New Spatial Methods for Characterizing Dynamic Environmental Processes

表征动态环境过程的新空间方法

• 批准号: 327664-2013
• 负责人: Nelson, Trisalyn
• 金额: $ 2.91万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572092
• 关键词: New; Spatial; Methods; Characterizing; Dynamic

New technology has changed the nature of geographic environmental data. Take the example of snow cover. Currently, NASA freely provides daily data on indicators of snow cover at a spatial resolution of 1 km for most of the globe. The same characteristics that make new data sets exciting for environmental research also invalidate the assumptions of most analytical methods. For instance, standard statistical methods are invalidated when study areas are large and include multiple ecological processes. My goal is to develop new spatial analytical methods that allow novel geographic data to be utilized to test hypotheses of how dynamic environmental processes are changing. To meet this goal I will address three objectives. First, I will develop methods capable of detecting and mapping change in dynamic environmental processes and demonstrate approaches by assessing national scale change in biodiversity from 1987 to 2007. Second, I will improve methods for quantifying shifts in the distribution of species and apply methods to predict the nature of mountain pine beetle dispersal into Canada's boreal forests. Third, I will develop methods for analysing detailed GPS collar data and utilize methods to assess impacts of oil and gas activities on grizzly bears in the Canadian Rockies. Through proposed research, HPQ and I will address one of the most pressing issues in spatial science - the lack of quantitative methods that simultaneously consider spatial and temporal dimensions of data. Method development will be supported by freeware to aid transmission of novel methods to science and resource management communities. We will also contribute new knowledge to topics of international interest such as biodiversity monitoring, the mountain pine beetle epidemic, boreal forests, climate change, and wildlife management. HQP training will be the focus of this research and Canada will benefit as HQP are trained to conduct advanced spatial analysis and environmental research.

新技术改变了地理环境数据的性质。以积雪为例。目前，NASA 免费提供全球大部分地区空间分辨率为 1 公里的积雪指标每日数据。使新数据集对环境研究令人兴奋的相同特征也使大多数分析方法的假设失效。例如，当研究区域很大并且包括多个生态过程时，标准统计方法就失效了。 我的目标是开发新的空间分析方法，允许利用新的地理数据来测试动态环境过程如何变化的假设。为了实现这一目标，我将实现三个目标。首先，我将开发能够检测和绘制动态环境过程变化的方法，并通过评估 1987 年至 2007 年国家范围内生物多样性的变化来展示方法。其次，我将改进量化物种分布变化的方法，并应用方法来预测山松甲虫扩散到加拿大北方森林的性质。 第三，我将开发分析详细 GPS 项圈数据的方法，并利用方法评估石油和天然气活动对加拿大落基山脉灰熊的影响。 通过拟议的研究，HPQ 和我将解决空间科学中最紧迫的问题之一——缺乏同时考虑数据空间和时间维度的定量方法。方法开发将得到免费软件的支持，以帮助向科学和资源管理社区传播新方法。我们还将为生物多样性监测、山松甲虫流行、北方森林、气候变化和野生动物管理等国际关注的主题贡献新知识。 HQP 培训将是这项研究的重点，加拿大将从中受益，因为 HQP 接受了进行高级空间分析和环境研究的培训。