Zongrun Li, Kamal J Maji, Yongtao Hu, Ambarish Vaidyanathan, Susan M O'Neill, M Talat Odman, Armistead G Russell
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Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. 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The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. 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引用次数: 0
摘要
规定的焚烧是一种微细特定物质的主要来源,尤其是在美国东南部地区,准确量化焚烧作业的排放量是确定空气质量影响不可或缺的一部分。例如,计算火灾排放量的一个关键因素是确定火灾活动信息(如地点、日期/时间、火灾类型和燃烧面积),而之前用于计算排放量的规定火灾活动估算要么使用了燃烧许可记录,要么使用了基于卫星的遥感产品。虽然国家机构保存的燃烧许可记录是一个可靠的来源,但它们并不总是可用或随时可得。卫星遥感产品目前被用来填补数据空白,尤其是在区域研究中;但是,这些产品无法将规定的燃烧与其他类型的火灾区分开来。在这项研究中,我们开发了一种新型算法,以区分来自卫星产品--NCAR 的火灾清单(Fire INventory from NCAR,FINN)中的规定烧毁与野火和农业烧毁。我们在不同的空间尺度上对许可证记录和 FINN 中的燃烧面积进行了匹配和比较:单个火灾级别、4 千米网格级别和州级别。本研究开发的方法可用于区分燃烧类型、匹配和比较不同分辨率下两个数据集的燃烧面积以及估算规定燃烧的排放量。结果表明,在单个火灾层面,许可证和 FINN 数据集的燃烧面积相关性较弱,而在 4 千米网格和州层面,相关性要高得多。由于 4 千米网格级别的匹配显示出相对较高的相关性,而化学迁移模型通常使用基于网格的排放量,因此我们使用网格级别 FINN 与许可证燃烧面积之间的线性回归关系来调整 FINN 燃烧面积。这一调整使 FINN 烧毁面积减少了 34%。调整后的焚烧面积随后被用作 BlueSky 烟雾模拟框架的输入,以提供美国东南部长期、三维的规定焚烧排放量。在这项研究中,我们还比较了不同方法(FINN 或 BlueSky)和不同数据源(调整后的 FINN 或许可证)的排放量,以评估排放量估算的不确定性。比较结果显示了焚烧面积、方法和数据源对规定焚烧排放量估算的影响。
An Analysis of Prescribed Fire Activities and Emissions in the Southeastern United States from 2013 to 2020.
Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.