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Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 55623

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Special Issue Editors

Dr. Alessandro Sorichetta

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Guest Editor

School of Geography and Environmental Science, University of Southampton, Southampton, UK
Interests: GIS; remote sensing; human migration and mobility; epidemiology;urbanization; air quality; grondwater vulnerability

Dr. Andrea E. Gaughan

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Guest Editor

Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
Interests: human-environment dynamics; land systems; human population mapping; climate variability/change; remote sensing and geospatial analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Forrest R. Stevens

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Guest Editor

Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
Interests: integrated modeling; quantitative spatial analysis; human-environment and socio-ecological systems; natural resource management; land systems science; remote sensing; rural lands and livelihood

Special Issue Information

Dear Colleagues,

The regular and timely mapping and monitoring of human-settlements at multiple spatial levels, from local to global, is crucial for better understanding spatio-temporal variation of population distribution and supporting international frameworks such as the 2030 Agenda for Sustainable Development, Sendai Framework for Disaster Risk Reduction, Paris Agreement on Climate Change, and New Urban Agenda, as well as regional and local initiatives.

The increasing availability and quality of remote sensing data in recent years, alongside with an increasing computing power capability and storage capacity, has led to the production of an extensive range of valuable and accurate information regarding the characteristics, extent, and growth of human-settlement areas at various spatial and temporal resolutions.

This Special Issue presents an overview of the state-of-the-art of remote sensing-based products and methodologies addressing various aspects related to the presence of human-settlements including, but not limited to, identifying informal settlements, delineating urban/rural areas along with their transition zones mapping built-up areas and impervious surfaces, assessing infill, horizontal, and vertical urbanization, classifying building typologies, estimating building volumes, and modeling three-dimensional urban morphologies.

Critical and literature review contributions are welcomed, as well as perspective and research articles concerning inter-comparisons and validations or describing the use of novel sensors, remote sensing data, methods, and modeling approaches for mapping human-settlements, assessing their change over time, and predicting their dynamic nature and growth into the future.

Dr. Alessandro Sorichetta
Dr. Andrea E. Gaughan
Dr. Forrest R. Stevens
Guest Editors

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Published Papers (8 papers)

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Research

26 pages, 86078 KiB

Open AccessArticle

Global Harmonization of Urbanization Measures: Proceed with Care

by Deborah Balk, Stefan Leyk, Mark R. Montgomery and Hasim Engin

Remote Sens. 2021, 13(24), 4973; https://doi.org/10.3390/rs13244973 - 8 Dec 2021

Cited by 9 | Viewed by 4607

Abstract

By 2050, two-thirds of the world’s population is expected to be living in cities and towns, a marked increase from today’s level of 55 percent. If the general trend is unmistakable, efforts to measure it precisely have been beset with difficulties: the criteria defining urban areas, cities and towns differ from one country to the next and can also change over time for any given country. The past decade has seen great progress toward the long-awaited goal of scientifically comparable urbanization measures, thanks to the combined efforts of multiple disciplines. These efforts have been organized around what is termed the “statistical urbanization” concept, whereby urban areas are defined by population density, contiguity and total population size. Data derived from remote-sensing methods can now supply a variety of spatial proxies for urban areas defined in this way. However, it remains to be understood how such proxies complement, or depart from, meaningful country-specific alternatives. In this paper, we investigate finely resolved population census and satellite-derived data for the United States, Mexico and India, three countries with widely varying conceptions of urban places and long histories of debate and refinement of their national criteria. At the extremes of the urban–rural continuum, we find evidence of generally good agreement between the national and remote sensing-derived measures (albeit with variation by country), but identify significant disagreements in the middle ranges where today’s urban policies are often focused. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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25 pages, 18936 KiB

Open AccessArticle

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

by Johannes H. Uhl, Stefan Leyk, Zekun Li, Weiwei Duan, Basel Shbita, Yao-Yi Chiang and Craig A. Knoblock

Remote Sens. 2021, 13(18), 3672; https://doi.org/10.3390/rs13183672 - 14 Sep 2021

