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Planimetric accuracy of Ikonos 1-m panchromatic image products and their utility for local government GIS basemap applications

C.H. DAVIS and X. WANG

Department of Electrical Engineering University of Missouri - Columbia

Columbia, MO 65211 USA Phone: (573) 884-3789 FAX: (573) 882-0397

DavisCH@missouri.edu XiangyunW@hotmail.com

Abstract. A detailed assessment of the planimetric accuracy of high-resolution (1 m) panchromatic Ikonos orthoimage products for three different test sites located in the State of Missouri is presented. The main objective of this study was to evaluate the potential of Ikonos orthoimage products for use as digital image basemaps in local government GIS systems. For maximum utility and adoption in a wide variety of local government planning and management applications, a planimetric accuracy of 3-4 m CE90 is considered nominal. Evaluation of the planimetric accuracies of Ikonos orthoimages acquired under a Space Imaging/NASA Databuy agreement were found to be 1.1 m and 1.7 m CE90 at two different test sites. The terms of the NASA Databuy agreement require a 2 m CE90 planimetric accuracy. Thus, our independent assessment confirms that the Space Imaging products met this NASA Databuy specification. Low precision, low cost georeferenced Ikonos image products (Carterra Geo, 50 m CE90) were orthorectified using third party commercially available software and various custom NAPP DEMs. The planimetric accuracies of the resulting Ikonos orthoimages were found to vary between 2-4 m CE90. In addition, USGS DEMs were also used to orthorectify georeferenced Ikonos image products. The resulting Ikonos orthoimages were found to have planimetric accuracies from 2-7 m CE90. Planimetric accuracies of 2-3 m CE90 can be obtained from georeferenced Ikonos using USGS DEMs with RMS vertical accuracies on the order of 2 m. The approach demonstrated here can be used to deliver up-to-date, cost effective orthoimages from Ikonos Carterra Geo products that yield planimetric accuracies suitable for use as digital image basemaps by local governments. 1. Introduction Urban growth and change places a heavy demand on local governments to seek better planning and management approaches. Increasing urbanization puts pressure on natural resources and existing infrastructure. Decision makers within various local government bodies must deal with a wide-variety of issues that routinely have economic, social, and political consequences. In addition, state and federal governments have issued an increasing number of regulations that mandate the monitoring and tracking of numerous issues by local governments. Elected officials in these local governments require timely information products to support policy decisions on issues that are often interrelated and can span several political boundaries. Growth assessment, infrastructure inventory and planning, environmental assessment, and risk management impact and drive policy decisions for these managers.

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As a result, local governments have invested considerable resources in developing Geographic Information Systems (GIS) to aid them in their planning and decision making processes. A digital image basemap is a key information layer in many local government GIS systems. Image basemaps are used by city planners and engineers for tax assessment, inventory, construction planning (roads, bridges, etc.), stormwater management, and other civil planning activities (greenbelt preservation, emergency 911, etc.). A major stumbling block to the effective application of remote sensing imagery for digital basemap generation is the positional accuracy of the imagery. Existing vector data layers (road centerlines, parcel and zoning boundaries, etc.) are routinely superimposed upon the image basemap for planning and assessment applications. Figure 1 shows a typical situation where an existing GIS data layer (parcel boundaries) is overlain on an imagery basemap that has a lower positional accuracy. If vector data layers do not line up with the image basemap, then the basemap is perceived to be of limited value and will not be integrated into standard operations and decision-making processes within the local government. Thus, the horizontal or planimetric accuracy of a remote sensing image is a critical measure of its utility for application as a digital basemap in local government GIS systems. 2. Digital basemap sources Orthorectified digital images derived from aerial photographs are often used as basemaps in many local government GIS systems. Image orthorectification using an appropriate Digital Elevation Model (DEM) to correct for horizontal errors (due to terrain displacement) is required to produce a basemap with a high degree of positional accuracy. The most widely available high-resolution digital orthoimage dataset available to local governments in the USA are the Digital Orthorectified Quarter Quadrangles (DOQQs) produced by the United States Geological Survey (USGS 1996). The DOQQs are derived from 1:40,000-scale National Aerial Photography Program (NAPP) panchromatic aerial photographs acquired about every 5-6 years (Light 1993). The NAPP photographs are acquired with 60% N/S overlap to enable stereo processing for DEM extraction. The USGS 7.5-minute Level 1 DEMs are derived from stereo-processing of the NAPP imagery. The USGS DEMs have a 30-m horizontal resolution, a nominal RMS vertical accuracy of 3-5 m, and a worst-case vertical accuracy of 7 to 15 m (USGS 1997). The USGS DEMs have been used to orthorectify the original NAPP photographs to produce DOQQs. The DOQQs have a ground sample distance or pixel size of 1 m and a worst-case planar accuracy of 10 m CE90. The reported 10-m planar accuracy is a circular error at 90% probability (CE90) and corresponds to US National Map Accuracy Standards (NMAS). NMAS specifies that 90% of well-defined points must fall within the specified planar accuracy of the image or map. The DOQQ pixel size is sufficient for local government basemap applications. The worst-case planar accuracy is a limitation for widespread use. For example, typical road widths are on the order of 10 m, so a 5 m planar accuracy (CE90) would be required for road centerline vectors to lie within the road width. Nevertheless, many city and county governments have utilized

