Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3623-2023
© Author(s) 2023. 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-15-3623-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
Wanru He
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Xuecao Li
CORRESPONDING AUTHOR
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Yuyu Zhou
CORRESPONDING AUTHOR
Department of Geography, The University of Hong Kong, Hong Kong SAR 999077, China
Urban Systems Institute, The University of Hong Kong, Hong Kong SAR 999077, China
Zitong Shi
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Guojiang Yu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Tengyun Hu
Beijing Municipal Institute of City Planning and Design, Beijing 100045, China
Yixuan Wang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Jianxi Huang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Tiecheng Bai
School of Information Engineering, Tarim University, Alaer 843300, China
Zhongchang Sun
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100101, China
Xiaoping Liu
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Peng Gong
Department of Geography, The University of Hong Kong, Hong Kong SAR 999077, China
Urban Systems Institute, The University of Hong Kong, Hong Kong SAR 999077, China
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Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
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Hao Jiang, Mengjun Ku, Xia Zhou, Qiong Zheng, Yangxiaoyue Liu, Jianhui Xu, Dan Li, Chongyang Wang, Jiayi Wei, Jing Zhang, Shuisen Chen, and Jianxi Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-44, https://doi.org/10.5194/essd-2025-44, 2025
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-432, https://doi.org/10.5194/essd-2024-432, 2025
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Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
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Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
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Min Zhao, Changxiu Cheng, Yuyu Zhou, Xuecao Li, Shi Shen, and Changqing Song
Earth Syst. Sci. Data, 14, 517–534, https://doi.org/10.5194/essd-14-517-2022, https://doi.org/10.5194/essd-14-517-2022, 2022
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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.
Bowen Cao, Le Yu, Xuecao Li, Min Chen, Xia Li, Pengyu Hao, and Peng Gong
Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, https://doi.org/10.5194/essd-13-5403-2021, 2021
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In the study, the first 1 km global cropland proportion dataset for 10 000 BCE–2100 CE was produced through the harmonization and downscaling framework. The mapping result coincides well with widely used datasets at present. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The dataset will be valuable for long-term simulations and precise analyses. The framework can be extended to specific regions or other land use types.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
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For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Cited articles
Acuto, M., Parnell, S., and Seto, K. C.:
Building a global urban science, Nat. Sustain., 1, 2–4, https://doi.org/10.1038/s41893-017-0013-9, 2018.
Alberti, M., Correa, C., Marzluff John, M., Hendry Andrew, P., Palkovacs Eric, P., Gotanda Kiyoko, M., Hunt Victoria, M., Apgar Travis, M., and Zhou, Y.:
Global urban signatures of phenotypic change in animal and plant populations, P. Natl. Acad. Sci. USA, 114, 8951–8956, https://doi.org/10.1073/pnas.1606034114, 2017.
Borrelli, P., Robinson, D. A., Panagos, P., Lugato, E., Yang, J. E., Alewell, C., Wuepper, D., Montanarella, L., and Ballabio, C.:
Land use and climate change impacts on global soil erosion by water (2015-2070), P. Natl. Acad. Sci. USA, 117, 21994–22001, https://doi.org/10.1073/pnas.2001403117, 2020.
Brown de Colstoun, E. C., Huang, C., Wang, P., Tilton, J. C., Tan, B., Phillips, J., Niemczura, S., Ling, P.-Y., and Wolfe, R. E.: Global Man-made Impervious Surface (GMIS) Dataset From Landsat, NASA Socioeconomic Data and Applications Center (SEDAC) [data set], https://doi.org/10.7927/H4P55KKF, 2017.
Castán Broto, V. and Bulkeley, H.:
A survey of urban climate change experiments in 100 cities, Global Environ. Chang., 23, 92–102, https://doi.org/10.1016/j.gloenvcha.2012.07.005, 2013.
Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., Xu, X., Liao, W., Qiu, Y. A., Wu, Q., and Huang, K.:
Global projections of future urban land expansion under shared socioeconomic pathways, Nat. Commun., 11, 537, https://doi.org/10.1038/s41467-020-14386-x, 2020.
