Abstract Multi-spectral imagery from the Landsat family of satellites has been used to map forest properties for decades, but accurate forest type characterizations at a 30-m Landsat resolution have remained an ongoing challenge, especially over large areas. We combined existing Landsat time series algorithms to quantify both harmonic and phenological metrics in a new set of spectral-temporal features that can be produced seamlessly across many Landsat scenes. Harmonic metrics characterize mean annual reflectance and seasonal variability, while phenological metrics quantify the timing of seasonal events. We assessed the performance of spectral-temporal features derived from time series of all available observations (1985–2015) relative to more conventional single date and multi-date inputs. Performance was determined based on agreement with a reference dataset for eight New England forest types at both the pixel and polygon scale. We found that spectral-temporal features consistently and significantly (paired t-test, p ≪ 0.01) outperformed all feature sets derived from individual images and multi-date combinations in all measures of agreement considered. Harmonic features, such as annual amplitude and model fit error, aid in distinguishing deciduous hardwoods from conifer species, while phenology features, like the timing of autumn onset and growing season length, were useful in separating hardwood classes. This study represents an important step toward large-scale forest type mapping using spectral-temporal Landsat features by providing a quantitative assessment of the advantages of harmonic and phenology features derived from time series of Landsat data as compared with more conventional single-date and multi-date classification inputs.

中文翻译：

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使用光谱-时间 Landsat 特征改进森林类型的映射

摘要 几十年来，Landsat 系列卫星的多光谱图像已被用于绘制森林特性图，但以 30 米 Landsat 分辨率准确描述森林类型仍然是一个持续的挑战，尤其是在大面积区域。我们结合现有的 Landsat 时间序列算法来量化一组新的光谱 - 时间特征中的谐波和物候指标，这些特征可以在许多 Landsat 场景中无缝生成。谐波指标表征平均年度反射率和季节性变化，而物候指标量化季节性事件的时间。我们评估了从所有可用观测（1985-2015）的时间序列中得出的光谱-时间特征相对于更传统的单日期和多日期输入的性能。性能是根据与八种新英格兰森林类型在像素和多边形尺度上的参考数据集的一致性来确定的。我们发现光谱-时间特征一致且显着（配对 t 检验，p ≪ 0.01）在所有考虑的一致性度量中优于从单个图像和多日期组合派生的所有特征集。谐波特征，例如年振幅和模型拟合误差，有助于区分落叶阔叶树和针叶树物种，而物候特征，如秋季开始时间和生长季节长度，有助于区分阔叶树类别。