Initial Assessment of QuickBird Image Tiles for I-10 Project Corridor Mapping and Evaluation

1. TEAM Dr. Chuck O’Hara, PI Dr. Roger King, PI Eric Kolstad, Geospatial Analyst Sunil Reddy Repaka, Civil Engineer Initial Assessment of QuickBird Image Tiles for I-10 Project Corridor Mapping and Evaluation Overview The proposed highway corridor paths for CSX railroad relocation have potential impact on large portions of southern Mississippi. To gauge factors in construction planning such as environmental impact, high resolution spatial data is requisite for detailed study of route alternatives. Through a cooperative agreement with NASA, multi-spectral and panchromatic QuickBird imagery at 0.61m and 2.44m resolution respectively is being collected for this region. As this beta image set arrives, our initial focus will be on several tasks: 1) Arranging an overall map of image “footprints,” showing large-scale placement of specific tile sets and respective overlap between sets; 2) Manually outlining individual tiles’ boundary edges to assess artifacts or gaps that may be introduced by processing steps such as clipping and cell division; and 3) Assigning attribute metadata (filename, image type, acquisition date, etc.) incorporating a rough estimate of image quality (e.g. sharp/clear, cloudy, haze) to individual tile panels. Initial observations The first data set (6 ~4.7Gb DVDs of single- and multi-band data) includes an overlapping panchromatic image set, detailed in the large composite map at left. Complete images acquired at fixed intervals are visible along the QuickBird trajectory; each image has roughly 3.9km horizontal and 3.7km vertical overlap. Individual images are received UTM/WGS84 projected and split into a grid of 9-20 image tiles (~32Mb (multi-spectral) or 128Mb (panchromatic) each). These tiles may not be completely matched; depending on interpolation algorithm used for zoomed-in detail viewing (e.g. via ArcView or ERDAS) tile edges can display “black” borders, where occasional pixels translate to null data. Further discussion and interpretation is needed. Prior to receipt, all images & tile sets were clipped to the larger study area defined for satellite tasking. No-data regions are mapped beyond that border; some tiles are thus blank, partially or completely void of image data. The GeoTIFF images supplied (16-bit format) required specific extensions to view without conversion. Retention of pre-computed pyramid layers for this format also presented technical difficulties with COTS software. However, with few exceptions all appropriate scene images could be reviewed. At present basic quality information is attributed for multispectral image tiles e.g. "clear", "haze", "clouds", "blank" (containing no valid data), and panchromatic image quality is marked similarly, e.g. “clouds”, “NC” (no clouds) or “blank”. Cloud layers are apparent within several tiles in the northeast section of the map composite. Table 1: Example attribute table for highlighted image tile set (valid image area blocks shown). Figure 1: Panchromatic coverage area showing onlap and side lap regions. Blue, green and red denote successive acquisition paths. E-W overlap is not present between February and May images.

2. QuickBird Imagery for Dead Tiger Creek, Mississippi, 7.5' Quad DigitalGlobe QuickBird Multispectral Imagery DigitalGlobe QuickBird Panchromatic Imagery USGS DOQQ’s Team Dr. Roger L. King, PI Dr. Charles G. O’Hara, PI Sunil Reddy Repaka, Civil EngineerEric Kolstad, Geospatial Analyst Location Map Image Tiles Used Procedure Pan Multi Multispectral and panchromatic QuickBird images acquired through the NASA scientific data purchase have been used to create mosaiced image products. Individual QuickBird scenes were delivered as tiled image sets due to memory and file size limits. The individual sets were merged in ENVI to recreate the larger scenes. Erdas Imagine was used to composite and clip these to USGS 1:24,000 Topographic Quadrangle Map boundaries. Panchromatic Imagery: Image resampling was performed using Nearest Neighbor to retain band combination intensities. Inter-scene color balancing and histogram matching was configured with default manual options. Feathering was used to smooth transitions between image scenes. The output area was specified by an AOI outlining the Dead Tiger Creek Quad region. Multispectral Imagery: A similar procedure was utilized to create the multispectral image mosaics for the Dead Tiger Creek area. Color balancing and histogram matching proved more difficult for multiple bands, but acceptable results were achieved with the parabolic (automatic) surface method. 