Abstract

The USDA Forest Service uses satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time throughout the United States. We specifically focus on a 230, 400 hectare region in north-central Wisconsin between $2003$-$2012$. The auxiliary data from the satellite imagery of this region are relatively dense in space and time, and can be used to learn how forest conditions changed over that decade. However, these records have a significant proportion of missing values due to weather conditions and system failures that we fill in first using a spatiotemporal model. Subsequently, we use the complete imagery as functional predictors in a two-component mixture model to capture the spatial variation in yearly average live tree basal area, an attribute of interest measured on field plots. We further modify the regression equation to accommodate a biophysical constraint on how plot-level live tree basal area can change from one year to the next. Findings from our analysis, represented with a series of maps, match known spatial patterns across the landscape. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

摘要

美国农业部森林服务局使用卫星图像以及国家森林清单田地样本，监测和预测整个美国的森林状况随时间的变化。我们特别关注威斯康星州中北部的一个230、400公顷的地区$\mathrm{2003年}$--$\mathrm{2012年}$。来自该地区卫星图像的辅助数据在空间和时间上相对密集，可用于了解该十年来森林状况的变化。但是，由于我们首先使用时空模型填写的天气条件和系统故障，这些记录的缺失值有很大一部分。随后，我们将完整图像用作两成分混合模型中的功能预测变量，以捕获年平均活树基础面积的空间变化，这是在田间地块上测得的重要属性。我们进一步修改回归方程，以适应地块级活树基础面积如何从一年到下一年变化的生物物理约束。我们的分析结果以一系列地图表示，与整个景观中已知的空间格局相匹配。