Efficacy of Monitoring Management Activities in Longleaf Pine in North Carolina Using Remote Sensing

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2019-12-10

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Abstract

Using remote sensing as a tool to monitor forest management intervention may reduce the time and funds needed to actively visit landscapes. However, previous research typically studied the effects of large-scale disturbances, such as wildfires, to demonstrate the efficacy of using vegetation indices to track forest change. To better understand the limitations of these indices, Landsat 8 NDVI and NBRT values were calculated for 99 management units consisting of longleaf pine stands under stewardship of The Nature Conservancy of North Carolina. These units were across nine preserves held by TNC, in the Coastal Plain region of North Carolina. To assess change, indices values before and after management activity were compared, as well as indices values in management units with and without management intervention. These values were significant, but the changes were minimal. Linear mixed models were created to test the explanatory power that time since treatment, seasonality, treatment size, basal area, treatment type, and preserve locality had on the change in NDVI or NBRT. While these variables failed to explain the changes in indices values post-intervention, a variety of other factors may potentially express the reduction in NDVI or NBRT: other vegetative growth, climate variability, and the scale of the data may influence these indices’ results. As such, while the mixed models did not find these management characteristics explanatory, that alone does not reject the thesis that remote sensing may be useful for monitoring fine-scale change. Further study and extended data collection may prove useful.

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Leung, Emily (2019). Efficacy of Monitoring Management Activities in Longleaf Pine in North Carolina Using Remote Sensing. Master's project, Duke University. Retrieved from https://hdl.handle.net/10161/19571.


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