Wetlands MEaSUREs – Monitoring Instruments

Spaceborne microwave remote sensing offers effective tools for characterizing wetlands, as microwave sensors are particularly sensitive to surface water and to vegetation structure, allowing monitoring of large, inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination….

GISMO — Data Products

May 23, 2006, 150 MHz Data Products

Each flight line was segmented into approximately 25 km long sections with 20% overlap on each end. Each segment contains the following data in a zip file:

1. Thickness data 32 bit float binary, 10 m pixel resolution ‘seg#_thickness’
2. Thickness data GeoTIFF ‘seg#_thickness_geo.tif’
3. Thickness data graphic ‘seg#_thickness.gif’
4. Image data 32 bit float binary, 10 m pixel resolution ‘seg#_image’
5. Image data GeoTIFF ‘seg#_image_geo.tif’
6. Image data graphic ‘seg#_image.gif’
7. Header file with columns, rows, projection information, and corner map coordinates ‘seg#.hdr’
8. ASCII file with latitude, longitude, and aircraft altitude of corresponding segment ‘seg#_0_0.pos’ where the last two numbers represent waveform and channel. There are 4 waveforms and as many as 8 channels.

‘seg#’ refers to the segment number ie: 0_3, 3_7, 7_11 etc.

Products

Sept 10, 2007, 450 MHz Data Products

Each flight line was segmented into approximately 25 km long sections with 20% overlap on each end. Since there were only 4 channels for these data, good left-right separation was not possible, thus there is no image or thickness data available for 2007. The following is what is included in the product zip files:

1. ASCII file with latitude, longitude, and aircraft altitude of corresponding segment ‘seg#_0_0.pos’ where the last two numbers represent waveform and channel. There are 4 waveforms and as many as 8 channels.
2. 450 MHz (Sept 10) intensity data 32 bit float binary, 5X25 m (pixel x line) resolution which includes both the surface and base part. Slant range imagery can be used to get the nadir thickness information. ‘seg#_inten’
3. Azimuth compressed header file with column, row, resolution, wavelength, and # looks ‘seg#_0_0.azcom.par’
4. Azimuth compressed nadir file with latitude, longitude of first pixel of each azimuth line in the slant range image, aircraft altitude, and roll angle in degrees (last column) ‘seg#_0_0.azcom.nadir’
5. 450 MHz (Sept 10) Surface/Base profile gives an estimate (may not be exact in some areas) of the surface and the base profile. ‘seg#_surface_base.profile’ *note not all segments have this file*

Ice_elevation = (base_pixel – surface_pixel)*5.0/1.8 (m)

‘seg#’ refers to the segment number ie: 0_3, 3_7, 7_11, 100_110 etc.

GISMO – Schematic Labeling the Inbound and Outbound Flight Lines

Products

Sept 10, 2007, 450 MHz Outbound Flight Line A

Sept 10, 2007, 450 MHz Outbound Flight Line B

Sept 12, 2007, 150 MHz Data Products

Each flight line was segmented into approximately 25 km long sections with 20% overlap on each end. Since there were only 4 channels for these data, good left-right separation was not possible, thus there is no image or thickness data available. The following is what is included in the product zip files:

1. ASCII file with latitude, longitude, and aircraft altitude of corresponding segment ‘seg#_0_0.pos’
2. 150 MHz (Sept 12) Intensity data 32 bit float binary, 5X25 m (pixel x line) resolution which includes both the surface and base part. Slant range imagery can be used to get the nadir thickness information. ‘seg#_inten’
3. Azimuth compressed header file with column, row, resolution, wavelength, and # looks ‘seg#_0_0.azcom.par’
4. Azimuth compressed nadir file with latitude, longitude of first pixel of each azimuth line in the slant range image, aircraft altitude, and roll angle in degrees (last column) ‘seg#_0_0.azcom.nadir’
5. 150 MHz (Sept 10) Surface/Base profile gives an estimate (may not be exact in some areas) of the surface and the base profile. ‘seg#_surface_base.profile’

