Abstract

Historic changes in Alpine Treeline Ecotone were modeled using 21 topographic, climatic, geologic, and disturbance variables in a random forest model. Airborne LiDAR and oblique historic repeat photography were used to identify changes in canopy cover in the West Castle Watershed (WCW), Alberta, Canada (49.3° N, 114.4° W). A Random Forest model was trained on ∼30% of the watershed which was observable in oblique imagery, then used for a spatial extension to predict change classes in the unobserved regions of the watershed. Overall accuracy of the model was 77.3% and kappa showed moderate agreement at 0.56. The relative strength of each prediction variable was compared using permutation importance. Fire exposure, annual temperature, and annual solar radiation were the highest-ranking variables; canopy cover decreases on warm, fire-exposed aspects at high elevations, and increases on cool, non-fire-exposed aspects.

摘要

在随机森林模型中，使用21种地形，气候，地质和干扰变量对高山林线过渡带的历史变化进行了建模。机载LiDAR和倾斜的历史重复摄影被用于识别加拿大艾伯塔省西城堡集水区（WCW）（49.3°N，114.4°W）的冠层覆盖变化。在约30％的流域（在倾斜图像中可观察到）上训练了随机森林模型，然后将其用于空间扩展，以预测流域未观察到的区域中的变化类别。该模型的整体准确性为77.3％，kappa的适度一致性为0.56。使用排列重要性比较每个预测变量的相对强度。火灾暴露，年度温度和年度太阳辐射是排名最高的变量。温暖时树冠层会减少，