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Classification and Transition of Grassland in Qinghai, China, from 1986 to 2020 with Landsat Archives on Google Earth Engine

Author

Listed:
  • Pengfei He

    (School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yuli Shi

    (School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Haiyong Ding

    (School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Fangwen Yang

    (School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

The lack of long-duration, high-frequency grassland classification products limits further understanding of the grasslands’ long-term succession. This study first explored the annual mapping of grassland with fourteen categories at 30 m in Qinghai, China, from 1986 to 2020 based on Google Earth Engine (GEE) and the Integrated Orderly Classification System (IOCSG). Specifically, we proposed an image composite strategy to obtain annual source images for classification, by quarterly compositing multi-sensor and multi-temporal Landsat surface reflectance images. Subsequently, the 35-year area time series of each category was analyzed in terms of trend, degree of change, and succession of each category. The results indicate that the different grasslands of the IOCSG can be effectively differentiated by utilizing the designed feature bands of remote sensing data. Additionally, the proposed annual image composition strategy can not only decrease the invalid pixels but also promote classification accuracy. The grasslands transition analysis from 1986 to 2020 implies the progressive urbanization, warming, and wetting trend in Qinghai. The generated 35-year annual grassland thematic data in Qinghai can serve as an elementary dataset for further regional ecological and climate change studies. The proposed methodology of large-scale grassland classification can also be referenced to other applications like land use/cover mapping and ecological resource monitoring.

Suggested Citation

  • Pengfei He & Yuli Shi & Haiyong Ding & Fangwen Yang, 2023. "Classification and Transition of Grassland in Qinghai, China, from 1986 to 2020 with Landsat Archives on Google Earth Engine," Land, MDPI, vol. 12(9), pages 1-22, August.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:9:p:1686-:d:1227407
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    References listed on IDEAS

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    1. Xiaofan Ma & Haifeng Zhang, 2022. "Variations in the Value and Trade-Offs/Synergies of Ecosystem Services on Topographic Gradients in Qinghai Province, China," Sustainability, MDPI, vol. 14(23), pages 1-27, November.
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