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Abstract
The mixed prairie in Canada is characterized by its low to medium green vegetation cover, high amount of non‐photosynthetic materials, and ground level biological crust. It has proven to be a challenge for the application of remotely sensed data in extracting biophysical variables for the purpose of monitoring grassland health. Therefore, this study was conducted to evaluate the efficiency of broadband‐based reflectance and vegetation indices in extracting ground canopy information. The study area was Grasslands National Park (GNP) Canada and the surrounding pastures, which represent the northern mixed prairie. Fieldwork was conducted from late June to early July 2005. Biophysical variables—canopy height, cover, biomass, and species composition—were collected for 31 sites. Two satellite images, one SPOT 4 image on 22 June 2005, and one Landsat 5 TM image on 14 July 2005, were collected for the corresponding time period. Results show that the spectral curve of the grass canopy was similar to that of the bare soil with lower reflectance at each band. Consequently, commonly used vegetation indices were not necessarily better than reflectance when it comes to single wavelength regions at extracting biophysical information. Reflectance, NDVI, ATSAVI, and two new coined cover indices were good at extracting biophysical information.
†Present address: Department of Economics, Finance, Geography, and Urban Studies, Eastern Tennessee State University Johnson City, TN 37614 USA.
Acknowledgements
This study was supported by the Natural Sciences and Engineering Research Council (NSERC) awarded to Dr Guo and in part by the Parks Canada Species at Risk Recovery Action and Education Fund, a programme supported by the National Strategy for the Protection of Species at Risk. The authors would like to thank the Grasslands National Park of Canada for providing logistical support. Special thanks go to Dr Bai for providing critical comments on field design. Help was also obtained from Y. He and S. Black for fieldwork, grass sorting, satellite‐image processing, and proofreading. Finally, we would like to thank the anonymous reviewers who helped to improve the quality of this paper.
Notes
†Present address: Department of Economics, Finance, Geography, and Urban Studies, Eastern Tennessee State University Johnson City, TN 37614 USA.