Quality assured spatial dataset of wildfire containment firelines and engagement outcomes 2017 to 2024.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-05-28 DOI:10.1038/s41597-025-05208-0

Alexander Arkowitz, Scott M Ritter, Matthew P Thompson, Jesse D Young, Brad Pietruszka, David E Calkin

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引用次数: 0

Abstract

The escalation of wildfires in the USA, coupled with rising firefighting costs and decreasing workforce capacity, underscores the critical need to evaluate the efficiency and effectiveness of containment measures. However, the existing spatial data that records the locations and types of containment measures and wildfire perimeters contains numerous errors and redundancies. This paper presents a comprehensive fireline Quality Assurance and Quality Control dataset developed from the wildland firefighting operations data reported in the National Interagency Fire Center National Incident Feature Service. This improved dataset contains reliable spatial locations for fireline built during suppression operations, the associated verified fire perimeters, and identifies where containment was success or failure for fires greater than 1000 acres from 2017-2024. The improved final dataset represents critical information that was previously unavailable for assessing the success of fireline operations and incident management resource-use efficiency. The lessons learned from analyses utilizing this dataset are critical for improving the efficiency and effectiveness of the United States wildfire management system.

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2017 - 2024年有质量保证的野火控制火线空间数据集和参与成果。

美国野火的升级，加上消防成本的上升和劳动力能力的下降，凸显了评估遏制措施的效率和有效性的迫切需要。然而，记录遏制措施的位置和类型以及野火周长的现有空间数据存在许多错误和冗余。本文介绍了一个综合的火线质量保证和质量控制数据集，该数据集是根据国家机构间消防中心国家事件特征服务中心报告的荒地消防行动数据开发的。这个改进的数据集包含了在灭火行动期间建造的火线的可靠空间位置，相关的经过验证的火灾周长，并确定了2017-2024年超过1000英亩火灾的遏制成功或失败的地方。改进后的最终数据集代表了以前无法获得的关键信息，用于评估火线作业的成功和事故管理资源利用效率。从利用该数据集的分析中吸取的经验教训对于提高美国野火管理系统的效率和有效性至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.