Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-517-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-517-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Feb 2022
Data description paper |
| 08 Feb 2022
| 08 Feb 2022
A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Beijing Normal University, Beijing, 100875, China
Key Laboratory of Environmental Change and Natural Disaster, Beijing
Normal University, Beijing, 100875, China
Center for Geodata and Analysis, Faculty of Geographical Science,
Beijing Normal University, Beijing, 100875, China
Changxiu Cheng
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Beijing Normal University, Beijing, 100875, China
Key Laboratory of Environmental Change and Natural Disaster, Beijing
Normal University, Beijing, 100875, China
Center for Geodata and Analysis, Faculty of Geographical Science,
Beijing Normal University, Beijing, 100875, China
Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA 50011, USA
Xuecao Li
College of Land Science and Technology, China Agricultural
University, Beijing, 100083, China
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Beijing Normal University, Beijing, 100875, China
Key Laboratory of Environmental Change and Natural Disaster, Beijing
Normal University, Beijing, 100875, China
Center for Geodata and Analysis, Faculty of Geographical Science,
Beijing Normal University, Beijing, 100875, China
Changqing Song
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Beijing Normal University, Beijing, 100875, China
Center for Geodata and Analysis, Faculty of Geographical Science,
Beijing Normal University, Beijing, 100875, China
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Preprint withdrawn
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Antroposphere – Urban Environment
The Bellinge data set: open data and models for community-wide urban drainage systems research
An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration
Agnethe Nedergaard Pedersen, Jonas Wied Pedersen, Antonio Vigueras-Rodriguez, Annette Brink-Kjær, Morten Borup, and Peter Steen Mikkelsen
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A comprehensive data set from a combined sewer system in a 1.7 km2 suburban area is presented. Up to 10 years of observations (2010–2020) from level sensors, a flow meter, position and power sensors, rain gauges, X- and C-band weather radars, and a weather station; distributed hydrodynamic models; and CCTV pipe network data are included. This will enable independent testing and replication of results from future scientific developments within urban hydrology and urban drainage system research.
Zuoqi Chen, Bailang Yu, Chengshu Yang, Yuyu Zhou, Shenjun Yao, Xingjian Qian, Congxiao Wang, Bin Wu, and Jianping Wu
Earth Syst. Sci. Data, 13, 889–906, https://doi.org/10.5194/essd-13-889-2021, https://doi.org/10.5194/essd-13-889-2021, 2021
Short summary
Short summary
An extended time series (2000–2018) of NPP-VIIRS-like nighttime light (NTL) data was proposed through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and NPP-VIIRS NTL data (2013–2018). Compared with the annual composited NPP-VIIRS NTL data, our extended NPP-VIIRS-like NTL data have a high accuracy and also show a good spatial pattern and temporal consistency. It could be a useful proxy to monitor the dynamics of urbanization for a longer time period compared to existing NTL data.
Cited articles
Cao, Q., Yu, D., Georgescu, M., Wu, J., and Wang, W.: Impacts of future
urban expansion on summer climate and heat-related human health in eastern
China, Environ. Int., 112, 134–146,
https://doi.org/10.1016/j.envint.2017.12.027, 2018.
Center for International Earth Science Information Network:
Gridded Population of the World, Version 4 (GPWv4): Population Count,
Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC)
[data set], https://doi.org/10.7927/H4JW8BX5, 2018.
Chen, J., Chen, J., Liao, A. P., Cao, X., Chen, L. J., Chen, X. H., He, C.
Y., Han, G., Peng, S., Lu, M., Zhang, W. W., Tong, X. H., and Mills, J.:
Global land cover mapping at 30 m resolution: A POK-based operational
approach, ISPRS J. Photogramm., 103, 7–27,
https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Chen, Y. M., Li, X., Wang, S. J., Liu, X. P., and Ai, B.: Simulating Urban
Form and Energy Consumption in the Pearl River Delta Under Different
Development Strategies, Ann. Assoc. Am. Geogr., 103, 1567–1585,
https://doi.org/10.1080/00045608.2012.740360, 2013.
Chen, Z. Q., Yu, B. L., Song, W., Liu, H. X., Wu, Q. S., Shi, K. F., and Wu,
J. P.: A New Approach for Detecting Urban Centers and Their Spatial
Structure With Nighttime Light Remote Sensing, IEEE T.
Geosci. Remote, 55, 6305–6319, https://doi.org/10.1109/tgrs.2017.2725917,
2017.