Chen, J., Gong, P., He, C., Luo, W., Tamura, M., and Shi, P. J.:
Assessment of the urban development plan of Beijing by using a CA-based urban growth model, Photogramm. Eng. Rem. S., 68, 1063–1071, 2002.
Chen, Y., Li, X., Liu, X., and Ai, B.:
Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy, Int. J. Geogr. Inf. Sci., 28, 234–255, https://doi.org/10.1080/13658816.2013.831868, 2014.
Chen, Y., Li, X., Huang, K., Luo, M., and Gao, M.:
High-Resolution Gridded Population Projections for China Under the Shared Socioeconomic Pathways, Earths Future, 8, e2020EF001491, https://doi.org/10.1029/2020EF001491, 2020.
Dahal, K. R. and Chow, T. E.:
Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth, Int. J. Geogr. Inf. Sci., 29, 475–497, 2015.
DeFries, R. S., Rudel, T., Uriarte, M., and Hansen, M.:
Deforestation driven by urban population growth and agricultural trade in the twenty-first century, Nat. Geosci., 3, 178–181, https://doi.org/10.1038/ngeo756, 2010.
Doelman, J. C., Stehfest, E., Tabeau, A., van Meijl, H., Lassaletta, L., Gernaat, D. E. H. J., Hermans, K., Harmsen, M., Daioglou, V., Biemans, H., van der Sluis, S., and van Vuuren, D. P.:
Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation, Global Environ. Chang., 48, 119–135, https://doi.org/10.1016/j.gloenvcha.2017.11.014, 2018.
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., and Snyder, P. K.:
Global Consequences of Land Use, Science, 309, 570–574, https://doi.org/10.1126/science.1111772, 2005.
Gao, J. and O'Neill, B. C.:
Data-driven spatial modeling of global long-term urban land development: The SELECT model, Environ. Modell. Softw., 119, 458–471, https://doi.org/10.1016/j.envsoft.2019.06.015, 2019.
Gao, J. and O'Neill, B. C.:
Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways, Nat. Commun., 11, 2302, https://doi.org/10.1038/s41467-020-15788-7, 2020.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Gong, P., Liang, S., Carlton, E. J., Jiang, Q., Wu, J., Wang, L., and Remais, J. V.:
Urbanisation and health in China, Lancet, 379, 843–852, https://doi.org/10.1016/S0140-6736(11)61878-3, 2012.
Gong, P., Li, X., and Zhang, W.:
40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing, Sci. Bull., 64, 756–763, https://doi.org/10.1016/j.scib.2019.04.024, 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.
Güneralp, B., Lwasa, S., Masundire, H., Parnell, S., and Seto, K. C.:
Urbanization in Africa: challenges and opportunities for conservation, Environ. Res. Lett., 13, 015002, https://doi.org/10.1088/1748-9326/aa94fe, 2017.
He, W., Li, X., Zhou, Y., Shi, Z., Yu, G., Hu, T., Wang, Y., Huang, J., Bai, T., Wang, Y., Liu, X., and Gong, P.:
Global urban fractional changes at 1 km resolution under diverse SSP-RCP scenarios throughout 2100, figshare [data set], https://doi.org/10.6084/m9.figshare.20391117, 2022.
He, W., Li, X., Zhou, Y., Liu, X., Gong, P., Hu, T., Yin, P., Huang, J., Yang, J., Miao, S., Wang, X., and Wu, T.:
Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model, Cities, 133, 104146, https://doi.org/10.1016/j.cities.2022.104146, 2023.
Herold, M., Goldstein, N. C., and Clarke, K. C.:
The spatiotemporal form of urban growth: measurement, analysis and modeling, Remote Sens. Environ., 86, 286–302, https://doi.org/10.1016/S0034-4257(03)00075-0, 2003.
Hong, C., Burney, J. A., Pongratz, J., Nabel, J. E. M. S., Mueller, N. D., Jackson, R. B., and Davis, S. J.:
Global and regional drivers of land-use emissions in 1961–2017, Nature, 589, 554–561, https://doi.org/10.1038/s41586-020-03138-y, 2021.
