Digital technology has transformed environemental field data collection. Digital methods allow data to be collected more efficiently, more accurately, in greater quantity and processed more quickly than ever before. For Dudek biologists, archaeologists, urban foresters, hydrologists, and other science and engineering professionals that manage projects involving fieldwork, applying technology is a constant challenge as it evolves.
Three Dudek team members recently sat down to chat about the nexus of environmental sciences fieldwork and data technology:
- Brock Ortega, senior biologist/project manager
- Kam Muri, biologist/project manager
- Kyle Harper, applications developer
Brock and Kam, as biologists you’ve been involved in hundreds of field surveys large and small. What has been the single biggest impact of technology for environmental fieldwork?
Kam: Digital tools have ensured the highest quality data collection, so the work product has gotten better and better.
Brock: Technology has made it both more efficient to collect data and faster to get information into decision-makers’ hands. Applying technology to fieldwork constantly evolves. We have gone from pencils and notebooks in the field to inputting survey data into apps on tablets, and now, deploying unmanned aerial systems for certain data collection tasks.
Kam: As an example of efficiencies, we can train monitors and get them in the field more quickly because instead of printing and distributing paper maps of the project site, we issue pre-loaded tablets. This might sound like a minor logistical issue, but it can result in significant time savings and improve data collection accuracy when we’re pulling together a large field staff for a project involving thousands of acres.
Brock mentioned unmanned aerial systems (UAS). Let’s talk about that technology for a minute.
Brock: This technology is intriguing because it has become mainstream so quickly for so many applications, and is so visible––like Amazon proposing to fly deliveries to your porch. UAS are another tool for us. We can equip aerial platforms with cameras for imagery; sensors for data, such as lidar; noise meters; air sensors; temperature gauges; and so on. They offer significant advantages in data collection efficiency and frequency, cost-effectiveness, and safety. UAVs are a game-changing tool for certain applications, but they need to be used the right way for the right task to have a benefit.
UASs are a game-changing tool for certain applications, but they need to be used the right way for the right task to have a benefit.
Kyle: In a way, UAS can be considered an example of the Internet of Things (IoT) with its network of “smart” sensor-enabled devices that connect through the Internet. In the consumer world, the Internet of Things are the sensors in the refrigerator monitoring how much milk you have or in the thermostat that lets you adjust the temperature from your smart phone. The environmental practice has been building its Internet of Things like remote telemetry on streams and groundwater wells.
Brock: Or, game cameras in wildlife corridors that transmit photos wirelessly in real-time rather than having someone go out periodically to download images.
Kam: And, biology survey apps on smartphones let us capture, track and share a gnatcatcher’s movements in real-time. A big advantage of technology-driven environmental data collection is closing data gaps. For example, we have a project with a water agency to monitor riparian habitat conditions. Traditionally that meant collecting habitat information once a year and matching it with groundwater data.
Now, we’re using infrared cameras on UAS to collect imagery on plant production once a month rather than once a year. This gives us finer detail and allows us to tease out more information and trends.
Kyle: As you mention those examples, I see three main issues with the way digital has impacted environmental field data collection. First, how is the appropriate technology selected from all the available choices for a particular task or project? Second, how are the increasingly large amounts of data, particularly from UAS flights, cost-effectively stored and secured? Third, how are the collected data converted to integrated, actionable information as quickly as possible?
Those are good points. Can you talk first about selecting the appropriate technology for the project?
Kam: This is an important point, and a common pitfall. It goes back to Brock’s point about UAS being a tool. Just because you’ve collected data digitally doesn’t mean you have useful information. A tool is only as good as the person using it. The starting place in selecting technology for field data project is to link the subject-matter expert who knows the project goals with the application developer.
A big advantage of technology-driven environmental data collection is closing data gaps
Brock: We see our subject-matter experts––whether biologists or archeologists or urban foresters–– also becoming very conversant in technology. So, field data collection is really connecting subject-matter expertise and technology capability. UAS are a good example. There is an ever-growing choice of types of aerial vehicles and software options. But it takes experience to select the correct UAS and sensor depending on a number of variables, such as biological resource, topography, vegetative cover, goal, accessibility, timing, season, and many others. A biologist who is conversant with the processing software and UAS platform is much more likely to choose an efficient method–– or determine that UAS is not the way to go for this particular project.
Kyle: And, there is a tendency in the tech world to over-hype new products.
Brock: That’s right. We stay away from using products that are on technology’s “bleeding edge,” and instead focus on the best, proven technology to accomplish data collection. Experience is the best teacher. When we kick off a field data collection project involving technology tools, we’re happy to share “war stories” with clients.
