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An Approach for Determining Relationships Between Disturbance and Habitat Selection Using Bi-weekly Synthetic Images and Telemetry Data

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Multitemporal Remote Sensing

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 20))

Abstract

Ecological studies can be limited by the mismatch in spatial-temporal scales between wildlife GPS telemetry data, collected sub-hourly, and the large-area maps used to identify disturbances, generally updated annually. Recent advancements in remote sensing, data fusion modeling, mapping, and change detection approaches offer environmental data products representing every 16-day period through the growing season. Here we highlight opportunities and challenges for integrating wildlife location data with high spatial and temporal resolution landscape disturbance data sets, available from remotely sensed imagery. We integrated 16-day outputs from the Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) disturbance maps with grizzly bear (Ursus arctos) telemetry data. Our results indicate that males and females avoided same-year disturbances, while male bears were most likely to avoid recently disturbed areas in summer. When intra-year (disturbances mapped at a 16-day time-step) analysis of disturbance was compared to traditional annual time-step analysis, annual aggregation of disturbance data resulted in an increase in the observed selection of same-year disturbed habitat, although change was not statistically significant (α 0.05). We caution the use of low-temporal resolution disturbance data to evaluate short-term impacts on wildlife and highlight the need for further development of probabilistic- and model-based techniques for overcoming spatial-temporal differences between datasets.

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Authors and Affiliations

  1. Department of Geography and Environmental Studies, Carleton University, Ottawa, ON, Canada

    Nicholas D. A. Brown

  2. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

    Trisalyn Nelson

  3. Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, Victoria, BC, Canada

    Michael A. Wulder

  4. Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada

    Nicholas C. Coops

  5. Department of Geography and Environment, University of Southampton, Highfield Rd, Southampton, SO17 1BJ, UK

    Thomas Hilker

  6. Forest Management Branch, Forestry Division, Alberta Agriculture and Forestry, Edmonton, AB, Canada

    Christopher W. Bater

  7. School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, UK

    Rachel Gaulton

  8. Foothills Research Institute, Hinton, AB, Canada

    Gordon B. Stenhouse

Authors
  1. Nicholas D. A. Brown
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  2. Trisalyn Nelson
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  3. Michael A. Wulder
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  4. Nicholas C. Coops
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  5. Thomas Hilker
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  6. Christopher W. Bater
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  7. Rachel Gaulton
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  8. Gordon B. Stenhouse
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Corresponding author

Correspondence to Trisalyn Nelson .

Editor information

Editors and Affiliations

  1. Division of Geoinformatics, KTH Royal Institute of Technology, Stockholm, Sweden

    Yifang Ban

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Brown, N.D.A. et al. (2016). An Approach for Determining Relationships Between Disturbance and Habitat Selection Using Bi-weekly Synthetic Images and Telemetry Data. In: Ban, Y. (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-47037-5_16

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