Landsat LDCM Sample Image Now Available for Download

As VermontView Director Jaralth O’Neil-Dunne mentioned in the March 29th AmericaView post, LDCM / Landsat 8 is in orbit and being prepped to acquire data, carrying on the record-setting Landsat legacy. Knowing that this blog is read by numerous early adopters, it seems apropos to help distribute far and wide a link to a sample of the data recently made available by the good folks at USGS Land Remote Sensing. The image (shown below) was acquired on March 18th, 2013 and is provided for those of us who’d like a first look at the sensor’s 16-bit data. Check it out and spread the word. Feel free to comment and let us know what you think. We’ll pass your thoughts along to the USGS. They’d appreciate the feedback. Alternatively, send them your thoughts directly from here.

NOTE: This sample image is considered Engineering data, which implies that the data do not meet the exact specification as when the Landsat Data Continuity Mission (LDCM) is declared operational in May. More information regarding caveats is available at the USGS sample data post here.

What you should know about Landsat 8

Landsat 8 launched successfully and is orbiting the earth, so it's time to get ready to work with all this cool data!  I need to acknowledge that at the time this blog post was written Landsat 8 is not officially "Landsat 8," it is called the "Landsat Data Continuity Mission (LDCM)."  Once testing is complete (scheduled for May 30th), NASA will pass control to USGS and we will have an "operational" satellite whose official name is "Landsat 8!"

From an end user's perspective one of the most important things to keep in mind is that the band configuration for Landsat 8 (LDCM) is different from the Landat TM and ETM+ missions as Landsat 8 added two new bands.  Want to make a natural color composite from Landsat 8?  It will now be R-G-B = 4-3-2 (for Landsat 4,5, and 7 it was 3-2-1).  The USGS has a very helpful FAQ page listing the band numbers and wavelengths for all the Landsat missions.  There is another handy USGS page that displays the band combinations of Landsat 8 to Landsat 7 (see graphic below)

Source: NASA

Landsat 8 will be 12-bit data, compared to the 8-bit data used for previous Landsat missions.  Pixel values will now range from 0-4095 (4096 possible values) for Landsat 8, as compared to 0-255 (256 possible values) for the previous missions.  This should make it easier to distinguish features that are spectrally similar and shadow penetration will be much improved.

Because Landsat 8 is not yet officially operational you cannot download scenes from sites like GloVis just yet, but you can download a sample dataset to play around with courtesy of the USGS.

Close-Range Photogrammetry: The 3D Optical Bridge Evaluation System (3DOBS)

According to the American Society of Civil Engineers (ASCE), more than 26% of the nation’s bridges in 2009 were classified as either structurally deficient or functionally obsolete. Two years later, structurally deficient or functionally obsolete bridges still made up close to 24% of the nation's total bridge infrastructure. A report by the Federal Highway Administration (FHWA) indicates that, given more time and funding to complete bridge inspections, the use of non-destructive evaluation (NDE) methods would increase among state and county transportation agencies (Highway Bridge Inspection: State-of-the-Practice Survey, 2001). NDE promises a way to enhance the allocation of funding by improving the information these decisions are based on and by improving the assessment of existing bridge conditions and through increased safety of inspection crews, reducing traffic disruption, and increasing the frequency, objectivity, and accuracy of bridge condition assessment.

As part of research funded by USDOT-RITA, the 3D Optical Bridge Evaluation System (3DOBS) was developed to quickly assess the condition of bridges while minimizing traffic disruptions and limiting inspection crews' exposure to traffic. The system is composed of a Digital Single Lens Reflex (DSLR) camera mounted on a truck, close range photogrammetry software (Agisoft PhotoScan Pro) and an automated spall detection algorithm. For close range photogrammetry to be achieved, the photos need to be collected with at least 60% overlap. Early testing of the photogrammetry software showed that collecting imagery with greater overlap produced better results.

Prior to the collection of photos, the bridges had to be marked with reference points for Agisoft to set up a coordinate system and to create a DEM. These reference points were marked duct tape that was placed on the bridge deck in a grid pattern. The tape was placed at four foot intervals in the transverse direction and at ten foot intervals in the longitudinal direction. Carrier phase GPS points were collected with a Trimble GPS (with an accuracy of <1m) at each of the four corners of the bridge deck and at various other points on the deck to be able to correctly spatially reference our data.

For the collection of the photos, a standard consumer grade Nikon D5000 DSLR with a resolution of 12.3 megapixels (MP) and a 27 mm focal length lens were used. In order to capture a full lane in one pass the camera needed to be mounted 9 ft above the bridge deck. In order to achieve this height, a wooden vehicle mount was constructed to fit into the bed of a standard pickup truck. During field collections a control board was programmed to trigger the camera shutter at a rate of one image per second. With the camera mounted, the truck was driven across the bridge deck at a speed of about 2 mph. This speed ensured that images were captured with the required 60% overlap between the photos.

