LiPheStream - A 18-month high spatiotemporal resolution point cloud time series of Boreal trees from Finland.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-25 DOI:10.1038/s41597-024-04143-w

Samantha Wittke, Mariana Campos, Lassi Ruoppa, Rami Echriti, Yunsheng Wang, Antoni Gołoś, Antero Kukko, Juha Hyyppä, Eetu Puttonen

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Abstract

In the present paper, we introduce a high-resolution spatiotemporal point cloud time series, acquired using a LiDAR sensor mounted 30 metres above ground on a flux observation tower monitoring a boreal forest. The dataset comprises a 18-month long (April 2020 - September 2021) time series with an average interval of 3.5 days between observations. The data acquisition, transfer, and storage systems established at Hyytiälä (Finland) are named the LiDAR Phenology station (LiPhe). The dataset consists of 103 time points of LiDAR point clouds covering a total of 458 individual trees, comprising three distinct Boreal species. Additional reference information includes the respective location, the species, and the initial height (at the first time point) of each individual tree. The processing scripts are included to outline the workflow used to generate the individual tree point clouds (LiPheKit). The presented dataset offers a comprehensive insight into inter- and intra-species variations of the individual trees regarding their growth strategies, phenological dynamics, and other functioning processes over two growth seasons.

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在本文中，我们介绍了一个高分辨率时空点云时间序列，该序列是使用安装在监测北方森林的通量观测塔上距地面 30 米处的激光雷达传感器获取的。该数据集包括一个长达 18 个月（2020 年 4 月至 2021 年 9 月）的时间序列，平均观测间隔为 3.5 天。在 Hyytiälä（芬兰）建立的数据采集、传输和存储系统被命名为激光雷达气候学站（LiPhe）。数据集由 103 个时间点的激光雷达点云组成，共涵盖 458 棵树，包括三个不同的北方树种。其他参考信息包括每棵树的位置、树种和初始高度（在第一个时间点）。数据集包含处理脚本，概述了用于生成单个树木点云（LiPheKit）的工作流程。所提供的数据集能让人全面了解单棵树木在两个生长季节的生长策略、物候动态和其他功能过程方面的种间和种内变化。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.