Articles | Volume 13, issue 1
Earth Syst. Sci. Data, 13, 63–82, 2021
https://doi.org/10.5194/essd-13-63-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Earth Syst. Sci. Data, 13, 63–82, 2021
https://doi.org/10.5194/essd-13-63-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper 14 Jan 2021
Data description paper | 14 Jan 2021
A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018
Wenhui Kuang et al.
Related authors
Wenhui Kuang, Shu Zhang, Xiaoyong Li, and Dengsheng Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-65, https://doi.org/10.5194/essd-2019-65, 2019
Revised manuscript not accepted
Short summary
Short summary
Urban land use/cover dynamics datasets play a vital role in urban planning and management. However, a series of national urban land-cover data covering more than 15 years is relatively rare. Here we developed a new data subset called CLUD-Urban from 2000 to 2015 at five-year intervals with a 30 m resolution. The total urban area of China was 62800 km2 in 2015, with average fractions of 70.70 % and 26.54 % for ISA and UGS, respectively. CLUD-Urban will be useful in urban environment.
Wenhui Kuang, Shu Zhang, Xiaoyong Li, and Dengsheng Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-65, https://doi.org/10.5194/essd-2019-65, 2019
Revised manuscript not accepted
Short summary
Short summary
Urban land use/cover dynamics datasets play a vital role in urban planning and management. However, a series of national urban land-cover data covering more than 15 years is relatively rare. Here we developed a new data subset called CLUD-Urban from 2000 to 2015 at five-year intervals with a 30 m resolution. The total urban area of China was 62800 km2 in 2015, with average fractions of 70.70 % and 26.54 % for ISA and UGS, respectively. CLUD-Urban will be useful in urban environment.
Related subject area
Antroposhere - Land Cover and Land Use
A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps
Early-season mapping of winter wheat in China based on Landsat and Sentinel images
Key landscapes for conservation land cover and change monitoring, thematic and validation datasets for sub-Saharan Africa
Fine-grained, spatio-temporal datasets measuring 200 years of land development in the United States
Earth transformed: detailed mapping of global human modification from 1990 to 2017
A cultivated planet in 2010 – Part 1: The global synergy cropland map
Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform
Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016
ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
Short summary
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SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
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
Short summary
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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.
Zoltan Szantoi, Andreas Brink, Andrea Lupi, Claudio Mammone, and Gabriel Jaffrain
Earth Syst. Sci. Data, 12, 3001–3019, https://doi.org/10.5194/essd-12-3001-2020, https://doi.org/10.5194/essd-12-3001-2020, 2020
Short summary
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Larger ecological zones and wildlife corridors in sub-Saharan Africa require monitoring, as social and economic demands put high pressure on them. Copernicus’ Hot-Spot Monitoring service developed a satellite-imagery-based monitoring workflow to map such areas. Here, we present a total of 560 442 km2 from which 153 665 km2 is mapped with eight land cover classes while 406 776 km2 is mapped with up to 32 classes. Besides presenting the thematic products, we also present our validation datasets.
Johannes H. Uhl, Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, and Deborah Balk
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-217, https://doi.org/10.5194/essd-2020-217, 2020
Revised manuscript accepted for ESSD
Short summary
Short summary
Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior to the era of remote-sensing based earth observation. In this paper, we present datasets derived from a large, novel building stock database, enabling the spatially explicit analysis of 200 years of land development in the United States, at unprecedented spatial and temporal resolution. These datasets greatly facilitate long-term studies of socio-environmental systems in the conterminous United States.
David M. Theobald, Christina Kennedy, Bin Chen, James Oakleaf, Sharon Baruch-Mordo, and Joe Kiesecker
Earth Syst. Sci. Data, 12, 1953–1972, https://doi.org/10.5194/essd-12-1953-2020, https://doi.org/10.5194/essd-12-1953-2020, 2020
Short summary
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We developed a global, high-resolution dataset and quantified recent rates of land transformation and current patterns of human modification for 2017, globally. Briefly, we found that increased human activities and land use modification have caused 1.6 × 106 km2 of natural land to be lost between 1990 and 2015 and the rate of loss has increased over that time. While troubling, we believe these findings are invaluable to underpinning global and national discussions of conservation priorities.
