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Remote Sens. 2018, 10(9), 1498; https://doi.org/10.3390/rs10091498

Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS)

1
Earth System Research Center, University of New Hampshire, 8 College Rd, Durham NH 03824, UK
2
Department of Earth Sciences, University of New Hampshire, 56 College Rd, Durham NH 03824, UK
3
Virginia Commonwealth University Center for Environmental Studies, 1000 West Cary St, Richmond, VA 23284, USA
4
Quantum Spatial, 1100 NE Circle Blvd #126, Corvallis, OR 97333, USA
5
Department of Ecology & Evolutionary Biology. University of Arizona, P.O. Box 210088, Tuscon, AZ 85721, USA
6
Department of Biological Sciences, Northern Arizona University, 617 S Beaver St, Flagstaff, AZ 86011, USA
7
School of Life Sciences, Rochester Institute of Technology, 85 Lomb Memorial Drive, Rochester, NY 14623, USA
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 11 September 2018 / Accepted: 15 September 2018 / Published: 19 September 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Full-Text   |   PDF [12695 KB, uploaded 19 September 2018]   |  

Abstract

Rising global temperatures tied to increases in greenhouse gas emissions are impacting high latitude regions, leading to changes in vegetation composition and feedbacks to climate through increased methane (CH4) emissions. In subarctic peatlands, permafrost collapse has led to shifts in vegetation species on landscape scales with high spatial heterogeneity. Our goal was to provide a baseline for vegetation distribution related to permafrost collapse and changes in biogeochemical processes. We collected unmanned aerial system (UAS) imagery at Stordalen Mire, Abisko, Sweden to classify vegetation cover types. A series of digital image processing routines were used to generate texture attributes within the image for the purpose of characterizing vegetative cover types. An artificial neural network (ANN) was developed to classify the image. The ANN used all texture variables and color bands (three spectral bands and six metrics) to generate a probability map for each of the eight cover classes. We used the highest probability for a class at each pixel to designate the cover type in the final map. Our overall misclassification rate was 32%, while omission and commission error by class ranged from 0% to 50%. We found that within our area of interest, cover classes most indicative of underlying permafrost (hummock and tall shrub) comprised 43.9% percent of the landscape. Our effort showed the capability of an ANN applied to UAS high-resolution imagery to develop a classification that focuses on vegetation types associated with permafrost status and therefore potentially changes in greenhouse gas exchange. We also used a method to examine the multiple probabilities representing cover class prediction at the pixel level to examine model confusion. UAS image collection can be inexpensive and a repeatable avenue to determine vegetation change at high latitudes, which can further be used to estimate and scale corresponding changes in CH4 emissions. View Full-Text
Keywords: unmanned aerial system (UAS); artificial neural network; mire vegetation; Stordalen; tundra; drone; classification unmanned aerial system (UAS); artificial neural network; mire vegetation; Stordalen; tundra; drone; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Palace, M.; Herrick, C.; DelGreco, J.; Finnell, D.; Garnello, A.J.; McCalley, C.; McArthur, K.; Sullivan, F.; Varner, R.K. Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS). Remote Sens. 2018, 10, 1498.

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