Kam: There is another aspect to having the subject matter expert and technologists jointly involved, specifically on long-term monitoring projects. If data is not structured correctly at the outset, you sort of paint yourself in a corner because you can only look at the data in a limited way. Structuring data right gives you the flexibility to look back on multiple years of data and slice things in different ways than you may have originally intended, which can end up being very valuable. Now, your data gets a much longer shelf life and becomes more useful.
So you’ve determined the right technology for the project need. Now you’ve collected a ton of data. What’s next?
Kyle: This is a big issue. The amount of data being stored is growing astronomically. For our projects, we store and host giant datasets such as UAS imagery for our clients who may not have the infrastructure or staff to take on this burden. Some of our clients are interested in being more hands-on, or want to host the data themselves—in these cases, we’re happy to work together with them to do the up-front heavy lifting and gradually transition to their infrastructure. Cloud storage options are getting cheaper and cheaper, and can offer a scalable, cost-effective solution. In other cases, on-premise servers and storage make more sense. It really depends on the applications and goals of the project. Context is everything.
The amount of data being stored is growing astronomically. For our projects, we store and host giant datasets, such as UAS imagery, for our clients who may not have the infrastructure or staff to take on this burden.
Kam: Also, software licensing isn’t cheap.
Kyle: Very true. Just having the storage and server capacity is the starting point, but then there are a host of software considerations. Using open source systems where possible is a great way to save on recurring licensing costs. Open source platforms, database management systems, and geospatial services can be superior to proprietary systems. There are always trade-offs to consider before making the leap to open source, however. We find a hybrid approach can be very effective—go open source where you can, but keep the proprietary tools that are adding value.
Kam: Another thing to consider is security. Data security and integrity is key to protect sensitive data and to keep it useful over the long-term.
Kyle: Yes, access control is an important part of any data management system. For instance, who gets to see the data, do they have access to all the data or just a subset of it, and for how long into the future? Do certain data points come with a shelf life, after which their quality should be reduced? What data standards are we going to commit to supporting long term, and how are the storage and computing costs properly accounted for? These are important questions to ask when designing a data management system.
How does all the data that has been collected and stored get converted to actionable information?
Brock: This is a huge advantage technology provides. Turn-around time for field collected data used to be days. Now it can be hours. We were assessing cultural resources on an 8,000-acre site by combining UAS-collected data with GIS modeling to provide archival research and a mapping database. Project managers had information days earlier than usual so they could evaluate potential impacts of cultural resources.
Kam: We address strategies for data distribution up front on a project. Considerations include what quality control steps to apply to data before it is disseminated and considerations about data that could potentially be part of a public record.
Brock: Which brings us back to what we discussed earlier about the importance of subject-matter experts providing guidance on data management tasks. Technology gives us tremendous capabilities to collect and distribute data, but the real key is knowing how to manage data appropriately and effectively.
Technology gives us tremendous capabilities to collect and distribute data, but the real key is knowing how to manage data appropriately and effectively.
Kyle: From a technical standpoint, distributing meaningful, relevant information has become trickier with more data flowing in from multiple streams and in multiple formats. You don’t want to look at siloed data. I see data being used in two ways: either transactional or integrated.
What do you mean by transactional data?
Kam: Monitoring surveys are a good example. Data comes in from the field from multiple biologists via automated email reports. These transactional reports are extremely valuable in tracking the progress of field surveys, and let me know that things are being completed correctly and as scheduled. On many occasions, I’ve used the immediate feedback from the field to adjust mobile data forms on the fly to improve the information being collected or to capture something we weren’t aware of at the planning stage.
What’s an example of integrated data use?
Kyle: Integrated data involves running processes behind the scenes to monitor multiple streams of data, test for certain conditions, and send appropriate notifications or updates when thresholds are crossed.
For example, we have a nightly process that integrates several biological data sources to assist with compliance on a large construction project. The latest GIS habitat models sync with field monitors’ incoming data collection forms to test for potential outliers, update our Web dashboard for the team to track total acreages and occurrences, and send alerts to the team when environmentally sensitive areas are established in the field.
Another example: we are involved with Habitat Conservation Plans (HCPs) that are obligated to preserve a certain amount of land. When developers apply for new projects, we track how the developer’s plans would impact natural habitats, and how that would affect the overall preserve requirements to stay ahead of certain preservation goals. Our integrations run behind the scenes keep track of the changes.
We’re talking about a lot of dynamic information. As part of our development, we build interactive Web dashboards that let clients visualize and interact with data, as well as run ad hoc analyses on demand. Without asking a GIS analyst for assistance, a landowner can define a certain extent to analyze on the map and run their own analysis, getting the latest information-rich reports about the natural resources and compliance requirements on their property in minutes or even seconds.