Mathematical Geophysics Project Update

Streamlining Field Data Collection with Mobile Apps

With the availability and affordability of new customized apps on smartphones and tablets, data collected in the field are becoming easier to input, store, and share.

By Reid J. Camp and Joseph M. Wheaton

Fieldwork is a major component of nearly every geoscience discipline. Over the past 3 decades, scientists have amassed an array of specialized instrumentation and equipment to help them measure and monitor a staggering assortment of geophysical phenomena.

Although this equipment gives scientists valuable insight into the physical world, it is not without drawbacks. Much of this specialized equipment comes with hefty price tags and is often difficult or impossible to customize. Despite the degree of sophistication of much of the instrumentation, scientists often lack the flexibility to adapt data collection to best meet their own experimental or monitoring needs. In addition, technology ironically often stifles creativity and discourages scientists from harnessing their powers of basic observation—if there is not a button to click or a box to fill, all too often basic observations and critical insights go unrecorded.

Just 5 years ago, building custom apps was beyond the skill sets of most geoscientists and was a relatively costly investment. Today, users no longer need to be expert programmers to build and deploy their own apps with the help of programs that expedite development by using predefined scripts and layouts. What’s more, it is easy to leverage and integrate onboard sensors in smartphones and tablets such as cameras, GPS, accelerometers, and light sensors, among others. Users can also combine the sensors and the agility of building custom apps with an assortment of cases (e.g., Lifeproof® and OtterBox®) that make smartphones and tablets ruggedized, even waterproof.

The ability to develop customizable apps to help with fieldwork is becoming increasingly accessible to projects at all funding levels. Taking advantage of the advances, scientists and software developers from Utah State University’s (USU’s) Ecogeomorphology and Topographic Analysis Lab, USU’s Fluvial Habitats Center, and Eco Logical Research, Inc., work together to develop custom apps to increase efficiency and data quality by providing a template for data entry with quality control enforced by validation rules (see, e.g., Figure 1). The many custom apps are designed specifically to facilitate data collection, including one to design, implement, and monitor a large-scale restoration project.

Why Build Apps?

Smartphones and tablets are much cheaper than data collection devices designed specifically for field use ($100–$600 versus $1500–$5000). Rugged and waterproof cases are also available for nearly any mobile device ($50–$150) to make them fieldworthy. Mobile app stores have an ever-increasing list of practical apps to aid field work, such as geographic information systems (GIS) and data-syncing tools. Importantly, most people are now familiar with the use of smartphones and tablets, making these tools intuitive and practical to use. These benefits make common smartphones and tablets the ideal field tool.

Interestingly, geoscientists are not exploiting these devices en masse. In the geosciences, field data are still frequently recorded on data sheets and manually entered into a database for storage. This transcription process is prone to errors, and information can be lost completely because of misplaced or soiled data sheets.

Mobile database applications increase data integrity by allowing users to enter information into a structured database during initial collection. In some cases, data can even be synced to a central server when the user has an internet or cellular connection. Mobile applications have been developed for anything from monitoring air pollution from user photos to ribotyping bacteria [Showstack, 2010; Guertler and Grando, 2013]. The benefits to individual projects are apparent, but the limits of specific methods are still being explored [Teacher et al., 2013].

To bridge the gap between the technology people use every day and the outdated or expensive technologies scientists use in the field, scientists and programmers have developed mobile database apps for a range of basic field data collection and observations, including geotagged field voice and video recordings, fluvial audits, fish surveys, habitat inventories, beaver dam surveys, geomatics survey notebooks, geomorphic unit mapping, and logs created for use with River Styles, among others.

Case Study: Stream Restoration Design and Effectiveness Monitoring

To illustrate the power of using apps in the field, we describe an example of a custom app developed by scientists at Utah State University. This app, named High Density Large Woody Debris (HDLWD) Effectiveness, has improved proficiency at transparently documenting a field design, cataloging the construction process, and facilitating explicit testing of design hypotheses for a large-scale stream restoration project.

The HDLWD Effectiveness app is used to specifically monitor the implementation and effectiveness of a large-scale experimental stream restoration project on Asotin Creek in southeast Washington State. The project involves the addition of a high density of large woody debris to three streams in the Asotin Creek watershed [Bennett et al., 2012; Wheaton et al., 2012] to increase or improve juvenile steelhead trout habitat. Historic land use practices have left the channel in a static, degraded state composed mostly of uniform runs and rapids. The goal of the restoration project is to return LWD densities to historic levels and thereby to facilitate the creation of pools and bars that can shelter young steelhead. This application incorporates the dynamic design, implementation, and recurrent annual monitoring of the more than 600 restoration structures—woody debris deliberately placed along the creek—built for the project (Figure 1).

Fig. 2. Screenshot of the app showing the channel unit assemblage builder, used to record the size, location, and pertinent attributes of geomorphic units within 50 meters of every structure (red = run, orange = rapid, gray = bar, blue = pool, yellow = undercut bank). The assemblages can be exported from the app as spatially explicit rasters for analysis.
Fig. 2. Screenshot of the app showing the channel unit assemblage builder, used to record the size, location, and pertinent attributes of geomorphic units within 50 meters of every structure (red = run, orange = rapid, gray = bar, blue = pool, yellow = undercut bank). The assemblages can be exported from the app as spatially explicit rasters for analysis.