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DOQQs as their data source for orthoimage basemaps because the cost to obtain these data is essentially zero. However, problems with positional displacements between individual DOQQs in the overall basemap and problems with proper registration of vector data layers are routinely experienced. Another limitation of the DOQQs is that these are produced from NAPP aerial photographs that are only acquired about every 5-6 years. Thus, the DOQQs are often outdated in many areas and do not reflect the current situation within the city and/or county, especially in areas that experience even moderate urban development and/or change. The recent launch of the Ikonos satellite has opened up a new area for acquiring up-to-date high-resolution panchromatic digital imagery for use as digital orthoimage basemaps. Ikonos panchromatic imagery has a nominal pixel size of 1 m and an 11-bit information content. The 1-m pixel size is identical to the DOQQ pixel size while the 11-bit imagery provides image contrast and quality that is superior to the DOQQs. The Carterra Geo Ikonos product costs $21/km2, bit its horizontal precision is poor with a planar accuracy of only 50 m CE90 (Space Imaging 2000). While this product is affordable for most local government entities, the planar accuracy of 50 m CE90 renders this product unusable for traditional basemap applications. Our interaction and discussion with basemap users within various local government user communities indicates that planar accuracies of 3-5 m CE90 are required for 1-m resolution digital basemaps to be useful for GIS applications. The Carterra Precision Ikonos panchromatic image product costs $63/km2 and has a planar accuracy of 4 m CE90 (Space Imaging 2000). While this meets the planar accuracy requirement for basemap implementation, it is three times more expensive than the Carterra Geo product. This cost can be prohibitive for many local government entities with limited fiscal resources. For example, a typical county within the State of Missouri, USA covers an area of approximately 2,400 km2. Thus, for complete county coverage the Geo and Precision Ikonos products would cost $50,400 and $151,200 (US), respectively. The former figure is cost-competitive with traditional aerial photographic surveys, while the latter figure is beyond the budgetary capability of most local county governments. The challenge then becomes to develop a methodology that would enable the use of the lower-cost Carterra Geo products for creation of digital basemaps. In this paper, we present results from a comprehensive examination of the planimetric accuracy of various Ikonos 1-m panchromatic image datasets. This analysis includes both the Geo and Precision imagery from multiple test sites. The impact of DEM resolution and error on the planimetric accuracy of the resulting orthoimages is also evaluated. In addition, the planimetric accuracy of USGS DOQQs and other custom NAPP-based orthoimages are evaluated for comparative purposes. The results are used to assess the cost/benefit of various digital basemap sources for adoption and use in local government GIS applications. 3. Methods and data sets 3.1 Study areas and image data Table 1 provides a summary of the study area and Ikonos image characteristics for the three test sites used in this study. The first study area was in Southern Boone County (SBC), Missouri

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just south of the City of Columbia (population 70,000). The study area is about 220 km2 (14 x 16 km) and is part of the US 63 highway corridor connecting Columbia, MO and the state capital of Jefferson City, MO. The study area contains the small town of Ashland (population 1,500). This is an environmentally sensitive area containing several state parks. The area is under pressure from urban expansion (primarily single family housing developments) pushing south out of Columbia and north out of Ashland. The second study area is located in a rapidly growing suburban area in St. Charles County (SCC), Missouri. SCC is a bedroom community near St. Louis, MO and is one of the fastest growing counties in the Midwest. The final study area is located in an urban area of Springfield, MO. This area has been designated as an Urban Validation Site (UVS) for testing and evaluation of a large variety of remote sensing datasets for urban applications (Hipple and Daugherty 2000). The SCC and UVS study locations cover areas of 49 km2 (7 x 7 km) and 21 km2 (3 x 7 km), respectively. A Carterra Geo Ikonos panchromatic image was acquired for the SBC study area on 4/30/2000 with a nominal viewing angle of 37° (elevation angle measured with respect to satellite nadir). The Carterra Geo products are not corrected for terrain distortions and consequently these have the poorest planar accuracy of all products available from Space Imaging (SI). The worst-case planar accuracy of the Carterra Geo product is 50 m CE90 over non-mountainous terrain (Space Imaging 2000). For both the SCC and UVS study areas, Precision Plus Ikonos orthoimages were obtained under the NASA Phase II Scientific Databuy agreement (NASA 2001) with Space Imaging. Under this agreement, the planar accuracy of these products is 2 m CE90 compared to the 4 m CE90 accuracy of the Carterra Precision product that is commercially available from Space Imaging. In addition, Carterra Geo Ikonos images were also obtained for each test site so that these could be independently processed to produce orthoimage basemaps. For the SCC study area, the georeferenced Ikonos image was acquired on 2/29/2000 at a viewing angle of 18°. This is the same source image that what used by SI to produce the Precision Plus orthoimage under the NASA Phase II Databuy agreement. For the UVS study area, the SI product was acquired on 3/28/2000 at a viewing angle of 12°, whereas the georeferenced Ikonos image was acquired separately on 9/17/2000 at a viewing angle of 20°. 3.2 Digital Elevation Models A Digital Elevation Model (DEM) of some form is needed to process georeferenced image data (satellite or airborne) to remove planar distortions in the image caused by terrain variations. This process is called orthorectification and is required to produce image basemaps with a high degree of positional accuracy. We created custom DEMs with various horizontal resolutions and vertical accuracies by stereo-processing aerial photos for all three study areas. For each study area, B/W film positives were obtained from the NAPP archive at a cost of only $10/photo. The photos were acquired by the NAPP program in quasi leaf-off conditions in the spring of 1996 for all three locations. The overlap in the photos is approximately 60% N/S and 30% E/W (Light 1993). Stereo-coverage from 6-10 photos, depending on site location, was needed to produce the DEMs. The B/W film positives were precision scanned at 1200 dpi (0.85 m pixel size) by a third party vendor at a total cost of $200 ($20/photo). Precision scanning is required to preserve the geometric integrity of the B/W photos.

