Browsing by Author "Derwin, Jill"

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  • Automated Landsat Classification of Tropical Forest Disturbances for Large Scale Identification in the Amazon
    Derwin, Jill (2014)
    Under-reporting of selective logging and forest disturbance has posed an issue for forest health and deforestation estimations in the tropics due to difficulty of detection by satellite data. Several studies have proposed methods for the delineation of these areas using a variety of data and models. In the interest of supporting the study of large-scale ecosystem and climate dynamics in this region for the purposes of prioritization of critical focal points in climate mitigation policy and incentive programs, I hope to test one such methodology over different regions across Amazonia. Landsat-derived Multiple Soil Adjusted Vegetation Index (MSAVI) and alternately Multiple Soil Adjusted Vegetation Index with Aerosol Resistance (MSAVIaf) have been shown to be relatively successful in obtaining green fractional percentage (fc) when incorporated into a linear mixture model. I plan to replicate this technique, followed by a multi-temporal comparison of fc to locate disturbed and degraded areas, and then expand the analysis over an extended study area to review the potential for automation to a state-wide or national scale.
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    Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation
    Cao, Qianqian; Dettman, Garret, T.; Radtke, Philip J.; Coulston, John W.; Derwin, Jill; Thomas, Valerie A.; Burkhart, Harold E.; Wynne, Randolph H. (Frontiers Media, 2022-04-26)
    Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimates at finer geographic scales than most currently implemented in operational estimates using NFI sample data. In the past decade small area estimation techniques have been shown to increase precision in forest inventory estimates by combining field observations and remote-sensing.We sought to demonstrate the potential for improving the precision of forest inventory growing stock volume estimates for counties in United States of North Carolina, Tennessee, and Virginia, by pairing canopy height models from digital aerial photogrammetry (DAP) and field plot data from the United States NFI. Area-level Fay-Herriot estimators were used to avoid the need for precise (GPS) coordinates of field plots. Reductions in standard errors averaging 30% for North Carolina county estimates were observed, with 19% average reductions in standard errors in both Tennessee and Virginia. Accounting for spatial autocorrelation among adjacent counties provided further gains in precision when the three states were treated as a single forest land population; however, analyses conducted one state at a time showed that good results could be achieved without accounting for spatial autocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to 128% in North Carolina, compared to the current number of inventory plots. Results should allow for determining whether acquisition of statewide DAP would be costeffective as a means for increasing the accuracy of county-level forest volume estimates in the United States NFI.