The app is used to monitor the presence of hydraulic and geomorphic responses that are specific to hypotheses in the project design. When size, location, and descriptions of specific channel units are entered into this app, it automatically creates spatially explicit maps of how channel units (e.g., pools, bars, runs) surrounding every structure are connected (Figure 2). The app gets very detailed—there are many specific types of channel units, and each one is created through specific fluvial processes. Armed with these data, scientists can better determine the efficacy of the restoration project in altering hydraulic and geomorphic complexity in the study streams.

Aspects of the project are divided into easily navigable tabs within one application, and data are stored in a single database. Data validation rules are set up to keep numeric values within an acceptable range, and drop-down lists are used for common and repeatable inputs.

Using this app, researchers can collect much more data than was previously feasible in one field season. The ability to store videos of each structure nearly eliminates confusion for the implementation crews when they are expected to operate remotely and unsupervised. Photos are directly stored within “container” fields in the database, making it operate like a digital photo library as well. In addition, by incorporating the bulk of the restoration monitoring into a single application, the data are readily accessible and sharable among the working group.

Apps for Citizen Science

Citizen science projects are becoming more popular but are typically limited in scope by a nonspecialist user’s knowledge and ability. Providing a mobile application with immediate quality control and integrated help features can greatly expand the expectations and dependability of crowdsourcing data.

The scientists and programmers at USU recently launched a statewide citizen science monitoring program with Utah State University’s Water Quality Extension group to monitor beaver dams throughout the state. The data are being used both to guide wildlife management and to validate predictive models developed to assess the capacity of riverscapes to support dam-building activity by beavers (J. M. Wheaton and W. W. MacFarlane, Modeling the capacity of riverscapes to support beaver dams, submitted to Ecohydrology, 2013). Using custom applications to control data entry can make crowdsourcing a viable option for more geoscience projects.

Simple Apps Do the Heavy Lifting

Native custom mobile database applications—apps that require extensive programming knowledge because they are built from the ground up—are ideal but expensive. Every project and researcher would benefit from using native apps, but few researchers could afford it, and even fewer have the knowledge to develop the apps themselves.

The apps developed by the USU groups and Eco Logical Research, Inc., do not reinvent the wheel—they pull from other codes and software, tailoring them for specific projects. For example, the FileMaker Go app was used to deploy FileMaker Pro databases and data entry forms on iOS devices (think of this as an app within an app) so that the developers of the HDLWD Effectiveness app did not have to program anything for device interaction and basic app infrastructure. This saved time and money. Other database-driven apps (e.g., GISPro and HanDBase) can be employed for the same benefit.

By leveraging simple database-driven apps and software that already exist to do the heavy lifting, the time and cost to develop a custom application are dramatically reduced. In this way, geoscientists may be able to develop and use mobile devices and custom apps more regularly to aid their fieldwork.


For more information on the apps discussed in this article or on using mobile database applications, contact the corresponding author. We would like to thank the Utah State University Fluvial Habitats Center and Eco Logical Research, Inc., in particular, Nick Weber, for developing the FileMaker workflow. Steve Bennett and Nick Bouwes were instrumental in the structure design and monitoring process that facilitated the development of the HDLWD app.


Bennett, S., R. Camp, N. Trahan, and N. Bouwes (2012), Southeast Washington Intensively Monitored Watershed Project in Asotin Creek: Year 4 pretreatment monitoring summary report, Wash. State Recreation and Conserv. Off., Olympia.

Guertler, V., and D. Grando (2013), Reprint of New opportunities for improved ribotyping of C. difficile clinical isolates by exploring their genomes, J. Microbiol. Methods, 95(3), 425–440, doi:10.1016/j.mimet.2013.09.009.

Showstack, R. (2010), Air pollution app, Eos Trans. AGU, 91(41), 371, doi:10.1029/2010EO410004.

Teacher, A. G. F., D. J. Griffiths, D. J. Hodgson, and R. Inger (2013), Smartphones in ecology and evolution: A guide for the app-rehensive, Ecol. Evol., 3(16), 5268–5278, doi:10.1002/ece3.888.

Wheaton, J., S. Bennett, N. Bouwes, and R. Camp (2012), Asotin Creek Intensively Monitored Watershed: Restoration plan for Charley Creek, North Fork Asotin, & South Fork Asotin Creeks, Snake River Salmon Recovery Board, Dayton, Wash.

—Reid J. Camp and Joseph M. Wheaton, Department of Watershed Sciences, Utah State University, Logan; email: [email protected]


Citation: Camp, R. J., and J. M. Wheaton (2014), Streamlining field data collection with mobile apps, Eos Trans. AGU, 95(49), 453–454, doi:10.1002/2014EO490001.

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  • Dana Rehm

    Its fascinating to see the progress in the use of new tools to gather field data.