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A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia

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  • Xiulian Bai
  • Ram C. Sharma
  • Ryutaro Tateishi
  • Akihiko Kondoh
  • Bayaer Wuliangha
  • Gegen Tana

Abstract

Land use and land cover (LULC) change plays a key role in the process of land degradation and desertification in the Horqin Sandy Land, Inner Mongolia. This research presents a detailed and high-resolution (30 m) LULC change analysis over the past 16 years in Ongniud Banner, western part of the Horqin Sandy Land. The LULC classification was performed by combining multiple features calculated from the Landsat Archive products using the Support Vector Machine (SVM) based supervised classification approach. LULC maps with 17 secondary classes were produced for the year of 2000, 2009, and 2015 in the study area. The results showed that the multifeatures combination approach is crucial for improving the accuracy of the secondary-level LULC classification. The LULC change analyses over three different periods, 2000–2009, 2009–2015, and 2000–2015, identified significant changes as well as different trends of the secondary-level LULC in study area. Over the past 16 years, irrigated farming lands and salinized areas were expanded, whereas the waterbodies and sandy lands decreased. This implies increasing demand of water and indicates that the conservation of water resources is crucial for protecting the sensitive ecological zones in the Horqin Sandy Land.

Suggested Citation

  • Xiulian Bai & Ram C. Sharma & Ryutaro Tateishi & Akihiko Kondoh & Bayaer Wuliangha & Gegen Tana, 2017. "A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, January.
  • Handle: RePEc:hin:jnlmpe:1316505
    DOI: 10.1155/2017/1316505
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    Cited by:

    1. Andrew Clark & Stuart Phinn & Peter Scarth, 2023. "Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation," Land, MDPI, vol. 12(7), pages 1-25, June.

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