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Mapping Fences in Xilingol Grassland Using High Spatiotemporal Resolution Remote Sensing Data

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Geoinformatics and Data Analysis (ICGDA 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 143))

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Abstract

As a management tool, fences used in grasslands around the world are becoming increasingly ubiquitous, and the impact on wild species and ecosystems has attracted global attention. However, lacking of large scale accurate fence mapping data has become a limitation of monitoring the influence of fence on ecosystems. In this study, a method is proposed to identify fences based on fused high spatiotemporal resolution remote sensing data in Xilingol grassland, China. The study results indicate that fences as boundaries of adjacent pastures that could be mapped using the fused data by principal component analysis (PCA) feature extraction, multiresolution segmentation and cell edges removal. Tests in our study area for this method showed an overall accuracy of 81.75%, outperformed the single NDVI used result (65.95%).

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Authors and Affiliations

  1. Yunnan Normal University, Kunming, 650500, Yunnan, China

    Tao Liu & Shupeng Gao

  2. Inner Mongolia University of Technology, Hohhot, 010051, Inner Mongolia, China

    Xiaolong Liu & Libiao Guo

Authors
  1. Tao Liu
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  2. Xiaolong Liu
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  3. Libiao Guo
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  4. Shupeng Gao
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Corresponding author

Correspondence to Xiaolong Liu .

Editor information

Editors and Affiliations

  1. Multidimensional Signal Processing Group, Ecole Centrale Marseille, Marseille Cedex 13, France

    Salah Bourennane

  2. Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic

    Petr Kubicek

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Liu, T., Liu, X., Guo, L., Gao, S. (2022). Mapping Fences in Xilingol Grassland Using High Spatiotemporal Resolution Remote Sensing Data. In: Bourennane, S., Kubicek, P. (eds) Geoinformatics and Data Analysis. ICGDA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-031-08017-3_7

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