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A rapid-static differential GPS survey was conducted in each study area to obtain anywhere from 35-45 ground control points (GCPs) for DEM registration/generation. In addition, kinematic GPS data were collected between GCPs to generate independent check points (ICPs) for DEM validation. The accuracy of the rapid-static GCPs was estimated by comparing GPS coordinate solutions with known positions at multiple monument sites. The accuracy of the GCPs was found to be 3-5 cm RMS for all three coordinates (x, y, and z). The vertical accuracy of the kinematic GPS points, used as ICPs for DEM validation, was found to be 10 cm RMS from crossing-point comparisons following procedures described in Davis and Wang (2001). The NAPP photos were processed using commercially available software for DEM extraction (Apex v7.0 - PCI Geomatics 2001). Thus, the results presented here could be easily reproduced by other parties (e.g. third-party consulting companies, GIS specialists in city/count governments, etc.). The GCPs are used to generate a highly accurate coordinate reference frame (CRF) for triangulation and registration of the NAPP photos. DEMs with 3, 10, and 30 m horizontal resolutions were extracted via automated stereo-correlation processing for the SBC study area. DEMs with 10 m and 30 m resolution were extracted for the SCC and UVS study areas. A more complete description of the procedures used here for DEM generation from stereo-NAPP photographs can be found in Davis and Wang (2001). DEMs with different horizontal resolutions were created to evaluate the impact of DEM resolution, and the corresponding vertical accuracy, on the planimetric accuracy of the orthorectified Ikonos images. A 3-m resolution DEM was not created for the SCC and UVS study areas because of the poor planimetric accuracy results obtained for the Ikonos images in the SBC study area (Section 4). In addition to the custom NAPP-based DEMs, 30-m resolution Level 1 DEMs were obtained from the USGS for each of the study areas. This was done to demonstrate the utility of the USGS DEMs for generation of orthoimage basemaps. Since the USGS DEMs are publicly available, these represent an important no-cost elevation data source with easy access for use when no other elevation data are readily available. ICP datasets were derived from the kinematic GPS data to assess the vertical accuracies of the various DEMs in each study area. For each study area, the ICP dataset was a small subset of the kinematic GPS data. The kinematic GPS data were processed first by eliminating low-accuracy kinematic positions (due to obstructions caused by trees and buildings during the survey). In addition, the ICP data were selected: a) for uniform distribution throughout the study area, and b) for a minimum separation between ICPs of 10 m. The ICP datasets contained anywhere from 3000-8000 check points depending on the study area. The RMS vertical accuracy estimated using the ICP datasets for the various DEMs are shown in table 2. The results show that the RMS vertical accuracies of the custom NAPP DEMs varied between 1.2-3.0 m. The RMS vertical accuracy of the 3 m NAPP DEM in the SBC study area is significantly larger than the 10-m and 30-m resolution NAPP DEMs because it becomes more difficult for the automated stereo-correlation software to precisely determine the location of the matching pixel pairs in the stereo images as the DEM resolution increases (Davis and Wang 2001). The RMS vertical accuracies of the USGS DEMs were 1.6 m and 1.7 m for the SBC and UVS study areas, respectively. This is significantly better than the nominal (i.e. target) 3-5 m vertical accuracy for the USGS DEMs. However, the 16.8 m RMS vertical accuracy of the

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USGS DEM in the SCC study area is greater than the worst-case specification of 15 m (USGS 1997). Nevertheless, the majority of the DEMs used in this study have RMS vertical accuracies between 1.2-2.3 m. These accuracies have been determined using thousands of highly accurate ICPs derived from kinematic GPS surveys. These RMS vertical accuracies are very good and the corresponding DEMs are appropriate for orthorectification of the georeferenced Ikonos panchromatic imagery. 3.3 Orthorectification Digital orthorectified image basemaps were created and/or evaluated using a variety of image data sources and DEMs. As discussed in Section 3.1, Precision Plus orthorectified Ikonos images with a specified 2-m CE90 planimetric accuracy were obtained from Space Imaging, via the NASA Phase II Databuy, for the SCC and UVS study areas. In addition, the USGS DOQQs were also obtained for all three study areas with a worst-case accuracy of 10 m CE90 (USGS 1996). Orthoimage basemaps were produced from the NAPP and Ikonos Carterra Geo images using commercially available software (OrthoEngine v7.0 – PCI Geomatics 2001). Thus, these results could easily reproduced by third parties as needed. This is an important issue for widespread adoption by the private sector and/or local government entities. Proprietary algorithms and non-commercial software are in impediment for widespread development and application of remote-sensing information products. First, the custom NAPP DEMs with various horizontal resolutions (table 2) were used to orthorectify the digitally-scanned NAPP aerial photos to produce NAPP digital basemaps for all three study areas. In this way the effect of DEM resolution and accuracy could be evaluated in terms of the impact on the visual quality and planar accuracy of the resulting NAPP orthoimage basemaps. Note that these digital basemaps are produced from the same NAPP data used by the USGS to produce the DOQQ products. Next, the low-precision Ikonos Carterra Geo images were orthorectified using a variety of techniques and DEMs for each of the three study areas. In addition to the DEM, the orthorectification process requires GCP input data to develop corrections for planar distortions in the imagery resulting from the Ikonos sensor viewing geometry. There are several methods available for developing these corrections. These include the simple polynomial (SP), the rational function polynomial (RFP), and the more rigorous sensor model (SM). The SP method was not used as this is known to produce very poor results compared to the other two methods (Toutin and Cheng 2000). The RFP method uses a ratio of polynomial transformations that take into account ground elevation in addition to horizontal location to correct for planar distortions in the imagery. The RFP method does not attempt to model and/or estimate the satellite sensor viewing geometry. Instead the RFP method uses the GCP data to develop local estimates for the planar distortion caused by the viewing geometry. Since the RFP method relies exclusively upon the polynomial transformations derived from GCP data, the RFP corrections are only valid for areas in the vicinity of the GCP. As a result, planimetric distortions are not entirely eliminated between GCP locations. Thus, the RFP method is best suited for small areas with gentle terrain and a large number of GCPs.

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The SM method uses the GCP data to develop a rigorous model of the sensor viewing geometry (Toutin 1995) that is valid for the entire image scene. Even though the detailed sensor information for Ikonos has not been released by Space Imaging, a valid SM solution can be obtained for Ikonos using GCP input and basic information from the image metadata files (Toutin and Cheng 2000). A distinct advantage of the SM solution is that only a limited number of ground control points (4-6) are required to develop a model that is valid for the entire image scene. Both the RFP and SM methods were used to generate different Coordinate Reference Frame (CRF) solutions for each Ikonos Carterra Geo image in a given study area. A total of 10 GCPs obtained from the rapid-static GPS survey (section 3.2), uniformly distributed over the study area, were used to develop the RFP and SM solutions. The remaining rapid-static GPS points in each study area where then set aside to be used as Independent Check Points (ICPs) for validation of the planimetric accuracy of the resulting orthoimage basemaps. Figure 2 shows the spatial distribution of the GCPs and ICPs in the Ikonos basemap that was produced for the SBC study area using the 10-m NAPP DEM and the SM coordinate solution. Table 3 summarizes the RMS residual errors for the RFP and SM solutions obtained from least-squares bundle adjustment of the GCP input data. The same 10 GCP points were used in both the RFP and SM solutions for each study area. For all three study areas the CRF RMS errors are excellent and are less than one-half pixel (< 0.5 m). This is due to: 1) the high horizontal and vertical positional accuracies of the GCP input data, 2) the a priori selection of the GCP locations at point targets in the imagery with high contrast and visual clarity, and 3) the ability of the software operator to select the GCP locations in the imagery with sub-pixel accuracy. After the two CRF solutions were obtained for each Ikonos Carterra Geo image, the images were then orthorectified using various DEMs summarize in table 2 for each study area. The planimetric quality and accuracy of the DOQQ, NAPP, and all the various Ikonos orthoimage basemaps were then evaluated. The planimetric quality was subjectively measured by examining the linearity of known linear features (primarily roads and buildings) in the imagery. The planimetric accuracy was assessed using ICP datasets that contained 20, 24, and 35 points uniformly distributed (e.g. figure 2) throughout the SBC, SCC, and UVS study areas, respectively. As noted previously, the ICP coordinates were obtained from rapid-static GPS survey data and had RMS x and y accuracies between 3-5 cm (section 3.2). The ICPs were selected a priori at locations in the imagery that were sharp and distinct point features. This facilitated the identification of these points in the orthoimage basemaps and subsequent comparison with the known GPS-based ICP locations. The ICPs are distinct from the GCPs used for the RFP and SM solutions for the Ikonos Carterra Geo images (e.g. figure 2). The RMS Radial Error (RMSRE) and the Circular Error @ 90% probability (CE90) were calculated for each orthoimage basemap using the ICP datasets. The RMSRE is given by

