Data fusion methods for combining remote sensing and wildlife tracking data

这项提议的总体目标是将最先进的遥感技术与了解野生动物运动行为的创新方法相结合。考虑到遥感技术的快速发展，结合新的记录仪来追踪野生动物的位置，我们的研究是及时的。特别是，我的计划将专注于方法论研究的两个主要领域：1)量化野生动物空间使用的模型和2)理解个体之间相互作用的方法，例如人类对野生动物的干扰。我和我的团队将通过地方、国家和国际合作，将我们开发的方法应用于野生动物空间生态学的应用研究问题。理解高分辨率野生动物跟踪数据中的时空模式是GIScience的主要方法论挑战。当前方法的一个关键局限性是无法表征运动模式和环境变化之间的关系。当我们无法捕捉到野生动物如何对动态的环境条件做出反应时，我们为管理或保护活动做出合理决策的能力就会受到限制。因此，将高分辨率野生动物跟踪数据与遥感数据相结合的新方法对于更好地捕捉和了解野生动物运动模式与动态、快速变化的环境之间的关系是必要的。目前将野生动物跟踪数据与遥感数据相结合的方法通常使用单一日期或单一卫星组合，因此，它们捕捉环境中细微的空间和时间变化的能力仍然有限。因此，卫星数据融合--结合来自多个传感器的卫星数据--是改善野生动物运动研究中理解的一个重大机会。我的课程重点是HQP的培训。具体地说，我的提议将支持13名当前和未来的HQP(本科生、研究生、技术人员)，他们将接受地理信息系统、遥感和野生动物保护方面的培训。我的受训人员学到了各种技术技能，这些技能在学术界内外都很有市场。我们的工作将发表在影响力很大的科学期刊上，并在国内和国际学术会议上发表。我们将通过与地方、国家和国际合作伙伴在应用野生动物保护方面的合作，以及通过开发免费和开源的地理空间分析软件工具，实现广泛的社会影响。

The overall objective of this proposal is to integrate state-of-the-art remote sensing techniques with innovative methods for understanding wildlife movement behavior. Our research is timely given the rapid development of remote sensing technology, combined with new loggers for tracking wildlife locations. In particular, my program will focus on two primary areas of methodological research: 1) models for quantifying wildlife space use and 2) methods for understanding inter-individual interactions, for example human disturbance of wildlife. My team and I will apply the methods we develop to applied research problems in wildlife spatial ecology through local, national, and international collaborations. Understanding space-time patterns in high-resolution wildlife tracking data is a major methodological challenge in GIScience. A key limitation of current methods is the ability to characterize relationships between movement patterns and environmental change. When we fail to capture how wildlife respond to dynamic environmental conditions, we are limited in our ability to make sound decisions for management or conservation activities. Therefore, new methods for combining high resolution wildlife tracking data with remotely sensed data are necessary to better capture and understand how patterns of wildlife movement relate to dynamic, rapidly changing environments. Current methods for integrating wildlife tracking data with remotely sensed data typically use single-date or single-satellite composites, and therefore, remain limited in their ability to capture fine-scale spatial and temporal changes in the environment. Therefore, satellite data fusion - combining satellite data from multiple sensors - represents a major opportunity for improved understanding in wildlife movement studies. My program emphasizes the training of HQP. Specifically, my proposal will support 13 current and future HQP (undergraduate, graduate, technical staff), who will be trained in GIS, remote sensing, and wildlife conservation. My trainees are taught a variety of technical skills which are highly marketable within and outside of academia. Our work will be published in high-impact scientific journals and presented at national and international academic conferences. We will achieve broad societal impact by working with local, national, and international partners in applied wildlife conservation and through the development of free and open source software tools for geospatial analysis.