County currently uses the Stateplane projection for all of its spatial data. The projection gives different options as to how to perform the resampling. Cubic convolution was used as the resampling procedure because it was felt that cubic convolution maintains the spatial integrity of the data, although it does tend to interfere with the spectral integrity. It was felt that maintaining the spatial integrity of the DOQQs was more important than maintaining the spectral integrity of the DOQQs because the DOQQs were ultimately going to be used for the county wide base map. Therefore, cubic convolution, as opposed to nearest neighbor, was selected as the resampling process used when projecting the grids. Once the grids were projected to Stateplane, they could then be converted back into images so that further processing could be performed on them in an image processing software. The gridimage command was used to convert the grids into an image format that could be read in ENVI image processing software. The image file selected was the TIFF file format. The TIFFs were then brought into ENVI. The TIFF DOQQ image files were then histogram matched before any warping or other processing was done. The histogram matching was done to make the DOQQs more visually appealing. Histogram matching was done in order to make the brightness distribution of the two images as close as possible. All of the DOQQs were histogram matched to the same originating DOQQ. This DOQQ was Brownsne.tif. The northeastern quadrant of Browns was chosen as the matching DOQQ for the rest of the DOQQs because it contained a distribution of rural to urban land that was proportionate to the county as a whole. Once all the DOQQs were histogram matched an evaluation began that used the centerlines that were collected by the Boone County Public Works Department as a source of ground truth. The centerlines were created from their GPS kinematic data collection that was done for the whole county. These lines were not the exact centerlines because the vehicle used in the collection process often had to drive down one side of the road or the other. Nevertheless, the lines should when overlain on images fall within the road encasement. The evaluation involved overlaying the centerlines on various DOQQs throughout the county to see if they fell within the encasements of the roads. The majority of the areas that were visually inspected showed that the centerlines did in fact fall within the encasement of the roads. Nevertheless, a ground team was activated in order to collect image based ground control points for various portions of the county. Over 200 ground control points

4 were collected for the majority of the central to southern portion of the county. These ground control points were used to try and warp the individual DOQQs to see if warping them would produce more accurate x and y results. The results of warping the histogram matched DOQQs were encouraging when looking at the individual DOQQs, however, when trying to mosaic the warped DOQQs they would not line up properly along the edges of the DOQQs. A further evaluation of the ground control points along with the centerlines showed that there was an error of approximately 4 to 6 feet, between what was considered truth (the GCPs and the centerlines) and the DOQQs. The county and city requirements were that the DOQQs be positionally accurate within 10 feet of ground truth. Because the DOQQs were within the range defined by the county and city, it was decided not to try and make the DOQQs any more positionally accurate (Figure 3). Figure 3: Unwarped DOQQs with centerlines overlain The next step involved in the DOQQ base map was the mosaicking or all the DOQQs into one seamless database. The individual DOQQs were first mosaicked together into their respective quads. Then each of the quads was put together in tiles running north to south. Once these tiles were put together, they were then mosaicked together into one database. In all, there were around sixty individual DOQQs, which were mosaicked into 15 quads, which were mosaicked into 5 tiles, and finally which were mosaicked into the DOQQ base map database. Each time a mosaic was done, feathering was performed to help smooth out the seams along the

5 edge of the DOQQs. A one hundred-meter feather was applied to each mosaic performed during the building up process of the base map. Figures 4 and 5 show a comparison of a non-feathered, non-histogram matched mosaic section, with an example of a feathered mosaic section. Figure 4: Non-feathered, non-histogram matched DOQQ mosaic Figure 5: Feathered and histogram matched DOQQ mosaic Once the DOQQ base map was completed, the IKONOS (April 2000) imagery was integrated into the DOQQ database to give a more current and up-to-date idea of what was occurring in the county. The IKONOS database covered roughly one half of the county. Its

6 geographical extent ranged from the north central portion of the county down through the southern portion of the county. The lowest precision IKONOS was acquired from Space Imaging. Two separate swaths were purchased, an eastern swath and a western swath (Figure 6). Figure 6: IKONOS coverage for Boone County The IKONOS imagery had to be co-registered to the DOQQ base map database. Before co-registration began, the IKONOS had to be projected from its original UTM projection to Stateplane, as was the case for the DOQQs. Co-registration was labor intensive and tedious. Roughly 1600 image-to-image registration points were selected to register the IKONOS to the DOQQ database. So many points had to be used because the integrity of the spatial accuracy was at stake when registering to the DOQQ database. As well, the IKONOS imagery was not ortho-rectified so we were attempting to accomplish both the registration and orthofication of the IKONOS imagery with this process. When fewer points were tried, the IKONOS would lose some of its spatial accuracy. Using more points increased the co-registration accuracy of the IKONOS. It is important to note that the maximum accuracy of the IKONOS co-registration would only be as good as the base accuracy of the DOQQs. The resulting IKONOS accuracy was roughly similar to the DOQQs (approximately 5.5 feet). Once the IKONOS was registered to the DOQQs, it was possible to integrate the IKONOS with the DOQQ to get a seamless database with the IKONOS coverage overtop of the DOQQ data (Figure 7). Once these DOQQ database and the IKONOS/DOQQ database were completed, each were compressed using the commercial product MrSID in order for file transfer and distribution to be made easier.

Figure 7: IKONOS (right) and DOQQ (left) Mosaic with centerlines overlain PRODUCTS CREATED The deliverables for this segment of the BOCOMO project was a countywide image base with a user defined

7 Figure 7: IKONOS (right) and DOQQ (left) Mosaic with centerlines overlain PRODUCTS CREATED The deliverables for this segment of the BOCOMO project was a countywide image base with a user defined spatial accuracy of plus or minus 10 feet. The deliverables included: 1. Countywide 1995 DOQQ seamless mosaic (RMSE 5.1 feet) 2. Countywide 1995 DOQQ seamless mosaic with IKONOS data integrated where coverage was available (RMSE 5.5 feet) 3. Vector migration protocols for conversion of spatially inaccurate vector data to the image base. ANTICIPATED IMPLEMENTATION PROBLEMS IN LOCAL GOVERNMENT The users will still not be able to see all the features that they would like to. A procedure for building tools to allow for inventory of directly visible objects (transmission towers, bridges, maintenance buildings, etc.) as well as referential inventory of objects too small for the IKONOS imagery to delineate (fire hydrants, utility poles, storm water inlets, etc.) is needed and will be sought in Synergy Phase II. Work will also need to continue with the users to define decision rules whereby the conversion process for the migration of vector files to the image base can be automated to the extent possible. This is critical work as it is this migration and spatial referencing capacity which will allow the legacy systems and data bases to be integrated with the

8 remote sensing data available today. The tool kits and interfaces to allow for this conversion process will be pursued within in Phase II. As well, training and education in the use and utility of these data is needed to ensure proper implementation and integration within the local government applications. ADDITIONAL WORK 1. Tool kit development for inventory of features from the base map 2. Decision trees for the automation of vector migration tools 3. Feature extraction from the image base for new features (roads, building footprints, etc.) 4. Continuing study of accuracy assessment and cost/benefit analyses based on various methods of building and maintaining an image base map. PROJECT PARTICIPANTS Timothy L. Haithcoat, Dan Daugherty, Jim Dunajcik, and Derek Smith, Geographic Resources Center (GRC), Department of Geography, University of Missouri under the auspices of ICREST. User clients included: Ross Short, City of Columbia and Boone County GIS Coordinator; David Storvick, City of Columbia, Engineering Department; J.R. Richardson, Boone Electric Cooperative;

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