Recently I was fortunate to work with the IGIS team in Santa Rosa and Sonoma to explore why so many homes and buildings were lost in the October Tubbs and Nuns Fires. With the IGIS's Shane Feirer we collected drone-based video to record how the fires burned through the vegetation near and around the lost structures.
We observed several sites where there was little fire activity in the forests or woodlands, yet the homes burned. This type of video helps us document how devastating a wind-driven ember fire can be and of the important lessons we can learn to be better prepared for wildfire.
From this experience I came away with a painful reminder that we all need to do a better job at focusing on fuels near our homes (e.g. combustible wood mulches used in landscaping, lawn furniture, leaf accumulations, dry landscape plants, etc.), especially in the 5 feet immediately adjacent to our homes. While the Tubbs Fire originated in grassy area in Calistoga it easily picked up embers from the burning vegetation which were moved by the 40-70 mph winds and created spot fires ahead of the flaming front. In short time these embers were blasted into homes via attic or soffit vents (critical to let moisture out of a building) or they ignited combustible materials close to buildings; these types of exposures are the primary way the Tubbs Fire started to consume homes. Eventually the Tubbs Fire moved to the more densely populated areas of the Fountain Grove subdivision in Santa Rosa and with each new home that was ignited a new source of embers were created. The embers that came from the burning buildings included 2 x 4s, chunks of wood the size of a frisbee, and other materials. These materials were blasted over Highway 101 on to homes and businesses in the urban center of Santa Rosa- a place most thought could not be impacted by wildfire. The winds persisted till mid-morning on October 9th providing considerable time for an ember to find a weakness in the home. All of us hope we never have a fire like this again, but as history shows us, California's most damaging fires typically occur in the September and October and are often wind-driven.
For many years UC has worked in educating homeowners about fire preparedness in the Wildland Urban Interface (WUI). These fires have resulted in the largest number of structure losses to date in California and we all need tools to better understand how to learn from these experiences. I greatly appreciate IGIS's willingness to help me collect some critical data in a time sensitive manner.
- Author: Andy Lyons
The National Agriculture Imagery Program (NAIP) is a USDA service which has been collecting aerial imagery once a year during the growing season for the entire continental US. Established in 2003, the images are taken from airplanes and then stitched together to create a four band (blue, green, red, & near-infrared) digital orthomosiac at a one-meter resolution.
This dataset has become the foundation for many important analyses across the country, including crop distribution maps and agricultural forecasts, and has served as the basemap for countless maps. For us in IGIS, NAIP imagery is one of our go-to datasets for map backdrops or raw data for things like classification or georeferencing drone images. ESRI makes NAIP imagery easy to use by distributing it through their ArcGIS online platform.
Aside from its national scope and high quality, a big reason why NAIP imagery has been so widely used is because it has always been public domain, available at no cost through the USDA Geospatial Data Gateway. But this may soon be changing. As recently reported on GIS Lounge, the USDA Farm Services Agency (FSA) is considering making NAIP a Commercial-Off-The-Shelf (COTS) product subject to a license. This means everyone, including government agencies and researchers, would have to start paying for the data, and have limitations on what they can do.
According to a recent presentation by John Mootz, Imagery Program Manager at the FSA Aerial Photography Field Office, the switch to a license model may become necessary because the current funding model isn't working. NAIP is funded under an innovative arrangement where costs are shared by state and federal agencies (not unlike Cooperative Extension). However, some states haven't been paying their bills, leaving a $3.1 million shortfall over the past few years. Aside from being financially unsustainable, late or missing payments cause delays in scheduling flights which can result in images being collected past the peak agriculture growth season. The time it takes to process the data, which has already stretched from 2 years to 3 years, is also affected.
