Abstract Monitoring and classifying forest disturbance using Landsat time series has improved greatly over the past decade, with many new algorithms taking advantage of the high-quality, cost free data in the archive. Much of the innovation has been focused on use of sophisticated workflows that consist of a logical sequence of processes and rules, multiple statistical functions, and parameter sets that must be calibrated to accurately classify disturbance. For many algorithms, calibration has been local to areas of interest and the algorithm's classification performance has been good under those circumstances. When applied elsewhere, however, algorithm performance has suffered. An alternative strategy for calibration may be to use the locally tested parameter values in conjunction with a statistical approach (e.g., Random Forests; RF) to align algorithm classification with a reference disturbance dataset, a process we call secondary classification. We tested that strategy here using RF with LandTrendr, an algorithm that runs on one spectral band or index. Disturbance detection using secondary classification was spectral band- or index-dependent, with each spectral dimension providing some unique detections and different error rates. Using secondary classification, we tested whether an integrated multispectral LandTrendr ensemble, with various combinations of the six basic Landsat reflectance bands and seven common spectral indices, improves algorithm performance. Results indicated a substantial reduction in errors relative to secondary classification based on single bands/indices, revealing the importance of a multispectral approach to forest disturbance detection. To explain the importance of specific bands and spectral indices in the multispectral ensemble, we developed a disturbance signal-to-noise metric that clearly highlighted the value of shortwave-infrared reflectance, especially when paired with near-infrared reflectance.

中文翻译：

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用于森林干扰检测的 LandTrendr 多光谱集成

摘要 在过去十年中，使用 Landsat 时间序列对森林干扰进行监测和分类有了很大改进，许多新算法利用了存档中的高质量、免费数据。大部分创新都集中在复杂工作流程的使用上，这些工作流程由流程和规则的逻辑序列、多个统计函数和必须校准以准确分类干扰的参数集组成。对于许多算法，校准是局部于感兴趣的区域，并且在这些情况下算法的分类性能很好。然而，当应用于其他地方时，算法性能受到了影响。另一种校准策略可能是结合使用本地测试的参数值和统计方法（例如，随机森林；RF) 将算法分类与参考干扰数据集对齐，我们称之为二次分类的过程。我们在这里使用 RF 和 LandTrendr 测试了该策略，LandTrendr 是一种在一个光谱带或索引上运行的算法。使用二级分类的干扰检测依赖于光谱带或指数，每个光谱维度提供一些独特的检测和不同的错误率。使用二级分类，我们测试了一个集成的多光谱 LandTrendr 集合，具有六个基本 Landsat 反射波段和七个常见光谱指数的各种组合，是否可以提高算法性能。结果表明，相对于基于单波段/指数的二级分类，错误大大减少，揭示了多光谱方法对森林干扰检测的重要性。