Cited by 21 | Viewed by 4800

Abstract

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. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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31 pages, 28098 KiB

Open AccessArticle

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

by Jamon Van Den Hoek and Hannah K. Friedrich

Remote Sens. 2021, 13(18), 3574; https://doi.org/10.3390/rs13183574 - 8 Sep 2021

Cited by 16 | Viewed by 4850

Abstract

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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22 pages, 6697 KiB

Open AccessArticle

Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam

by Khanh D. Ngo, Son V. Nghiem, Alex M. Lechner and Tuong T. Vu

Remote Sens. 2021, 13(13), 2439; https://doi.org/10.3390/rs13132439 - 22 Jun 2021

Cited by 1 | Viewed by 3159

Abstract

Mapping building structures is crucial for environmental change and impact assessment, and is especially important to accurately estimate fossil fuel CO₂ emissions from human settlements. In this regard, the objective of this study is to develop novel and robust methods using time-series data acquired from Sentinel-1 synthetic aperture radar (SAR) to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. From annual composites of SAR data in the two-dimensional VV-VH polarization space, we determined the VV-VH domain for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, we validated the methods and found that the results were accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The algorithm could also detect small houses in rural settlements and in small islands such as in Hòn Sơn and Hòn Tre. Over sea surfaces, the algorithm effectively identified lines of power poles connecting islands to the mainland, guard shacks in marine blood clam farms in Kiên Giang, individual wind towers in the off-shore wind farm in Bạc Liêu, and oilrigs in the Vũng Tàu oil fields. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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14 pages, 5075 KiB

Open AccessArticle

Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019

by Christopher D. Elvidge, Mikhail Zhizhin, Tilottama Ghosh, Feng-Chi Hsu and Jay Taneja

Remote Sens. 2021, 13(5), 922; https://doi.org/10.3390/rs13050922 - 2 Mar 2021

Cited by 279 | Viewed by 20724

Abstract

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm²/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm²/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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21 pages, 9397 KiB

Open AccessArticle

Towards a Large-Scale 3D Modeling of the Built Environment—Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

by Thomas Esch, Julian Zeidler, Daniela Palacios-Lopez, Mattia Marconcini, Achim Roth, Milena Mönks, Benjamin Leutner, Elisabeth Brzoska, Annekatrin Metz-Marconcini, Felix Bachofer, Sveinung Loekken and Stefan Dech

Remote Sens. 2020, 12(15), 2391; https://doi.org/10.3390/rs12152391 - 25 Jul 2020

Cited by 29 | Viewed by 5852

Abstract

Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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28 pages, 16193 KiB

Open AccessArticle

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

by Annett Bartsch, Georg Pointner, Thomas Ingeman-Nielsen and Wenjun Lu

Remote Sens. 2020, 12(15), 2368; https://doi.org/10.3390/rs12152368 - 23 Jul 2020

Cited by 30 | Viewed by 4802

Abstract

Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads which are typical for gas and oil fields. A combination of results from both GBM (Sentinel-1 and -2 combined) and DL (Sentinel-2; Sentinel-1 optional) is therefore suggested for circumpolar mapping. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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26 pages, 8355 KiB

Open AccessArticle

Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations

by Jeremiah J. Nieves, Maksym Bondarenko, Alessandro Sorichetta, Jessica E. Steele, David Kerr, Alessandra Carioli, Forrest R. Stevens, Andrea E. Gaughan and Andrew J. Tatem

Remote Sens. 2020, 12(10), 1545; https://doi.org/10.3390/rs12101545 - 12 May 2020

Cited by 4 | Viewed by 4366

Abstract

Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000–2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover “Urban Areas” class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011–2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of “urban” or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available “off-the-shelf” datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement expansion. Further, this framework shows promise for predicting near-future settlement extents and provides a foundation for forecasts further into the future. Full article

(This article belongs to the Special Issue Mapping Human-Settlements from, between, and beyond Remotely-Sensed Observations)

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