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. (December 2022) - Yao, Jiaqi; Mo, Fan; Zhai, Haoran; Sun, Shiyi; Feger, Karl-Heinz; Zhang, Lulu; Tang, Xinming; Li, Guoyuan; Zhu, Hong
Graphical abstract: Highlights: The measurement errors in long-time series remote sensing images are corrected by statistical and spatio-temporal prediction model. The ecological degradation of the Miyun Reservoir basin from 1985 to 2021 is mainly caused by anthropogenic factors. Through statistical theory, the ecological development trend in the experimental area is analyzed, and the conclusion is verified by land use evolution. 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.