Preprint Article Version 3 This version is not peer-reviewed

Annually Modelling Built-settlements between Remotely-sensed Observations Using Relative Changes in Subnational Populations and Lights at Night

Version 1 : Received: 18 December 2018 / Approved: 20 December 2018 / Online: 20 December 2018 (12:55:37 CET)
Version 2 : Received: 8 April 2019 / Approved: 8 April 2019 / Online: 8 April 2019 (13:24:22 CEST)
Version 3 : Received: 6 October 2019 / Approved: 9 October 2019 / Online: 9 October 2019 (10:48:20 CEST)

A peer-reviewed article of this Preprint also exists.

Journal reference: Computers Environment and Urban Systems 2019, 80
DOI: 10.1016/j.compenvurbsys.2019.101444

Abstract

Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category “built” in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.

Subject Areas

Built-settlements; urban features; spatial growth;, random forest; dasymetric modelling; population

Comments (1)

Comment 1
Received: 9 October 2019
Commenter: Jeremiah J. Nieves
Commenter's Conflict of Interests: Author
Comment: Moving of content from supplemental to main text and vice versa. Major reorganization and clarification of the Methods section. Addition of a simplified and more graphical process diagram and addition of maps of the resultant modeled data to give better context for the results. Overall cleaning and clarification of the text. Greater specification added to article title.
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