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Mapping annual global land cover changes at a 30 m resolution from 2000 to 2015

  • role： First author第一作者
  • Affiliation：

    School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China

  • Email：xuxiaocong@mail.sysu.edu.cn
  • Introduction：许晓聪，1989年生，男，副研究员，研究方向为土地利用变化与生态环境响应。E-mail： xuxiaocong@mail.sysu.edu.cn
XU Xiaocong1,  
  • Affiliation：

    School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China

LI Bingjie1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China

    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080 China

  • Email：liuxp3@mail.sysu.edu.cn
  • Introduction：刘小平，1978年生，男，教授，研究方向为土地利用模拟与生态环境响应。E-mail： liuxp3@mail.sysu.edu.cn
LIU Xiaoping12*,  
  • Affiliation：

    School of Geographic Sciences, East China Normal University, Shanghai 200241, China

LI Xia3,  
  • Affiliation：

    School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China

SHI Qian1

résumé

High-spatiotemporal-resolution global multi-class land cover data play a critical role in the studies of biogeochemical cycles of the Earth system and global climate change. Among the existing open products, higher-resolution global multi-class land cover data are available for a single or short period of time, while annual ones have limited class, preventing longer-term analysis of land cover change in fine spatial detail. Therefore, long time, high spatial resolution and high temporal frequency global land cover are needed.In this paper, with the support of the Google Earth Engine platform, we proposed a method composed of data fusion, change detection and machine learning based on existing global land cover maps in 2015, Landsat imagery and samples after manual interpretation to develop annual global land cover data (Annual Global Land Cover-2000-2015) from 2000 to 2015 at 30 m resolution. Based on AGLC-2000-2015 data, we selectively analyzed dynamic change of land cover in three typical regions (the Pearl River Delta of China, Selin Co lake of Tibetan Plateau and the Amazon rainforest of Brazil).Our results show relatively high accuracy of AGLC-2000-2015. Mean overall accuracy and Kappa coefficient of global land cover product for the year 2015 (AGLC-2015) are 76.10% and 0.72, respectively. The accuracies are considerably higher than that of existing global land covet products at 30m resolution, such as Globeland30 (OA=63.49%, Kappa=0.58), FROM-GLC (OA=61.41%, Kappa=0.55), and GLC-FCS30 (OA=63.46%, Kappa=0.57). The overall accuracy and Kappa coefficient of the Random Forest classification model are 84.10% and 0.81, respectively. In addition, the mean overall accuracy of Random Forest classification model at continental scale is more than 80.00%, showing that this model has good performance on global multi-class land cover mapping.We analyzed the land cover changes for the 2000—2015 period in three selected regions. Urban area has increased substantially by an average of 195.96 km2 in the Pearl River Delta of China, with 85% of the newly developed impervious surface encroaching on cropland. Selin Co lake responses significantly to the warming climate with expanding at a rate of 17.95 km2, and the expansion is most pronounced in the north bank. Forest in the south part of Amazon has decreased by a total area of 46356.53 km2, most of which is converted to cropland, showing forest destruction for cropland.AGLC-2000-2015 can effectively reflect the distribution and annual change of global land cover classes at 30 m resolution from 2000 to 2015, providing fundamental data for research and application related to land surface processes.

mots-clés

remote sensing;global land cover;30 m resolution;Landsat imagery;annual land cover change

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