Hosmer Jr., D. W., Lemeshow, S., and Sturdivant, R. X.: Applied logistic regression, John Wiley & Sons, https://doi.org/10.1002/9781118548387, 2013.
Hu, Z. and Lo, C. P.:
Modeling urban growth in Atlanta using logistic regression, Computers, Environment and Urban Systems, 31, 667–688, https://doi.org/10.1016/j.compenvurbsys.2006.11.001, 2007.
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, Science China Earth Sciences, 64, 1922–1933, https://doi.org/10.1007/s11430-020-9797-9, 2021.
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones, C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P., and Wang, Y. P.:
Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands, Climatic Change, 109, 117, https://doi.org/10.1007/s10584-011-0153-2, 2011.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.:
Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Klein Goldewijk, K., Beusen, A., and Janssen, P.:
Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1, Holocene, 20, 565–573, https://doi.org/10.1177/0959683609356587, 2010.
Kocabas, V. and Dragicevic, S.:
Assessing cellular automata model behaviour using a sensitivity analysis approach, Computers, Environment and Urban Systems, 30, 921–953, https://doi.org/10.1016/j.compenvurbsys.2006.01.001, 2006.
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. and Liu, X.:
Defining agents' behaviors to simulate complex residential development using multicriteria evaluation, J. Environ. Manage., 85, 1063–1075, https://doi.org/10.1016/j.jenvman.2006.11.006, 2007.
Li, X., Liu, X., and Yu, L.:
A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules, Int. J. Geogr. Inf. Sci., 28, 1317–1335, https://doi.org/10.1080/13658816.2014.883079, 2014.
Li, X., 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.
Li, X., Yu, L., Sohl, T., Clinton, N., Li, W., Zhu, Z., Liu, X., and Gong, P.:
A cellular automata downscaling based 1 km global land use datasets (2010–2100), Sci. Bull., 61, 1651–1661, https://doi.org/10.1007/s11434-016-1148-1, 2016.
Li, X., Chen, G., Liu, X., Liang, X., Wang, S., Chen, Y., Pei, F., and Xu, X.:
A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions, Ann. Am. Assoc. Geogr., 107, 1040–1059, https://doi.org/10.1080/24694452.2017.1303357, 2017.
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, Earths Future, 7, 351–362, https://doi.org/10.1029/2019EF001152, 2019a.
Li, X., Zhou, Y., Yu, S., Jia, G., Li, H., and Li, W.:
Urban heat island impacts on building energy consumption: A review of approaches and findings, Energy, 174, 407–419, https://doi.org/10.1016/j.energy.2019.02.183, 2019b.
Li, X., Zhou, Y., and Chen, W.:
An improved urban cellular automata model by using the trend-adjusted neighborhood, Ecol. Process., 9, 28, https://doi.org/10.1186/s13717-020-00234-9, 2020.
Li, X., Zhou, Y., Hejazi, M., Wise, M., Vernon, C., Iyer, G., and Chen, W.:
Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling, Communications Earth & Environment, 2, 201, https://doi.org/10.1038/s43247-021-00273-w, 2021.
Liao, J., Tang, L., Shao, G., Su, X., Chen, D., and Xu, T.:
Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations, Environ. Modell. Softw., 75, 163–175, 2016.
Liu, X., Li, X., Liu, L., He, J., and Ai, B.:
A bottom-up approach to discover transition rules of cellular automata using ant intelligence, Int. J. Geogr. Inf. Sci., 22, 1247–1269, https://doi.org/10.1080/13658810701757510, 2008.
Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., and Pei, F.:
A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects, Landscape Urban Plan., 168, 94–116, https://doi.org/10.1016/j.landurbplan.2017.09.019, 2017.
Liu, X., Hu, G., Ai, B., Li, X., Tian, G., Chen, Y., and Li, S.:
Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics, Int. J. Geogr. Inf. Sci., 32, 73–101, https://doi.org/10.1080/13658816.2017.1376065, 2018.
