Date of Graduation
Davis College of Agriculture, Natural Resources and Design
Division of Forestry and Natural Resources
Petra Bohall Wood
James T. Anderson
Amy E. Hessl
Michael P. Strager
Timothy A. Warner
In this study, I tested the potential for remote sensing data with a high spatial resolution to model breeding forest bird species and their habitat at a fine spatial scale. The research took place on ridgetops in a large, relatively contiguous Appalachian mature deciduous forest in northwestern WV, USA. The remote sensing data sources were a leaf-on QuickBird satellite image (0.6-m panchromatic and 2.4-m multispectral) and a 3-m digital elevation model (DEM). For the first part of the study, I extracted spectral and textural measures from the satellite image and terrain information from the DEM. I then used these data to analyze avian community survey and habitat data collected at circular plots (n = 68) distributed across the ridgetops. The primary results of this analysis indicated that the satellite image provided information about trends in forest composition and structure across the study site, and further that a relatively simple plot-level measure of image texture (the panchromatic pixel standard deviation calculated at plot radii of 50 and 100 m) was a useful proxy of environmental heterogeneity for predicting the distributions of certain forest canopy gap-dependent bird species. For the second part of the study, I analyzed the habitat and remote sensing data at a finer spatial scale to develop remote sensing-based indices of forest structure and composition. These indices provided further insight into local variation in forest characteristics (e.g., in relation to topographic aspect) on the ridgetops. I also tested these indices, the DEM, and anthropogenic forest edge for modeling the breeding territory distributions of three focal species (Cerulean Warbler, Setophaga cerulea; Hooded Warbler, S. citrina; and Ovenbird, Seiurus aurocapilla) mapped over ~11 km of ridgetop transects. These models indicated the importance of local influences of terrain (e.g., east-facing aspects for Cerulean and Hooded Warbler, west-facing aspects for Ovenbird, and knolls for Cerulean Warbler), and forest edges (positive for Cerulean Warbler and negative for Ovenbird) on their distributions. Among the remotely-sensed indices, the index of forest structural complexity was primarily useful as a strong predictor of the distribution of the canopy gap-dependent Hooded Warbler. For the third and final part of the study, I used the locations of singing males of the three focal species collected across a greater extent of the site (~28 km of ridgetop transects) in point pattern analyses that incorporated the remote sensing data and the potential for intraspecific interactions (attraction and repulsion) between neighboring individuals. The results of these analyses supported that intraspecific interactions in addition to environmental influences as indicated by the remote sensing data explained the species’ fine-scale distribution patterns. While the individuals of all three species exhibited regular spacing over short distances that was consistent with competition for territorial space, Cerulean Warbler individuals exhibited more clustering than could be statistically accounted for by the remote sensing data, suggesting the importance of conspecific attraction in its distribution. In summary, my findings supported the potential application of fine-scale remote sensing data for purposes such as complementing coarse-scale environmental data (e.g., land cover maps) in predicting forest breeding bird species distributions, and for comparative analyses of the local spatial distributions of these species. The capacity for remote sensing data to provide useful environmental information at a fine spatial scale is likely to improve as the technology continues to develop.
Sheehan, James, "The Utility of Fine-Scale Remote Sensing Data for Modeling Habitat Characteristics and Breeding Bird Species Distributions in an Appalachian Mature Deciduous Forest." (2017). Graduate Theses, Dissertations, and Problem Reports. 6624.