Ice_elevation = (base_pixel – surface_pixel)*5.0/1.8 (m)

6. Image, thickness, and header data for two segments 450_460 and 480_490 ‘seg#_image, seg#_thickness, and seg#.hdr’
7. Image and thickness GeoTIFF’s for segments 450_460 and 480_490 ‘seg#_image_geo.tif’ and ‘seg#_thickness_geo.tif’
8. Thickness data graphic for segments 450_460 and 480_490 ‘seg#.thickness.tiff’

‘seg#’ refers to the segment number ie: 0_3, 3_7, 7_11, 100_110 etc.

Products

Sept 12, 2007, 150 MHz Outbound Flight Line

Sept 12, 2007, 150 MHz Inbound Flight Line

2008 Low Aircraft Elevation Data Products

Each flight line was segmented into approximately 25 km long sections with 20% overlap on each end. Each segment contains the following data in a zip file:

1. Thickness data 32 bit float binary, 20 m pixel resolution ‘seg#_thickness’
2. Thickness data GeoTIFF, 20 m, ‘seg#_thickness_geo.tif’
3. Thickness data graphic ‘seg#.thickness.tiff’
4. Image data 32 bit float binary, 20 m pixel resolution ‘seg#_image’
5. Image data GeoTIFF, 20 m, ‘seg#_image_geo.tif’
6. Header file with columns, rows, projection information, and corner map coordinates ‘seg#.hdr’
7. ASCII file with latitude, longitude, and aircraft altitude of corresponding segment ‘seg#_0_0.pos’ where the last two numbers represent waveform and channel. There are 4 waveforms and as many as 8 channels.
8. Intensity graphic ‘seg#_inten.tif’ where the along track is in the vertical direction and the cross track (slant range) is in the horizontal direction from left to right. The brightest part on the left is the ice surface. There are discontinuities between the surface and the base resulting from different azimuth looks. Only two looks are used to create the surface portion (left) and 20 looks are used to create the base portion (right). The white line (right) represents the base and is an estimate of the nadir profile.

‘seg#’ refers to the segment number ie: 0_3, 3_7, 7_11 etc.

Example of intensity
 tif 310_320_inten.tif

Low aircraft elevation data and high elevation data overlap.
thick red line = high elevation
thin black dotted line = low aircraft elevation

Products

2008 High Aircraft Elevation Data Products

Each flight line was segmented into approximately 25km long sections with 20% overlap on each end. Each segment contains the following data in a zip file:

1. Elevation data 32 bit float binary, 20m pixel resolution ‘seg#_elevation’
2. Elevation data GEOtiff, 20m, ‘seg#_elevation_geo.tif’
3. Elevation data graphic ‘seg#.elevation.tiff’
4. Image data 32 bit float binary, 20m pixel resolution ‘seg#_image’
5. Image data GEOtiff, 20m, ‘seg#_image_geo.tif’
6. Header file with columns, rows, projection information, and corner map coordinates ‘seg#.hdr’
7. Base Intensity data 32 bit float binary, 5X25m (pixel x line) resolution which include both the surface and base part. This slant range image can be used to get the nadir elevation information and as a reference to the elevation maps. ‘seg#_base_inten’
8. Azimuth compressed header file with column, row, resolution, wavelength, and # looks ‘seg#_0_0.azcom.par’
9. Azimuth compressed nadir file with latitude, longitude of first pixel of each azimuth line in the slant range image and roll angle in degrees (last column) ‘seg#_0_0.azcom.nadir’
10. Surface/Base profile gives an estimate (may not be exact in some areas) of the surface and the base profile. ‘seg#_surface_base.profile’ *note: not all segments have this file*

Ice_thickness = (base_pixel – surface_pixel)*5.0/1.8(m)

‘seg#’ refers to the segment number ie: 0_3, 3_7, 7_11, 100_110 etc.