22yx RMSRMSRMSRE += (1)

where RMSx and RMSy are the RMS errors in the x and y directions, respectively. The RMS error in each coordinate is computed using

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1

)(1

2

−

−=∑=

N

PPRMS

N

iIMAGEICP ii

(2)

where PICP and PIMAGE are the x or y positions obtained from the ICPs and the image locations, respectively. The CE90 is estimated by rank ordering the radial errors from smallest to largest and selecting the radial error corresponding to the 90th percentile, i.e. the abscissa value of the 90% point of the Cumulative Distribution Function (CDF) of the radial (circular) error is used. For example, in an ICP dataset of N = 20 points, the 18th (0.9 × 20) radial error value in the rank ordered dataset is used to estimate CE90. For a purely random error distribution in both x and y coordinates, the relationship between the circular error @ 90% and the RMSRE is CE90 = 1.54 × RMSRE. The actual relationship will vary slightly from this due to small bias errors in the x and y directions. 4. Results and discussion 4.1 DOQQ and NAPP basemaps The planimetric quality and accuracy of the DOQQ and custom NAPP digital basemaps are summarized in tables 4-6. The USGS DOQQ basemaps had CE90 values between 2.7 m and 3.4 m. Thus, the DOQQ basemaps in this study had planar accuracies that are suitable for most local government applications where a nominal accuracy of 3-4 m CE90 is required. Note that the specified worst-case planar accuracy of the DOQQs is 10 m CE90 (USGS 1996), so the results presented here certainly may not be valid for all DOQQs. However, we believe that the 10-m CE90 specification is indeed a worst-case value and that many DOQQs will have planar accuracies substantially better than this, just as the case for the three test sites in this study. The custom NAPP basemaps, which are derived from the same aerial photographs as the USGS DOQQs, had CE90 values that ranged between 0.9 m and 3.1 m depending on study area and the DEM source used for orthorectification. For the SBC study area, the lowest and highest accuracy custom NAPP basemaps were orthorectified using the 3-m and 30-m NAPP DEMs respectively. We attribute that higher planar accuracy of the NAPP basemap orthorectified using the lower resolution 30-m DEM to the better vertical accuracy and coarser spatial resolution. As noted previously, the 3-m NAPP DEM had an RMS vertical accuracy of 3.0 m while the 30-m NAPP DEM had an RMS vertical accuracy of 2.2 m (table 2). The coarser resolution of the 30-m DEM in effect represents a spatial average of the elevation data relative to the 3-m NAPP DEM. Abrupt (erroneous) elevation discontinuities present in the 3-m NAPP DEM are smoothed out in the coarser resolution 30-m NAPP DEM. Figure 3 shows a comparison of how well the DOQQ and custom NAPP basemaps preserved the linearity of roads in the SBC study area. Subjective rankings of poor, fair, good, and excellent are provided in tables 4-6 to describe the planimetric quality of the various image basemaps based upon the preservation, or lack thereof, of known linear features. Strong distortions are present in the NAPP basemap derived from the 3-m NAPP DEM. Moderate distortions are present in the NAPP basemap derived from the 10-m NAPP DEM, whereas no

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significant distortions occur in the DOQQ and the NAPP basemap derived from the 30-m NAPP DEM. Note also that the contrast and clarity of the custom NAPP images are slightly better than the DOQQ. There is clearly a tradeoff between horizontal resolution and vertical accuracy of a DEM and its subsequent effect on the planar accuracy and quality of basemaps derived from DEMs via the orthorectification process. In general, it is desirable to use the highest resolution DEM possible for orthorectification. This is especially true in urban areas where there are substantial topographic variations over short spatial scales (e.g. buildings). In these areas, high resolution DEMs are needed to preserve the planar accuracy of orthorectified image features that are often adjacent to each other and have substantially different elevations (e.g. roads and buildings). However, the vertical accuracy of DEMs derived from stereo-correlation procedures usually degrades as the resolution increases (assuming the source image resolution is the same). Thus, the planimetric accuracy and visual quality tends to degrade as the DEM resolution increases because the vertical accuracy worsens. In more rural areas with less elevation variability over short spatial scales, it is preferable to utilize moderate resolution DEMs that maintain an acceptable vertical accuracy. The results in table 4-6 show, in general, that the custom NAPP basemap derived from the 30-m NAPP DEM provides the best planar accuracies and quality amongst the NAPP/DOQQ basemaps. In a more urban setting, a 30-m DEM may not be the best choice for orthorectification. Obviously we desire the highest resolution and highest accuracy DEM for orthorectification. However, in most situations one must critically evaluate both the resolution and vertical accuracy of the DEM to determine the proper choice for digital basemap generation via orthorectification. The planimetric accuracies for the custom NAPP basemaps derived using the 30-m NAPP DEM were around 1.5 m CE90 for all three study areas (tables 4-6). This is significantly better the DOQQ planar accuracies for any of the areas studied here. Thus, the raw aerial photographs in the NAPP archives can be exploited to produce orthoimage basemaps with excellent planar accuracies when the planar accuracies of the DOQQs are found to be insufficient or when specific applications require planar accuracies on the order of 1-2 m CE90. Note that DEMs with suitable horizontal resolutions (10-30 m) and RMS vertical accuracies (~ 2 m) are required to achieve the 1-2 m CE90 planar accuracy. 4.2 Ikonos Basemaps The planimetric quality and accuracy of the Ikonos digital basemaps are also summarized in tables 4-6. The low-precision Ikonos Carterra Geo products had planar accuracies between 9 m and 24 m CE90. This is significantly better than the 50-m CE90 worst-case specification (Space Imaging 2000) and is due largely to the small variations in terrain and/or small coverage areas (table 1). As expected, the largest planar error occurred for the SBC test site which had greater terrain variations over larger spatial scales than the other two test sites. The planimetric accuracies of the Ikonos Precision Plus basemaps provided by Space Imaging were 1.1 m and 1.7 m CE90 for the SCC and UVS study areas, respectively. A 2-m CE90 planar accuracy was required under the terms of the NASA Phase II Databuy agreement with Space Imaging. Thus, the Space Imaging products were found to meet the NASA Phase II