Miao Lu, Wenbin Wu, Liangzhi You, Linda See, Steffen Fritz, Qiangyi Yu, Yanbing Wei, Di Chen, Peng Yang, and Bing Xue
Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, https://doi.org/10.5194/essd-12-1913-2020, 2020
Short summary
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Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
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The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Yidi Xu, Le Yu, Wei Li, Philippe Ciais, Yuqi Cheng, and Peng Gong
Earth Syst. Sci. Data, 12, 847–867, https://doi.org/10.5194/essd-12-847-2020, https://doi.org/10.5194/essd-12-847-2020, 2020
Short summary
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The first annual oil palm area dataset (AOPD) for Malaysia and Indonesia from 2001 to 2016 was produced by integrating multiple satellite datasets and a change-detection algorithm (BFAST). This dataset reveals that oil palm plantations have expanded from 5.69 to 19.05 M ha in the two countries during the past 16 years. The AOPD is useful in understanding the deforestation process in Southeast Asia and may serve as land-use change inputs in dynamic global vegetation models.
Yuchuan Luo, Zhao Zhang, Yi Chen, Ziyue Li, and Fulu Tao
Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, https://doi.org/10.5194/essd-12-197-2020, 2020
Short summary
Short summary
For the first time, we generated a 1 km gridded-phenology product for three staple crops in China during 2000–2015, called ChinaCropPhen1km. Compared with the phenological observations from the agricultural meteorological stations, the dataset had high accuracy, with errors of retrieved phenological date of less than 10 d. The well-validated dataset is sufficiently reliable for many applications, including improving the agricultural-system or earth-system modeling over a large area.
Cited articles
As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W., and Nuarsa, I. W.: Enhanced
built-up and bareness index (EBBI) for mapping built-up and bare land in an
urban area, Remote Sens., 4, 2957–2970,
https://doi.org/10.3390/rs4102957, 2012.
Bai, X., Shi, P., and Liu, Y.: Society: realizing China's urban dream,
Nature, 509, 158–160, https://doi.org/10.1038/509158a, 2014.
Bai, X., Dawson, R. J., Urge-Vorsatz, D., Delgado, G. C., Barau, A. S., Dhakal,
S., Dodman, D., Leonardsen, L., Masson-Delmotte, V., Roberts, D., and
Schultz, S.: Six research priorities for cities and climate change, Nature,
555, 19–21, https://doi.org/10.1038/d41586-018-02409-z, 2018.
Belward, A. (Ed.): The IGBP-DIS global 1 km land cover data set “DISCover”:
proposal and implementation plans. Report of the Land Cover Working Group of
the IGBP-DIS, IGBP-DIS Working Paper, No. 13, Stockholm, 1996.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X., 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.
Dong, J., Kuang, W., and Liu, J.: Continuous land cover change monitoring in
the remote sensing big data era, Sci. China Earth Sci., 60,
2223–2224, https://doi.org/10.1007/s11430-017-9143-3, 2017.
Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M., Roth, A., Zeidler, J.,
Dech, S., and Strano, E.: Breaking new ground in mapping human settlements from
space – the Global Urban Footprint, Isprs J. Photogramm., 134, 30–42,
https://doi.org/10.1016/j.isprsjprs.2017.10.012, 2017.
Esch, T., Bachofer, F., Heldens, W., Hirner, A., Marconcini, M., Palacios-Lopez,
D., Roth, A., Üreyen, S., Zeidler, J., Dech, S., and Gorelick, N.: Where we
live – a summary of the achievements and planned evolution of the global
urban footprint, Remote Sens., 10, 895, https://doi.org/10.3390/rs10060895,
2018.
Falcone, J. A. and Homer, C. G.: Generation of a U.S. national urban
land-use product, Photogramm. Eng. Rem. S., 78, 1057–1068,
https://doi.org/10.14358/PERS.78.10.1057, 2012.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: algorithm
refinements and characterization of new datasets, Remote Sens. Environ.,
114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang,
X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X.,
Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng,
Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu,
X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C.,
Clinton, N., Zhu, Z., 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., 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., Chen, B., Li, X., Liu, H., Wang, J., Bai, Y., Chen, J., Chen, X.,
Fang, L., and Feng, S.: Mapping essential urban land use categories in China
(EULUC-China): preliminary results for 2018, Sci. Bull., 65, 182–187,
https://doi.org/10.1016/j.scib.2019.12.007, 2020a.