What does this mean for ag? For cash-strapped agencies, researchers, and members of the public, losing access to a valuable dataset is never a good thing. But a lot of questions are still unknown. Collecting geospatial data is expensive, particularly for the entire USA, and as we've already seen with NAIP funding shortfalls can affect the quality and timeliness of the data. A lot of other data collection is funded through license models, which can work well if they are affordable and licenses tailored to the different needs of users. How will the state agencies who have been funding NAIP respond if FSA switches to a license model? Could other technologies fill the gap, such as the many commercial high resolution satellites that are now in orbit? Can political will be mobilized to convince USDA that collecting data that serves a public good is a good role for government, or has that cow left the barn?
FSA needs to make a decision by May 1, 2018 whether or not to change the distribution model for the NAIP 2019 data (to be collected in summer 2019). They are currently collecting impact statements "to allow FSA leadership a clear understanding before they make the final decision". If this affects you, please leave a comment below and also consider letting the FSA know how your work would be impacted, for better or worse, if NAIP data become licensed.
- Author: Sean Hogan
ESRI held its first ‘Imagery Education Summit' in Redlands California this week, and even though I came with high expectations, I was still pleasantly surprised with the caliber of the summit's presentations. It is very difficult to pick out a favorite among these talks; however, I can say that I particularly enjoyed the presentations by Jarlath O'Neil Dunn from the University of Vermont (pictured below), on ‘Success Stories and Progress' in image analysis and mapping, and by Jason Ur from Harvard University, regarding his work with ‘Drones and Archaeology Case Studies' in Iraq. The innovative approaches that they and others at the summit presented were truly inspiring!
One take-away from this event is that ESRI is making huge strides to incorporate more remote sensing processing options into ArcGIS Pro's ‘Image Analysis' toolbox. Speaking for myself, as both a remote sensing and GIS practitioner, I am excited about the prospect of being able to do more of my work within just one application environment, as opposed to doing my image stitching in Pix4D, image analysis in ENVI, and then finally my spatial analysis and mapping in ArcGIS. For the sake of efficiency, I look very forward to the day that I can do all of this in just one app.
For you drone enthusiasts out there, one neat new feature in the ArcGIS Pro Image Analysis tools is basic image stitching for producing color balanced orthomosaics and digital surface model outputs. This new function is not at the level of what Pix4D or Drone-to-Map can do yet, but for basic RGB image processing it may be good enough for many people's needs. Plus, it is a brand new tool that is bound to improve over time.
A couple more neat news items that were mentioned at the summit include:
- ESRI now offers a FREE ‘Schools Mapping Software Bundle', which includes both fully functional ArcGIS Pro and ArcGIS Online licenses for every K-12 school worldwide. http://www.esri.com/industries/education/software-bundle#.
- ESRI also now offers free ‘Massive Open Online Courses' (MOOCs), which among other things include approximately 30 courses on working with imagery alone, http://www.esri.com/mooc/imagery.
The future for spatial science has never looked brighter!
- Author: Sean Hogan
Last week IGIS was very pleased to partner with CERES Imaging Inc. (http://www.ceresimaging.net/) to provide a workshop on GIS and Remote Sensing for Crop Agriculture, in Davis CA. This particular event represents an example of how UC ANR's IGIS Program is working with private industries to better deliver valuable services (in this case training) to public audiences who are eager to put the services/information into action; reinforcing UC ANR's priorities of public service and Cooperative Extension.
This event was partially funded by a Department of Water Resources (DWR) grant to CERES, which among other things has helped CERES to provide very affordable, high resolution, multi-spectral, thermal and NDVI data to agriculturalists around California. The objective of the DWR funding was to help facilitate more agile farming practices for water conservation, through the adoption of newly available aerial image products, while introducing farmers to contemporary image processing and mapping methods. By partnering with CERES in this effort, IGIS is helping farmers to better utilize CERES's image products, to ideally make their operations more efficient and profitable.
- Author: Andy Lyons
GO FAIR is an initiative to promote and support data stewardship that allows data to be Findable, Accessible, Interoperable, and Reusable. I was pleased to attend the launch of the first North American FAIR network last week at the UC San Diego Supercomputing Center.
