Data Set ID: 

IceBridge CAMBOT L1B Geolocated Images, Version 2

This data set contains high-resolution imagery taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) system over Antarctica and Greenland. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

This is the most recent version of these data.

Version Summary: 

The raw, Level-0 JPG images were orthorectified and geolocated to create the GeoTIFF files in this data set.

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Data: Data integrity and usability verified

Documentation: Key metadata and user guide available

User Support: Assistance with data access and usage; guidance on use of data in tools

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Data Format(s):
  • GeoTIFF
Spatial Coverage:
N: 90, 
N: -53, 
S: 60, 
S: -90, 
E: 180, 
E: 180, 
W: -180
W: -180
Platform(s):DC-8, G-V, P-3B
Spatial Resolution:
  • Varies x Varies
Temporal Coverage:
  • 19 October 2018 to 9 September 2019
Temporal ResolutionVariesMetadata XML:View Metadata Record
Data Contributor(s):Michael Studinger

Geographic Coverage

Other Access Options

Other Access Options


As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Studinger, M. and J. Harbeck. 2019. IceBridge CAMBOT L1B Geolocated Images, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].
20 November 2019
Last modified: 
17 February 2020

Data Description


This data set contains orthorectified and geolocated images collected over the Arctic and Antarctic by the Continuous Airborne Mapping By Optical Translator (CAMBOT), a part of the Airborne Topographic Mapper (ATM) instrument suite.

File Information


The data files are provided in GeoTIFF (.tif) format. Each image is a three-channel, natural-color RGB image and is stored using lossless Lempel-Ziv-Welch (LZW) compression in TIFF format. The projection data are stored in the GeoTIFF portion of the metadata. The image metadata, including the camera settings, are written to the file using EXIF format standards.

File Contents

Figure 1 shows imagery from the file IOCAM1B_2018_AN_NASA_20181019_154311.4217.tif.

Figure 1. Imagery of Antarctic sea ice from 19 October 2018.

Naming Convention

Example file name:


The files are named according to the following convention, which is described in more detail in Table 1.

Table 1. File Naming Convention
Segment of file name Description
IOCAM1B Data set ID

NASA campaign identifier:

YYYY: 4-digit campaign year
LO: location; AN = Antarctica, GR = Greenland/Arctic
Example: 2018_AN_NASA
yyyymmdd Year, month, and day of image capture
HHMMSS Hour, minute, and second of image capture in UTC time
DDDD Fraction of a second of image capture

File type:

.tif: GeoTIFF data file

Spatial Information


This data set covers areas within the following geographical boundaries:

Southernmost latitude: 60° N
Northernmost latitude: 90° N
Westernmost longitude: 180° W
Easternmost longitude: 180° E

Southernmost latitude: 90° S
Northernmost latitude: 53° S
Westernmost longitude: 180° W
Easternmost longitude: 180° E


The pixel size is dependent on flight altitude and referenced DEM; thus, the spatial resolution typically ranges from 0.06 m to 0.30 m. For a nominal flight altitude of 1,500 ft above ground level, the spatial resolution is 0.087 m.


The following table provides details about the coordinate systems used in this data set:

Table 2. Geolocation Details for the Arctic/Greenland and Antarctica Data
Arctic/Greenland Antarctica
Geographic coordinate system WGS 84 WGS 84
Projected coordinate system WGS 84 / NSIDC Sea Ice Polar Stereographic North WGS 84 / Antarctic Polar Stereographic
Longitude of true origin -45° E
Latitude of true origin 70° N 71° S
Scale factor at longitude of true origin 1 1
Datum WGS 84 WGS 84
Ellipsoid/spheroid WGS 84 WGS 84
Units meters meters
False easting 0 0
False northing 0 0
EPSG code 3413 3031
PROJ4 string +proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs

Temporal Information


19 October 2018 to 09 September 2019


IceBridge campaigns are conducted on an annually repeating basis. Arctic and Greenland campaigns are typically conducted in March, April, and May; Antarctic campaigns are typically conducted in October and November.

Data Acquisition and Processing


CAMBOT is a digital image acquisition system that supports analysis of laser altimeter data collected by the Airborne Topographic Mapper (ATM). The CAMBOT images provide a qualitative assessment of the surface structure and lower atmosphere conditions sensed by the ATM. CAMBOT is a passive instrument that uses sunlight as the source of illumination. As such, CAMBOT has some limitations, including shadows, daytime-only operation, and reduced data value under high-cloud or low mist conditions. However, the images provide a high-resolution record of ice structures, nunataks, snow, crevasses, sastrugi, leads, and sea ice type.

The CAMBOT raw images are collected by an Allied Vision Prosilica GT4905C camera. The Prosilica GT4905C, a 16 MP machine vision camera designed for extreme environments, operates over ethernet via the GigE command interface. The camera is fitted with a Zeiss Distagon 28 mm f/2 ZF.2 lens to yield an image width below the aircraft that is marginally wider than the ATM wide-scan lidar swath. The camera is rigidly mounted in each science aircraft platform above a down-looking optical window (either glass or acrylic) to provide nadir imagery of the surface below. Finally, the camera is connected to three components: a power supply, an acquisition/control computer, and an intervalometer. The CAMBOT data acquisition and control computer is a SuperLogics Microbox PC that provides an ethernet connection to the camera for transferring data and managing the camera settings. Custom software is used to set up the camera, transfer images, display QA thumbnails, and organize collected images into one-minute duration tar files.

