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Expected Pine Height Map Using NAIP Point Clouds of Virginia, North Carolina, and Tennessee, United States

Version 2 2022-06-15, 19:57
Version 1 2022-06-15, 16:27
posted on 2022-06-15, 19:57 authored by Alison RitzAlison Ritz, Valerie ThomasValerie Thomas, Randolph WynneRandolph Wynne, Patrick GreenPatrick Green, Todd Schroeder, Timothy AlbaughTimothy Albaugh, Harold Burkhart, David CarterDavid Carter, Rachel Cook, Otavio Campoe, Rafael Rubilar, Jim Rakestraw

This study’s objective was to use a reduced major axis (RMA) regression to develop a model that would predict the height of loblolly pine trees in plantations using values from NAIP point clouds and apply the model to a statewide map of pines for Virginia, North Carolina, and Tennessee. The NAIP point clouds were first normalized by subtracting the ground elevation DEM from the point cloud z values. Then a 5m x 5m grid was created for each state and the 90th percentile of height was extracted for each grid cell. These grid cells were then converted to a raster and mosaiced into one large raster for each state. Next, using National Landcover Dataset (NLCD) information, the raster was extracted for pixels only classified as evergreen (class 42). Lastly, the predicted pine height (PPH) model (PPH = 0.81 + (0.88 * 90th Percentile of Height)) was applied to the extracted raster. It should be noted that the model, and therefore the maps, are most applicable to areas of loblolly pine that are not located in areas where heavy thinning (removal of a large portion of trees) has occurred.


A remote sensing and socioeconomic approach to modeling landowner decision making, forest attributes, and productivity in the managed southeastern US, International Paper



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Virginia, North Carolina, and Tennessee, United States