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, 2020b.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27,
https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Grekousis, G., Mountrakis, G., and Kavouras, M.: An overview of 21 global
and 43 regional land-cover mapping products, Int. J. Remote Sens., 36,
5309–5335, https://doi.org/10.1080/01431161.2015.1093195, 2015.
Haase, D., Larondelle, N., Andersson, E., Artmann, M., Borgström, S.,
Breuste, J., Gomez-Baggethun, E., Gren, Å., Hamstead, Z., Hansen, R.,
Kabisch, N., Kremer, P., Langemeyer, J., Rall, E. L., McPhearson, T.,
Pauleit, S., Qureshi, S., Schwarz, N., Voigt, A., Wurster, D., and Elmqvist,
T.: A quantitative review of urban ecosystem service assessments: concepts,
models, and implementation, Ambio, 43, 413–433,
https://doi.org/10.1007/s13280-014-0504-0, 2014.
Hamdi, R. and Schayes, G.: Sensitivity study of the urban heat island intensity
to urban characteristics, Int. J. Climatol., 28, 973–982, https://doi.org/10.1002/joc.1598, 2007.
Hansen, M. C., Defries, R. S., Townshend, J. R. G., and Sohlberg, R.: Global
land cover classification at 1 km spatial resolution using a classification
tree approach, Int. J. Remote Sens., 21, 1331–1364,
https://doi.org/10.1080/014311600210209, 2000.
He, C., Liu, Z., Gou, S., Zhang, Q., Zhang, J., and Xu, L.: Detecting global urban
expansion over the last three decades using a fully convolutional network,
Environ. Res. Lett., 14, 34008, https://doi.org/10.1088/1748-9326/aaf936,
2019.
Huang, C., Yang, J., and Jiang, P.: Assessing impacts of urban form on landscape
structure of urban green spaces in China using Landsat images based on
Google Earth Engine, Environ. Res. Lett., 10, 054011,
https://doi.org/10.1088/1748-9326/10/5/054011, 2018.
Kuang, W.: Simulating dynamic urban expansion at regional scale in
Beijing-Tianjin-Tangshan Metropolitan Area, J. Geogr. Sci., 21, 317–330,
https://doi.org/10.1007/s11442-011-0847-4, 2011.
Kuang, W.: Evaluating impervious surface growth and its impacts on water
environment in Beijing-Tianjin-Tangshan metropolitan area, J. Geogr. Sci.,
22, 535–547, https://doi.org/10.1007/s11442-012-0945-y, 2012.
Kuang, W.: 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, 2020a.
Kuang, W.: National urban land-use/cover change since the beginning of the
21st century and its policy implications in China, Land Use Pol., 97,
104747, https://doi.org/10.1016/j.landusepol.2020.104747, 2020b.
Kuang, W. and Dou, Y.: Investigating the patterns and dynamics of urban green
space in China's 70 major cities using satellite remote sensing, Remote
Sens., 12, 1929, https://doi.org/10.3390/rs12121929, 2020.
Kuang, W. and Yan, F.: Urban structural evolution over a century in Changchun
city, Northeast China, J. Geogr. Sci., 28, 1877–1895,
https://doi.org/10.1007/s11442-018-1569-7, 2018.
Kuang, W., Liu, J., Zhang, Z., Lu, D., and Xiang, B.: Spatiotemporal
dynamics of impervious surface areas across China during the early 21st
century, Chin. Sci. Bull., 58, 1691–1701,
https://doi.org/10.1007/s11434-012-5568-2, 2013.
Kuang, W., Chi, W., Lu, D., and Dou, Y.: A comparative analysis of megacity
expansions in China and the U.S.: patterns, rates and driving forces,
Landscape Urban Plan., 132, 121–135,
https://doi.org/10.1016/j.landurbplan.2014.08.015, 2014.
Kuang, W., Dou, Y., Zhang, C., Chi, W., Liu, A., Liu, Y., Zhang, R., and
Liu, J.: Quantifying the heat flux regulation of metropolitan land use/land
cover components by coupling remote sensing modeling with in situ
measurement, J. Geophys. Res.-Atmos., 120, 113–130,
https://doi.org/10.1002/2014JD022249, 2015.