The intervalometer used to trigger the CAMBOT image collection is a Javad Delta/Sigma GPS receiver set to output two pulses per second (PPS). The 2 PPS signal is delivered on every half second, which provides accurate timing of each image acquired.

Input Data

The ancillary data used to create this data product include an Applanix-smoothed best estimate of trajectory (SBET) and a range-to-surface measurement provided by the ATM lidar. The Applanix SBET is a post-processed PPP solution created from the GPS/IMU data collected by an Applanix POS AV 610 sensor, which provides the position (latitude, longitude, altitude) and attitude (pitch, roll, yaw) of the camera. The ATM lidar instruments measure the time-of-flight of laser pulses that are transmitted from the instrument to the ground surface below and back. Once calibrated and averaged, an estimated range, corrected for off-nadir pointing angle, is provided for each image. The combination of these two data sets determines the origin, direction, and magnitude of each pixel vector.

Geolocation Procedure

Since the sensor is mounted on an aircraft which is constantly in motion, a series of coordinate transformations are applied to convert position and attitude as measured by the GPS and INS systems to obtain the geolocation for each recorded image pixel. In this procedure, the camera orientation is here assumed that if the airplane is traveling due north the image (0, 0) pixel is assumed to be in the upper left (northwest) corner of the image.

Lens distortion

To account for lens distortion effects prior to flight operation, a checkerboard calibration target was imaged with both the primary and backup CAMBOT cameras in a laboratory to determine the lens radial and tangential distortion coefficients. The sizes of the checkerboard squares were measured with a scale and the calibration target was imaged at different orientations. The images were analyzed using the Caltech Calibration Toolbox software package to determine the lens distortion coefficients (Bouget, 2016). An inverse model was then applied to convert between the actual image pixel position (xi,yi) and the effective pixel position (xip,yip) after lens distortion.

Geolocation procedure for the camera sensor

  • For each image, differential GPS and aircraft attitude measurements are synced with the camera time stamp.
  • To determine the position of the camera sensor from this information, the spatial distance between the CAMBOT camera and GPS antenna, rant, is measured prior to each Operation IceBridge campaign using a North, East, Down (NED) coordinate system.
  • Coordinate transformations are then applied to correct for the camera offset from the GPS antenna location and determine the camera sensor location.

Geolocation procedure for image pixels

  • To determine the geolocation of each camera pixel, the vector between an image pixel and the surface is calculated using a simple projection camera model. This approach is similar to the methodology described in Barber et al. (2006) but with modifications to account for the measurement system used on the IceBridge aircraft.
  • Two transformation matrices accounting for aircraft motion and mounting biases are constructed using homogeneous coordinates.
  • To account for mounting biases of the camera, another transformation matrix is calculated in an equivalent manner but using u, v, and w, which correspond to the bias in the camera pitch, roll, and heading, respectively. The procedure for determining the mounting biases is described in the Quality, Errors, and Limitations section.
Note: for more detailed information on the geolocation procedures for the camera sensor and the image pixels, including mathematical equations, see the Geolocation of CAMBOT Imagery from Operation IceBridge Surveys document.

Quality, Errors, and Limitations

Mounting biases for airborne data are determined by using surveyed targets on or near airport ramps, which are used by the IceBridge ATM laser altimeter system for calibration. The image pixels of the survey targets were identified in a series of images and the mounting biases were found by an iterative method, adjusting the camera pitch, roll, heading, and timing biases to minimize the error in the distance between the known and calculated locations.

Software and Tools

Software that recognizes the GeoTIFF file format is recommended for these images, such as the GIS software QGIS or ArcGIS. See also the libGeoTIFF and GDAL websites for more information.

Version History

Table 3 provides a version history for this data set.

Table 3. Version History
Version Number Time Frame Description
1 31 March 2009 to 26 April 2013 The data files were provided as non-geolocated JPG images.
2 19 October 2018 to present The raw, Level-0 JPG images were orthorectified and geolocated to create the GeoTIFF files in this data set.

Related Data Sets

IceBridge ATM L1B Qfit Elevation and Return Strength
IceBridge DMS L0 Raw Imagery
IceBridge DMS L1B Geolocated and Orthorectified Images

Related Websites

Operation IceBridge web page at NSIDC
Operation IceBridge web page at NASA
Caltech Camera Calibration Toolbox for Matlab (Bouget, 2016)

Contacts and Acknowledgments

Principal Investigator: Michael Studinger
Cryospheric Sciences Laboratory
NASA Goddard Space Flight Center
Greenbelt, Maryland, USA

Technical Contact: Jeremy Harbeck
Cryospheric Sciences Laboratory
NASA Goddard Space Flight Center
Greenbelt, Maryland, USA


The ATM project team would like to acknowledge the dedicated NASA P-3 and DC-8 flight crews, whose efforts allowed the safe and efficient collection of the data over some of the most isolated and extreme regions on this planet.


Barber, D. B., Redding, J. D., McLain, T. W., Beard, R. W., & Taylor, C. N. (2006). Vision-based Target Geo-location using a Fixed-wing Miniature Air Vehicle. Journal of Intelligent and Robotic Systems, 47(4), 361–382.

Bowring, B. R. (1976). TRANSFORMATION FROM SPATIAL TO GEOGRAPHICAL COORDINATES. Survey Review, 23(181), 323–327.

Harpold, R., Yungel, J., Linkswiler, M., & Studinger, M. (2016). Intra-scan intersection method for the determination of pointing biases of an airborne altimeter. International Journal of Remote Sensing, 37(3), 648–668.

How To

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