| Title | Rapid mapping of soil electrical conductivity by remote sensing: Implication for landmine detection and vehicle mobility |
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| Author | Katsube, T J; McNairn, H; Das, Y; Gauthier, E; Holt, R M; Singhroy, V; DiLabio, R; Connell-Madore, S; Dyke, L |
| Source | Proceedings of SPIE, the International Society of Optical Engineering vol. 5794, no. PART I, 16, 2005 p. 144-156, https://doi.org/10.1117/12.602825 |
| Year | 2005 |
| Alt Series | Natural Resources Canada, Contribution Series 20181201 |
| Publisher | SPIE |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | geophysics; remote sensing |
| Program | Canada Centre for Remote Sensing Divsion |
| Released | 2005 06 10 |
| Abstract | Many soil physical and chemical properties interfere with landmine detection signals. Since prior knowledge of these property distributions would allow appropriate technology selection and efficient
demining operations, rapid mapping of these properties over wide areas are considered for meeting military and economic constraints. As soil electrical conductivity (EC) interferes with widely used detection systems, such as metal detectors and
ground penetrating radar, we have started with developing a rapid mapping technique for EC using remote sensing. Electromagnetic surveys are proven methods for mapping EC, but do not provide all information required for demining. Therefore, EC
prediction by imaging of soil moisture change using radar satellite imagery acquired by RADARSAT is being tested in eastern Alberta (Canada) and northern Mississippi (U.S.A.). Areas of little soil moisture change with time are associated with high
moisture retention and high clay content, suggesting higher EC. These soil characteristics are also associated with trafficability. RADARSAT soil moisture change detection images for eastern Alberta identified five areas with possible high moisture
retention characteristics. Validation by soil and trafficability maps verified the predictions for more than half of the areas. Lack of some prediction accuracy is considered due to image acquisition timing and lack of physical property knowledge of
some soil constituents. |
| GEOSCAN ID | 311555 |
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