4.7 Article

Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada

期刊

GISCIENCE & REMOTE SENSING
卷 53, 期 3, 页码 382-401

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/15481603.2015.1137112

关键词

change detection; data integration; change trends; remote sensing time series

资金

  1. Canadian Space Agency, Government Related Initiatives Program IMOU [13MOA41001]

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The 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 +/- 1year. 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.

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