A spatio-temporal prediction model theory based on deep learning to evaluate the ecological changes of the largest reservoir in North China from 1985 to 2021.

Yao, Jiaqi, Mo, Fan, Zhai, Haoran, Sun, Shiyi, Feger, Karl-Heinz, Zhang, Lulu, Tang, Xinming, Li, Guoyuan and Zhu, Hong, (2022). A spatio-temporal prediction model theory based on deep learning to evaluate the ecological changes of the largest reservoir in North China from 1985 to 2021.. Ecological Indicators, 145 109618-n/a

Document type:
Article
Collection:
UNU-FLORES Articles

Metadata
Links
Versions
Statistics
  • Sub-type Journal article
    Author Yao, Jiaqi
    Mo, Fan
    Zhai, Haoran
    Sun, Shiyi
    Feger, Karl-Heinz
    Zhang, Lulu
    Tang, Xinming
    Li, Guoyuan
    Zhu, Hong
    Title A spatio-temporal prediction model theory based on deep learning to evaluate the ecological changes of the largest reservoir in North China from 1985 to 2021.
    Appearing in Ecological Indicators
    Volume 145
    Publication Date 2022
    Place of Publication Amsterdam, Netherlands
    Publisher Elsevier
    Start page 109618
    End page n/a
    Language eng
    Abstract Miyun Reservoir, located in the Miyun District, Beijing, China, is the largest comprehensive water conservancy project and is an important ecological protection area in the North China region. Changes within the basin are the driving factors affecting the ecosystem in the watershed; therefore, it is important to analyze the changes in the ecological environment of Miyun Reservoir. For the analysis of a long time series of image data remotely sensed by satellite, the outliers caused by atmospheric, lighting, and sensor measurement errors are significant, and it is difficult for traditional algorithms to effectively recover the true image value. To address this, this paper proposes a theoretical model for predicting spatio-temporal variation based on deep learning to identify and correct invalid and anomalous values in extended time series data. This study corrected and analyzed the results of Remote Sensing based Ecological Index inversion of Landsat data of the Miyun Reservoir watershed from 1985 to 2021. The findings and conclusions of this study are important for the analysis of long time series image data from satellite remote sensing and for improving regional ecological evaluation and sustainable development planning.
    Keyword Miyun reservoir
    E3d-LSTM
    Deep learning
    Mann-Kendall test
    Ecological environment
    RSEI
    Copyright Holder The Authors
    Copyright Year 2022
    Copyright type Creative commons
    ISSN 1470-160X
    DOI https://doi.org/10.1016/j.ecolind.2022.109618
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 101 Abstract Views  -  Detailed Statistics
    Created: Sat, 30 Sep 2023, 03:52:30 JST by Věra Greschner Farkavcová on behalf of UNU FLORES