Abstract The southeastern United States is unique in terms of both the intensity and scale of forest management, which includes substantial thinning and other forms of harvesting. Because thinning is not a land use transition, and the disturbance signal is relatively subtle compared to a clear cut, there is a dearth of studies that attempt to detect thinning over large areas. Our goal was to detect pine thins as an indicator of active forest management using Landsat data. Areas which undergo thinning are indicative of active forest management in the region. Our approach uses a machine learning method which combines first-order harmonics and metrics from 3-year Fourier regression of Landsat time series stacks, layers from the Global Forest Change product, and other vetted national products into a random forests model to classify forest thins in the southeastern US. Forest Harvest Inspection Records for Virginia were used for training and validation. Models were successful separating thins from clear cuts and non-harvested pines (overall accuracy 86%, clear cut accuracy 90%, thin accuracy 83% for a simplified 10-predictor variable model). Examination of variable importance illustrates the physical meaning behind the models. The curve fit statistics (R2 or RMSE) of the NDVI, Pan, and SWIR1 harmonic curve fits, which are an indication of a departure from typical vegetation phenology caused by thinning or other disturbances, were consistently among the top predictors. The harmonic regression constant, sine and cosine from the Landsat 8 panchromatic band were also important. These describe the visible reflectance (500–680 nm) phenology over the time period at a high spatial resolution (15 m). The Loss Year from the Global Forest Change product, which is an indication of stand replacing disturbance, was also consistently among the most important variables in the classifiers. High performance computing, such as Google Earth Engine, and analysis-ready data are important for this approach. This work has importance for quantification of actively managed forests in a region of the world where production forestry is the dominant land disturbance signal and a significant economic engine.

中文翻译：

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使用 Landsat 时间序列堆栈绘制薄层以识别南部松树人工林的活跃森林管理

摘要 美国东南部在森林管理的强度和规模方面都是独一无二的，其中包括大量间伐和其他形式的采伐。由于间伐不是土地利用的转变，并且干扰信号与清晰的切割相比相对微妙，因此缺乏试图检测大面积间伐的研究。我们的目标是使用 Landsat 数据检测松树薄作为积极森林管理的指标。进行间伐的地区表明该地区的森林管理活跃。我们的方法使用机器学习方法，该方法结合了来自 Landsat 时间序列堆栈的 3 年傅立叶回归的一阶谐波和度量、来自全球森林变化产品的层、和其他经过审查的国家产品进入随机森林模型，以对美国东南部的森林薄层进行分类。弗吉尼亚州的森林收获检查记录用于培训和验证。模型成功地将薄木与未采伐的松树和未收获的松树区分开来（对于简化的 10 预测变量模型，总体准确度为 86%，清除准确度为 90%，薄准确度为 83%）。对变量重要性的检查说明了模型背后的物理意义。NDVI、Pan 和 SWIR1 谐波曲线拟合的曲线拟合统计数据（R2 或 RMSE），表明与由变薄或其他干扰引起的典型植被物候不同，始终是最高的预测因子。Landsat 8 全色波段的谐波回归常数、正弦和余弦也很重要。这些描述了在高空间分辨率 (15 m) 下一段时间内的可见反射率 (500–680 nm) 物候。全球森林变化产品的损失年是林分替代干扰的指标，也是分类器中最重要的变量之一。高性能计算（例如 Google Earth Engine）和分析就绪数据对于这种方法很重要。这项工作对于量化世界上生产性林业是主要土地干扰信号和重要经济引擎的地区的积极管理森林具有重要意义。也始终是分类器中最重要的变量之一。高性能计算（例如 Google Earth Engine）和分析就绪数据对于这种方法很重要。这项工作对于量化世界上生产性林业是主要土地干扰信号和重要经济引擎的地区积极管理的森林具有重要意义。也始终是分类器中最重要的变量之一。高性能计算（例如 Google Earth Engine）和分析就绪数据对于这种方法很重要。这项工作对于量化世界上生产性林业是主要土地干扰信号和重要经济引擎的地区积极管理的森林具有重要意义。