Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change

Abstract

The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in order to infer urban land use and its changes over time. We conducted a case study in the Washington D.C.&ndash

Baltimore metropolitan area to identify the pattern of land use change from undeveloped to developed land, including residential and non-residential uses for a period covering 1986&ndash

2008. The proposed approach modeled physical and behavioral features of land parcels from a satellite-based impervious surface cover change product and georeferenced Tweets, respectively. A model assessment with random forests classifiers showed that the proposed classification workflow could classify residential and non-residential land uses at an accuracy of 81%, 4% better than modeling the same land uses from physical features alone. Using the timestamps of the impervious surface cover change product, the study also reconstructed the timeline of the identified land uses. The results indicated that the proposed approach was capable of mapping detailed land use and change in an urban region, and represents a new and viable way forward for urban land use surveying that could be especially useful for surveying and tracking changes in cities where traditional approaches and mapping products (i.e., from remote sensing products) may have a limited capacity to capture change.

Document type: Article

Full document

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/rs11242965 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/rs11242965 under the license http://creativecommons.org/licenses/by/3.0/

• http://dx.doi.org/10.5167/uzh-181475 under the license https://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/2072-4292/11/24/2965/pdf

• https://www.zora.uzh.ch/id/eprint/181475/1/2019_Fu_remotesensing2019-fu-song-stewart.pdf under the license cc-by

• https://dblp.uni-trier.de/db/journals/remotesensing/remotesensing11.html#FuSS19,

https://www.zora.uzh.ch/id/eprint/181475/1/2019_Fu_remotesensing2019-fu-song-stewart.pdf,

https://www.zora.uzh.ch/id/eprint/181475,

https://academic.microsoft.com/#/detail/2999853307 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.mdpi.com/2072-4292/11/24/2965/pdf,

http://dx.doi.org/10.3390/rs11242965

under the license https://creativecommons.org/licenses/by/4.0/

DOIS: 10.3390/rs11242965 10.5167/uzh-181475