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Global cultivated land mapping at 30 m spatial resolution

Science China Earth Sciences 2016年第12期59卷 2275-2284页

作者： CAO Xin CHEN XueHong ZHANG WeiWei LIAO AnPing CHEN LiJun CHEN ZhiGang CHEN Jin State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University National Geomatics Center of China College of Geomatics Anhui University of Science and Technology

Cultivated land is one of the most important types of land cover in the global mapping of land cover, and its variation influences economic development, food security, and ecological environment protection. Existing products of global cultivated land mapping have a low resolution, and high spatial resolution products are in demand. This study uses global remote sensing image datasets in 2000/2010 with a spatial resolution of 30 m(Landsat Tm/ETm+, HJ-1), mODIS 250 m NDVI time-serial data, and many types of reference data. An three-layer extraction method based on pixels, objects, and knowledge(POK) was adopted to ease cultivated land extraction in global-scale 30 m images, i.e., cultivated land classification based on pixel-scale multi-feature optimization, cultivated land automatic identification based on objects, and interactive object processing based on information service and priori knowledge. Global 30 m cultivated land mapping was accomplished for the two reference years(2000 and 2010), and statistical analysis was conducted on the data. Results showed that the total cultivated land area was 1.903 billion ha and 1.960 billion ha, respectively. Accuracy assessments showed that overall accuracy of global cultivated land mapping are higher than 92% for both the two reference years. The global cultivated land products in 2000/2010 developed in this research are superior to their international counterparts in terms of spatial resolution and classification accuracy. They also provide significant basic data on global food security, ecological environment supervision, and global change.

关键词： Cultivated land global 30 m resolution Remote sensing mapping NDVI time series

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