Low aircraft elevation data and high elevation data overlap.
thick red line = high elevation
thin black dotted line = low aircraft elevation

Products

GISMO - 2008 Southbound Flight Line

2008 Southbound Flight Line

GISMO - 2008 Northbound Flight Line

2008 Northbound Flight Line

products for segment 190_198 | 190_198.elevation.tiff
products for segment 198_205 | 198_205.elevation.tiff
products for segment 207_213 | 207_213.elevation.tiff
products for segment 271_281 | 271_281.elevation.tiff
products for segment 283_290 | 283_290.elevation.tiff

2008 Coastal Flight Line

2008 Coastal Flight Line

products for segment 290_300 | 290_300.elevation.tiff
products for segment 300_310 | 300_310.elevation.tiff
no surface_base.profile file
products for segment 310_316 | 310_316.elevation.tiff
no surface_base.profile file

GISMO Flight lines

Global Ice Sheet Mapping Orbiter (GISMO)

GISMO Overview

The Global Ice-Sheet Mapping Observatory (GISMO) spaceborne radar system was a part of the NASA Instrument Incubator Project (IIP). The IIP program was designed to provide a testing opportunity for emerging technology in order to create smaller and more efficient flight instruments. GISMO had a specific focus in measuring the surface topography of ice sheets, ice-sheet thickness, and in uncovering physical properties of the glacier bed using synthetic aperture radar (SAR).

The GIMSO project lasted for three years and had documented flight lines over the Greenland Ice Sheet in 2006, 2007, and 2008. It utilized VHF and P-band interferometric radars and tested different methods of clutter rejection in order to find the method most suitable for the project’s focus.

GISMO achieved mapping the physical properties of a glacier bed through up to 5 km of ice. It also created an effective clutter rejection technique for measuring the ice sheet’s surface and base. GISMO has applications in predicting the effects of climate change on ice sheets and in exploring planets with icy areas.

GISMO Documentation

Public Access Documents. Team documents available only with prior approval.

January 2006 Mid-year Review
Vu-graph Package

February 2006 Airborne Experiment Meeting WFF
Vu-graph Package
Meeting Summary

May 2006 Team Meeting, Vexcel Corp
Agenda, Summary, Plans
Radar Status
Airborne Simulations
May 06 Field Study Summary
Surface-based SAR Summary
Cards Data Format
Scattering Models

Planning Meeting for April 07 — WFF October 06
Overview (Jezek)
Review of May 06 Radar (Gogineni)
Review of May 06 Navigation (Sonntag)
Lessons Learned
Radar for 07 (Gogineni)
Flight Organization (Valiant)

2006 Reports
Jan 2006
March 2006
May 2006
Year 1 Annual Report (.doc)
Year 1 Annual Report (.pdf)
Year 1 Review (.ppt)
August 2006
December 2006

January 2007 Mid-year Review
Mid-year Review Vu-graphs
Mid-year Review Vu-graphs Updated Post Review

GISMO Team Meeting JPL, January 31
PARCA Presentation (Rodriguez)

Progress to Date
Status and Summary of Mid-year Review, Budget Issues (Jezek)

Planning for April 07 Experiment
Science and Engineering Objectives for April 2007 (Jezek)
Radar and Antenna Status (Gogineni)
Vexcel Data Processing Status, Interface and Readiness (Refraction, Motion Compensation, Calibration) (Wu)
JPL Data Processing Status, Interface and Readiness (Refraction, Motion Compensation, Calibration) (Rodriguez)
Navigation (Sonntag)
Arctic 07 Planning Status and Key Milestones (Krabill)
Proposed Flight Lines 123 (Fahnestock and Sonntag) and Discussion
April 07 Experiment Plan and Discussion (Jezek) (see May 06 vu-graphs for sample experiment plan form)

