30 m annual land cover and its dynamics in China from 1990 to 2019

Abstract

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China Land Cover Dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics of China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China’s Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Using 335,709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5,463 visually-interpreted samples. A further assessment based on 5,131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC, and GlobaLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China’s LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease of cropland (−4.85 %) and grassland (−3.29 %), increase of forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g., Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at http://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

Urbanization Amplified Asymmetrical Changes of Rainfall and Exacerbated Drought: Analysis Over Five Urban Agglomerations in the Yangtze River Basin, China

“…The 30 m China's Land‐Use/Cover Datasets (CLUDs) from 1985 to 2019 was adopted in this study to characterize the urban expansion process (J. Yang & Huang, 2021). CLUD was generated by a random forest classifier using 335,709 Landsat images on the Google Earth Engine.…”

Large‐Scale Afforestation Enhances Precipitation by Intensifying the Atmospheric Water Cycle Over the Chinese Loess Plateau

“…This ensemble ET has a temporal coverage from 1982 to 2012 that is the intersected period shared by these datasets. In addition, the China Land Cover Data set (CLCD) was applied to reveal the LC changes in the LP over the past several decades (Yang & Huang, 2021). The CLCD is an annual LC product (1985, and covers China in its entirety with a fine spatial resolution of 30 m. We selected the CLCD data set in 1985 and 2015 to reflect the LC changes over the LP before and after the GFGP.…”

“…The JRA-55 reanalysis data set was obtained from the Research Data Archive of the National Center for Atmospheric Research via https://doi.org/10.5065/D60G3H5B (Japan Meteorological Agency, 2013). The CLCD data set was derived from the Zenodo (Yang & Huang, 2021) and can be accessed at https://zenodo.org/record/4417810 The CMFD precipitation and temperature datasets were provided by the National Tibetan Plateau Data Center (TPDC) of the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang & He, 2019) and can be accessed at https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file The National Earth System Science Data Center, National Science and Technology Infrastructure of China provided the FVC (https://doi. org/10.12041/geodata.GLASS_FVC_modis(0.05D).ver1.db; Liang, 2018), GLASS ET datasets (https://doi.…”

Processes and mechanisms of vegetation ecosystem responding to climate and ecological restoration in China

“…The land-use dataset used in this study was to obtain the forests area and it was operated on the Google Earth Engine platform, with a time scale of 1990 to 2018 and a spatial resolution of 30 m. For details, please refer to Yang and Huang (2021) . Long-term data on CO 2 emissions and population density were obtained from the China Energy Statistical Yearbook and China Demographic Yearbook respectively.…”