Browsing by Author "Subedi, Bipana"

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    Improving Precision in Forest Inventory through Small Area Estimation for Loblolly Pine Plantations in Coastal Georgia
    Subedi, Bipana (Virginia Tech, 2025-01-31)
    The use of small area estimation (SAE) in forest inventory has shown promise for improving the precision of estimates needed for informed decision-making when sample data are sparse. We evaluated the potential of unit-level SAE for increasing the precision of stand-level estimates of basal area, volume, and above-ground biomass estimates in loblolly pine plantations in coastal Georgia. Following the unit-level approach, field plots sampled in plantations owned by Rayonier Inc. were georeferenced to aerial lidar data using high-quality GPS field coordinates. Results focused on A) gains in precision for stand-level basal area, volume, and above-ground biomass estimates achieved by combining data from field plots with lidar-derived canopy height models in a SAE framework, B) impacts of small sample sizes on the precision of estimated stand level attributes, and C) the effects of nonrandom field plot placement in stands of interest when using unit-level SAE. Findings indicate that higher precision is achievable with greater variance stability than what is possible from very small samples of field data alone. This was true for all three attributes of interest. With careful attention to checking assumptions of the unit-level SAE approach, the use of non-random sampling does not appear to impair SAE's ability to deliver unbiased estimates for forest plantation stands. Simulating the entire population's basal area to test for the effects of non-random plot placement showed that SAE is robust to the type of sampling technique used. However, results can be affected when sampling is intentionally biased. This work can be useful to landowners and forest managers working with southern loblolly pine plantations. By leveraging simulation techniques to generate non-random sampling data from the available random sampling data, this study attempted to bridge the gap between the available empirical data and the desired sampling framework, ultimately widening the applicability of SAE in forest inventory settings.