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TitleLand change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada
AuthorPouliot, D; Latifovic, R
SourceGIScience and Remote Sensing vol. 53, no. 3, 2016 p. 382-401, https://doi.org/10.1080/15481603.2015.1137112
Year2016
Alt SeriesNatural Resources Canada, Contribution Series 20181614
PublisherInforma UK Limited
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
Released2016 03 03
AbstractThe Alberta Oil Sands (AOS) is a unique area in Canada undergoing significant disturbance and recovery due to a variety of anthropogenic and natural factors. Accurately quantifying these changes in space and time is important for assessing ecosystem status and trends. In this research, we implemented an approach to combine Landsat time series for the period 1984-2012 with ancillary change datasets to derive detailed change attribution in the AOS. Detected changes were attributed to causes including fire, forest harvest, surface mining, insect damage, flooding, regeneration, and several generic change classes (abrupt/gradual, with/without regeneration) with accuracies ranging from 74% to 100% for classes that occurred frequently. Lower accuracies were found for the generic gradual change classes which accounted for less than 3% of the affected area. Timing of abrupt change events were generally well captured to within ±1 year. For gradual changes timing was less accurate and variable by change type. A land-cover time series was also created to provide information on "from-to" change. A basic accuracy assessment of the land cover showed it to be of moderate accuracy, approximately 69%. Results show that fire was the major cause of change in the region. As expected, surface mine development and related activities have increased since 2000. Insect damage has become a more significant agent of change in the region. Further investigation is required to determine if insect damage is greater than past historical events and to determine if industrial development is linked to the increasing trend observed.
GEOSCAN ID311969