Abstract
ABSTRACTUnderstanding spatial and temporal characteristics of landscape patterns is critical in ecology, since human interactions with their natural environment can significantly impact ecological processes. The common approach to detect changes in landscape patterns is to evaluate the spatial and temporal variation of well known, established metrics. Examples of such metrics include the composition of different land-use class types and the spatial heterogeneity of individual patches. However, computing such metrics over large geographic areas and at fine levels of granularity requires significant computing resources. In addition, conventional software often lack a visual component that is essential for the detection of changes in landscape patterns and knowledge discovery. In this paper, we propose a cloud-based framework to facilitate the estimation and visualization of landscape pattern analysis in both space and time, capitalizing on the cloud computing facilities provided by Amazon EC2. We illustrate the merit of our approach on landscape metrics across the USA for the years 1992, 2001, and 2011 at the county level. Leveraging cloud computing technology provides the flexibility, scalability and portability to different study regions and at variable scales.
Highlights
The ecological impacts of land use and land cover change are profound, and numerous examples in the literature have highlighted environmental impacts of future urban development (Gustafson 1998; Nagendra, Munroe, and Southworth 2004)
We proposed a platform which combines the computation, analysis, and visualization of landscape metrics to support the detection of changing landscape patterns and enhancing knowledge discovery
Illinois Pinal, Arizona Hemphill, Texas Cameron Parish, Louisiana and it provides the flexibility for users to select specific spatial units, scales, land cover classifications, and landscape metrics
Summary
The ecological impacts of land use and land cover change are profound, and numerous examples in the literature have highlighted environmental impacts of future urban development (Gustafson 1998; Nagendra, Munroe, and Southworth 2004). To better understand human interactions with natural systems, landscape ecology plays an important role to analyse land use change pattern and spatial fragmentation (Turner and Lynn Ruscher 1988; Turner 1989, 1990). Despite the fact that methods and metrics for analysing land use change are well developed, calculating and visualizing them over large geographic areas, at high spatial and temporal resolutions, poses significant computational challenges. In the light of these issues and recent improvement in high-performance computing, our paper is motivated by the need to develop a robust, automated approach that can estimate and visualize significant changes of landscape metrics over a large spatial extent.
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