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USING GIS TO ASSESS SOIL CARBON DISTRIBUTION UNDER DIFFERENT FOREST MANAGEMENT REGIMES **

Abstract

Carbon sequestration in forest soils is important for climate mitigation. Understanding how management regimes influence soil carbon stocks requires measurements that capture spatial variability across the landscape. However, field data collection can be time consuming and expensive. GIS could provide a way to evaluate sampling designs by allowing field measurements to be spatially analyzed, interpolated, and compared against environmental gradients. The goal of this study was to determine if differences in percent soil organic carbon (SOC) between management regimes can be explained by physical parameters and if GIS can accurately interpolate field data at large scales. Soil samples were collected from two pine forest stands with differing management histories and an unmanaged hardwood stand in Bartram Wildlife Management Forest in Milledgeville, Georgia. The coordinates of 20 samples were recorded from each 20-acre forest stand. The loss-on-ignition approach was used to assess SOC. Soil texture was determined using the hydrometer method. Elevation, vegetation indices, and tree cover percent were obtained from remote sensing and compared to SOC field data to determine if the difference in SOC showed significant correlation with site physical characteristics. Field data was interpolated using Kriging in ArcGIS Pro. Random points were generated for 20, 10, and 6 samples and interpolated SOC data was compared to the full field SOC data using t-tests. Preliminary results show that there is a significant positive correlation between SOC and % tree cover and a significant negative correlation between SOC and elevation. This emphasizes the importance of choosing sample locations representative of the physical variation in a site when assessing soil carbon. Interpolation results show that as few as 6 field samples provide an accurate representation of SOC across a site. GIS can, therefore, be used alongside field data to capture natural variation in a soil characteristic at a large scale.

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