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      Optical and radar remotely sensed data for large-area wildlife habitat mapping

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      WangPhDThesis2011.pdf (3.674Mb)
      Date
      2011-06
      Author
      Wang, Kai
      Type
      Thesis
      Degree Level
      Doctoral
      Metadata
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      Abstract
      Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models.
      Degree
      Doctor of Philosophy (Ph.D.)
      Department
      Geography
      Program
      Geography
      Supervisor
      Guo, Xulin; Franklin, Steven
      Copyright Date
      June 2011
      URI
      http://hdl.handle.net/10388/etd-07202011-104155
      Subject
      Remote Sensing
      Habitat mapping
      Landscape
      Small satellite constellation
      RADARSAT-2
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