TY - JOUR
T1 - An interactive platform for the analysis of landscape patterns
T2 - a cloud-based parallel approach
AU - Deng, Jing
AU - Desjardins, Michael R.
AU - Delmelle, Eric M.
N1 - Funding Information:
The authors would like to acknowledge the feedback and computational support provided by Dr. Wenwu Tang from the Center for Applied Geographic Information Science at the University of North Carolina at Charlotte
Publisher Copyright:
© 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Nanjing Normal University.
PY - 2019/4/3
Y1 - 2019/4/3
N2 - 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.
AB - 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.
KW - cloud computing
KW - GIS
KW - interactive visualization
KW - Landscape metrics
KW - spatial pattern analysis
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U2 - 10.1080/19475683.2019.1615550
DO - 10.1080/19475683.2019.1615550
M3 - Article
AN - SCOPUS:85065839382
SN - 1947-5683
VL - 25
SP - 99
EP - 111
JO - Annals of GIS
JF - Annals of GIS
IS - 2
ER -