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A S pace‐ T ime Raster GIS Data Model for Spatiotemporal Analysis of Vegetation Responses to a Freeze Event
Author(s) -
Zhao Ziliang,
Shaw ShihLung,
Wang Dali
Publication year - 2015
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12088
Subject(s) - raster graphics , event (particle physics) , focus (optics) , computer science , perspective (graphical) , object (grammar) , data mining , vegetation (pathology) , raster data , temporal database , data model (gis) , remote sensing , cartography , geography , artificial intelligence , medicine , physics , pathology , quantum mechanics , optics
Many past space‐time GIS data models viewed the world mainly from a spatial perspective. They attached a time stamp to each state of an entity or the entire area of study. This approach is less efficient for certain spatio‐temporal analyses that focus on how locations change over time, which require researchers to view each location from a temporal perspective. In this article, we present a data model to organize multi‐temporal remote sensing datasets and track their changes at the individual pixel level. This data model can also integrate raster datasets from heterogeneous sources under a unified framework. The proposed data model consists of several object classes under a hierarchical structure. Each object class is associated with specific properties and behaviors to facilitate efficient spatio‐temporal analyses. We apply this data model to a case study of analyzing the impact of the 2007 freeze in Knoxville, Tennessee. The characteristics of different vegetation clusters before, during, and after the 2007 freeze event are compared. Our findings indicate that the majority of the study area is impacted by this freeze event, and different vegetation types show different response patterns to this freeze.