Kuang, W., Liu, J., Dong, J., Chi, W., and Zhang, C.: The rapid and massive
urban and industrial land expansions in China between 1990 and 2010: a
CLUD-based analysis of their trajectories, patterns, and drivers, Landscape
Urban Plan., 145, 21–33, https://doi.org/10.1016/j.landurbplan.2015.10.001,
2016.
Kuang, W., Yang, T., Liu, A., Zhang, C., Lu, D., and Chi, W.: An EcoCity
model for regulating urban land cover structure and thermal environment:
taking Beijing as an example, Sci. China Ser. D-Earth Sci., 60, 1098–1109,
https://doi.org/10.1007/s11430-016-9032-9, 2017.
Kuang, W., Yang, T., and Yan, F.: Examining urban land-cover characteristics and
ecological regulation during the construction of Xiong'an New District,
Hebei Province, China, J. Geogr. Sci., 28, 109–123,
https://doi.org/10.1007/s11442-018-1462-4, 2018.
Kuang, W., Zhang, S., Li, X., and Lu, D.: A 30-meter resolution dataset of
impervious surface area and green space fractions of China's cities,
2000–2018, Zenodo, https://doi.org/10.5281/zenodo.3778424, 2020a.
Kuang, W., Du, G., and Lu, D.: Global observation of urban expansion and
land-cover dynamics using satellite big-data, Sci. Bull., accepted,
https://doi.org/10.1016/j.scib.2020.10.022, 2020b.
Li, H., Wang, C., Zhong, C., Su, A., Xiong, C., Wang, J., and Liu, J.: Mapping
urban bare land automatically from Landsat imagery with a simple index,
Remote Sens., 9, 249, https://doi.org/10.3390/rs9030249, 2019.
Li, X., Zhou, Y., Zhu, Z., Liang, L., Yu, B., and Cao, W.: Mapping annual
urban dynamics (1985–2015) using time series of Landsat data, Remote Sens.
Environ., 216, 674–683, https://doi.org/10.1016/j.rse.2018.07.030, 2018.
Li, X., Zhou, Y., Zhu, Z., and Cao, W.: A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States, Earth Syst. Sci. Data, 12, 357–371, https://doi.org/10.5194/essd-12-357-2020, 2020.
Lin, Y., Zhang, H., Lin, H., Gamba, P. E., and Liu, X.: Incorporating synthetic
aperture radar and optical images to investigate the annual dynamics of
anthropogenic impervious surface at large scale, Remote Sens. Environ., 242,
111757, https://doi.org/10.1016/j.rse.2020.111757, 2020.
Liu, J., Liu, M., Tian, H., Zhuang, D., Zhang, Z., Zhang, W., Tang, X., and
Deng, X.: Spatial and temporal patterns of China's cropland during
1990–2000: an analysis based on Landsat TM data, Remote Sens. Environ., 98,
442–456, https://doi.org/10.1016/j.rse.2005.08.012, 2005a.
Liu, J., Liu, M., Zhuang, D., Zhang, Z., and Deng, X.: Study on spatial pattern
of land-use change in China during 1995–2000, Sci. China Ser. D-Earth Sci.,
46, 3732003, https://doi.org/10.1360/03yd9033, 2005b.
Liu, J., Zhang, Z., Xu, X., Kuang, W., Zhou, W., Zhang, S., Li, R., Yan, C.,
Yu, D., Wu, S., and Jiang, N.: Spatial patterns and driving forces of land
use change in China during the early 21st century, J. Geogr. Sci., 20,
483–494, https://doi.org/10.1007/s11442-010-0483-4, 2010.
Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang,
S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., and Chi,
W.: Spatiotemporal characteristics, patterns, and causes of land-use changes
in China since the late 1980s, J. Geogr. Sci., 24, 195–210,
https://doi.org/10.1007/s11442-014-1082-6, 2014.
Lu, D. and Weng, Q.: Spectral mixture analysis of the urban landscape in
Indianapolis with Landsat ETM plus imagery, Photogramm. Eng. Rem. S., 70,
1053–1062, https://doi.org/10.14358/PERS.70.9.1053, 2004.
