I’ve been away from the blog for awhile, but thought I’d catch up a bit. I am in beautiful Madison Wisconsin (Lake Mendota! 90 degrees! Rain! Fried cheese curds!) for the NASA LP DAAC User Working Group meeting. This is a cool deal where imagery and product users meet with NASA team leaders to review products and tools. Since this UWG process is new to me, I am highlighting some of the key fun things I learned.
What is a DAAC?
A DAAC is a Distributed Active Archive Center, run by NASA Earth Observing System Data and Information System (EOSDIS). These are discipline-specific facilities located throughout the United States. These institutions are custodians of EOS mission data and ensure that data will be easily accessible to users. Each of the 12 EOSDIS DAACs process, archive, document, and distribute data from NASA's past and current Earth-observing satellites and field measurement programs. For example, if you want to know about snow and ice data, visit the National Snow and Ice Data Center (NSIDC) DAAC. Want to know about social and population data? Visit the Socioeconomic Data and Applications Data Center (SEDAC). These centers of excellence are our taxpayer money at work collecting, storing, and sharing earth systems data that are critical to science, sustainability, economy, and well-being.
What is the LP DAAC?
The Land Processes Distributed Active Archive Center (LP DAAC) is one of several discipline-specific data centers within the NASA Earth Observing System Data and Information System (EOSDIS). The LP DAAC is located at the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. LP DAAC promotes interdisciplinary study and understanding of terrestrial phenomena by providing data for mapping, modeling, and monitoring land-surface patterns and processes. To meet this mission, the LP DAAC ingests, processes, distributes, documents, and archives data from land-related sensors and provides the science support, user assistance, and outreach required to foster the understanding and use of these data within the land remote sensing community.
Why am I here?
Each NASA DAAC has established a User Working Group (UWG). There are 18 people on the LP DAAC committee, 12 members from the land remote sensing community at large, like me! Some cool stuff going on. Such as...
Two upcoming launches are super interesting and important to what we are working on. First, GEDI (Global Ecosystem Dynamics Investigation) will produce the first high resolution laser ranging observations of the 3D structure of the Earth. Second, ECOSTRESS (The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), will measure the temperature of plants: stressed plants get warmer than plants with sufficient water. ECOSTRESS will use a multispectral thermal infrared radiometer to measure surface temperature. The radiometer will acquire the most detailed temperature images of the surface ever acquired from space and will be able to measure the temperature of an individual farmer's field. Both of these sensors will be deployed on the International Space Station, so data will be in swaths, not continuous global coverage. Also, we got an update from USGS on the USGS/NASA plan for the development and deployment of Landsat 10. Landsat 9 comes 2020, Landsat 10 comes ~2027.
Other Data Projects
We heard from other data providers, and of course we heard from NEON! Remember I posted a series of blogs about the excellent NEON open remote sensing workshop I attended last year. NEON also hosts a ton of important ecological data, and has been thinking through the issues associated with cloud hosting. Tristin Goulden was here to give an overview.
NASA staff gave us a series of demos on their WebGIS services; AppEEARS; and their data website. Their webGIS site uses ArcGIS Enterprise, and serves web image services, web coverage services and web mapping services from the LP DAAC collection. This might provide some key help for us in IGIS and our REC ArcGIS online toolkits. AppEEARS us their way of providing bundles of LP DAAC data to scientists. It is a data extraction and exploration tool. Their LP DAAC data website redesign (website coming soon), which was necessitated by the requirement for a permanent DOI for each data product.
LP DAAC is going full-force in user engagement: they do workshops, collect user testimonials, write great short pieces on “data in action”, work with the press, and generally get the story out about how NASA LP DAAC data is used to do good work. This is a pretty great legacy and they are committed to keep developing it. Lyndsey Harriman highlighted their excellent work here.
Grand Challenges for remote sensing
Some thoughts about our Grand Challenges: 1) Scaling: From drones to satellites. It occurs to me that an integration between the ground-to-airborne data that NEON provides and the satellite data that NASA provides had better happen soon; 2) Data Fusion/Data Assimilation/Data Synthesis, whatever you want to call it. Discovery through datasets meeting for the first time; 3) Training: new users and consumers of geospatial data and remote sensing will need to be trained; 4) Remote Sensible: Making remote sensing data work for society.
A primer on cloud computing
We spent some time on cloud computing. It has been said that cloud computing is just putting your stuff on “someone else’s computer”, but it is also making your stuff “someone else’s problem”, because cloud handles all the painful aspects of serving data: power requirements, buying servers, speccing floor space for your servers, etc. Plus, there are many advantages of cloud computing. Including: Elasticity. Elastic in computing and storage: you can scale up, or scale down or scale sideways. Elastic in terms of money: You pay for only what you use. Speed. Commercial clouds CPUs are faster than ours, and you can use as many as you want. Near real time processing, massive processing, compute intensive analysis, deep learning. Size. You can customize this; you can be fast and expensive or slow and cheap. You use as much as you need. Short-term storage of large interim results or long-term storage of data that you might use one day.
