Data fusion methods for combining remote sensing and wildlife tracking data

结合遥感和野生动物追踪数据的数据融合方法

• 批准号: RGPIN-2020-04994
• 负责人: Long, Jed
• 金额: $ 1.82万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717482
• 关键词: Data; fusion; methods; combining; remote

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.

该提案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。我们的研究是及时的，因为遥感技术的快速发展，加上新的记录器跟踪野生动物的位置。特别是，我的计划将侧重于方法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。 了解高分辨率野生动物跟踪数据的时空模式是GIScience的一个主要方法挑战。目前的方法的一个主要局限是无法描述移动模式与环境变化之间的关系。当我们无法捕捉野生动物对动态环境条件的反应时，我们就无法为管理或保护活动做出合理的决策。因此，有必要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，以更好地捕捉和了解野生动物运动模式与动态、快速变化的环境的关系。目前用于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合成数据，因此，其捕捉环境中精细尺度空间和时间变化的能力仍然有限。因此，将来自多个传感器的卫星数据结合起来的卫星数据融合是提高对野生动物运动研究的认识的一个重要机会。 我的计划强调HQP的培训。具体来说，我的建议将支持13个当前和未来的HQP（本科生，研究生，技术人员），谁将在地理信息系统，遥感和野生动物保护的培训。我的学员学习各种技术技能，这些技能在学术界内外都很有市场。我们的工作将发表在高影响力的科学期刊上，并在国家和国际学术会议上发表。我们将通过与当地，国家和国际合作伙伴在应用野生动物保护和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。