Lu, D. and Weng, Q.: Use of impervious surface in urban land-use
classification, Remote Sens. Environ., 102, 146–160,
https://doi.org/10.1016/j.rse.2006.02.010, 2006.
Lu, D., Tian, H., Zhou, G., and Ge, H.: Regional mapping of human settlements in
southeastern China with multisensor remotely sensed data, Remote Sens.
Environ., 112, 3668–3679, https://doi.org/10.1016/j.rse.2008.05.009, 2008.
Lu, D., Li, G., Kuang, W., and Moran, E.: Methods to extract impervious
surface areas from satellite images, Int. J. Digit. Earth, 7, 93–112,
https://doi.org/10.1080/17538947.2013.866173, 2014.
Lu, D., Li, L., Li, G., Fan, P., Ouyang, Z., and Moran, E.: Examining spatial patterns
of urban distribution and impacts of physical conditions on urbanization in
coastal and inland metropoles, Remote Sens., 10, 1101,
https://doi.org/10.3390/rs10071101, 2018.
Ma, Q., He, C., Wu, J., Liu, Z., Zhang, Q., and Sun, Z.: Quantifying
spatiotemporal patterns of urban impervious surfaces in China: an improved
assessment using nighttime light data, Landscape Urban Plan., 130, 36–49,
https://doi.org/10.1016/j.landurbplan.2014.06.009, 2014.
Ning, J., Liu, J., Kuang, W., Xu, X., Zhang, S., Yan, C., Li, R., Wu, S., Hu, Y., Du,
G., Chi, W., Pan, T., and Ning, J.: Spatiotemporal patterns and characteristics of
land-use change in China during 2010–2015, J. Geogr. Sci., 28, 547–662,
https://doi.org/10.1007/s11442-018-1490-0, 2018.
Nowak, D. J. and Greenfield, E. J.: Tree and impervious cover in the United
States, Landscape Urban Plan., 107, 21–30,
https://doi.org/10.1016/j.landurbplan.2012.04.005, 2012.
Peng, J., Shen, H., Wu, W., Liu, Y., and Wang, Y.: Net primary productivity
(NPP) dynamics and associated urbanization driving forces in metropolitan
areas: a case study in Beijing City, China, Landscape Ecol., 31, 1077–1092,
https://doi.org/10.1007/s10980-015-0319-9, 2016.
Pesaresi, M., Huadong, G., Blaes, X., Ehrlich, D., Ferri, S., Gueguen, L., Halkia,
M., Kauffmann, M., Kemper, T., Lu, L., Marin-Herrera, M. A., Ouzounis, G. K.,
Scavazzon, M., Soille, P., Syrris, V., and Zanchetta, L.: A Global Human
Settlement Layer from optical hr/vhrrs data: concept and first results, Ieee
J-Stars., 6, 2102–2131, https://doi.org/10.1109/JSTARS.2013.2271445, 2013.
Reba, M. and Seto, K. C.: A systematic review and assessment of algorithms to
detect, characterize, and monitor urban land change, Remote Sens. Environ.,
242, 111739, https://doi.org/10.1016/j.rse.2020.111739, 2020.
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,
https://doi.org/10.1016/j.rse.2010.03.003, 2010.
Seto, K. C., Guneralp, 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.
Sexton, J. O., Song, X., Huang, C., Channan, S., Baker, M. E., and Townshend, J.
R.: Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region
from 1984 to 2010 by annual, Landsat-based estimates of impervious cover,
Remote Sens. Environ., 129, 42–53,
https://doi.org/10.1016/j.rse.2012.10.025, 2013.
Small, C. and Milesi, C.: Multi-scale standardized spectral mixture models,
Remote Sens. Environ., 136, 442–454,
https://doi.org/10.1016/j.rse.2013.05.024, 2013.
Wang, H., Lu, S., Wu, B., and Li, X.: Advances in remote sensing of
impervious surfaces extraction and its applications, Adv. Earth
Sci., 28, 327–336, 2013.
Weng, Q.: Remote sensing of impervious surfaces in the urban areas:
requirements, methods, and trends, Remote Sens. Environ., 117, 34–49,
https://doi.org/10.1016/j.rse.2011.02.030, 2012.
