面向森林火灾远程跟踪的卫星热点数据聚类组织算法

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2023-08-26 DOI:10.32614/rj-2023-022
Weihao Li, Emily Dodwell, Dianne Cook
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引用次数: 0

摘要

本文提出了一种时空聚类算法,并在R包spotoroo中实现。这项工作的动机是澳大利亚2019-2020年夏季的灾难性森林大火,并通过卫星热点数据的可用性使其成为可能。该算法的灵感来自于两种现有的时空聚类算法,但在空间上结合点在连续时间段内的运动进行了增强。如果需要,它还允许对不同地点和卫星数据源的关键参数进行调整。来自澳大利亚维多利亚州的森林大火数据被用来说明该算法及其在软件包中的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Clustering Algorithm to Organize Satellite Hotspot Data for the Purpose of Tracking Bushfires Remotely
This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package.
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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