PS 81-75 - A framework for mapping carbon storage hot spots and determining optimal forest structure and management regime characteristics

Friday, August 12, 2011
Exhibit Hall 3, Austin Convention Center
Nilesh Timilsina1, Francisco Escobedo1, Amr Abd-Elrahman2, Wendell P. Cropper Jr.1, Sonia Delphin1 and Samuel Lambert3, (1)School of Forest Resources and Conservation, University of Florida, Gainesville, FL, (2)Gulf Coast Research and Education Center, University of Florida, (3)Spatial Data Services, U. S. Forest Service, Knoxville, TN

Mapping areas of high carbon storage within a region, and identifying the corresponding land use and management drivers could provide insights into key forest structural characteristics and management activities that are optimal for carbon sequestration and storage. Spatially explicit analyses of ecosystem system services (i.e. carbon sequestration) that are directly relevant to both land use management decision making and conservation are rare.

We used USDA Forest Service Forest Inventory and Analysis Data, and Geographical Information Systems (ArcGIS) to develop a framework for mapping “carbon storage hot spots” (i.e. areas of significantly high tree and understory above ground carbon storage) across a range of forest land covers in the State of  Florida USA. Cold spots (i.e. areas with low carbon storage) were also mapped.  We related the areas of high carbon value and the cold spots to biophysical variables (e.g. forest types, fire, hurricanes, tenure, management activities) using generalized linear mixed modeling to evaluate the expected drivers behind the hotspots. 


Most of the hot spots were located in the Northern third of the state of Florida and there were no identifiable hot spots in South Florida. Land tenure and treatments (e.g. site preparation, thinning, logging, etc) were not significant predictors of hot spots. Forest types, site quality, and stand age were significant predictors with mixed upland hardwood forests having the highest probability of being a carbon hot spot. Higher site quality and stand age increased the odds of classification as a hot spot. Disturbance type and time since disturbance were not significant predictors in our analyses. This study introduces a new framework for analyzing accessible carbon data; identification of ecosystems service provision areas and of forest management strategies that could optimize carbon storage in forests of the US Coastal Plain.

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