Weng, Q., Lu, D., and Schubring, J.: Estimation of land surface
temperature–vegetation abundance relationship for urban heat island
studies, Remote Sens. Environ., 89, 467–483,
https://doi.org/10.1016/j.rse.2003.11.005, 2004.
Wu, C. and Murray, A. T.: Estimating impervious surface distribution by
spectral mixture analysis, Remote Sens. Environ., 84, 493–505,
https://doi.org/10.1016/S0034-4257(02)00136-0, 2003.
Wu, J., Xiang, W., and Zhao, J.: Urban ecology in China: historical
developments and future directions, Landscape Urban Plan., 125, 222–233,
https://doi.org/10.1016/j.landurbplan.2014.02.010, 2014.
Xu, H.: Modification of normalised difference water index (NDWI) to enhance
open water features in remotely sensed imagery, Int. J. Remote Sens., 27,
3025–3033, https://doi.org/10.1080/01431160600589179, 2006.
Xu, J., Zhao, Y., Zhong, K., Zhang, F., Liu, X., and Sun, C.: Measuring
spatio-temporal dynamics of impervious surface in Guangzhou, China, from
1988 to 2015, using time-series Landsat imagery, Sci. Total Environ., 627,
264–281, https://doi.org/10.1016/j.scitotenv.2018.01.155, 2018.
Xu, X. and Min, X.: Quantifying spatiotemporal patterns of urban expansion
in China using remote sensing data, Cities, 35, 104–113,
https://doi.org/10.1016/j.cities.2013.05.002, 2013.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., Case, A.,
Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B., Liknes, G. C., Rigge,
M., and Xian, G.: A new generation of the United States National Land Cover
Database: Requirements, research priorities, design, and implementation
strategies, Isprs J. Photogramm., 146, 108–123,
https://doi.org/10.1016/j.isprsjprs.2018.09.006, 2018.
Zhang, C., Kuang, W., Wu, J., Liu, J., and Tian, H.: Industrial land
expansion in rural China threatens food and environmental securities, Front.
Env. Sci. Eng., 15, 29, https://doi.org/10.1007/s11783-020-1321-2, 2021.
Zhang, L. and Weng, Q.: Annual dynamics of impervious surface in the Pearl
River delta, China, from 1988 to 2013, using time series Landsat imagery,
ISPRS J. Photogramm., 113, 86–96,
https://doi.org/10.1016/j.isprsjprs.2016.01.003, 2016.
Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., and Zhang, B.: Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform, Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, 2020.
Zhang, Y., Odeh, I. O. A., and Han, C.: Bi-temporal characterization of land
surface temperature in relation to impervious surface area, NDVI and NDBI,
using a sub-pixel image analysis, Int. J. Appl. Earth. Obs., 11, 256–264,
https://doi.org/10.1016/j.jag.2009.03.001, 2009.
Zhang, Z., Wang, X., Zhao, X., Liu, B., Liu, F., Yi, L., Zuo, L., Wen, Q.,
Xu, J., and Hu, S.: A 2010 update of National Land Use/Cover Database of
China at 1:100 000 scale using medium spatial resolution satellite images,
Remote Sens. Environ., 149, 142–154,
https://doi.org/10.1016/j.rse.2014.04.004, 2014.
Zhang, Z., Wang, W., Cheng, M., Liu, S., Xu, J., He, Y., and Meng, F.: The
contribution of residential coal combustion to PM2.5 pollution over China's
Beijing-Tianjin-Hebei region in winter, Atmos. Environ., 159, 147–161,
https://doi.org/10.1016/j.atmosenv.2017.03.054, 2017.
Zhou, Y., Smith, J. S., Elvidge, C. D., Zhao, K., Thomson, A. M., and
Imhoff, L. 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.
Zhou, Y., Smith, J. S., Zhao, K., Imhoff, L. M., Thomson, A. M.,
Bondlamberty, B., Asrar, G., 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., 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.
Short summary
We propose a hierarchical principle for remotely sensed urban land use and land cover change for mapping intra-urban structure and component dynamics. China’s Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness and green surface conditions in cities from 2000 to 2018. The newly developed datasets can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and on urban dwellers' environments.
We propose a hierarchical principle for remotely sensed urban land use and land cover change for...
