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Carbon accreditation of forest development projects is essential for sequestering atmospheric CO2 under the provisions of the Kyoto Protocol. The carbon sequestration potential of surface coal-mined lands is not well known. The purpose of this work was to determine how to measure carbon sequestration and estimate the additional amount that could be sequestered using different reforestation methods compared to the common practice of establishing grasslands. I developed a thermal oxidation technique for differentiating sequestered soil carbon from inorganic and fossilized carbon found at high levels in mine soils along with a geospatial and statistical protocol for carbon monitoring and accounting. I used existing tree, litter, and soil carbon data for 14 mined and 8 adjacent, non-mined forests in the Midwestern and Eastern coal regions to determine, and model sequestered carbon across the spectrum of site index and stand age in pine, mixed, and hardwood forest stands. Finally, I developed the framework of a decision support system consisting of the first iteration of a dynamic model to predict carbon sequestration for a 60-year period for three forest types (white pine, hybrid poplar, and native hardwoods) at three levels of management intensity: low (weed control), medium (weed control and tillage) and high (weed control, tillage, and fertilization). On average, the highest amount of ecosystem carbon on mined land was sequestered by pine stands (148 Mg ha-1), followed by hardwood (130 Mg ha-1) and mixed stands (118 Mg ha-1). Non-mined hardwood stands contained 210 Mg C ha-1, which was about 62% higher than the average of all mined stands. After 60 years, the net carbon in ecosystem components, wood products, and landfills ranged from 20 to 235 Mg ha-1 among all scenarios. The highest net amount of carbon was estimated under mixed hardwood vegetation established by the highest intensity treatment. Under this scenario, a surface-mined land of average site quality would sequester net carbon stock at 235 Mg C ha-1, at a rate of 3.9 Mg C ha-1 yr-1, which was 100% greater than a grassland scenario. Reforestation is a logical choice for mined land reclamation if carbon sequestration is a management objective. Ph. D.

Soil organic carbon represents the largest constituent of the global C pool and carbon budgets are studied by researchers and modelers in C cycling, global climate change, and soil quality studies. Pedon and soil interpretation record databases are used with soil and ecological maps to estimate regional SOC even though these databases are rarely complete for surface litter and mineral subsurface horizons. The first main objective of the project is to improve the ability to produce soil organic carbon estimates from existing spatial soils datasets, such as STATSGO. All records in the STATSGO Layer table that were incomplete or appeared to be incorrectly filled with a null or zero value were considered invalid. Data sorting procedures and texture lookup tables were used to identify exiting correct (valid) data entries that were used to substitute invalid records. STATSGO soil property data were grouped by soil order, MLRA, layer number, and texture to produce replacement values for all invalid data used to calculate mass SOC. Grouping criteria was specific to each variable and was based on texture designations. The resulting filled and unfilled tables were used with procedures assuming Normal and Lognormal distribution of parameters in order to analyze variation of mass SOC estimates caused by using different computation techniques. We estimated mass SOC to 2 m in Maine and Minnesota using filled and unfilled STATSGO data tables. Up to 54% of the records in Maine and up to 80% of the records in Minnesota contained null or zero values (mostly in fields related to rock fragments) that were replaced. After filling, the database resulted in 1.5 times higher area-weighted SOC. SOC calculated using the Normal distribution assumption were 1.2 to 1.5 times higher than those using the Lognormal transformation. SOC maps using the filled tables had more logical geographic SOC distribution than those using unfilled tables. The USDA Forest Service collects and maintains detailed inventory data for the condition and trends of all forested lands in the United States. A wide range of researchers and landowners use the resulting Forest Inventory and Analysis (FIA) database for analytical and decision making tasks. FIA data is available to the public in transformed or aggregate format in order to ensure confidentiality of data suppliers. The second main objective of this project was to compute SOC (kg m-2) results by FIA forest type and forest type group for three depth categories (25 cm, 1 m, and 2 m) at a regional scale for the 48 contiguous United States. There were four sets of results derived from the filled STATSGO and FIA datasets for each depth class by region: (1) SOC computed by the Lognormal distribution approach for (1a) all soil orders, (1b) without Histosols; and (2) SOC computed by the Normal distribution approach for (2a) all soil orders, (2b) without Histosols. Two spatial forest cover datasets were relevant to this project, FIA and AVHRR. We investigated the effects of FIA inventory data masking for Maine and Minnesota, such as plot coordinates rounding to the nearest 100 arc-second, and the use of 1 km resolution satellite-derived forest cover classes from AVHRR data, on SOC estimates to 2 m by forest type group. SOC estimates by soil mapping unit were derived from fixed STATSGO database tables and were computed by the Lognormal distribution approach including all soil orders. The methods in this study can be used for a variety of ecological and resource inventory assessments and the automated procedures can be easily updated and improved for future uses. The procedures in this study point out areas that could benefit the most during future revisions of STATSGO. The resulting SOC maps are dynamic and can be rapidly redrawn using GIS whenever STATSGO spatial or tabular data undergo updating. Use of pedon data to define representative values for all properties in all STATSGO layers and correlation of STATSGO layers to soil horizons will lead to vast improvement of the STATSGO Layer table and promote its use for mass SOC estimation over large regions. Master of Science