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Databuy specification. This independent assessment therefore confirms that Space Imaging precision orthoimages can produce planar accuracies on the order of 2-m CE90 under suitable conditions (e.g. viewing angle, GCP input, high-resolution DEM, etc.). The planar accuracy of these products approaches the 1-m pixel size of the panchromatic Ikonos imagery and is likely the best that can be achieved for these data. The 1-2 m CE90 planar accuracy of these orthoimages combined with the overall clarity and contrast of the Ikonos panchromatic imagery represents an extremely high quality product for digital basemap applications. The only factor limiting the widespread adoption of the Space Imaging precision orthoimages by local governments is the higher cost of the Carterra Precision products ($63/km2) relative to the Carterra Geo products ($21/km2). The custom Ikonos orthoimages generated by orthorectifying the Ikonos Carterra Geo source images using the 10-m and 30-m NAPP DEMs and the RFP and SM coordinate solutions yielded planimetric accuracies that ranged from 2.7-4.6 m CE90, 1.8-4.4 m CE90, and 1.1-2.0 m CE90 for the SBC, SCC, and UVS study areas respectively (tables 4-6). In all study areas, the RFP solutions appear to produce slightly better planar accuracies relative to the corresponding SM solutions for the same source DEM. For example, the RFP basemaps for the SBC study area had CE90 values ranging from 2.7-3.1 m CE90, while the SM basemaps had CE90 values ranging from 3.2-4.6 m. This differs substantially from the results obtained by Toutin and Cheng (2000) who found that the SM solution was far better than the RFP solution (RMSRE ~ 1.8 m for SM vs. 5.6 m for RFP) for one Ikonos test image of Richmond Hill, Ontario. Just as was the case in the Toutin and Cheng (2000) study, only ICPs were used (not GCPs) for assessing the planar accuracy, and these were uniformly distributed throughout the study area (e.g. figure 2). These issues are important because the RFP method corrects locally at the GCP. The accuracy assessment could be significantly biased if only GCPs are used in computing positional errors or if the ICPs are located in the vicinity of the GCPs. Since this is not the case in our study, this cannot satisfactorily explain the difference between the RFP and SM results obtained here and those from the Toutin and Cheng (2000) study. One possible explanation for this may be due to the number of GCPs used in the RFP coordinate solutions. We used 10 GCPs for all RFP solutions compared to 7 GCPs for the Toutin and Cheng (2000) study. As a result, a higher order RFP solution was obtained for our study and more local areas are adjusted as a result. This could account for the observed differences between the RFP and SM planar accuracies between the two studies. However, even though all the ICP datasets indicate that the Ikonos RFP basemaps have better planar accuracies than the Ikonos SM basemaps, visual examination shows that substantial linear distortions (i.e. warping) may occur for the RFP basemaps. Figure 4 presents a comparison of the planimetric quality of the RFP and SM basemaps for the SBC study area for various DEM data sources. This is the same location as that portrayed in figure 3. The comparison in figure 4 shows that the RFP method can produce substantial linear distortions that render these basemaps less desirable relative to the corresponding SM basemaps. This difference is especially noticeable for the basemaps derived from the higher resolution DEM (10-m NAPP). This occurs even though the ICP dataset indicates the planar accuracies are slightly worse for the SM solution (table 4).

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It is very likely that distortions in the RFP basemaps, caused by the local nature of the RFP solution, are not present in the vicinity of the ICPs, and this therefore accounts for the apparently higher planar accuracy relative to the SM basemaps. In reality, the Ikonos SM basemaps have better planar accuracies relative to their RFP counterparts for the SBC study area when the same source DEM is used. The significant differences in the preservation of the linear features resulted in higher planimetric quality rankings for the SM solutions compared to the RFP solutions for the SBC study area. For the SCC study area, the planimetric quality differences between the SM and RFP solutions were still noticeable but were not as great as the differences for the SBC study area. For the UVS study area, there were no noticeable differences in the planimetric quality between the SM and RFP solutions when the same source DEM is used. The planimetric quality rankings for the SCC and UVS study areas (tables 5 and 6) reflect these observations. The differences in the planimetric quality between the SM and RFP solutions are significant, noticeable, and non-existent for the SBC, SCC, and UVS study areas, respectively. This variation is directly related to the size differences of the study areas (table 1). Recall that 10 GCPs were used for the SM and RFP solutions for each study area. Since the RFP method only corrects for planimetric distortions locally at each GCP, the GCP density per unit area is an important characteristic affecting the quality of the RFP basemaps. The GCP density per km2 was 0.48, 0.20, and 0.04 for the UVS, SCC, and SBC study areas respectively. The factor of 10 difference in the GCP density between the UVS and SBC study is responsible for the substantial differences in the planimetric quality between the two study areas. Since the RFP method only corrects locally at each GCP for planimetric distortions, it is only suitable for small study areas or areas were a large number of highly accurate GCPs are available. The results here suggest that a GCP density of roughly 0.4/km2 is required for the RFP method to produce no noticeable linear distortions in the orthorectified image basemap. Since a typical Ikonos image scene is 11 x 11 km (121 km2), this would require 48 GCPs to produce an RFP solution without noticeable linear distortions in the image area. This is clearly impractical for most applications, and thus the RFP method should only be utilized for small study areas. A distinct advantage of the SM solution is that only a limited number of ground control points are required to develop a model that is valid for the entire image scene. For the SM solutions, the custom Ikonos orthoimages generated by orthorectifying the Ikonos Carterra Geo source images using the 10-m and 30-m NAPP DEMs produced planimetric accuracies that ranged between 1.9 m and 4.6 m CE90 for the three study areas. The best planar accuracies for the SM solutions using the NAPP DEMs were 2.3 m and 1.9 m CE90 for the SCC and UVS study areas, respectively. These are significantly better than the 3.2 m CE90 planar accuracy obtained in the SBC study area from the SM solution and the 10-m NAPP DEM. We attribute this directly to the smaller viewing angles (18° and 20°) of the Ikonos Geo images for the SCC and UVS study areas relative to the 37° viewing angle for the SBC study area (see table 1). Small viewing angles are better suited for accurate orthoimage generation because horizontal pixel displacements caused by topographic variations and the corresponding susceptence to DEM error will be minimized. For the commercial SI Ikonos orthoimage products, the SI technical specifications require a nominal viewing angle <15° for production of precision orthoimages with 4 m CE90 (Space Imaging 2000). Thus, generation of the highest precision

