Open Collections

Characterising moose habitat, abundance and ecosystem variability using satellite-derived indicators

University of British Columbia

Natural variability and disturbance events drive spatial and temporal variation in ecosystem processes and play key roles in ecosystem variety and the maintenance of species diversity. As a result, an improved understanding of the links between natural environmental variability and species diversity is needed to guide prioritisation of conservation and management actions. Ontario, the second largest province in Canada, covering approximately 1 million km², is environmentally diverse and is subject to a large amount of natural and anthropogenic disturbances. Remote sensing is uniquely capable of monitoring dynamic ecosystems over large areas in a repeatable and cost effective manner and has been shown to provide considerable benefit to assess species distribution and biodiversity. This thesis (1) examines an approach for detecting natural variability and disturbances of vegetation productivity from a remote sensing time-series and (2) demonstrates the use of satellite-derived indicators for the characterisation of moose habitat across Ontario. First, an approach was developed to assess temporal trends in vegetation productivity which utilised a Theil-Sen’s non-parametric statistical trend test over a 6-year period (2003-2008) of ten-day composites of Medium Resolution Imaging Spectroradiometer (MERIS) fraction of Photosynthetically Active Radiation (fPAR). Results indicated that this novel remote sensing approach can be used to characterise trends in landscape productivity patterns over large areas and can aid in provincial and national monitoring activities. Second, the research investigated the application of remotely sensed indicators such as vegetation productivity, land cover, topography, snow cover and natural and anthropogenic disturbances to predict moose occurrence and abundance. Results indicated that remotely sensed indicators were significantly correlated to moose habitat suitability with moose distribution being more accurately estimated than moose abundance. In addition to providing insights into the relative importance of the predictor covariates for moose occurrence and abundance, this study creates opportunities for further development of spatial models that closely examine the occurrence/abundance-habitat relationships which are highly valuable for habitat management decisions.

Text

2012-01-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/40207

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/