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Field spectroscopy data from non-arable grass-dominated objects in an intensively used agricultural landscape in East Anglia UK

机译:来自英国东安格利亚频繁使用的农业景观中非耕地草类占主导地位的物体的现场光谱数据

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摘要

Remote sensing of vegetation provides important information for ecological applications and environmental assessments. The association between vegetation composition and structure with its spectral signal can most fully be assessed with hyperspectral data. Particularly field spectroscopy data can improve such understanding as the spectral data can be linked with the vegetation under consideration without the geographic registration uncertainties of aerial or satellite imagery. The data provided in this article contain field spectroscopy measurements from non-arable, grass-dominated objects on four farms in an intensively used agricultural landscape in the South-East of the UK. Detailed data on the plant species composition of the objects are also supplied with this article to support further analysis. Reuse potential includes linking the vegetation data with the spectral response using spectral unmixing techniques to map certain plant species or including the field spectroscopy data in a larger study with data from a wider area. This data article is related to the paper ‘Classifying grass-dominated habitats from remotely sensed data: the influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution’ (Bradter et al., 2019) in which the ability to classify the recorded vegetation from the field spectroscopy data was analysed.
机译:植被遥感为生态应用和环境评估提供了重要信息。可以用高光谱数据最全面地评估植被组成与结构及其光谱信号之间的关联。特别地,现场光谱数据可以改善这种理解,因为光谱数据可以与所考虑的植被相关联,而没有航空或卫星图像的地理配准不确定性。本文提供的数据包含对英国东南部一个频繁使用的农业景观中的四个农场中非耕地,草为主的物体的现场光谱测量。本文还提供了有关对象植物物种组成的详细数据,以支持进一步的分析。重复利用潜力包括使用光谱分解技术将植被数据与光谱响应联系起来,以绘制某些植物种类的图谱,或者在较大的研究中将田间光谱数据与来自更广范围的数据结合起来。该数据文章与论文``根据遥感数据对草类栖息地进行分类:光谱分辨率,采集时间和植被分类系统对准确性和主题分辨率的影响''(Bradter等人,2019年)中的能力从现场光谱数据中对记录的植被进行分类。

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