Articles | Volume 13, issue 1
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.
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
| 14 Jan 2021
A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018
Wenhui Kuang
CORRESPONDING AUTHOR
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
Shu Zhang
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 10049, China
Xiaoyong Li
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 10049, China
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Dengsheng Lu
School of Geographical Sciences, Fujian Normal University, Fuzhou
350007, China
Fujian Provincial Key Laboratory of Subtropical Resources and
Environment, Fujian Normal University, Fuzhou 350007, China
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Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
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Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
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The study provided an annual 100 m resolution glimpse into the grazing activities across the Qinghai–Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
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This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
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We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
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Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
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Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
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Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou
Earth Syst. Sci. Data, 16, 3705–3718, https://doi.org/10.5194/essd-16-3705-2024, https://doi.org/10.5194/essd-16-3705-2024, 2024
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Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-279, https://doi.org/10.5194/essd-2024-279, 2024
Revised manuscript accepted for ESSD
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We present a global geospatial database of cropland and pastures representing the year 2015. We made it using machine learning models to merge satellite-based land cover data with agricultural census data that we compiled. This database is an update to an earlier version representing the year 2000. It can be used to study issues such as land use, food security, climate change and biodiversity loss. We provide a reproducible code base to easily update the product for future years.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
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We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
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Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
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The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
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In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
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The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
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We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
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Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
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We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
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This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
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To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
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To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
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Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
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.
Bontemps, S., Defourny, P., Bogaert, E., Arino, O., Kalogirou, V., and Perez, J.:
Globcover 2009. Products Description and Validation Reports, available at:
https://epic.awi.de/31014/16/GLOBCOVER2009_Validation_Report_2-2.pdf (last access:
18 April 2019), 2011.
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.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., and Wang, S.:
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.
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.
Wang, P., Huang, C., Brown de Colstoun, E. C., Tilton, J. C., and Tan, B.:
Global Human Built-up And Settlement Extent (HBASE) dataset from Landsat,
NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY,
https://doi.org/10.7927/H4DN434S, 2017.
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...
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