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orthoimage basemaps from georeferenced Ikonos imagery require acquisition of the image from a small viewing angle. However, we note that a 3.2 m CE90 accuracy was obtained for the SBC study area from the georeferenced Ikonos source image with a 37° viewing angle. Thus, it is possible to achieve planar accuracies comparable to the commercial SI Carterra Precision product with 4 m CE90 even when the source image has a larger than nominal viewing angle. The results presented here clearly demonstrate that Ikonos orthoimage basemaps with planar accuracies of ~2.0 m CE90 can be obtained using the low cost, low precision Ikonos georeferenced imagery with viewing angles ≤ 20° (e.g. SCC and UVS test sites). This accuracy level was achieved using: 1) commercially available orthorectification software, 2) a small number of GCPs (e.g. 10) to produce a valid sensor model solution, and 3) NAPP DEMs with horizontal resolutions of 10-30 m and RMS vertical accuracies on the order of 2 m. This effectively demonstrates that low cost, low precision georeferenced Ikonos imagery can be used by independent parties (e.g. no affiliation with SI) to produce orthorectified digital basemaps with planar accuracies on the order of 2 m CE90. This is comparable to the SI/NASA Databuy products evaluated here and better than the highest precision orthoimage products commercially available from SI (Carterra Precision, 4 m CE90). From the standpoint of local governments, the most important benefit of this approach is the cost savings, as the georeferenced Ikonos image products are available for a fraction of the cost of the precision orthoimage products ($21/km2 vs. $63/km2). The results presented in the preceding paragraphs were generated using custom NAPP DEMs for performing the Ikonos image orthorectification. This can be an impediment for local governments who do not already possess sufficiently accurate digital elevation data and who do not have the technical expertise to generate custom NAPP DEMs. Since the USGS 30-m DEMs are readily available throughout most of the U.S., these existing DEMs are a potential solution to this problem. The planar accuracy of the Ikonos SM basemap generated using the USGS DEM was 2.0 m CE90 (table 6) for the UVS study area. For the SBC study area, the planar accuracy of the Ikonos SM basemap generated using the USGS DEM was 4.6 m CE90 (table 4). Thus, it is clear that the USGS DEMs can be used to orthorectify georeferenced Ikonos data to yield imagery with planar accuracies that are sufficient for local government basemap applications. However, we note that the resulting planar accuracy will of course depend on the vertical accuracy of the USGS DEM. Planar accuracies of 2-4 m CE90 will not automatically be obtained using USGS DEMs. For the SBC and UVS study areas, the RMS vertical accuracies of the 30-m USGS DEMs were found to be 1.6 m and 1.7 m, respectively, and these produced Ikonos SM basemaps with 4.6 m and 2.0 m CE90 planar accuracies. However, for the SCC study area, the Ikonos SM basemap derived from the USGS DEM yielded a planar accuracy of only 7.0 m CE90. This comparatively poor result is due to the poor RMS vertical accuracy (16.6 m) of the particular USGS DEM available for the SCC study area. Thus, it is essential that some validation of the vertical accuracy of USGS DEMs be done prior to using them for production of Ikonos orthoimage basemaps. Finally, the planimetric accuracy results presented in tables 4-6 were estimated using ICP datasets, derived from rapid-static GPS survey, that contained anywhere between 20-35 points. Even though the ICPs were uniformly distributed throughout each study area, this limited

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amount of checkpoints will imperfectly estimate the RMS and CE90 accuracies. Due to sampling variability alone, the RMS and CE90 values reported in tables 4-6 are accurate to about 20-30%. To further validate the utility of the SM solutions for orthorectification of Ikonos georeferenced imagery, we collected 230 rapid-static GPS positions in a 630-km2 study area (19 x 33 km) located in Boone County, Missouri. Two georeferenced Ikonos images were acquired in April 2000 covering the complete study area. The viewing angles of the two images were 27° and 37° respectively. A set of 10 uniformly distributed GCPs was used to generate the SM solutions for each image. USGS DEMs with 30-m horizontal resolution were used to orthorectify the Ikonos images, and the two orthorectified images were combined into a single mosaic. An ICP datasets comprised of 210 rapid-static GPS positions was used to validate the planimetric accuracy of the orthomosaic. Figure 5 shows a scatter plot of planimetric errors in Ikonos orthomosaic. The distribution of the planimetric errors is clearly random and the corresponding planimetric accuracies were 3.1 m RMSRE and 4.5 m CE90. These are consistent with the SBC results in table 4 which utilized an Ikonos image with a large viewing angle as well (27°). This further demonstrates that the low cost, low accuracy georeferenced Ikonos image products can be orthorectified by third parties using existing DEM data sources and limited amounts of ground control to produce digital image basemaps with planimetric accuracies suitable for local government applications. Planimetric accuracies on the order of 2.0 m CE90 can be obtained when the acquisition viewing angle is ≤ 20°. The comparatively lower cost of the georeferenced Ikonos imagery makes these very attractive for use by local governments for developing up-to-date digital image basemaps for incorporation into GIS systems. 5. Summary and conclusions In this study we presented a detailed assessment of the planimetric accuracy of high-resolution (1 m) panchromatic Ikonos orthoimage products for three different test sites located in the State of Missouri. The main objective of this study was to evaluate the potential of Ikonos orthoimage products for use as digital image basemaps for use in local government GIS systems. For maximum utility and adoption in a wide variety of local government planning and management applications, a planimetric accuracy of 3-4 m CE90 is considered nominal. The planimetric accuracy of USGS DOQQs and custom orthoimages derived from NAPP aerial photography were also evaluated for comparative purposes. A variety of DEM data sources with different horizontal resolutions and vertical accuracies were used to determine their affect on the planimetric accuracy of orthoimage products derived from the DEMs. Key findings from this study are: (1) The USGS DOQQs had planimetric accuracies on the order of 3 m CE90 for the test sites

in this study. This is substantially better than the 10 m CE90 specified by the USGS. As a result, we believe that many DOQQ products are likely to meet the desired accuracy of 3-4 m CE90 for local government applications.