After the photos were collected they were processed in Agisoft PhotoScan Pro. This process was mostly automated as the software aligned the photos and generated a 3D model without any user input. After the model was generated it was necessary to manually add "KeyPoints" to mark the location of each one of the duct tape markers with the latitude and longitude coordinates. This allowed PhotoScan to set up a coordinate system and to accurately reference the model and create a Digital Elevation Model (DEM). The DEMs that were generated have a resolution of 5 mm in the x, y directions and a z resolution of 2 mm.

The spall detection algorithm was written in Python programming language and uses ArcPy to interface with ArcGIS and utilize some of ESRI’s available geospatial tools. The tool used to detect spalls is called Focal Statistics analyzes each cell in the raster and calculates statistics based on a specified neighborhood of cells around it. Additional functionality was added so that the user can remove bridge joints by creating a shapefile that defines the bridge joints. Spalls can also be identified based upon their area. This feature allows for the detected spalls to correspond to minimum size definitions and allow for the removal of small artifacts in the DEM. The data processing is automated, and only requires the user to set the working directory, file names for the DEM and bridge joint shapefile, focal statistics sensitivity and the minimum spall size.

Enhancements are currently being made to this system through a project sponsored by the Michigan Department of Transportation (MDOT). These enhancements will include improving the camera so that the system would be able to operate at near highway speeds (40 mph) and the construction of a more sturdy vehicle mount. The spall detection algorithm will also be improved. The current version of the algorithm simply looks for a change in the elevation and therefore it also detects the edges of patches on the bridge and reports them as spalls. This will be changed so that the algorithm will only detect negative changes or those that represent spalls on the bridge deck.

This work is supported as part of a larger program (Bridge Condition Assessment Using Remote Sensors) sponsored by the Commercial Remote Sensing and Spatial Information program of the Research and Innovative Technology Administration (RITA), U.S. Department of Transportation (USDOT), Cooperative Agreement # DTOS59-10-H-00001, with additional support provided by the Michigan Department of Transportation, the Michigan Tech Transportation Institute, the Michigan Tech Research Institute, and the Center for Automotive Research. The views, opinions, findings, and conclusions reflected in this paper are the responsibility of the authors only and do not represent the official policy or position of the USDOT/RITA, or any state or other entity. Further information regarding remote sensing technologies and the decision support system for bridge condition assessment and about this project can be found at <http://www.mtri.org/bridgecondition>.

LDCM / Landsat 8

I'm guessing that most people who work in the applied natural sciences would agree on the need for synoptic, multitemporal, objective, easily and freely accessible data of the Earth's surface.

Without argument, no Earth observing system meets that need as well as has the Landsat Program. Providing the longest, most comprehensive record of Earth surface changes, Landsat is unprecedented.

The next generation of Landsat systems, the Landsat Data Continuity Mission aka Landsat 8, is scheduled for launch today from Vandenberg AFB on the coast of southcentral California.  Members of the AmericaView consortium work daily with Landsat data though our 300+ academic, agency, non-profit, and industry partners. Below, in no particular order, is a sample of what Landsat means to AV and our partners to both demonstrate the breadth of applications and to celebrate today's launch.


Landsat imagery extends human vision to see our Earth’s surface not just over previous years, but over previous decades - Dr. Jim Campbell, Virginia Tech / VirginiaView

Landsat: still the premiere moderate resolution terrestrial imaging program after 41 years - Dr. Tim Warner, West Virginia University / West VirginiaView

Landsat, the first and best satellite sensing system for mapping, monitoring and analysis of land and water resources over time and space (and my favorite system for the past 40 years) - Dr. Marvin Bauer, University of Minnesota / MinnesotaView

We could not have fulfilled our Legislative mandates to assess the quality of all lakes in the state without your Landsat remote sensing technologies. These data are being used for a wide variety of water quality trend detection, impairment evaluations and watershed management actions - Bruce Wilson, senior scientist, Minnesota Pollution Control Agency / MinnesotaView

From the early 1970s to the present, Landsat satellite imagery has been used to create four comprehensive land cover maps of Kansas – Landsat’s spectral capabilities, spatial resolution, and repeat coverage have made it an ideal resource for studying the Kansas landscape - Dr. Steve Egbert, University of Kansas / KansasView


The Landsat image archive stretches back over 40 years and covers the entire globe: nothing else even comes close - Kevin Dobbs, University of Kansas / KansasView


Creeping landcover changes, invisible from the ground, suddenly revealed in their full extent and proximity – shocking! - Dr. Rebecca Dodge, Midland State University / TexasView

Landsat: My magic carpet ride to see the wonders of Planet Earth - Teresa Howard, The University of Texas at Austin / TexasView