Liu, Y.:
Modelling sustainable urban growth in a rapidly urbanising region using a fuzzy-constrained cellular automata approach, Int. J. Geogr. Inf. Sci., 26, 151–167, https://doi.org/10.1080/13658816.2011.577434, 2012.
Liu, Y. and Phinn, S. R.:
Modelling urban development with cellular automata incorporating fuzzy-set approaches, Computers, Environment and Urban Systems, 27, 637–658, https://doi.org/10.1016/S0198-9715(02)00069-8, 2003.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W.:
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000.
Mu, H., Li, X., Zhou, Y., Gong, P., Huang, J., Du, X., Guo, J., Cao, W., Sun, Z., Xu, C., and Liu, D.:
Identifying discrepant regions in urban mapping from historical and projected global urban extents, All Earth, 34, 167–178, https://doi.org/10.1080/27669645.2022.2104990, 2022.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.:
The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Global Environ. Chang., 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Potere, D., Schneider, A., Angel, S., and Civco, D. L.:
Mapping urban areas on a global scale: which of the eight maps now available is more accurate?, Int. J. Remote Sens., 30, 6531–6558, https://doi.org/10.1080/01431160903121134, 2009.
Santé, I., García, A. M., Miranda, D., and Crecente, R.:
Cellular automata models for the simulation of real-world urban processes: A review and analysis, Landscape Urban Plan., 96, 108–122, https://doi.org/10.1016/j.landurbplan.2010.03.001, 2010.
Seto, K. C. and Ramankutty, N.:
Hidden linkages between urbanization and food systems, Science, 352, 943–945, https://doi.org/10.1126/science.aaf7439, 2016.
Seto, K. C., Güneralp, 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, https://doi.org/10.1073/pnas.1211658109, 2012.
Shi, L., Ling, F., Ge, Y., Foody, G. M., Li, X., Wang, L., Zhang, Y., and Du, Y.: Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016, Remote Sens., 9, 1148, https://doi.org/10.3390/rs9111148, 2017.
Song, X.-P., Sexton, J. O., Huang, C., Channan, S., and Townshend, J. R.: Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover, Remote Sens. Environ., 175, 1–13, https://doi.org/10.1016/j.rse.2015.12.027, 2016.
Sunde, M. G., He, H. S., Zhou, B., Hubbart, J. A., and Spicci, A.:
Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth, Landscape Urban Plan., 124, 104–108, https://doi.org/10.1016/j.landurbplan.2014.01.007, 2014.
United Nations:
World urbanization prospects: the 2018 revision, Department of Economic and Social Affairs, Population Division, New York, USA, 2019.
Verburg, P. H., Schulp, C. J. E., Witte, N., and Veldkamp, A.:
Downscaling of land use change scenarios to assess the dynamics of European landscapes, Agr. Ecosyst. Environ., 114, 39–56, https://doi.org/10.1016/j.agee.2005.11.024, 2006.
Wu, F.:
Calibration of stochastic cellular automata: the application to rural-urban land conversions, Int. J. Geogr. Inf. Sci., 16, 795–818, https://doi.org/10.1080/13658810210157769, 2002.
Wu, H., Zhou, L., Chi, X., Li, Y., and Sun, Y.:
Quantifying and analyzing neighborhood configuration characteristics to cellular automata for land use simulation considering data source error, Earth Sci. Inform., 5, 77–86, https://doi.org/10.1007/s12145-012-0097-8, 2012.
Wu, H., Li, Z., Clarke, K. C., Shi, W., Fang, L., Lin, A., and Zhou, J.:
Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change, Int. J. Geogr. Inf. Sci., 33, 1040–1061, 2019.
Zhou, Y., Varquez, A. C. G., and Kanda, M.:
High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model, Scientific Data, 6, 34, https://doi.org/10.1038/s41597-019-0048-z, 2019.
Zhou, Y., Li, X., Chen, W., Meng, L., Wu, Q., Gong, P., and Seto, K. C.:
Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South, P. Natl. Acad. Sci. USA, 119, e2214813119, https://doi.org/10.1073/pnas.2214813119, 2022.
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Most existing global urban products with future projections were developed in urban and...
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