Image courtesy of Chris Lynnes
We can use the cloud as infrastructure, for sharing data and results, and as software (e.g. ArcGIS Online, Google Earth Engine). Above is a cool graphic showing one vision of the cloud as a scaled and optimized workflow that takes advantage of the cloud: from pre-processing, to analytics-optimized data store, to analysis, to visualization. Why this is a better vision: some massive processing engines, such as SPARC or others, require that data be organized in a particular way (e.g. Google Big Table, Parquet, or DataCube). This means we can really crank on processing, especially with giant raster stacks. And at each step in the workflow, end-users (be they machines or people) can interact with the data. Those are the green boxes in the figure above. Super fun discussion, leading to importance of training, and how to do this best. Tristan also mentioned Cyverse, a new NSF project, which they are testing out for their workshops.
Image attribution: Corey Coyle
Super fun couple of days. Plus: Wisconsin is green. And warm. And Lake Mendota is lovely. We were hosted at the University of Wisconsin by Mutlu Ozdogan. The campus is gorgeous! On the banks of Lake Mendota (image attribution: Corey Coyle), the 933-acre (378 ha) main campus is verdant and hilly, with tons of gorgeous 19th-century stone buildings, as well as modern ones. UW was founded when Wisconsin achieved statehood in 1848, UW–Madison is the flagship campus of the UW System. It was the first public university established in Wisconsin and remains the oldest and largest public university in the state. It became a land-grant institution in 1866. UW hosts nearly 45K undergrad and graduate students. It is big! It has a med school and a law school on campus. We were hosted in the UW red-brick Romanesque-style Science Building (opened in 1887). Not only is it the host building for the geography department, it also has the distinction of being the first buildings in the country to be constructed of all masonry and metal materials (wood was used only in window and door frames and for some floors), and may be the only one still extant. How about that! Bye Wisconsin!
Esri has been holding these Imagery and Mapping Forum days prior to the main UC. I was here for the day, as an invited panelist for the Executive Panel and Closing Remarks session on Sunday. During the day Ihung out in the Imaging and Innovation Zone, in front of the Drone Zone (gotta get one of these for ANR).
Notes from the day: Saw demos from a range of vendors, including:
- Aldo Facchin from Leica gave a slideshow about the Leica Pegasus: Backpack. Their backpack unit workflow uses SLAM; challenges include fusion of indoor and outdoor environments (from transportation networks above and below ground). Main use cases were industrial, urban, infrastructure. http://leica-geosystems.com/en-us/products/mobile-sensor-platforms/capture-platforms/leica-pegasus-backpack
- Jamie Ritche from Urthecast talked about "Bringing Imagery to Life". He says our field is "a teenager that needs to be an adult". By this he means that in many cases businesses don't know what they need to know. Their solution is in apps- "the simple and the quick": quick, easy, disposable and useful. 4 themes: revisit, coverage, time, quality. Their portfolio includes DEIMOS 1, Theia, Iris, DEIMOIS-2, PanGeo + . Deimos-1 focuses on agriculture. UrtheDaily: 5m pixels, 20TB daily, (40x the Sentinel output); available in 2019. They see their constellation and products as very comparable to Sentinel, Landsat, RapidEye. They've been working with Land O Lakes as their main imagery delivery. Stressing the ability of apps and cloud image services to deliver quick, meaningful information to users. https://www.urthecast.com/
- Briton Vorhees from SenseFly gave an overview of: "senseFly's Drone Designed Sensors". They are owned by Parrot, and have a fleet of fixed wing drones (e.g. the eBee models); also drone optimized cameras, shock-proof, fixed lens, etc (e.g. SODA). These can be used as a fleet of sensors (gave an citizen-science example from Zanzibar (ahhh Zanzibar)). They also use Sequoia cameras on eBees for a range of applications. https://www.sensefly.com/drones/ebee.html
- Rebecca Lasica and Jarod Skulavik from Harris Geospatial Solutions: The Connected Desktop". They showcased their new ENVI workflow implemented in ArcGIS Pro. Through a Geospatial Services Framework that "lifts" ENVI off the desktop; and creates an ENVI Engine. They showed some interesting crop applications - they call it "Crop Science". This http://www.harrisgeospatial.com/
- Jeff Cozart and McCain McMurray from Juniper Unmanned shared "The Effectiveness of Drone-Based Lidar" and talked about the advantages of drone-based lidar for terrain mapping and other applications. They talked through a few projects, and wanted to demonstrate the economies for drone-based lidar. The main advantages are in the data, not in the economics per se. They partner with Reigl and YellowScan from France. Showcased an example in Colorado between lidar (DJI Matrice was platform) and survey - the cost was 1/24th as expensive as the field survey. They did a live demo of some of the ArcGIS tools: classification of ground, feature extraction, etc. http://juniperunmanned.com/
- Aerial Imaging Productions talked about their indoor scanning - this linking indoor to outdoor - making data truly geo - is a big theme here. Also OBJ is a data format. From Wikipedia: "The OBJ file format is a simple data-format that represents 3D geometry alone — namely, the position of each vertex, the UV position of each texture coordinate vertex, vertex normals, and the faces that make each polygon defined as a list of vertices, and texture vertices." Used for 3D graphics, but increasingly for indoor point clouds in our field.
