Use of High Resolution UAS Imagery to Classify Sub-Arctic Vegetation Types

Wednesday, 17 December 2014
Christina Herrick1, Michael W Palace1, Daniel R Finnell2, AJ Garnello3, Franklin Sullivan1, Samantha Marie Anderson1 and Ruth K Varner1, (1)University of New Hampshire (UNH), Institute for the Study of Earth, Oceans, and Space (EOS), Durham, NH, United States, (2)Virginia Commonwealth University, Richmond, VA, United States, (3)University of Arizona, Tucson, AZ, United States
Sub-arctic permafrost regions are now experiencing annual warming with a resulting thaw that induces changes to the vegetative landscape. This warming trend is directly correlated to increases in annual greenhouse gas emissions including methane (CH4). Vegetation species and composition are indirect indicators of CH4 flux, and may serve as a proxy for estimating changes in CH4emission over time. Three WorldView-2 images (2m2 spatial resolution, 8 multispectral bands) were acquired in Jul/Aug of 2012-2014 over the Abisko region in northern Sweden. Color infrared (CIR) sub-meter imagery was also collected over a 4km2 area in 2014 using both a multi-rotor helicopter and a fixed wing unmanned aircraft system (UAS). Fifty 1m2 ground sample plots were established; these plots cover 5 major ground cover vegetation classes and were used in classification efforts. Texture analysis was conducted on both UAS and WV-2 imagery. Both an unsupervised k-means clustering algorithm to predict vegetation classes and a supervised classification using both random forests and neural networks were conducted; similar texture analysis and clustering were also performed on the UAS imagery. Classifications of the two imagery types were compared with promising results, thus supporting the use of UAS and high resolution satellite image collection to provide landscape level characterization of vegetation.