Doctoral Dissertation Research: An Integrated Spatio-Temporal Approach for Reconstructing, Analyzing, and Representing Paleo-Fire Regimes

博士论文研究：用于重建、分析和表示古火状态的综合时空方法

• 批准号: 0508984
• 负责人: Amy Hessl
• 金额: $ 0.91万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0508984&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Integrated; Spatio

The spatial characteristics of paleoecological disturbances are critical to understanding the natural variability of environmental processes. However, many areas of paleoecological inquiry, particularly those relying on data collected at discrete points, such as pollen and charcoal studies, emphasize temporal variability and minimize or neglect the spatial dimension. This is particularly true of fire history research that is based on fire-scar data collected from individual trees. Many such studies result in a fire return interval that estimates how frequently a portion of the landscape burned. These fire-return intervals are spatially ambiguous and sensitive to the extent of the area for which they are calculated, making interpretation challenging. Furthermore, past studies that have addressed spatial variability have used discrete representations of fires, principally mapped outlines of the estimated fire extent. This format is difficult to evaluate because presents spatial and temporal variability separately, and it precludes independent interpretation. The purpose of this Doctoral Dissertation Research Improvement project is to develop an integrated spatial-temporal representation system for paleo-fire. The project will incorporate fire-history science, landscape ecology, and geographic information science, particularly space-time and spatial cognition theory. The doctoral candidate will use georeferenced fire-scarred tree data in a geographic information system. The representation will integrate field and object models of past fires and incorporate an iconic visualization language that will illustrate the change in state of individual trees in space and through time. This system will facilitate novel approaches to analyzing paleo-fire. A spatially explicit approach for estimating fire return interval will be presented. Spatial metrics extracted from the representation system will be used to evaluate the influence of top-down climatic factors and bottom-up topographic factors on spatial-temporal variability of past fires.Current explosive wildfires can only be understood within their historical ecological context. This project will improve basic understanding of natural wildfire regimes by contributing new analytical methods and a cognitively sensitive approach to data representation. These new approaches to data representation, including maps of paleoecological fires, will be accessible to a wide range of interested parties, including scientists, managers, and concerned members of the public. This will improve the understanding of paleo-fire and present educational opportunities using complex paleoecological data. Finally, this project can benefit other types of paleoecological research where deep time point data are used. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

古生态扰动的空间特征对于理解环境过程的自然变化至关重要。 然而，许多领域的古生态调查，特别是那些依赖于在离散点收集的数据，如花粉和木炭的研究，强调时间的变化和最小化或忽略的空间维度。 这是特别真实的火灾历史的研究，是基于从个别树木收集的火痕数据。 许多这样的研究导致火灾重现间隔，估计有多频繁的一部分景观烧毁。 这些回击间隔在空间上模糊不清，对计算区域的范围很敏感，因此难以解释。 此外，过去的研究已经解决了空间变异性使用离散表示火灾，主要是绘制轮廓的估计火灾范围。 这种格式很难评价，因为空间和时间的变化是分开的，它排除了独立的解释。 这个博士论文研究改进项目的目的是开发一个集成的时空表示系统的古火。 该项目将结合火灾历史科学，景观生态学和地理信息科学，特别是时空和空间认知理论。 博士生将在地理信息系统中使用地理参考火灾疤痕树数据。 该表示将整合过去火灾的场和对象模型，并采用一种标志性的可视化语言，以说明单个树木在空间和时间上的状态变化。 该系统将促进新的方法来分析古火。 提出了一种空间显式的火灾重现时间间隔估计方法。 从代表系统中提取的空间指标将用于评估自上而下的气候因素和自下而上的地形因素对过去火灾时空变化的影响。 该项目将通过提供新的分析方法和认知敏感的数据表示方法，提高对自然野火状况的基本了解。 这些新的数据表示方法，包括古生态火灾地图，将提供给广泛的利益相关方，包括科学家，管理人员和关心的公众成员。 这将提高对古火的理解，并利用复杂的古生态数据提供教育机会。 最后，该项目可以有益于其他类型的古生态研究，其中使用深时间点数据。 作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。