- My-Linh Truong from Reigl talked about their new static, mobile, airborne, uav lidar platforms. They've designed some mini lidar sensors for smaller UAVas (3lbs; 100kHz; 250m range; ~40pts/m2). Their ESRI workflow is called LMAP, and it relies on some proprietary REIGL software processing at the front end, then transfer to ArcGIS Pro (I think). http://www.rieglusa.com/index.html
We wrapped up the day with a panel discussion, moderated by Esri's Kurt Schwoppe, and including Lawrie Jordan from Esri, Greg Koeln from MDA, Dustin Gard-Weiss from NGA, Amy Minnick from DigitalGlobe, Hobie Perry from USFS-FIA, David Day from PASCO, and me. We talked about the promise and barriers associated with remote sensing and image processing from all of our perspectives. Some fun things that came out of the panel discussion were:
- Lawrie Jordan started Erdas!
- Digital Globe launched their 30cm resolution WorldView-4. One key case study was a partnership with Associated Press to find a pirate fishing vessel in action in Indonesia. They found it, and busted it, and found on board 2,000 slaves.
- The FIA is increasingly working on understanding uncertainty in their product, and they are moving for an image-base to a raster-based method for stratification.
- Greg Koeln, from MDA (he of the rad tie) says: "I'm a fan of high resolution imagery...but the world is a big place".
- Multi=sensor triangulation (or georeferencing a stack of imagery from multiple sources to you and me) is a continual problem, and its going to get worse before it gets better with more imagery from UAVs. On that note, Esri bought the patent for "SIFT" an automated tool for relative registration of an image stack.
- Space Junk!
Notes and stray thoughts:
- Esri puts on a quality show always. San Diego always manages to feel simultaneously busy and fun, while not being crowded and claustrophobic. Must be the ocean or the air.
- Gotta get behind the ubiquitous "analytics" replacement of "analysis" in talks. I am not convinced everyone is using the term correctly, but hey, it's a thing now: https://en.wikipedia.org/wiki/Analytics#Analytics_vs._analysis
- 10 years ago I had a wonderful visitor to my lab from Spain - Francisco Javier Lozano - and we wrote a paper: http://www.sciencedirect.com/science/article/pii/S003442570700243X. He left to work at some crazy company called Deimos in Spain, and Lo and Behold, he is still there, and the company is going strong. The Deimos satellites are part of the UrtheCast fleet. Small world!
- The gender balance at the Imagery portion of the Esri UC is not. One presenter at a talk said to the audience with a pointed stare at me: "Thanks for coming Lady and Gentlemen".
Good fun! Now more from Shane and Robert at the week-long Esri UC!
Made this for fun for the GIF from NASA's "abcs from space" site: http://earthobservatory.nasa.gov/Features/ABC/. Was going to do "Kellylab" but ran out of time.
- G: This image of Pinaki Island was captured by astronauts on the International Space Station in April 2001.
- I: On February 10, 2007, the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured this image of the Andaman Islands. The thin, bright rings surrounding several of the islands are coral reefs that were lifted up by a massive earthquake near Sumatra in 2004.
- F: The Operational Land Imager (OLI) on Landsat 8 acquired this false-color image of valleys and snow-covered mountain ranges in southeastern Tibet on August 4, 2014. Firn is a granular type of snow often found on the surface of a glacier before it has been compressed into ice.
Big Data Helps Scientists Dig Deeper: Supercomputing + 40 Years of Landsat Images = A New Era for Earth Science
In 2008, the U.S. Geological Survey took 3.6 million images acquired by Landsat satellites and made them free and openly available on the Internet. Dating back to 1972, the images are detailed enough to show the impact of human decisions on the land, and they provide the longest continuous view of Earth’s landscape from space.
Since January 1, 2000, more than 4.3 million scenes have been captured by Landsat satellites and made available to the public. Landsat 8 was launched in February 2013, significantly boosting the rate to as many as 1,400 images each day. (Graph by Joshua Stevens, using data collected from the U.S. Geological Survey acquisitions archives.)
They found that the economic value of just one year of Landsat data far exceeds the multi-year total cost of building, launching, and managing Landsat satellites and sensors. This would be considered a stunning return on investment in any conventional business setting.