(2) Custom orthoimages derived from NAPP aerial photographs were produced and were

found to typically have planimetric accuracies on the order of 1-2 m CE90. These accuracies

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are significantly better than the DOQQs produced by the USGS from the same NAPP imagery. The raw aerial photographs in the NAPP archives can be used to produce basemaps with excellent planimetric accuracies when the accuracy of the DOQQs is found to be insufficient.

(3) DEMs with horizontal resolutions of 10-30 m and RMS vertical accuracies of 2 m are

required to achieve planimetric accuracies on the order of 2-4 m CE90 in orthoimage products derived from the DEMs.

(4) The planimetric accuracies of Ikonos orthoimages acquired under a Space

Imaging/NASA Databuy agreement were found to be 1.1 m and 1.7 m CE90 at two different test sites. The terms of the NASA Databuy agreement required a 2 m CE90 planimetric accuracy. Our independent assessment confirms that the Space Imaging products met this NASA Databuy specification.

(5) Low precision georeferenced Ikonos image products (Carterra Geo, 50 m CE90) were

orthorectified using third party commercially available software and various custom NAPP DEMs. The planimetric accuracies of the resulting Ikonos orthoimages were found to vary between 2-4 m CE90.

(6) Existing USGS DEMs were used to orthorectify georeferenced Ikonos image products.

The resulting Ikonos orthoimages were found to have planimetric accuracies from 2-7 m CE90. Planimetric accuracies of 2-4 m CE90 can be obtained from georeferenced Ikonos using USGS DEMs with RMS vertical accuracies on the order of 2 m. An image acquisition viewing angle for the Ikonos image would likely need to be ≤ 20° to obtain the highest planimetric accuracies (e.g. 2 m CE90).

The most important results from this study are with respect to cost effective acquisition of orthoimagery suitable for use as digital image basemaps in local government GIS applications. The planimetric accuracies of the USGS DOQQs and custom NAPP orthoimages were found to meet or exceed the desired basemap accuracy of 3-4 m CE90. These orthoimage products represent very low cost GIS basemap data sources. The main disadvantage of these products is that they are based on NAPP aerial photographs that are acquired only once every 5-6 years. Thus, these images are often outdated in urban areas that experience even moderate growth. This therefore limits the use of these orthoimages in many local government planning and management applications (tax assessment, E-911, etc.). The recent launch of the Ikonos satellite has opened up a new area for acquiring up-to-date high-resolution panchromatic digital imagery for use as digital orthoimage basemaps. The Ikonos panchromatic imagery has a nominal pixel size of 1 m and an 11-bit information content. The 11-bit imagery provides image contrast and clarity that is far superior to the DOQQs. The Ikonos panchromatic image product with the lowest horizontal precision (Carterra Geo) costs $21/km2 and has a planar accuracy of only 50 m (CE90). While this product is affordable for most local government entities, the planar accuracy of 50 m renders this product unusable for basemap applications. The results presented here independently demonstrate that the Carterra Geo Ikonos products can be orthorectified using the readily available USGS 30 m DEMs to

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produce digital basemaps with planimetric accuracies on the order of 2-4 m CE90. This level of accuracy is comparable to the highest accuracy Ikonos image products (Carterra Precision, 4 m CE90) commercially available from Space Imaging at a cost of $63/km2 (3x more expensive than Carterra Geo). A cost for complete coverage of a typical county (2400 km2) within the State of Missouri would be $50,400 and $151,200 for the Geo and Precision Space Imaging products, respectively. The latter figure is well beyond the budgetary capability of most local governments. The former figure is cost-competitive with traditional aerial photography surveys for the same resolution and coverage. Thus, the approach demonstrated here can be used to develop up-to-date, cost effective orthoimages from Ikonos Carterra Geo products that yield planimetric accuracies suitable for use as digital image basemaps by local governments. Acknowledgements The SBC work was supported by the Raytheon Synergy program under subcontract #012100MJ-3 from NASA. The SCC and UVS work was supported by NASA Stennis Space Center under contract # NAG13-99014 and by the Institute for the development of Commercial REmote Sensing Technologies (ICREST) at the University of Missouri-Columbia.

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References Davis, C.H. and X. Wang, 2001, “High resolution DEMs for urban applications from NAPP photography,” Photogrammetric Engineering & Remote Sensing, Vol. 67, No. 5, pp. 585-592. Hipple, J. and D. Daugherty, 2000, “Urban validation site for testing impervious surface models derived from remotely sensed imagery,” Proceedings of International Geoscience and Remote Sensing Symposium, Vol. 7, pp. 2885-2889, Honolulu, Hawaii, 24 - 28 July, 2000. Light, D.L, 1993, “The National Aerial Photography Program as a Geographic Information System Resource,” Photogrammetric Engineering & Remote Sensing, Vol. 59, No. 1, pp. 61-65. PCI Geomatics, 2001, OrthoEngine V7.0, URL: http://www.orthoengine.com, PCI Geomatics, Ontario, Canada. Space Imaging, 2000, “Space Imaging Catalog of Products and Services,” Volume 1 Supplement (55p.), Thornton, CO. United States Geological Survey, 1996, “Standards for Digital Orthophotos, Part 1: General (9 p.); Part 2: Specifications (37 p.),” Department of the Interior, Washington, DC. United States Geological Survey, 1997, “Standards for Digital Elevation Models, Part 1: General (17 p.); Part 2: Specifications (70 p.); Part 3: Quality Control (10 p.),” Department of the Interior, Washington, DC. NASA, 2001, Scientific Databuy – Phase II, http://www.crsp.ssc.nasa.gov/scripts/databuy, NASA Stennis Space Center, Stennis, MS. Toutin, T. and P. Cheng, 2000, “Demystification of IKONOS,” Earth Observation Magazine, Vol. 9, No.7.

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List of Tables

Table 1. Study area and Ikonos image characteristics.