Corbane, C., Florczyk, A., Pesaresi, M., Politis, P., and Syrris, V.: GHS-BUILT R2018A – GHS built-up grid, derived from Landsat, multitemporal (1975–1990–2000–2014), European Commission, Joint Research Centre (JRC) [data set], https://doi.org/10.2905/jrc-ghsl-10007, 2018.
Defries, R. S., Bounoua, L., and Collatz, G. J.: Human modification of the
landscape and surface climate in the next fifty years, Glob. Change
Biol., 8, 438–458, 2002.
Elvidge, C. D., Daniel, Z., Baugh, K. E., Tuttle, B. T., and Mikhail, Z.: A
Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite
Data, Energies, 2, 595–622, https://doi.org/10.3390/en20300595,
2009.
Esch, T., Marconcini, M., Felbier, A., Roth, A., Heldens, W., Huber, M.,
Schwinger, M., Taubenbock, H., Muller, A., and Dech, S.: Urban Footprint
Processor-Fully Automated Processing Chain Generating Settlement Masks From
Global Data of the TanDEM-X Mission, IEEE Geosci. Remote S., 10,
1617–1621, https://doi.org/10.1109/lgrs.2013.2272953, 2013.
Florczyk, A. J., Melchiorri, M., Corbane, C., Schiavina, M., Maffenini, M., Pesaresi, M., Politis, P., Sabo, S., Freire, S., Ehrlich, D., Kemper, T., Tommasi, P., Airaghi, D., and Zanchetta, L.: Description of the GHS Urban Centre Database 2015, Publications Office of the European Union [data set],
https://doi.org/10.2760/037310, 2019.
Gong, P., Wang, J., Yu, L., Zhao, Y. C., Zhao, Y. Y., Liang, L., Niu, Z. G.,
Huang, X. M., Fu, H. H., Liu, S., Li, C. C., Li, X. Y., Fu, W., Liu, C. X.,
Xu, Y., Wang, X. Y., Cheng, Q., Hu, L. Y., Yao, W. B., Zhang, H., Zhu, P.,
Zhao, Z. Y., Zhang, H. Y., Zheng, Y. M., Ji, L. Y., Zhang, Y. W., Chen, H.,
Yan, A., Guo, J. H., Yu, L., Wang, L., Liu, X. J., Shi, T. T., Zhu, M. H.,
Chen, Y. L., Yang, G. W., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z.
L., Chen, J., and Chen, J.: Finer resolution observation and monitoring of
global land cover: first mapping results with Landsat TM and ETM+ data,
Int. J. Remote Sens., 34, 2607–2654,
https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M. N., Li, C. C., Wang, J., Huang, H. B., Clinton,
N., Ji, L. Y., Li, W. Y., Bai, Y. Q., Chen, B., Xu, B., Zhu, Z. L., Yuan,
C., Suen, H. P., Guo, J., Xu, N., Li, W. J., Zhao, Y. Y., Yang, J., Yu, C.
Q., Wang, X., Fu, H. H., Yu, L., Dronova, I., Hui, F. M., Cheng, X., Shi, X.
L., Xiao, F. J., Liu, Q. F., and Song, L. C.: Stable classification with
limited sample: transferring a 30-m resolution sample set collected in 2015
to mapping 10-m resolution global land cover in 2017, Sci. Bull., 64,
370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B.,
Yang, J., Zhang, W., and Zhou, Y.: Annual maps of global artificial
impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236,
111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Guneralp, B., Zhou, Y., Urge-Vorsatz, D., Gupta, M., Yu, S., Patel, P. L.,
Fragkias, M., Li, X., and Seto, K. C.: Global scenarios of urban density and
its impacts on building energy use through 2050, P. Natl. Acad. Sci. USA,
114, 8945–8950, https://doi.org/10.1073/pnas.1606035114, 2017.
Homer, C., Dewitz, J., Yang, L. M., Jin, S., Danielson, P., Xian, G.,
Coulston, J., Herold, N., Wickham, J., and Megown, K.: Completion of the
2011 National Land Cover Database for the Conterminous United States –
Representing a Decade of Land Cover Change Information, Photogramm. Eng.
Remote Sens., 81, 345–354, https://doi.org/10.1016/S0099-1112(15)30100-2, 2015.
Hou, D., Meng, F., and Prishchepov, A. V.: How is urbanization shaping
agricultural land-use? Unraveling the nexus between farmland abandonment and
urbanization in China, Landscape Urban Plan., 214, 104170, https://doi.org/10.1016/j.landurbplan.2021.104170, 2021.
Huang, X., Li, J., Yang, J., Zhang, Z., Li, D., and Liu, X.: 30 m global
impervious surface area dynamics and urban expansion pattern observed by
Landsat satellites: From 1972 to 2019, Sci. China Earth Sci., 64, 1922–1933,
https://doi.org/10.1007/s11430-020-9797-9, 2021.
