An interactive platform for the analysis of landscape patterns: a cloud-based parallel approach

Abstract

Understanding 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.