Logistic Regression Samples - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA

Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the central Cascade Mountain area, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest patches identified in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras of logging trends that align with prevailing regulations and economic conditions. We used multiple logistic regression to determine the biophysical and anthropogenic factors that influence fine-scale selection of harvest stands in each time period. Results show that private forestland became significantly reduced and more fragmented from 1985 to 2014. Variables linked to parameters of site conditions, location, climate, and vegetation greenness consistently distinguished harvest selection for each distinct era. This study demonstrates the utility of annual LULC data for investigating the underlying factors that influence land cover change.