02feb17164500-p2as_r2c2-000000041925_01_p005.tif 02feb17164500-p2as_r2c3-000000041925_01_p005.tif 02feb17164500-p2as_r2c4-000000041925_01_p005.tif 02feb17164500-p2as_r3c2-000000041925_01_p005.tif 02feb17164500-p2as_r3c3-000000041925_01_p005.tif 02feb17164500-p2as_r3c4-000000041925_01_p005.tif 02feb17164503-p2as_r1c2-000000041925_01_p006.tif 02feb17164503-p2as_r1c3-000000041925_01_p006.tif 02feb17164503-p2as_r1c4-000000041925_01_p006.tif 02feb17164503-p2as_r2c2-000000041925_01_p006.tif 02feb17164503-p2as_r2c3-000000041925_01_p006.tif 02feb17164503-p2as_r2c4-000000041925_01_p006.tif 02feb17164503-p2as_r3c2-000000041925_01_p006.tif 02feb17164503-p2as_r3c3-000000041925_01_p006.tif 02feb17164503-p2as_r3c4-000000041925_01_p006.tif 02may23164914-p2as_r1c1-000000041926_01_p002.tif 02may23164914-p2as_r2c1-000000041926_01_p002.tif 02may23164914-p2as_r3c1-000000041926_01_p002.tif 02may23164914-p2as_r4c1-000000041926_01_p002.tif 02feb17164503-m2as_r1c2-000000041925_01_p006.tif 02feb17164503-m2as_r1c3-000000041925_01_p006.tif 02feb17164503-m2as_r1c4-000000041925_01_p006.tif 02feb17164503-m2as_r2c2-000000041925_01_p006.tif 02feb17164503-m2as_r2c3-000000041925_01_p006.tif 02feb17164503-m2as_r2c4-000000041925_01_p006.tif 02feb17164503-m2as_r3c2-000000041925_01_p006.tif 02feb17164503-m2as_r3c3-000000041925_01_p006.tif 02feb17164503-m2as_r3c4-000000041925_01_p006.tif 02may23164917-m2as_r1c1-000000041926_01_p003.tif 02may23164914-m2as_r1c1-000000041926_01_p002.tif 02may23164914-m2as_r2c1-000000041926_01_p002.tif 02may23164914-m2as_r3c1-000000041926_01_p002.tif 02feb17164500-m2as_r2c2-000000029365_01_p001.tif 02feb17164500-m2as_r2c1-000000029365_01_p001.tif

3. PROJECT CSX Corridor Relocation I-10 Corridor: QuickBird Image Accuracy Assessment TEAM Dr. Chuck O’Hara, PI Dr. Roger King, PI Eric Kolstad, Geospatial Analyst Sunil Reddy Repaka, Civil Engineer Wade Givens, Research Associate Fixed (known) reference points In order to establish centimeter-accuracy reference data, we plan to utilize NGS monument sites in the High Accuracy Reference Network (e.g. yellow triangles in Fig. 1). Only those points with A- or B- level accuracy will be considered. “A HARN is a statewide or regional upgrade in accuracy of NAD 83 coordinates using Global Positioning System (GPS) observations. … Horizontal A-order stations have a relative accuracy of 5mm +/- 1:10,000,000 relative to other A-order stations. Horizontal B-order stations have a relative accuracy of 8mm +/- 1:1,000,000 relative to other A-order and B-order stations.” [http://www.ngs.noaa.gov/faq.shtml] HARNs consist of regularly spaced (20-100km), easily accessible control points with clear horizons for satellite reception [http://www.ngs.noaa.gov/PC_PROD/WorkShops/PPT/SPCS/sld070.htm] A differential GPS system incorporating a base station equipped with a radio or cellular phone link can provide Real-Time Kinematic (RTK) surveying with accuracy of +/-10cm. This base station will be set up on the A- and B-order HARN monuments with precisely known locations, allowing calculation of correctional factors which can be applied to readings taken at non-registered sites within a 5-mile radius. Field notes including marker ID, antenna height above ground, location description, # satellites visible, date/time and weather conditions are to be acquired. Antenna height is a key factor in proper horizontal control; spheroid datum calculations benefit from - if not rely upon - this information for centimeter-level accurate positioning. Roving receiver measurements should be recorded similarly, with attention to operational requirements such as minimum acquisition time to achieve the desired level of accuracy. Introduction When evaluating different imagery sources for potential to characterize features (such as roads/highways, land cover type etc.) it is important to examine spatial accuracy in classification. To assess image alignment and feature locations in this context, image-identifiable pixel locations must be compared to their real-world counterparts via ground truth techniques. In georeferencing images, precise image-identifiable control positions are required. Potential bias due to errors in the measurement process should be kept to a minimum. Error sources include instrument accuracy, differences due to sensor position and/or orientation, and user error. These and other inherent errors can be estimated and either eliminated or substantially reduced. What remains thereafter should indicate the discrepancies in horizontal X and Y location of image pixels vs. observed real-world positions due to (potentially systematic) satellite position variance and/or common translational offset. Such discrepancies may thus be described and removed from the system to some extent. Control point selection Prior to fieldwork, a carefully chosen network of sample points was created. Sets of points are concentrated near NGS monuments within bounds defined by the limits of the GPS receiver and base station. (approx. a 5-mi. radius at present). QuickBird image pixels are roughly 