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.

华盛顿州的森林通过私有林地上的商品材采伐创造了可观的经济收益。为探究喀斯喀特山脉中部区域私有林块的森林采伐速率、成因及时空格局，本研究采用了新一代年度土地利用/土地覆被（Land Use/Land Cover, LULC）数据集产品——该数据集通过将连续变化检测与分类（Continuous Change Detection and Classification, CCDC）算法应用于1985至2014年采集的陆地卫星（Landsat）影像生成。研究人员通过提取各年度影像图层中的完整森林斑块与已采伐森林斑块，计算景观格局指标以解析整个时间序列内的森林动态变化。斑块动态分析揭示了四个截然不同的采伐趋势阶段，各阶段均与当时的主流监管政策及经济环境高度匹配。本研究采用多元逻辑回归模型，识别了各时期影响采伐林分精细尺度选择的生物物理与人为活动因子。结果表明，1985至2014年间，私有林地面积显著缩减，且景观破碎化程度不断加剧。与立地条件、空间区位、气候及植被绿度相关的变量，始终能够有效区分不同阶段的采伐选择偏好。本研究证实了年度LULC数据集在探究土地覆被变化潜在驱动因子方面的应用价值。