Jiang, L., Deng, X., and Seto, K. C.: The impact of urban expansion on
agricultural land use intensity in China, Land Use Policy, 35, 33–39,
https://doi.org/10.1016/j.landusepol.2013.04.011, 2013.
Kamarajugedda, S. A., Mandapaka, P. V., and Lo, E. Y. M.: Assessing urban
growth dynamics of major Southeast Asian cities using night-time light data,
Int. J. Remote Sens., 38, 6073–6093,
https://doi.org/10.1080/01431161.2017.1346846, 2017.
Kuang, W. H.: 70 years of urban expansion across China: trajectory, pattern,
and national policies, Sci. Bull., 65, 1970–1974,
https://doi.org/10.1016/j.scib.2020.07.005, 2020.
Kuang, W. H., Zhang, S., Li, X., and Lu, D.: A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018, Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, 2021a.
Kuang, W. H., Du, G. M., Lu, D. S., Dou, Y. Y., Li, X. Y., Zhang, S., Chi,
W. F., Dong, J. W., Chen, G. S., Yin, Z. R., Pan, T., Hamdi, R., Hou, Y. L.,
Chen, C. Y., Li, H., and Miao, C.: Global observation of urban expansion and
land-cover dynamics using satellite big-data, Sci. Bull., 66, 297–300,
https://doi.org/10.1016/j.scib.2020.10.022, 2021b.
Lawler, J. J., Lewis, D. J., Nelson, E., Plantinga, A. J., Polasky, S.,
Withey, J. C., Helmers, D. P., Martinuzzi, S., Pennington, D., and Radeloff,
V. C.: Projected land-use change impacts on ecosystem services in the United States, P. Natl. Acad. Sci. USA, 111, 7492–7497, https://doi.org/10.1073/pnas.1405557111,
2014.
Li, X. and Gong, P.: Urban growth models: progress and perspective, Sci.
Bull., 61, 1637–1650, https://doi.org/10.1007/s11434-016-1111-1, 2016.
Li, X., Zhou, Y., Eom, J., Yu, S., and Asrar, G. R.: Projecting Global Urban
Area Growth Through 2100 Based on Historical Time Series Data and Future
Shared Socioeconomic Pathways, Earth's Future, 7, 351–362,
https://doi.org/10.1029/2019ef001152, 2019.
Li, X., Zhou, Y., Zhao, M., and Zhao, X.: A harmonized global nighttime
light dataset 1992–2018, Sci. Data, 7, 168, https://doi.org/10.1038/s41597-020-0510-y, 2020a.
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y.,
Xu, B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P.,
Li, X., Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping
global urban boundaries from the global artificial impervious area (GAIA)
data, Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3,
2020b.
Li, X., Zhou, Y., Zhao, M., and Zhao, X.: Harmonization of DMSP and VIIRS
nighttime light data from 1992–2020 at the global scale, figshare [data set],
https://doi.org/10.6084/m9.figshare.9828827.v5, 2021.
Li, X. C. and Zhou, Y. Y.: A Stepwise Calibration of Global DMSP/OLS Stable
Nighttime Light Data (1992–2013), Remote Sens., 9, 637, https://doi.org/10.3390/rs9060637,
2017a.
Li, X. C. and Zhou, Y. Y.: Urban mapping using DMSP/OLS stable night-time
light: a review, Int. J. Remote Sens., 38, 6030–6046,
https://doi.org/10.1080/01431161.2016.1274451, 2017b.
Li, X. C., Gong, P., and Liang, L.: A 30-year (1984–2013) record of annual
urban dynamics of Beijing City derived from Landsat data, Remote Sens.
Environ., 166, 78–90, https://doi.org/10.1016/j.rse.2015.06.007, 2015.
Liang, L., Wang, Z., and Li, J.: The effect of urbanization on environmental
pollution in rapidly developing urban agglomerations, J. Clean.
Product., 237, 117649,
https://doi.org/10.1016/j.jclepro.2019.117649, 2019.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K.,
Ziegler, A. D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S.,
Wu, Q., Huang, K., Estes, L., and Zeng, Z.: High-spatiotemporal-resolution
mapping of global urban change from 1985 to 2015, Nat. Sustain., 3,
564–570, https://doi.org/10.1038/s41893-020-0521-x, 2020.
Liu, X. P., Hu, G. H., Chen, Y. M., Li, X., Xu, X. C., Li, S. Y., Pei, F.