Algorithms, February 1
Tomography Algorithm Status (Wu) (covered above)
InSAR Algorithm Status (Rodriguez)
Update on Multiaperture Beam Processing (Gogineni) (covered above)
Plans for Reducing May 2006 Data to Topography (Wu, Rodriguez, Forster)
Plans for Investigating Ionospheric Effects and Corrections (Freeman)
Data Management Issues, Schedule for Delivery of April 07 Data, Action Items (Jezek)

Vexcel Algorithm Meeting, June 2007
Agenda
Processor Review
Summary and Action Items
Updated InSAR Equation

June 2007 Mid-year Review
2007 Annual Review
Freeman Backup Vu-graphs to Annual Review

August 2007 Radar Readiness Review (KU)

Readiness Review (Gogineni)
Digital Systems (Ledford)
Calibration (Rodriguez)
Processor Status (Wu)
Experiment Plan (Jezek)
Thin Layer Model (Niamsuwan)

2007 Biannual Reports
Feb 07
Jul 07
Sep 07
Nov 07

AGU 05 Abstract
WAIS O5 Poster
JGR Planets PDF File
EUSAR 2006 PDF File
AGU 06 3 Abstracts
EUSAR 08 Jezek : Rodriguez-Morales
EUSAR 2010 Wu
TGARSS 2010 Preprint

Glacier Speed – How to Cite

Citing Glacier Speed Data or Imagery

Cite data in publications such as journal papers, articles, presentations, posters, and websites. Please send copies of, or links to, published works citing data, imagery, or tools accessed through ASF to uso@asf.alaska.edu with “New Publication” on subject line.

Format Example
Dataset: Glacier Flow Speed, Burgess et al. 2013. Retrieved from ASF [day month year of data access]. Includes Material © JAXA/METI 2007-2011, archived at ASF DAAC. Dataset: Glacier Flow Speed, Burgess et al. 2013. Retrieved from ASF 7 June 2015. Includes Material © JAXA/METI 2007-2011, archived at ASF DAAC.

Recommended: Also cite first publication of findings: Burgess, E. W. et al. Flow velocities of Alaskan glaciers. Nat. Commun. 4:2146 doi: 10.1038/ncomms3146 (2013).

Crediting Glacier Speed Imagery

Include credit with each image shown in publications such as journal papers, articles, presentations, posters, and websites. 

Example
Burgess et al. 2013; Includes Material © JAXA/METI 2007-2011.
Icebergs float from the calving Mendenhall glacier, which originates in Alaska's Coast Range. The glacier velocity dataset reveals that about 40 percent (approximately 20 cubic km) of ice lost annually in Alaska is due to calving alone, mostly from a few coastal glaciers. © UAF

Glacier Speed – Applications

Improving projections of future sea level rise will require a thorough understanding of how climate change will affect glacier flow speeds. Flow speeds of ocean-calving glaciers originating from ice sheets have been closely monitored for many years, leading to better understanding of ocean-ice sheet interactions. But flow velocities of mountain glaciers have not been as widely monitored as ice sheets, and current projections of mountain-glacier mass loss are limited to extrapolating melt and snowfall observations on only a few glaciers to large regions.

Icebergs float from the calving Mendenhall glacier, which originates in Alaska’s Coast Range. The glacier velocity dataset reveals that about 40 percent (approximately 20 cubic km) of ice lost annually in Alaska is due to calving alone, mostly from a few coastal glaciers. © UAF

Alaska is losing about 50 cubic km of ice per year, but much of the new-water sea-level rise expected over the next 100 years may come from changes in mountain glacier calving and ice flow, which are completely unconstrained and unincorporated into mass loss projections.

Over half of the downstream ice flux from more than 20,000 glaciers throughout Alaska comes from fewer than 12 coastal glaciers. The flow dynamics on these rapid-flow systems will operate differently than from those of other glaciers, because they are able to maintain higher flow speeds without losing mass. Understanding the dynamics of these glaciers is critical for future efforts to estimate flow-speed-related mass loss in Alaska.

