Abstract The Landsat archive offers great potential for monitoring forest cover change, and new approaches moving from categorical towards continuous change products emerge rapidly. Most approaches, however, require vast amounts of high-quality reference data, limiting their applicability across space and time. We here propose the use of a generalized regression-based unmixing approach to overcome this limitation. The unmixing approach relies on temporally generalized machine learning regression models (random forest regression [RFR] and support vector regression [SVR]), which are trained on synthetically mixed data from a multi-year library of pure and hence easy to identify image spectra. We apply the model to three decades of Landsat data, mapping both overall forest cover and broadleaved/coniferous forest cover fractions across space and time. The resulting maps well represented the spatial-temporal patterns of forest (change) in our study region. The SVR model outperformed the RFR model, yielding accuracies of r2 = 0.74/RMSE = 0.18 for the forest cover fraction maps, r2 = 0.50/RMSE = 0.24 for the broadleaved forest cover fraction maps, and r2 = 0.59/RMSE = 0.23 for coniferous forest cover fraction maps, respectively. Highest map errors were found in mature stands, residential areas, and recently disturbed forests. We also found some variability in forest cover fractions for stable forest pixels over time, which were explained by variation in Landsat image acquisition dates. We conclude that regression-based unmixing using synthetically mixed training data from a multi-year spectral library offers an innovative strategy for mapping forest cover fractions and forest types throughout the Landsat archive that likely can be extended to large areas.

中文翻译：

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一种基于广义回归的解混模型，用于映射三年 Landsat 数据中的森林覆盖率

摘要 Landsat 档案为监测森林覆盖变化提供了巨大的潜力，从分类产品到持续变化产品的新方法迅速出现。然而，大多数方法需要大量高质量的参考数据，限制了它们在空间和时间上的适用性。我们在这里建议使用基于广义回归的解混方法来克服这一限制。解混方法依赖于时间广义机器学习回归模型（随机森林回归 [RFR] 和支持向量回归 [SVR]），这些模型是在来自多年纯库的合成混合数据上训练的，因此易于识别图像光谱。我们将该模型应用于三个十年的 Landsat 数据，绘制了整个空间和时间的整体森林覆盖率和阔叶/针叶林覆盖率比例。由此产生的地图很好地代表了我们研究区域森林（变化）的时空模式。SVR 模型优于 RFR 模型，森林覆盖率图的精度为 r2 = 0.74/RMSE = 0.18，阔叶林覆盖率图的精度为 r2 = 0.50/RMSE = 0.24，针叶林的精度为 r2 = 0.59/RMSE = 0.23分别为森林覆盖率分布图。在成熟的林分、住宅区和最近受到干扰的森林中发现了最高的地图错误。我们还发现随着时间的推移，稳定森林像素的森林覆盖率存在一些变化，这可以通过 Landsat 图像采集日期的变化来解释。