Graduate and undergraduate students on our campus use Landsat data in their research and training each year – to date nearly 400 UAF students directly benefited from free Landsat data - Dr. Anupma Prakash, University of Alaska Fairbanks / AlaskaView



Landsat has been instrumental in helping the state of Alabama monitor land use and associated impacts on its many natural resources - Dr. Luke Marzen, Auburn University / AlabamaView

With the frequent synoptic views of California agriculture provided by Landsat, we have deepened our understanding of the relationship of phenology and crop production across the state - Pia van Benthem, UC Davis / CaliforniaView

Landsat is the only source for historic time-series data for my study site - Dr. Teki Sankey, Idaho State University / IdahoView

All current coastal land loss work in Louisiana is Landsat TM based - it's the heart of the Coastwide Reference Monitoring System (CRMS) landscape level monitoring effort -  Brent Yantis, University of Louisiana Lafayette / LouisianaView 

Landsat imagery is simply the best available data for studying the impacts of pervasive flooding on agriculture in the Devils Lake Basin of North Dakota, and an ideal tool for teaching about remote sensing – it is the “go-to” data source for most students working on projects for my remote sensing courses - Dr. Brad Runquist, University of North Dakota / North DakotaView

Freely available Landsat data has enabled 27 students at the University of Toledo to complete their masters and PhD degrees, and many of these students have gone on to work for local and state governments, the National Guard, and the National Geospatial Intelligence Agency - Dr. Kevin Czajkowski, University of Toledo / OhioView

We have 30 years of Landsat imagery for our area, available for a wide range of applications - that is unparalleled accessibility - Dr. Pete Clapham, Cleveland State University / OhioView

Freely available Landsat data has allowed college students to not only better understand remote sensing but the world around them - Dr. Tom Mueller, California University of Pennsylvania / PennsylvaniaView

South Dakota farmers have found Landsat imagery to be of great value for precision agriculture, especially for purposes such as delineating management zones within a field - Mary O’Neill, University of South Dakota / South DakotaView

Thoughts on post-event damage detection

Thanks to an AmericaView mini-grant and the labor of some intrepid students we were able to do a detailed damage assessment of some of the areas in Vermont hardest hit by Hurricane Irene back in 2011.  The pre-event imagery was sourced from the National Agricultural Imagery Program and we obtained post-event WorldView-2 imagery from the USGS Hazard Data Distribution System (HDDS).  Below are some key take-away points for this type of work.

• It's all about the resolution.  As much as I love Landsat you really need high-resolution imagery, such as WorldView2, to see the type of damage causes by an Irene-type event.
• Manual image interpretation.  While we would like to think that all we need is imagery from two different dates and a press of the Staples Easy Button to get results, the reality is much more complex.  This is particularly true in post-event disaster response.  Automated detection may help highlight areas of change, but chances are you will have to do it the old way to precisely quantify the damage.  No one beats a human when it comes to high-resolution image analysis.
• Georegistration is a challenge.  Much of the imagery that was acquired post-Irene was done so at rather extreme look angles.  Orthorectificaton did not yield promising results and it was time consuming.  Thus, all the damage mapping was done on the more accurate pre-event imagery through good old fashioned terrain association.  The offset is shown quite clearly in the above graphic.
• Come up with a damage class domain.  Lots of interpreters may be working on the same project and thus it really helps if you can come up with a list of damage types so that all of the datasets are consistent.
• Work locally.  The cloud and networks tend not to fare too well during disasters.  Digitizing goes a heck of a lot faster when the imagery is stored locally.

Hazard Data Distribution System (HDDS) tutorial

Geospatial data, particularly imagery, are crucial during disaster response.  The USGS has a wonderful web-based application called the Hazard Data Distribution System (HDDS) that provides the capability to download incident related imagery.  This video provides a short overview of HDDS, demonstrating how to you can use HDDS to locate and download imagery for your area of interest.  Please note that if you are involved in disaster response you will need to request permissions from the USGS to obtain access to the licensed imagery.  For more info on  HDDS please read the help pages.

Land Remote Sensing: Now for the iPad

Landspotting (www.landspotting.org), a recently released game for the iPad, allows players to characterize the landscape into simple and general land cover types (e.g. Urban, Trees, Grass, Water, Snow and Ice, Unknown, etc.) using their fingers to paint on high resolution satellite imagery. It is a tower-defense-game whereby players earn coins to buy new buildings, more warriors, and resources. The better the player paints, the more houses added to your village and the more warriors you have to protect against invaders.

However, the underlying goal of this game is crowd-sourcing the validation of global land cover types in order to improve global satellite-based land cover maps and products. The game was developed in cooperation with the Geo-Wiki.org project (http://www.geo-wiki.org). The game can be downloaded from iTunes.