Next Article in Journal
Assessment of Empirical and Semi-Analytical Algorithms Using MODIS-Aqua for Representing In-Situ Chromophoric Dissolved Organic Matter (CDOM) in the Bering, Chukchi, and Western Beaufort Seas of the Pacific Arctic Region
Previous Article in Journal
PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information
Previous Article in Special Issue
Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda
Article

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

1
Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO 80309, USA
2
Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO 80309, USA
3
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
4
Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USA
5
Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
6
Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Alessandro Sorichetta, Andrea E. Gaughan and Forrest R. Stevens
Remote Sens. 2021, 13(18), 3672; https://doi.org/10.3390/rs13183672
Received: 1 July 2021 / Revised: 29 August 2021 / Accepted: 10 September 2021 / Published: 14 September 2021
Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.
View Full-Text
Keywords: urbanization; long-term settlement patterns; built-up land data; global human settlement layer; historical maps; topographic map processing; data integration urbanization; long-term settlement patterns; built-up land data; global human settlement layer; historical maps; topographic map processing; data integration
Show Figures

Graphical abstract

MDPI and ACS Style

Uhl, J.H.; Leyk, S.; Li, Z.; Duan, W.; Shbita, B.; Chiang, Y.-Y.; Knoblock, C.A. Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents. Remote Sens. 2021, 13, 3672. https://doi.org/10.3390/rs13183672

AMA Style

Uhl JH, Leyk S, Li Z, Duan W, Shbita B, Chiang Y-Y, Knoblock CA. Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents. Remote Sensing. 2021; 13(18):3672. https://doi.org/10.3390/rs13183672

Chicago/Turabian Style

Uhl, Johannes H., Stefan Leyk, Zekun Li, Weiwei Duan, Basel Shbita, Yao-Yi Chiang, and Craig A. Knoblock 2021. "Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents" Remote Sensing 13, no. 18: 3672. https://doi.org/10.3390/rs13183672

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop