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Fractional Vegetation Cover Estimation of Different Vegetation Types in the Qaidam Basin

Author

Listed:
  • Siqi Zhang

    (Hebei Key Laboratory of Environmental Change and Ecological Construction, College of Resources and Environment Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Hui Chen

    (Hebei Key Laboratory of Environmental Change and Ecological Construction, College of Resources and Environment Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Yang Fu

    (Hebei Key Laboratory of Environmental Change and Ecological Construction, College of Resources and Environment Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Huihui Niu

    (Hebei Key Laboratory of Environmental Change and Ecological Construction, College of Resources and Environment Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Yi Yang

    (Hebei Key Laboratory of Environmental Change and Ecological Construction, College of Resources and Environment Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Boxiong Zhang

    (Hebei Key Laboratory of Environmental Change and Ecological Construction, College of Resources and Environment Sciences, Hebei Normal University, Shijiazhuang 050024, China)

Abstract

The estimation of fractional vegetation cover (FVC) by using remote sensing images has become feasible. Based on Landsat8-OLI images and field data obtained from an unmanned aerial vehicle, we established an empirical model (EM) and a pixel decomposition model (PDM) of FVC in the desert vegetation region, steppe vegetation region, meadow vegetation region and mixed vegetation region (the three vegetation region types) of the Qaidam Basin, and the inversion accuracies of the models were compared. The results show the following: (1) Vegetation classification inversion (VCI) provides a promising approach for FVC estimation. The accuracy of FVC by VCI was obviously better than that achieved using vegetation mixed inversion (VMI); (2) Differences were observed in the FVC estimation between VCI and VMI by the EM in areas with relatively high-density vegetation cover (FVC > 60%). The FVC in some parts of steppe region in the basin was slightly overestimated by VMI of the EM; 3) VCI estimated by the PDM resulted in lower inversion values for extremely low-density vegetation cover (FVC ≤ 10%) and higher inversion values for high-density vegetation cover (FVC > 80%). The FVC inversion was underestimated by the PDM in steppe and meadow regions with FVC > 15% in the basin. The application of VCI in different models can provide new ideas for the sustainable study of vegetation in arid regions.

Suggested Citation

  • Siqi Zhang & Hui Chen & Yang Fu & Huihui Niu & Yi Yang & Boxiong Zhang, 2019. "Fractional Vegetation Cover Estimation of Different Vegetation Types in the Qaidam Basin," Sustainability, MDPI, vol. 11(3), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:864-:d:204108
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    Citations

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    Cited by:

    1. Emmanuel Fundisi & Walter Musakwa & Fethi B Ahmed & Solomon G Tesfamichael, 2020. "Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    2. Jiří Šandera & Přemysl Štych, 2020. "Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier," Land, MDPI, vol. 9(11), pages 1-20, October.
    3. Yaqun Liu & Changhe Lu, 2021. "Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019," IJERPH, MDPI, vol. 18(2), pages 1-18, January.
    4. Alberto Luis Cantoral & Estrella Alfaro & Raquel Alonso-Redondo & Marta Eva García-González, 2019. "Temporal Indices of Landscape Change: A Proposal to Measure Variations in the Conservation Status of Vegetation at Fine Resolution," Sustainability, MDPI, vol. 11(21), pages 1-16, October.

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