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Remote Sensing
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Remote Sensing Application to Population Mapping
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Remote Sensing Application to Population Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 16766

Special Issue Editors

Prof. Dr. Christopher Small

E-Mail Website
Guest Editor
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10027, USA
Interests: geophysics; spectroscopy; land surface processes; spatiotemporal dynamics; urban environment
Special Issues, Collections and Topics in MDPI journals
Mr. Greg Yetman

E-Mail Website
Guest Editor
CIESIN Columbia University, Palisades, NY, USA
Interests: geographic information system (GIS) technologies; population geography; natural disasters; environmental assessment

Special Issue Information

Dear Colleagues,

Recent and ongoing advances in the mapping of human-modified landscapes and built environments have attained sufficient maturity to contribute to efforts to map ambient (daytime) population distribution at global scales. Spatially explicit estimates of population density generally require both land use and census data, as well as assumptions about the relationship(s) between population and land use. As such, advances in population mapping depend on, but are distinct from, general land use mapping. Remote sensing based on synoptic imaging is now complemented by mobile sensing technologies, which are able to quantify more granular population at higher spatial and temporal resolutions.

We invite contributions focused specifically on the combined use of remote sensing (both synoptic and mobile) and population metrics for spatially explicit mapping of ambient population at local to global scales. Specific aspects could include, but are not limited to, dasymetric mapping, disaggregation methodology, multimodal fusion, mobility inference, population displacement, land use assignment, and uncertainty quantification in population estimates.

Prof. Christopher Small
Mr. Greg Yetman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • population
  • mapping
  • remote sensing
  • mobile sensing
  • census
  • survey
  • dasymetric

Published Papers (3 papers)

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Research

20 pages, 2710 KiB  
Article
Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings
by Christopher T. Lloyd, Hugh J. W. Sturrock, Douglas R. Leasure, Warren C. Jochem, Attila N. Lázár and Andrew J. Tatem
Remote Sens. 2020, 12(23), 3847; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233847 - 24 Nov 2020
Cited by 19 | Viewed by 6390
Abstract
Utilising satellite images for planning and development is becoming a common practice as computational power and machine learning capabilities expand. In this paper, we explore the use of satellite image derived building footprint data to classify the residential status of urban buildings in [...] Read more.
Utilising satellite images for planning and development is becoming a common practice as computational power and machine learning capabilities expand. In this paper, we explore the use of satellite image derived building footprint data to classify the residential status of urban buildings in low and middle income countries. A recently developed ensemble machine learning building classification model is applied for the first time to the Democratic Republic of the Congo, and to Nigeria. The model is informed by building footprint and label data of greater completeness and attribute consistency than have previously been available for these countries. A GIS workflow is described that semiautomates the preparation of data for input to the model. The workflow is designed to be particularly useful to those who apply the model to additional countries and use input data from diverse sources. Results show that the ensemble model correctly classifies between 85% and 93% of structures as residential and nonresidential across both countries. The classification outputs are likely to be valuable in the modelling of human population distributions, as well as in a range of related applications such as urban planning, resource allocation, and service delivery. Full article
(This article belongs to the Special Issue Remote Sensing Application to Population Mapping)
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22 pages, 10625 KiB  
Article
Mapping the Population Density in Mainland China Using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model
by Yunchen Wang, Chunlin Huang, Minyan Zhao, Jinliang Hou, Ying Zhang and Juan Gu
Remote Sens. 2020, 12(21), 3645; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213645 - 06 Nov 2020
Cited by 34 | Viewed by 4218
Abstract
Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order [...] Read more.
Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as ‘Popi’). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14,839 and 7218, respectively, for Popi, and 18,014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP). Full article
(This article belongs to the Special Issue Remote Sensing Application to Population Mapping)
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21 pages, 6124 KiB  
Article
Upscaling Household Survey Data Using Remote Sensing to Map Socioeconomic Groups in Kampala, Uganda
by Lisa-Marie Hemerijckx, Sam Van Emelen, Joachim Rymenants, Jac Davis, Peter H. Verburg, Shuaib Lwasa and Anton Van Rompaey
Remote Sens. 2020, 12(20), 3468; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203468 - 21 Oct 2020
Cited by 7 | Viewed by 5457
Abstract
Sub-Saharan African cities are expanding horizontally, demonstrating spatial patterns of urban sprawl and socioeconomic segregation. An important research gap around the geographies of urban populations is that city-wide analyses mask local socioeconomic inequalities. This research focuses on those inequalities by identifying the spatial [...] Read more.
Sub-Saharan African cities are expanding horizontally, demonstrating spatial patterns of urban sprawl and socioeconomic segregation. An important research gap around the geographies of urban populations is that city-wide analyses mask local socioeconomic inequalities. This research focuses on those inequalities by identifying the spatial settlement patterns of socioeconomic groups within the Greater Kampala Metropolitan Area (Uganda). Findings are based on a novel dataset, an extensive household survey with 541 households, conducted in Kampala in 2019. To identify different socioeconomic groups, a k-prototypes clustering method was applied to the survey data. A maximum likelihood classification method was applied on a recent Landsat-8 image of the city and compared to the socioeconomic clustering through a fuzzy error matrix. The resulting maps show how different socioeconomic clusters are located around the city. We propose a simple method to upscale household survey responses to a larger study area, to use these data as a base map for further analysis or urban planning purposes. Obtaining a better understanding of the spatial variability in socioeconomic dynamics can aid urban policy-makers to target their decision-making processes towards a more favorable and sustainable future. Full article
(This article belongs to the Special Issue Remote Sensing Application to Population Mapping)
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