Significant declines in longleaf pine ecosystems during the last 150 years have motivated research and conservation programs focused on their restoration to perceived historical reference conditions. However, the ecological impacts of the historical naval stores industry on forest structure, fire regimes, and fuel conditions are not currently considered in reference conditions constructed from 18th, 19th, and 20th century sources. We present the Tar Kiln Feature Detection workflow (TKFD), an open-access, scripted, and replicable process developed in R and FIJI to identify archaeological tar kilns within high-resolution digital elevation models derived from aerial LiDAR datasets. The workflow is developed and validated on the entirety of the Francis Marion National Forest in coastal South Carolina. The TKFD has identified and measured over 2,700 tar kilns within our 420,000-acre study area and validation studies demonstrate a balanced identification accuracy of 90.6%. This is the most comprehensive dataset of tar production sites in North America and has implications for understanding the historical distribution of longleaf pine stands, anthropogenic impacts on fire and fuels, and the nature of these unique archaeological sites.

在过去 150 年中，长叶松生态系统的显着衰退促使研究和保护计划专注于将其恢复到可感知的历史参考条件。然而，从 18 世纪、19 世纪和 20 世纪的资源构建的参考条件目前并未考虑历史海军储备行业对森林结构、火灾状况和燃料条件的生态影响。我们介绍了焦油窑特征检测工作流程 (TKFD)，这是一种在 R 和 FIJI 中开发的开放访问、脚本化和可复制的过程，用于在源自航空 LiDAR 数据集的高分辨率数字高程模型中识别考古焦油窑。该工作流程是在南卡罗来纳州沿海的整个弗朗西斯马里恩国家森林中开发和验证的。TKFD 已识别和测量超过 2 个，我们 420,000 英亩的研究区域内的 700 座焦油窑和验证研究证明了 90.6% 的平衡识别准确度。这是北美最全面的焦油生产地点数据集，有助于了解长叶松林分的历史分布、人为对火灾和燃料的影响以及这些独特考古遗址的性质。