| Titre | Canada's forest cover indicator: Definition, methodology and results |
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| Auteur | Chen, W ; Moll, R
H; Haddon, B D; Leblanc, S; Pavlic, G; Fraser, R; Fernandes, R; Latfovic, R; Cihlar, J; Bridge, S R |
| Source | Natural Resource Modeling vol. 19, 1, 2006 p. 91-116, https://doi.org/10.1111/j.1939-7445.2006.tb00177.x |
| Année | 2006 |
| Séries alt. | Ressources naturelles Canada, Contribution externe 20190623 |
| Document | publication en série |
| Lang. | anglais |
| DOI | https://doi.org/10.1111/j.1939-7445.2006.tb00177.x |
| Media | papier; en ligne; numérique |
| Formats | pdf |
| Programme | Direction du Centre canadien de télédétection |
| Diffusé | 2008 06 28 |
| Résumé | (disponible en anglais seulement)
Spaceborne Synthetic Aperture Radar (SAR) instruments are effective tools for monitoring and mapping wetlands. With the availability of SAR instruments
providing various polarization options, the scope of this study is to evaluate the new compact polarization for wetland multitemporal change detection using simulated RADARSAT Constellation Mission (RCM) SAR data. A series of fully polarimetric (FP)
SAR images were collected over a test site located in Ontario, Canada, and used to simulate RCM compact polarimetric (CP) data. The simulated data were evaluated for multitemporal change detection and the results were compared to those from FP SAR
data. WorldView imagery and water level data were used for analysis and validation of the change detection results. The study shows potential for using the CP SAR for multitemporal change detection over three major wetland classes: Shallow water,
marsh, and swamp. Although FP SAR was slightly more effective in multitemporal change detection compared to CP SAR, the percentage of agreement between the change detection results of FP and CP SAR was always greater than 90% for all wetland classes.
The highest overall percentage of agreement (98.4%) between the results of FP and CP SAR was observed over the shallow water, while the lowest (92.5%) was observed over swamp. © 2019 IEEE. |
| GEOSCAN ID | 321952 |
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