The monitoring of forest ecosystems is significantly affected by the lack of consistent historical data of low-severity (forest partially disturbed) or gradual disturbance (e.g. eastern spruce budworm epidemic). The goal of this paper is to explore the use of a subset of Landsat time series and deep learning models to identify both the type and the year of disturbances, including low-severity and gradual disturbances, in the boreal forest of eastern Canada at the pixel level. Remote sensing data such as the spectral information from Landsat time series are the best available option for large scale observations of disturbances that go back decades. Traditional modeling approaches, like LandTrendr, require substantial handcrafted pre-processing to remove noise and to extract temporal features from the image sequences before using them as input to a classical machine-learning model. Deep-learning models can autonomously discern which features are relevant within the coarse temporal and spectral information from the Landsat annual dense time series.

中文翻译：

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评估应用于陆地卫星时间序列子序列以检测和分类北方森林干扰事件的深度学习方法：部分和渐进干扰的挑战

由于缺乏一致的低度（森林部分扰动）或渐进扰动（如东部云杉芽虫流行）的历史数据，森林生态系统的监测受到显着影响。本文的目标是探索使用陆地卫星时间序列的子集和深度学习模型来识别加拿大东部北方森林像素点的干扰类型和年份，包括低严重性和渐进性干扰等级。遥感数据（例如来自陆地卫星时间序列的光谱信息）是对数十年前的扰动进行大规模观测的最佳选择。传统的建模方法（例如 LandTrendr）需要大量的手工预处理来消除噪声并从图像序列中提取时间特征，然后再将其用作经典机器学习模型的输入。深度学习模型可以自主识别陆地卫星年度密集时间序列的粗略时间和光谱信息中哪些特征相关。