S., and Wang, S. J.: High-resolution multi-temporal mapping of global urban
land using Landsat images based on the Google Earth Engine Platform, Remote
Sens. Environ., 209, 227–239, https://doi.org/10.1016/j.rse.2018.02.055, 2018.
Liu, Z. F., He, C. Y., Zhou, Y. Y., and Wu, J. G.: How much of the world's
land has been urbanized, really? A hierarchical framework for avoiding
confusion, Landscape Ecol., 29, 763–771, https://doi.org/10.1007/s10980-014-0034-y, 2014.
Lu, X., Lin, C., Li, W., Chen, Y., Huang, Y., Fung, J. C. H., and Lau, A. K.
H.: Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in
China and the role of urbanization in aggravating the health burden, The
Sci. Total Environ., 652, 683–695,
https://doi.org/10.1016/j.scitotenv.2018.10.140, 2019.
Ma, T.: Multi-Level Relationships between Satellite-Derived Nighttime
Lighting Signals and Social Media-Derived Human Population Dynamics, Remote
Sens., 10, 1128,
https://doi.org/10.3390/rs10071128, 2018.
Ma, T., Zhou, Y. K., Zhou, C. H., Haynie, S., Pei, T., and Xu, T.:
Night-time light derived estimation of spatio-temporal characteristics of
urbanization dynamics using DMSP/OLS satellite data, Remote Sens. Environ.,
158, 453–464, https://doi.org/10.1016/j.rse.2014.11.022, 2015.
Ma, T., Yin, Z., and Zhou, A.: Delineating Spatial Patterns in Human
Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition
Approach, Remote Sens., 10, 465, https://doi.org/10.3390/rs10030465, 2018.
Parvati, K., Rao, B. S. P., and Das, M. M.: Image Segmentation Using
Gray-Scale Morphology and Marker-Controlled Watershed Transformation,
Discrete Dyn. Nat. Soc., 2008, 384346,
https://doi.org/10.1155/2008/384346, 2008.
Qiang, Z., He, K., and Hong, H.: Policy: Cleaning China's air, Nature, 484,
161–162, 2012.
Schneider, A., Friedl, M. A., and Potere, D.: A new map of global urban
extent from MODIS satellite data, Environ. Res. Lett., 4, 044003,
https://doi.org/10.1088/1748-9326/4/4/044003, 2009.
Schneider, A., Friedl, M. A., and Potere, D.: Mapping global urban areas
using MODIS 500-m data: New methods and datasets based on “urban
ecoregions”, Remote Sens. Environ., 114, 1733–1746,
10.1016/j.rse.2010.03.003, 2010.
Seto, K. C., Giineralp, B., and Hutyra, L. R.: Global forecasts of urban
expansion to 2030 and direct impacts on biodiversity and carbon pools,
P. Natl. Acad. Sci. USA, 109, 16083–16088, 2012.
Solecki, W., Seto, K. C., and Marcotullio, P. J.: It's Time for an
Urbanization Science, Environment, 55, 12–17, https://doi.org/10.1080/00139157.2013.748387, 2013.
Su, Y. X., Chen, X. Z., Wang, C. Y., Zhang, H. O., Liao, J. S., Ye, Y. Y.,
and Wang, C. J.: A new method for extracting built-up urban areas using
DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta,
southern China, Geosci. Remote Sens., 52, 218–238,
https://doi.org/10.1080/15481603.2015.1007778, 2015.
Taubenbock, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., and Dech, S.:
Monitoring urbanization in mega cities from space, Remote Sens. Environ.,
117, 162–176, https://doi.org/10.1016/j.rse.2011.09.015, 2012.
Wang, Y. P. and Shen, Z. H.: Comparing Luojia 1-01 and VIIRS Nighttime Light
Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel
Density Estimation, Remote Sens., 13, 1574,
https://doi.org/10.3390/rs13081574, 2021.
Yang, J., Siri, J. G., Remais, J. V., Cheng, Q., Zhang, H., Chan, K. K. Y.,
Sun, Z., Zhao, Y., Cong, N., Li, X., Zhang, W., Bai, Y., Bi, J., Cai, W.,
Chan, E. Y. Y., Chen, W., Fan, W., Fu, H., He, J., Huang, H., Ji, J. S.,
Jia, P., Jiang, X., Kwan, M.-P., Li, T., Li, X., Liang, S., Liang, X.,
Liang, L., Liu, Q., Lu, Y., Luo, Y., Ma, X., Schwartländer, B., Shen,
Z., Shi, P., Su, J., Wu, T., Yang, C., Yin, Y., Zhang, Q., Zhang, Y., Zhang,
Y., Xu, B., and Gong, P.: The Tsinghua–Lancet Commission on Healthy Cities
in China: unlocking the power of cities for a healthy China, Lancet,
391, 2140–2184, https://doi.org/10.1016/S0140-6736(18)30486-0,
2018.
