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LIU Tingxiang, ZHANG Shuwen, XU Xinliang, BU Kun, NING Jia, CHANG Liping. High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia[J]. Chinese Geographical Science, 2016, 26(4): 429-438. doi: 10.1007/s11769-016-0819-9
Citation: LIU Tingxiang, ZHANG Shuwen, XU Xinliang, BU Kun, NING Jia, CHANG Liping. High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia[J]. Chinese Geographical Science, 2016, 26(4): 429-438. doi: 10.1007/s11769-016-0819-9
shu

High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia

doi: 10.1007/s11769-016-0819-9
  • 1.

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;

  • 2.

    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Funds:  Under the auspices of National Natural Science Foundation of China (No. 41271416), Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA05090310)
More Information
  • Corresponding author: ZHANG Shuwen
  • Received Date: 2015-07-14
  • Rev Recd Date: 2015-11-06
  • Publish Date: 2016-08-27
  • Land cover is recognized as one of the fundamental terrestrial datasets required in land system change and other ecosystem related researches across the globe. The regional differentiation and spatial-temporal variation of land cover has significant impact on regional natural environment and socio-economic sustainable development. Under this context, we reconstructed the history land cover data in Siberia to provide a comparable datasets to the land cover datasets in China and abroad. In this paper, the European Space Agency (ESA) Global Land Cover Map (GlobCover), Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), Multispectral Scanner (MSS) images, Google Earth images and other additional data were used to produce the land cover datasets in 1975 and 2010 in Siberia. Data evaluation show that the total user's accuracy of land cover data in 2010 was 86.96%, which was higher than ESA GlobCover data in Siberia. The analysis on the land cover changes found that there were no big land cover changes in Siberia from 1975 to 2010 with only a few conversions between different natural forest types. The mainly changes are the conversion from deciduous needleleaf forest to deciduous broadleaf forest, deciduous needleleaf forest to mixed forest, savannas to deciduous needleleaf forest etc., indicating that the dominant driving factor of land cover changes in Siberia was natural element rather than human activities at some extent, which was very different from China. However, our purpose was not just to produce the land cover datasets at two time period or explore the driving factors of land cover changes in Siberia, we also paid attention on the significance and application of the datasets in various fields such as global climate change, geopolitics, cross-border cooperation and so on.
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High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia

doi: 10.1007/s11769-016-0819-9
  • 1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;
  • 2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41271416), Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA05090310)
    Corresponding author: ZHANG Shuwen
  • Received Date: 2015-07-14
  • Rev Recd Date: 2015-11-06
  • Publish Date: 2016-08-27

Abstract: Land cover is recognized as one of the fundamental terrestrial datasets required in land system change and other ecosystem related researches across the globe. The regional differentiation and spatial-temporal variation of land cover has significant impact on regional natural environment and socio-economic sustainable development. Under this context, we reconstructed the history land cover data in Siberia to provide a comparable datasets to the land cover datasets in China and abroad. In this paper, the European Space Agency (ESA) Global Land Cover Map (GlobCover), Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), Multispectral Scanner (MSS) images, Google Earth images and other additional data were used to produce the land cover datasets in 1975 and 2010 in Siberia. Data evaluation show that the total user's accuracy of land cover data in 2010 was 86.96%, which was higher than ESA GlobCover data in Siberia. The analysis on the land cover changes found that there were no big land cover changes in Siberia from 1975 to 2010 with only a few conversions between different natural forest types. The mainly changes are the conversion from deciduous needleleaf forest to deciduous broadleaf forest, deciduous needleleaf forest to mixed forest, savannas to deciduous needleleaf forest etc., indicating that the dominant driving factor of land cover changes in Siberia was natural element rather than human activities at some extent, which was very different from China. However, our purpose was not just to produce the land cover datasets at two time period or explore the driving factors of land cover changes in Siberia, we also paid attention on the significance and application of the datasets in various fields such as global climate change, geopolitics, cross-border cooperation and so on.

LIU Tingxiang, ZHANG Shuwen, XU Xinliang, BU Kun, NING Jia, CHANG Liping. High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia[J]. Chinese Geographical Science, 2016, 26(4): 429-438. doi: 10.1007/s11769-016-0819-9
Citation: LIU Tingxiang, ZHANG Shuwen, XU Xinliang, BU Kun, NING Jia, CHANG Liping. High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia[J]. Chinese Geographical Science, 2016, 26(4): 429-438. doi: 10.1007/s11769-016-0819-9
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