New Spatial Methods for Characterizing Dynamic Environmental Processes

新技术改变了地理环境数据的性质。以积雪为例。目前，美国航天局免费提供地球仪大部分地区积雪指数的每日数据，空间分辨率为1公里。同样的特点，使新的数据集令人兴奋的环境研究也无效的假设，大多数分析方法。例如，当研究区域很大并且包括多种生态过程时，标准的统计方法就失效了。 我的目标是开发新的空间分析方法，使新的地理数据被用来测试动态环境过程如何变化的假设。为了实现这一目标，我将提出三个目标。首先，我将开发能够检测和绘制动态环境过程中的变化的方法，并通过评估1987年至2007年生物多样性的国家尺度变化来展示方法。第二，我将改进的方法，量化的物种分布的变化和应用方法来预测山松甲虫扩散到加拿大的北方森林的性质。 第三，我将开发分析详细的GPS项圈数据的方法，并利用方法来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-缺乏同时考虑数据的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界传播新方法。我们还将为生物多样性监测、山地松甲虫流行、北方森林、气候变化和野生动物管理等国际感兴趣的主题贡献新知识。HQP培训将是这项研究的重点，加拿大将受益于HQP培训进行先进的空间分析和环境研究。

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.