0.6-0.7m (panchromatic) or 2.4-2.88m (multispectral). Points are chosen so as to be identifiable at a sub-pixel level. For accuracy assessment purposes, our primary focus will be on panchromatic imagery. Ideal control points are: - relatively close to a known reference point - within accurate measurement limits of equipment incl. base station - permanent features (e.g. paved roads rather than cropland) - clear and distinct spots, such as a near-perpendicular road edge/driveway or road/railway intersection - features of reasonable scale vs. pixel size (e.g. small roads or driveways instead of footpaths) - unobscured by nearby objects, overhanging trees etc. - in a fairly open area (optimally, with a 15-degree azimuth in several directions, to facilitate GPS satellite locks and minimize multipath interference) For varied reasons, certain control points will not prove viable in the field. As such, a larger than required number of points (min. 10-15 per image tile) should be chosen to ensure a reasonable sample set. Each NGS marker will encompass a set of usable points within a radial distance, though two NGS markers’ viable coverage areas may overlap in some cases. Collecting readings for common points with a base station at different NGS monuments is recommended, as this can reveal sources of error due to procedures involved in differential GPS measurement (rather than the imagery source). After selection, control points’ coordinates (in geographic or UTM projection) and IDs can be exported for rough navigation via handheld GPS. It has been found advantageous to have a laptop with multispectral and/or panchromatic image sets as well as target GCS monument and control point locations on-hand for field reference. Initial GPS results (esp. when observed inside a vehicle) were not always sufficient to clearly indicate correct ground locations for data points. Figure 1: Several HARN geodetic markers w/ nearby ground-control points.

4. PROJECT CSX Corridor Relocation I-10 Corridor: QuickBird Image Accuracy Assessment TEAM Dr. Chuck O’Hara, PI Dr. Roger King, PI Eric Kolstad, Geospatial Analyst Sunil Reddy Repaka, Civil Engineer Wade Givens, Research Associate CE90 determination The CE90 standard is commonly used to assess horizontal accuracy in map and image products. Established by the National Map Accuracy Standard (NMAS) in 1947, the CE90 measure states that for a set of well-defined points, 90% will fall within a specified radial distance. Greenwalt and Schultz (1968) state that if X and Y errors are part of a bivariate normal distribution and the X and Y errors are equal, independent, and zero-mean, then: CE90 = 2.1460 * RMSEx RMSEx is the root mean square summary of X error for a given point: RMSEx = sqrt(∑ (Ximage - Xcontrol) 2 / n) [RMSEy is found similarly] When X and Y errors are not equal, however, a linear combination of RMSEx and RMSEy is noted to be more accurate (Greenwalt and Schultz, 1968): RMSEc = 0.522* RMSEmin + 0.4778* RMSEmax where RMSEmin is the minimum value of RMSEx and RMSEy, and RMSEmax is the higher value. This linear combination was shown to be a good estimate of the circular error equivalent value for RMSEmin to RMSEmax ratios between 0.6 and 1.0. It was also found that a reasonable approximation for RMSEc is given by RMSEc≈ 0.5 * (RMSEmin + RMSEmax) Where RMSEmin / RMSEmax lies between 0.6 and 1.0, the above equation has been used by the NSSDA in the U.S. spatial data standard (Federal Geographic Data Committee, 1998). In terms of RMSEc, the estimate for CE90 looks the same as in the first equation (ref: NASA SSC Geometric Accuracy Assessment of DigitalGlobe QuickBird Imagery). When RMSEmin / RMSEmax is between 0.6 - 0.2, RMSEc must be adjusted using an interpolated value from statistical data relating RMSEmin / RMSEmaxto RMSEc / RMSEmax, according to the table at right (Greenwalt and Schultz, 1968). The positional accuracy of the initial test data set appears comparable with DigitalGlobe’s ~23m CE90 estimate, with a bias-adjusted CE90 of approximately ~25.25m. While the majority of data points were acquired in a similar timeframe, outlying data discrepancies (e.g. with W/SW- rather than NE-oriented offsets) seem correlated with satellite alignment on discrete dates. An extensive multi-county dataset will be acquired for detailed analysis of error sources & potential for bias correction. Figure 2: GPS coords. (black) and distance to image-based control pts (red vectors, 10x actual). Figure 4: Radial plot for pictured points with standard and bias-adjusted CE90 radii. Inner circles show potential CE90 for same-day scene. Figure 3: GPS readings (magenta circles) + offset to image-based coordinates (red vectors, exaggerated). Green bars show to-scale displacements for points in sample set. Figure 5: Absolute errors for sample points within regional image group.