Yu, L., Liu, X. X., Zhao, Y. Y., Yu, C. Q., and Gong, P.: Difficult to map
regions in 30 m global land cover mapping determined with a common
validation dataset, Int. J. Remote Sens., 39, 4077–4087,
https://doi.org/10.1080/01431161.2018.1455238, 2018.
Zhao, M., Zhou, Y., Li, X., Cao, W., He, C., Yu, B., Li, X., Elvidge, C. D., Cheng, W., and Zhou, C.: Applications of
Satellite Remote Sensing of Nighttime Light Observations: Advances,
Challenges, and Perspectives, Remote Sens., 11, 1971, https://doi.org/10.3390/rs11171971,
2019.
Zhao, M., Cheng, W., Zhou, C., Li, M., Huang, K., and Wang, N.: Assessing
Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia
Using Time Series of DMSP/OLS Nighttime Light Data, Remote Sens., 10, 47,
https://doi.org/10.3390/rs10010047, 2018.
Zhao, M., Zhou, Y., Li, X., Zhou, C., Cheng, W., Li, M., and Huang, K.:
Building a Series of Consistent Night-Time Light Data (1992–2018) in
Southeast Asia by Integrating DMSP-OLS and NPP-VIIRS, IEEE T.
Geosci. Remote, 58, 1843–1856, https://doi.org/10.1109/tgrs.2019.2949797,
2020a.
Zhao, M., Zhou, Y., Li, X., Cheng, W., Zhou, C., Ma, T., Li, M., and Huang,
K.: Mapping urban dynamics (1992–2018) in Southeast Asia using consistent
nighttime light data from DMSP and VIIRS, Remote Sens. Environ., 248,
111980, https://doi.org/10.1016/j.rse.2020.111980, 2020b.
Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., and Song, C.: A global
dataset of annual urban extents (1992–2020) from harmonized nighttime lights,
figshare [data set], https://doi.org/10.6084/m9.figshare.16602224.v1, 2021.
Zhao, X., Li, X., Zhou, Y. Y., and Li, D. R.: Analyzing Urban Spatial
Connectivity Using Night Light Observations: A Case Study of Three
Representative Urban Agglomerations in China, IEEE J. Sel.
Top. Appl., 13, 1097–1108,
https://doi.org/10.1109/jstars.2020.2980514, 2020.
Zheng, Q. M., Weng, Q. H., Huang, L. Y., Wang, K., Deng, J. S., Jiang, R.
W., Ye, Z. R., and Gan, M. Y.: A new source of multi-spectral high spatial
resolution night-time light imagery-JL1-3B, Remote Sens. Environ., 215,
300–312, https://doi.org/10.1016/j.rse.2018.06.016, 2018.
Zhou, Y., Li, X., Asrar, G. R., Smith, S. J., and Imhoff, M.: A global
record of annual urban dynamics (1992–2013) from nighttime lights, Remote
Sens. Environ., 219, 206–220, https://doi.org/10.1016/j.rse.2018.10.015, 2018.
Zhou, Y., Smith, S. J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty,
B., Asrar, G. R., Zhang, X., He, C., and Elvidge, C. D.: A global map of
urban extent from nightlights, Environ. Res. Lett., 10, 054011,
https://doi.org/10.1088/1748-9326/10/5/054011, 2015.
Zhou, Y. Y., Smith, S. J., Elvidge, C. D., Zhao, K. G., Thomson, A., and
Imhoff, M.: A cluster-based method to map urban area from DMSP/OLS
nightlights, Remote Sens. Environ., 147, 173–185, https://doi.org/10.1016/j.rse.2014.03.004,
2014.
Zhu, Z., Zhou, Y., Seto, K. C., Stokes, E. C., Deng, C., Pickett, S. T. A.,
and Taubenböck, H.: Understanding an urbanizing planet: Strategic
directions for remote sensing, Remote Sens. Environ., 228, 164–182,
https://doi.org/10.1016/j.rse.2019.04.020, 2019.
Short summary
We generated a unique dataset of global annual urban extents (1992–2020) using consistent nighttime light observations and analyzed global urban dynamics over the past 3 decades. Evaluations using other urbanization-related ancillary data indicate that the derived urban areas are reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. This dataset can provide unique information for studying urbanization and its impacts.
We generated a unique dataset of global annual urban extents (1992–2020) using consistent...
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