Year of Award

2019

Document Type

Thesis

Degree Type

Master of Science (MS)

Other Degree Name/Area of Focus

Computer Science

Department or School/College

Computer Science

Committee Chair

Travis Wheeler

Commitee Members

Kelsey Jencso, Rob Smith

Keywords

data loggers, spatially distributed data collection, embedded systems, environmental sensors

Publisher

University of Montana

Subject Categories

Computer Sciences

Abstract

Characterizing the processes that lead to differences in ecosystem productivity and watershed hydrology across complex terrain remains a challenge. This difficulty can be partially attributed to the cost of installing networks of proprietary data loggers that monitor differences in the biophysical factors contributing to vegetation growth or hydrological processes. Studies that aim to compare concurrent time-series data sets across multiple locations must therefore balance the high cost of these data logger systems with the need for spatial resolution in their data. Here, we present the design, implementation, and case study for an open-source “Pinecone” data logger system, released under the GNU General Public License that can be manufactured for under $70 USD per unit. The system was designed to accommodate a wide range of generic and proprietary environmental sensors, and to be inexpensive enough to build and deploy large numbers to a study site. A case study was performed in which 54 data loggers were deployed to North Fork Elk Creek, a mountainous watershed located in Lubrecht Experimental Forest in the Garnet mountain range in Northwest Montana for a one year period. The data loggers were deployed across 6 hillsides in the watershed, representing combinations of differing elevations and aspects, at 9 study locations on each hillslope. At each of these locations we recorded air temperature, vapor pressure, soil water content, sap flow velocity, and tree basal area at 30 minute intervals. We evaluated the reliability of the systems in a case study over an 8 month period in 2016 and 4 month period in 2017. Our results suggest that open-source technologies such as the Pinecone logger can make it possible to develop dependable and spatially distributed sensor network within the confines of a typical research budget.

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© Copyright 2019 Tim Anderson, Kelsey Jencso, and Zachary Hoylman