| Title | Quantification of anthropogenic and natural changes in oil sands mining infrastructure land based on RapidEye and SPOT5 |
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| Author | Zhang, Y; Guindon, B; Lantz, N; Shipman, T; Chao, D; Raymond, D |
| Source | International Journal of Applied Earth Observation and Geoinformation vol. 29, 2014 p. 31-43, https://doi.org/10.1016/j.jag.2013.11.013 |
| Image |  |
| Year | 2014 |
| Alt Series | Earth Sciences Sector, Contribution Series 20130345 |
| Publisher | Elsevier BV |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing; satellite imagery; land use; vegetation; RapidEye; SPOT5 |
| Illustrations | location maps; flow charts; satellite images |
| Program | Remote Sensing Science |
| Released | 2014 06 01 |
| Abstract | Natural resources development, spanning exploration, production and transportation activities, alters local land surface at various spatial scales. Quantification of these anthropogenic changes, both
permanent and reversible, is needed for compliance assessment and for development of effective sustainable management strategies. Multi-spectral high resolution imagery data from SPOT5 and RapidEye were used for extraction and quantification of the
anthropogenic and natural changes for a case study of Alberta bitumen (oil sands) mining located in the Western Boreal Plains near Fort McMurray, Canada. Two test sites representative of the major Alberta bitumen production extraction processes, open
pit and in-situ extraction, were selected. A hybrid change detection approach, combining pixel- and object-based target detection and extraction, is proposed based on Change Vector Analysis. The extraction results indicate that the changed
infrastructure landscapes of these two sites have different footprints linked with their differing oil sands production processes. Pixel- and object-based accuracy assessments have been applied for validation of the change detection results. For
manmade disturbances, except for those fine linear features such as the seismic lines, accuracies of about 80% have been achieved at the pixel level while, at the object level, these rise to 90-95%.
Since many disturbance features are transient,
a new landscape index, entitled the Re-growth Index, has been formulated at single object level specifically to monitor restoration of these features to their natural state. It is found that the temporal behaviour of the Re-growth Index in an
individual patch varies depending on the type of natural land cover. In addition, the Re-growth Index is also useful for assessing the detectability of disturbed sites. |
| Summary | (Plain Language Summary, not published)
Oil sands mining alters the land surface, due to exploration, extraction, and transportation activities. Detection and quantification of changes at oil
sands development sites is key information needed to ensure compliance with pertinent provincial regulations and to identify effective sustainable management strategies. Satellite EO data has the potential to make a major contribution in support of
operational regulatory compliance. In this work, two test sites, representative of the bitumen extraction processes of surface mining and in-situ extraction, are being intensively studied using high resolution satellite imagery. Methods have been
developed to extract anthropogenic changes at both test sites, and time series showing these changes have been generated. The methods have accuracies ranging from 80% to 95%. Using the extracted information, a new index has also been developed to
monitor restoration of some disturbances to their natural state. |
| GEOSCAN ID | 293381 |
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