Comparison of techniques for measuring forest carbon in British Columbia

• Main Author: Skrivanos, Pano Manolis
• Language: English
• Published: University of British Columbia 2012
• Online Access: http://hdl.handle.net/2429/42506

The Earth is currently in a period of rapid climate change and the anthropogenic release of carbon dioxide (CO₂) is widely considered to be the primary cause. Forests are an important part of the global carbon cycle and their ability to mitigate atmospheric CO₂ levels is increasingly being recognized. Forest carbon projects generate carbon offsets either through the application of specialized forest management practices, or through the protection or restoration of deforested or degraded land. The ability to quantify accurately the amount of carbon that would be sequestered by forest carbon project activities is critical to their success. A variety of forest carbon modeling techniques exist and use a variety of methods for data acquisition including forest inventory data, remotely sensed data, or ground measurements. However the accuracy of these modeling techniques varies, making their standard application difficult. This thesis contributes to the understanding of forest carbon quantification by comparing forest carbon estimates derived from a ground-based technique with forest carbon estimates derived from three forest carbon modeling techniques: Canadian Forest Service Carbon Budget Model (CBM-CFS3), Vegetative Resource Inventory Biomass Equations, and Private Woodland Planner. Both differential and least squares mean statistical analyses were conducted to determine which modeling technique estimated forest carbon closest to estimates derived from the ground-based technique. The hypothesis that framed this research was that CBM-CFS3 would be the most accurate modeling technique. However results indicated that forest carbon estimates from PWP are closest to those derived from the ground-based technique. Results derived from CBM-CFS3 are farthest from the ground-based technique. The results from this project suggest that an ideal forest carbon quantification technique incorporates field sampling with broad-based model estimates. Current research in forest carbon modeling techniques shows a trend towards more accurate and efficient estimates, which will allow project developers to better measure forest carbon stocks, improve forest conservation, and generate greater economic opportunities. These improvements will also increase the effectiveness of forest carbon projects and their role in mitigating the effects of climate change.