| Title | Synergistic use of RADARSAT-2 ultra fine and fine Quad-Pol data to map oilsands infrastructure land: Object-based approach |
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| Author | Jiao, X; Zhang, Y; Guindon, B |
| Source | International Journal of Applied Earth Observation and Geoinformation vol. 38, 2015 p. 193-203, https://doi.org/10.1016/j.jag.2015.01.007 |
| Image |  |
| Year | 2015 |
| Alt Series | Earth Sciences Sector, Contribution Series 20140253 |
| Publisher | Elsevier BV |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Province | Alberta |
| NTS | 73M/15 |
| Area | Christina Lake |
| Lat/Long WENS | -110.6667 -110.0000 55.6000 55.5000 |
| Subjects | oil sands; mining activities; mapping techniques; vegetation; Radar Vegetation Index (RVI) |
| Illustrations | location maps; satellite images; flow charts; tables; graphs |
| Program | Remote Sensing Science |
| Released | 2015 06 01 |
| Abstract | The landscape of Alberta's oilsands regions is undergoing extensive change due to the creation of infras- tructure associated with the exploration for and extraction of this resource. Since most oil
sands mining activities take place in remote forests or wetlands, one of the challenges is to collect up-to date and reliable information about the current state of land. Compared to optical sensors, SAR sensors have the advantage of being able to
routinely collect imagery for timely monitoring by regulatory agencies. This paper explores the capability of high resolution RADARSAT-2 Ultra Fine and Fine Quad-Pol imagery for mapping oilsands infrastructure land using an object-based
classification approach. Texture measure- ments extracted from Ultra Fine data are used to support an Ultra Fine based classification. Moreover, a radar vegetation index (RVI) calculated from PolSAR data is introduced for improved classification
perfor- mance. The RVI is helpful in reducing confusion between infrastructure land and low vegetation covered surfaces. When Ultra Fine and PolSAR data are used in combination, the kappa value of well pads and processing facilities detection reached
0.87. In this study, we also found that core hole sites can be iden- tified from early spring Ultra Fine data. With single-date image, kappa value of core hole sites ranged from 0.61 to 0.69. |
| Summary | (Plain Language Summary, not published)
The landscape of Alberta's oilsands region is undergoing extensive change due to the creation of infrastructure associated with the exploration for and
extraction of this resource. Since most oilsands mining activities take place in remote forests or wetlands, one of the challenges is to collect up-to date and reliable information about the current state of land. This report presents results of an
assessment using a particular type of satellite imagery - Synthetic Aperture Radar (SAR) data from Canada's RADARSAT-2 satellite, to map infrastructure developments in this region. Compared to optical sensors, SAR sensors have the advantage of being
able to routinely collect imagery. Methods were developed using two different satellite beam modes, a high resolution (high detail) mode and one which sends and receives the satellite signal in both horizontal and vertical planes, to identify
infrastructure such as well pads, well sites, and processing facilities. The preliminary results show that the use of a synergistic approach improves the results over the use of only one beam mode. |
| GEOSCAN ID | 295451 |
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