A global forest burn severity dataset from Landsat imagery (2003–2016)

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Kang He, Xinyi Shen, Emmanouil N. Anagnostou
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引用次数: 0

Abstract

Abstract. Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past 2 decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a 30 m resolution global forest burn severity (GFBS) dataset of the degree of biomass consumed by fires from 2003 to 2016. To develop this dataset, we used the Global Fire Atlas product to determine when and where forest fires occurred during that period and then we overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. We compared the GFBS dataset against the Canada Landsat Burned Severity (CanLaBS) product, showing better agreement than the existing Moderate Resolution Imaging Spectrometer (MODIS)-based global burn severity dataset (MOdis burn SEVerity, MOSEV) in representing the distribution of forest burn severity over Canada. Using the in situ burn severity category data available for the 2013 wildfires in southeastern Australia, we demonstrated that GFBS could provide burn severity estimation with clearer differentiation between the high-severity and moderate-/low-severity classes, while such differentiation among the in situ burn severity classes is not captured in the MOSEV product. Using the CONUS-wide composite burn index (CBI) as a ground truth, we showed that dNBR from GFBS was more strongly correlated with CBI (r=0.63) than dNBR from MOSEV (r=0.28). RdNBR from GFBS also exhibited better agreement with CBI (r=0.56) than RdNBR from MOSEV (r=0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at https://doi.org/10.5281/zenodo.10037629 (He et al., 2023).
从大地遥感卫星图像中提取的全球森林燃烧严重程度数据集(2003-2016 年)
摘要森林火灾虽然具有破坏性和危险性,但对生态系统的运作和更新非常重要。在过去的 20 年里,全球范围内大规模的严重森林火灾越来越频繁,而且随着火灾天气和干旱条件的加剧,预计森林火灾的风险还会增加。为了更好地量化森林火灾破坏的强度和范围,我们开发了一个 30 米分辨率的全球森林燃烧严重程度(GFBS)数据集,其中包含 2003 年至 2016 年火灾消耗的生物量程度。为了开发该数据集,我们使用了全球火灾图集产品来确定这一时期发生森林火灾的时间和地点,然后叠加现有的大地遥感卫星表面反射率产品,以获得每个被烧毁像素的火灾前和火灾后归一化烧毁率(NBR),并将两者之间的差值称为 dNBR,相对差值称为 RdNBR。我们将 GFBS 数据集与加拿大陆地卫星烧毁严重程度(CanLaBS)产品进行了比较,结果表明,在表示加拿大森林烧毁严重程度分布方面,GFBS 数据集比现有的基于中分辨率成像光谱仪(MODIS)的全球烧毁严重程度数据集(MODIS burn SEVerity,MOSEV)更一致。通过使用 2013 年澳大利亚东南部野火的原地燃烧严重程度类别数据,我们证明了全球森林燃烧严重程度数据集可以提供燃烧严重程度估算,并更清晰地区分严重程度等级和中/低严重程度等级,而 MOSEV 产品并未捕捉到原地燃烧严重程度等级之间的这种区分。使用全美烧伤综合指数(CBI)作为基本事实,我们发现,与 MOSEV 的 dNBR(r=0.28)相比,GFBS 的 dNBR 与 CBI 的相关性更强(r=0.63)。来自 GFBS 的 RdNBR 与 CBI 的一致性(r=0.56)也优于来自 MOSEV 的 RdNBR(r=0.20)。在全球范围内,虽然 GFBS 提取的 dNBR 和 RdNBR 空间模式与 MOSEV 相似,但 MOSEV 提供的烧伤严重程度往往高于 GFBS。我们将这种差异归因于反射率值的变化以及两颗卫星不同的空间分辨率。与现有数据集相比,GFBS 数据集能提供更精确、更可靠的燃烧严重程度评估。这些改进对于了解森林火灾的生态影响以及为全球受影响地区的管理和恢复工作提供信息至关重要。全球森林火灾数据集可在 https://doi.org/10.5281/zenodo.10037629 免费访问(He 等人,2023 年)。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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