Abstract Remote sensing estimates of forest canopy cover have frequently been used to support a variety of applications including wildlife habitat modeling, monitoring of watershed health, change detection, and are also correlated to various aspects of forest structure and ecosystem function. Although data from the long running Landsat earth observation program (1972–present) have been previously utilized to characterize forest canopy cover, the variability in spatial and spectral resolutions between the Landsat sensors has generally limited analyses to readily comparable imagery from the TM and ETM+ sensors, which omits large portions of the full temporal record. In this study, we present an R package, LandsatLinkr, which automates the processes for harmonizing Landsat MSS and OLI imagery to the spatial and spectral qualities of TM and ETM+ imagery, allowing for the generation of annual cloud-free composites of tasseled cap spectral indices across the entire Landsat archive. We demonstrate the utility of LandsatLinkr products, further enhanced through the LandTrendr segmentation algorithm, for characterizing forest attributes through time by developing annual forest masks and maps of estimated canopy cover for the state of Minnesota from 1973 to 2015. The forest mask model had an overall accuracy of 87%, with omission and commission errors for the forest class of 17% and 10%, respectively, and 9% and 16% for non-forest classification. Our resulting maps depicted a significant positive trend in forest cover across all ecological provinces of Minnesota during the study period. A random forest model used to predict continuous canopy cover had a pseudo R2 of 0.75, with a cross validation RMSE of 5%. Our results are comparable to previous Landsat-based canopy cover mapping efforts, but expand the evaluation time period as we were able to utilize the entire Landsat archive for assessment.

中文翻译：

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提取 Landsat 档案的全部价值：用于绘制明尼苏达森林冠层覆盖的传感器间协调（1973-2015）

摘要 森林冠层覆盖的遥感估计经常被用于支持各种应用，包括野生动物栖息地建模、流域健康监测、变化检测，并且还与森林结构和生态系统功能的各个方面相关。尽管来自长期运行的 Landsat 地球观测计划（1972 年至今）的数据以前曾被用于表征森林冠层覆盖，但 Landsat 传感器之间空间和光谱分辨率的可变性通常限制了对来自 TM 和 ETM+ 传感器的易于比较的图像的分析，它省略了完整时间记录的大部分。在这项研究中，我们展示了一个 R 包 LandsatLinkr，它将 Landsat MSS 和 OLI 影像与 TM 和 ETM+ 影像的空间和光谱质量相协调的过程自动化，从而允许在整个 Landsat 档案中生成流苏帽光谱指数的年度无云合成。我们展示了 LandsatLinkr 产品的效用，通过 LandTrendr 分割算法进一步增强，通过开发 1973 年至 2015 年明尼苏达州的年度森林掩膜和估计冠层覆盖地图来表征森林属性随时间的变化。森林掩膜模型具有整体准确率为 87%，森林分类的​​遗漏和委托误差分别为 17% 和 10%，非森林分类的​​遗漏和委托误差分别为 9% 和 16%。我们得到的地图描绘了在研究期间明尼苏达州所有生态省份森林覆盖率的显着积极趋势。用于预测连续冠层覆盖的随机森林模型的伪 R2 为 0.75，交叉验证 RMSE 为 5%。我们的结果与之前基于 Landsat 的冠层覆盖绘图工作相当，但由于我们能够利用整个 Landsat 档案进行评估，因此延长了评估时间段。