Canadian Forest Service Publications

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use. 2019. Comber, A.J.; Wulder, M.A. Transactions in GIS. 23:879–891.

Year: 2019

Issued by: Pacific Forestry Centre

Catalog ID: 40050

Language: English

Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1111/tgis.12559

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Abstract

Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community.

Plain Language Summary

  • Reviews utility of time series remote sensing for monitoring.
  • Data are increasingly spatio-temporal – they are collected some-where and at some-time. Tobler observed that near elements (in space) are expected to be more similar than far elements. Yet, overarching physical, seasonal, or climatic processes serve to confound these common expectations when the temporal dimension is considered.
  • Through the example of land cover / land use (LCLU) and related change (LCLUC), in this paper we offer that spatio-temporal analyses of big data should be grounded in an understanding of underlying process. Processes exhibit behaviours over both space and time dimensions and measurements may or may not coincide with the given change or process dynamic of interest.
  • When implementing Geographical Data Analysis of spatio-temporal data the aim should be to link the seriality (i.e., measurement interval, data density) of the spatio-temporal data to the phase of the process being considered.
  • We suggest that this consideration underpins the understanding and strength of spatio-temporal prediction, inference about process, analyses of changes in quality and condition, spatial temporal trajectories and potential future (LCLU) changes.