Coping with a Data Tsunami
To say that we live in a data rich world is an understatement. We live a data drenched world (a fact I'm constantly reminded of by the 'hard drive full' warnings that pop-up on my computer on a weekly basis). Thanks to simultaneous, order-of-magnitude, advances in our ability to produce, disseminate, and store all manner of data, people working in fields from economics to physics to agriculture are struggling to benefit from, rather than be paralyzed by, the volume and diversity of data we produce. And this is by no means a problem only affecting academics, as more and more individuals, private companies and organizations are collecting and working with large volumes of data, from personal health sensors to drones.
Adding to the challenge, there are often major barriers to get data to talk to each other. They may be stored in different formats, use different scales or units of analysis, or be under different restrictions. If you've ever carried personal health data from one doctors office to another by hand, you know what I mean.
FAIR Data Stewardship Principles
These are not new problems, but have taken on increased sense of urgency as the challenge gets worse and the demand for integrated analyses of complex problems grows. GO (Global Open) FAIR is a European based initiative that has two faces: i) a set a principles for data stewardship, and ii) a growing network of institutions and programs that are taking tangible steps toward a world in which data are Findable, Accessible, Interoperable, and Reusable. FAIR certainly doesn't mean that collected data have to be free or open access, but data stewardship should have a way to share information about the existence of data, and a means for access when appropriate.
The FAIR principles mirror what open science advocates have argued for many years. As a program, GO FAIR has gained more traction than many of its predecessors. Following endorsements from the European Commission and other international bodies, the EU has already committed €2 billion to the first phase of implementation. Starting in 2018, the major EU funding agencies will require applicants to submit data stewardship plans that align with the FAIR principles. The initiative is also investing a lot in training people to use metadata standards and tools, many of which already exist.
How is This Relevant for ANR?
ANR academics are impacted by the data psunami in at least two ways (neither for good). Like all practicing scientists, we have to deal with the usual challenges of managing large volumes of data, the frustrations of not being able to find or use data that others have collected, and the burden of all the gymnastics one must do to combine data from different sources into a robust, repeatable analysis. On top of that, as public servants whose work is funded by taxpayers, we have an additional moral and legal responsibility to be good stewards of all data collected for our public mission, which means ensuring the data we collect remains discoverable and accessible for other studies. Similarly, our extension mission also requires us to help California growers and land stewards get the most value from the data they collect, with tools that address their requirements for privacy and security.
While this may all seem like a lot to think about and additional work, the rewards are pretty exciting as the following video shows:
How Close are Your Data to Being FAIR?
For many us, putting the principles of FAIR data stewardship into practice will require a step or two we're not accustomed to, such as i) generating metadata in a format that can be read by both people and machines, and ii) storing our data (and metadata) for the long-term. The table below from a recent Nature article breaks down the gold standard a little further.
F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
F3. metadata clearly and explicitly include the identifier of the data it describes
F4. (meta)data are registered or indexed in a searchable resource
I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles
I3. (meta)data include qualified references to other (meta)data
A1. (meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where necessary
A2. metadata are accessible, even when the data are no longer available
R1. meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1. (meta)data are released with a clear and accessible data usage license
R1.2. (meta)data are associated with detailed provenance
R1.3. (meta)data meet domain-relevant community standards
Wilkinson, Mark D., et al. "The FAIR Guiding Principles for scientific data management and stewardship." Scientific Data 3 (2016): 160018.
As a research technology unit, I think we're doing fairly well in terms of keeping our data organized and accessible for the long-term. However after looking at our data management practices through the FAIR lens, I now see our metadata misses some important characteristics, a lot of the quality metrics aren't machine readable, and need to learn more about metadata repositories and discoverability, particularly for our drone data. These are challenges common to many new sources of geospatial data, and we look forward to engaging with the new arm of the GO FAIR network to develop solutions.