Johnson, BartKurtz, Lindsey2022-10-042022-10-042022-10-04https://hdl.handle.net/1794/27641Ecological classification systems are used to understand and restore complex heterogeneous landscapes. We explored an ecological classification methodology to determine fine-grained land units by combining field and remote sensing data. Regression trees were used to create these land units, which we term landtype phases. Oregon white oak was chosen as a test case for the methodology because of its conservation importance, the paucity of knowledge about how to sustain it in heterogeneous landscapes, and its wide range of growing conditions. We identified two landtype phases, the moist margins of harsh meadows and cooler locations away from the meadows. The fieldwork-based variables used to identify and classify these landtype phases were translated into remote-sensing variables using LiDAR, which allowed landtype phase mapping. Our results demonstrate how an integration of field-based and LiDAR-based approaches can provide useful guidance for restoration while highlighting the need for improved translation among the two data types.This thesis includes unpublished co-authored material.en-USAll Rights Reserved.Classification SystemsGeospatial Information SystemsOregon White OakRegression TreesRestoration EcologyElectronic Thesis or Dissertation