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Using RapidEye satellite imagery to detect forest disturbances in British Columbia Arnett, John Thomas Te Reo


Improving our ability to track and monitor changes on Earth’s surface will inevitably enhance our ability to manage and monitor the biosphere. Remote Sensing technologies developed to monitor the Earth’s surface have already improved our understating of dynamic land cover change at a variety of scales. Fundamental to the identification of land cover change is the detection of abrupt disturbance events. These events constitute direct changes to the composition and structure of ecological systems and may have long lasting effects. In a forestry context it is important to identify disturbances in a timely manner in order to inform management decisions. The RapidEye constellation is a series of five identical Earth orbiting optical sensors capable of achieving five meter spatial resolution imagery with a daily return time. In this thesis we present two studies which assess the capacity of RapidEye to detect (1) stand replacing disturbances and (2) non-stand replacing disturbances in British Columbia. In the first study we develop a robust method to identify stand-replacing disturbances across seven regions in British Columbia. Overall accuracy for the classification of forest disturbance ranged from 83.65 ± 0.77% to 97.65 ± 0.25% for individual 25 X 25 km test locations. In the second study the utility of the RapidEye constellation to detect and characterize a low severity fire in a dry Western Canadian Forest was examined. Estimates of burn severity from field data were correlated with a selected suite of common spectral vegetation indices. All correlations between the ground estimates and vegetation indices produced significant results (p < 0.01). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha-¹ to 11.72 ± 1.84 Mg ha-¹ across four extrapolated severity classes. The results of this thesis demonstrate the capacity of the RapidEye constellation to accurately detect stand replacing and non-stand replacing disturbances. We conclude by recommending the use of RapidEye to assess forest disturbances, with an emphasis on the spatial detail and temporal availability of imagery captured by the RapidEye sensors.

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