2018 Virginia NAIP Digital Ortho Photo Imagery

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DOQQ Production Process Description; USDA FSA APFO NAIP Program 2018; State: Virginia.

The imagery was collected using the following digital sensors: Leica ADS-100 (Serial Number

10510), Leica ADS-100 (Serial Number 10515), Leica ADS-100 (Serial Number 10522), Leica

ADS-100 (Serial Number 10530), Leica ADS-100 (Serial Number 10552), with Flight and Sensor

Control Management System (FCMS) firmware: v4.54, Cameras are calibrated radiometrically

and geometrically by the manufacturer and are all certified by the USGS. Collection was

performed using a combination of the following twin-engine aircraft with turbines flying at

16,457 ft above mean terrain. Plane types: C441, Tail numbers: N2NQ, N414EH, N440EH,

N441EH, N441FS, With these flying heights, there is a 30% sidelap, giving the collected data

nominal ground sampling distance of 0.40 meters at 16,457 feet. Based-upon the CCD Array

configuration present in the ADS digital sensor, imagery for each flight line is 20,000-pixels in

width. Red, Green, Blue, Near-Infrared and Panchromatic image bands were collected. The

ADS 100 has the following band specifications: Red 619-651, Green 525-585, Blue 435-495,

Near Infrared 808-882, all values are in nanometers. Collected data was downloaded to

portable hard drives and shipped to the processing facility daily. Raw flight data was extracted

from external data drives using XPro software. Airborne GPS / IMU data was post-processed

using Inertial Explorer software and reviewed to ensure sufficient accuracy for project

requirements. Using Xpro software, planar rectified images were generated from the

collected data for use in image quality review. The planar rectified images were generated at

native resolution using a two standard deviation histogram stretch. Factors considered during

this review included but were not limited to the presence of smoke and/or cloud cover,

contrails, light conditions, sun glint and any sensor or hardware-related issues that potentially

could result in faulty data. When necessary, image strips identified as not meeting image

quality specifications were re-flown to obtain suitable imagery.

Aero triangulation blocks

were defined primarily by order of acquisition and consisted of four to seventeen strips.

Image tie points providing the observations for the least squares bundle adjustment were

selected from the images using an auto correlation algorithm. Photogrammetric control

points consisted of photo identifiable control points, collected using GPS field survey

techniques. The control points were loaded into a softcopy workstation and measured in the

acquired image strips. A least squares bundle adjustment of image pass points, control points

and the ABGPS was performed to develop an aero triangulation solution for each block using

XPro software. Upon final bundle adjustment, the triangulated strips were ortho-rectified to

the digital elevation model (DEM). The most recent USGS 10-meter DEMs were used in the

rectification process. Positional accuracy was reviewed in the rectified imagery by visually

verifying the horizontal positioning of the known photo-identifiable survey locations using

ArcGIS software. The red, green, blue, and infrared bands were combined to generate a final

ortho-rectified image strip. The ADS sensor collects fourteen bit image data which requires

radiometric adjustment for output in standard eight bit image channels. The ortho-rectified

image strips were produced with the full 16 bit data range, allowing radiometric adjustment

to the 8 bit range to be performed on a strip by strip basis during the final mosaicking steps.

The 16 bit data range was adjusted for display in standard eight bit image channels by

defining a piecewise histogram stretch using OrthoVista Radiometrix software. A constant

stretch was defined for each image collection period, and then strip by strip adjustments

were made as needed to account for changes in sun angle and azimuth during the collection

period. Strip adjustments were also made to match the strips histograms as closely as

possible to APFO specified histogram metrics and color balance requirements. Automated

balancing algorithms were applied to account for bi-directional reflectance as a final step

before the conversion to 8 bit data range. The imagery was mosaicked using manual seam

line generation in OrthoVista