Glacier Speed – Download Data

Download a .kmz file that can be opened in Google Earth to show the statewide glacier-flow-speed map. Flow-speed is shown by color, as indicated in the scale at the bottom of the Google Earth screen. If clicking on the .kmz file gives you a message about a previous version of Google Earth, drag the file onto your Google Earth icon.

Download data

Contents:

  • Readme file
  • Three files (_speed.tif, _ids.tif, and .par) for each of the following regions:
  1. Central Alaska Range
  2. Chugach Mountains
  3. Coastal Range
  4. Delta Range
  5. Fairweather Range — Glacier Bay
  6. Hayes Range
  7. Kenai Mountains
  8. Tordrillo Mountains
  9. Wrangell Mountains — St. Elias Mountains 

The flow-speed data are gridded on 90-meter-resolution UTM grids as GeoTIFFs. The grids are divided into different regions and include a _speed file that contains the mosaicked flow speed in meters/day (32-bit float) and a _ids file that contains integer IDs that correspond to the image pair used for determining flow speed at each pixel (16-bit integer).

The dates of the image pairs used can be found by looking up image IDs in the corresponding .par file. In some cases, the .par file will contain IDs that are not in the _ids grid. In these cases, these image pairs were simply not needed in the final mosaic. The .par file also includes georeference information in a text format.

Glacier Speed – About the Glacier-Flow-Speed Dataset

Glacier surface velocities were derived using ALOS PALSAR fine beam data (L-Band, HH Polarization, 46-day orbit interval) provided through ASF. Repeat image pairs, acquired within one- to two-orbit intervals, were used to measure the ground displacement of features on glacier surfaces, such as crevasses or Synthetic Aperture Radar (SAR) speckle. Ground displacement was measured using image cross correlation, which provides estimates of flow speed both in azimuth and range directions. Displacements were calculated from slant-range Single Look Complex (SLC) files, then geocoded, topographically corrected, and corrected for image co-registration.

The final data products released here provide highly accurate estimates of flow speed; uncertainties are generally below 2 cm/day and overall biases are < 3 mm/day. While the data products are gridded at 90 m resolution, the method’s true spatial resolution is about three times larger due to the size-correlation windows. Nonetheless, these data are still able to resolve flow on Alaska glaciers of all sizes and speeds.

Region and timing: The dataset includes flow speeds of glaciers in the Wrangell-St. Elias Mountains, the Chugach Range, Kenai Peninsula, the Tordrillo and Necola Ranges, the Fairweathers, the Alaska Range, Glacier Bay, and the Coast Mountains of Southeast Alaska. Custom software was used to manually identify which of 344 image pairs could best represent each glacier and then mosaic chosen velocity fields together. In total, about 60 frames were chosen to produce the final maps. All images were acquired in winter between 2007 and 2010 (most were in January of 2008 and 2010). There were significant temporal changes in wintertime velocity, so continuity within each glacier basin was prioritized above optimal spatial coverage. Also, given the sometimes-erratic changes in velocity, the researchers caution against using these data to look at long-term trends in velocity.

Data format: The dataset available here includes geocoded grids of flow speed, metadata, and a Google Earth visualization of the full dataset for educators and scientists. The Google Earth visualization is self-explanatory for anyone with Google Earth. The metadata include geocoding information and the acquisition timing of the mosaicked flow-speed map.

For more information, see Burgess, E. W.; Forster, R.R.; and Larsen, C.F., Flow velocities of Alaskan glaciers. Nat. Commun. 4:2146 doi: 10.1038/ncomms3146 (2013).

Glacier surface-velocity map of the Wrangell and St. Elias Ranges. Light grey glacier outlines indicate missing data. Image credit: Evan Burgess, 2013. Includes Material © JAXA,METI 2006-2011

Glacier Speed

The first near-comprehensive dataset of wintertime glacier-flow speeds throughout Alaska — available here and described in Burgess et al., Nature Communications, 2013 — reveals complex patterns of glacier flow throughout the state….