A Sentinel-2 Machine Learning Dataset for Tree Species Classification in Germany

IF 11.2 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Earth System Science Data Pub Date : 2024-07-04 DOI:10.5194/essd-2024-206

Maximilian Freudenberg, Sebastian Schnell, Paul Magdon

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Abstract

Abstract. We present a machine learning dataset for tree species classification in Sentinel-2 satellite image time series of bottom of atmosphere reflectance. The dataset is based on the German national forest inventory of 2012, as well as analysis ready satellite imagery computed using the FORCE processing pipeline. From the national forest inventory data, we extracted the tree positions, filtered 387 775 trees in the upper canopy layer and automatically extracted the corresponding bottom of atmosphere reflectance time series from Sentinel-2 L2A images. These time series are labeled with the corresponding tree species, which allows pixel-wise classification tasks. Furthermore, we provide auxiliary information such as the approximate tree position, the year of possible disturbance events or the diameter at breast height. Temporally, the dataset spans the years from July 2015 to end of October 2022 with ca. 75.3 million data points for trees of 51 species and species groups, as well as 13.8 million observations for non-tree background. Spatially, it covers entire Germany. The dataset is available under following DOI (Freudenberg et al., 2024): https://doi.org/10.3220/DATA20240402122351-0

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用于德国树种分类的哨兵-2 机器学习数据集

摘要我们介绍了一个机器学习数据集，用于在哨兵-2 卫星图像的大气底部反射率时间序列中进行树种分类。该数据集基于 2012 年德国国家森林资源清查以及使用 FORCE 处理管道计算的卫星图像分析。我们从国家森林资源清查数据中提取了树木位置，过滤了树冠上层的 387 775 棵树木，并从哨兵-2 L2A 图像中自动提取了相应的大气底部反射率时间序列。这些时间序列标注了相应的树种，可用于像素分类任务。此外，我们还提供了一些辅助信息，如树木的大致位置、可能发生干扰事件的年份或胸径。从时间上看，数据集的时间跨度为 2015 年 7 月至 2022 年 10 月底，包含 51 个树种和树种群的约 7530 万个数据点，以及非树木背景的 1380 万个观测点。在空间上，该数据集覆盖了整个德国。该数据集的 DOI 如下（Freudenberg 等人，2024 年）： https://doi.org/10.3220/DATA20240402122351-0

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来源期刊

Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

18.00

自引率

5.30%

发文量

231

审稿时长

35 weeks

期刊介绍： Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.