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      Sensitivity of high-resolution satellite sensor imagery to regenerating forest age and site preparation for wildlife habitat analysis

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      WunderleAL-MScThesis-2006.pdf (5.258Mb)
      Date
      2006-03-27
      Author
      Wunderle, Ame Leontina
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      In west-central Alberta increased landscape fragmentation has lead to increased human use, having negative effects on wildlife such as the grizzly bear (Ursus arctos L.). Recently, grizzly bears in the Foothills Model Forest were found to select clear cuts of different age ranges as habitat and selected or avoided certain clear cuts depending on the site preparation process employed. Satellite remote sensing offers a practical and cost-effective method by which cut areas, their age, and site preparation activities can be quantified. This thesis examines the utility of spectral reflectance of SPOT-5 pansharpened imagery (2.5m spatial resolution) to identify and map 44 regenerating stands sampled in August 2005. Using object based classification with the Normalized Difference Moisture Index (NDMI), green, and short wave infrared (SWIR) bands, 90% accuracy can be achieved in the detection of forest disturbance. Forest structural parameters were used to calculate the structural complexity index (SCI), the first loading of a principal components analysis. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (R2=0.74). The best window size for the texture measures was 5x5, indicating that this is a measure only detectable at a very high spatial resolution. Age classes of these cut blocks were analysed using linear discriminant analysis and best separated (82.5%) with the SWIR and green spectral bands, second order correlation under a 25x25 window, and the predicted SCI. Site preparation was best classified (90.9%) using the NDMI and homogeneity texture under a 5x5 window. Future applications from this research include the selection of high probability grizzly habitat for high spatial resolution imagery acquisition for detailed mapping initiatives.
      Degree
      Master of Science (M.Sc.)
      Department
      Geography
      Program
      Geography
      Supervisor
      Franklin, Steven E.
      Committee
      Cattet, Marc R. L.; Bortolotti, Gary R.; Guo, Xulin
      Copyright Date
      March 2006
      URI
      http://hdl.handle.net/10388/etd-04102006-130406
      Subject
      GLCM texture analysis
      object-based classification
      site preparation
      forest structure
      forest age
      habitat analysis
      grizzly bear
      forestry
      remote sensing
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      • Graduate Theses and Dissertations
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