Ikonos Panchromatic Images

Test Site

Area Type

Test Area (km)

Terrain Variation

(m) Product Type Acquisition Viewing Angle

SBC Rural 14 x 16 160-290 Geo1 4/30/2000 37°

SCC Suburban 7 x 7 140-200

Geo

Prec Plus2

2/29/2000 2/29/2000

18° 18°

UVS Urban 3 x 7 330-430

Geo

Prec Plus

9/17/2000 3/28/2000

20° 12°

1 Geo = Carterra Geo product from Space Imaging with 50 m CE90 2 Prec Plus = Precision Plus product from Space Imaging with 2 m CE90 for NASA Databuy contract

Table 2. DEM horizontal resolutions and vertical accuracies for the three study areas.

Test Site DEM XY

Resolution(m)

RMS Z Accuracy

(m) NAPP 3 3.0 NAPP 10 2.3 NAPP 30 2.2 SBC

USGS 30 1.6 NAPP 10 1.2 NAPP 30 1.3 SCC USGS 30 16.8 NAPP 10 2.1 NAPP 30 2.3 UVS USGS 30 1.7

Table 3. RMS residual errors from the Rational Function Polynomial (RFP)

and Sensor Model (SM) solutions for Ikonos Geo imagery.

RFP Solution SM Solution Test Site RMS X

(m) RMS Y

(m) RMSRE

(m) RMS X

(m) RMS Y

(m) RMSRE

(m) SBC 0.26 0.12 0.29 0.33 0.33 0.47 SCC 0.26 0.19 0.32 0.25 0.23 0.34 UVS 0.23 0.22 0.32 0.27 0.17 0.32

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Table 4. Planimetric accuracy of orthoimage basemaps for Southern Boone County (SBC) study area.

Image Data

Source DEM Used

RMS Radial

Error (m)

Circular Error @ 90%

(m) Planimetric

Quality

DOQQ N/A 2.0 3.4 Good NAPP NAPP 3 m 1.9 3.0 Poor NAPP NAPP 10 m 1.8 3.1 Fair NAPP NAPP 30 m 1.0 1.5 Excellent

Ikonos – Geo N/A 19.5 23.7 Excellent Ikonos Ortho – RFP NAPP 10 m 1.8 2.9 Poor Ikonos Ortho – SM NAPP 10 m 2.2 3.2 Fair Ikonos Ortho – RFP NAPP 30 m 1.6 3.1 Fair Ikonos Ortho – SM NAPP 30 m 2.5 4.6 Good Ikonos Ortho – RFP USGS 30 m 2.1 2.7 Good Ikonos Ortho – SM USGS 30 m 2.9 4.6 Excellent

Table 5. Planimetric accuracy of orthoimage basemaps for St. Charles County (SCC) study area.

Image Data

Source DEM Used

RMS Radial

Error (m)

Circular Error @ 90%

(m) Planimetric

Quality

DOQQ N/A 1.9 2.8 Good NAPP NAPP 10 m 1.3 0.9 Good NAPP NAPP 30 m 0.9 1.7 Excellent

Ikonos – Geo N/A 5.4 9.0 Excellent Ikonos – Prec Plus N/A 0.9 1.1 Excellent Ikonos Ortho – RFP NAPP 10 m 1.2 1.8 Fair Ikonos Ortho – SM NAPP 10 m 1.6 2.3 Good Ikonos Ortho – RFP NAPP 30 m 1.7 2.9 Good Ikonos Ortho – SM NAPP 30 m 2.6 4.4 Excellent Ikonos Ortho – RFP USGS 30 m 5.8 8.0 Good Ikonos Ortho – SM USGS 30 m 4.5 7.0 Excellent

Table 6. Planimetric accuracy of orthoimage basemaps for Springfield UVS study area.

Image Data

Source DEM Used

RMS Radial

Error (m)

Circular Error @ 90%

(m) Planimetric

Quality

DOQQ N/A 1.9 2.7 Good NAPP NAPP 10 m 1.2 2.0 Good NAPP NAPP 30 m 1.1 1.7 Excellent

Ikonos – Geo N/A 11.3 13.5 Excellent Ikonos – Prec Plus N/A 1.1 1.7 Excellent Ikonos Ortho – RFP NAPP 10 m 0.7 1.1 Good Ikonos Ortho – SM NAPP 10 m 1.3 1.9 Good Ikonos Ortho – RFP NAPP 30 m 1.1 1.7 Excellent Ikonos Ortho – SM NAPP 30 m 1.2 2.0 Excellent Ikonos Ortho – RFP USGS 30 m 0.9 1.6 Excellent Ikonos Ortho – SM USGS 30 m 1.1 2.0 Excellent

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Figures

Figure 1. Vector data layer overlay of parcel boundaries on a sample USGS DOQQ. Note that the parcel boundaries divide many single-family residential houses due to the poor positional accuracy of the image basemap.

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Figure 2. Ikonos 1-m resolution orthoimage for SBC study area produced from low precision georeferenced Space Imaging product using 10-m NAPP DEM and SM solution. GCPs and ICPs are shown as red and yellow triangles, respectively, and are uniformly distributed throughout the study area.

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Figure 3. Examples of DOQQ and custom NAPP orthoimage basemaps for SBC study area illustrating differences in planimetric image quality from different DEM data sources. (a) DOQQ (b) NAPP with 3-m DEM (c) NAPP with 10-m DEM and (d) NAPP with 30-m DEM. Note the significant linear feature distortion in the NAPP orthoimages produced from the 3-m and 10-m NAPP DEMs.

(a) (b)

(c) (d)

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Figure 4. Examples of Ikonos orthoimage basemaps from SBC study area illustrating differences in planimetric quality resulting from different DEM data sources and coordinate solution methods. (a) RFP with 10 m NAPP DEM (b) SM with 10 m NAPP DEM (c) RFP with 30 m NAPP DEM (d) SM with 30 m NAPP DEM e) RFP with 30 m USGS DEM and f) SM with 30 m USGS DEM.

(a)

(c)

(b)

(e)

(d)

(f)

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Figure 5. Scatter plot of planimetric errors in orthorectified Ikonos image produced using 30-m USGS DEMs and SM coordinate solution. The ICP dataset contains 210 points derived from rapid-static GPS survey.

-6

-4

-2

0

2

4

6

-10 -8 -6 -4 -2 0 2 4 6 8 10

X Errror (m)

Y Er

ror (

m)

CE90 = 4.5 m

RMSRE = 3.1 m