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Accurately identifying the spatio-temporal information of surface changes will help to explore the law of development and evolution of surface natural environment and ecosystems, and support related scientific research and administrative management. Taking part of the implementation area of an ecological protection and restoration project in Henan Province as the study area, based on the Google Earth Engine (GEE) cloud platform, using 98view Landsat8/OLI remote sensing images from 2013 to 2020 as the data source, and the Breaks for additive season and trend (BFAST) algorithm theory was applied to extract and map information on land surface changes. The methodological experimental process included: firstly, the Landsat8/OLI land surface reflectance dataset was called and pre-processed based on GEE, the cloud shadow masking of the remote sensing dataset based on CFMask algorithm, the calculation of the spectral index (vegetation index NDVI) and the construction of the time series dataset. Secondly, based on the time series data set and the BFAST algorithm theory, a generalized linear regression model consisted of trend terms, seasonal terms and residual terms was constructed, and the unknown parameter set in the model was solved by the least square method, so as to further construct a time series fitting model and detect time-series structure changes in near real-time based on the Moving sums of the residuals (MOSUM) method. Finally, image element sample points were extracted from the detection results, and the results were validated and evaluated in terms of accuracy by overlaying with Google Earth high-resolution image data and visual interpretation. The analysis of the results showed that the method proposed had high detection accuracy in the detection of time-series land surface ecological changes in the study area (83.7% overall accuracy, 86.5%, 80.7% and 87.7% accuracy of the detection results in the sub-years from 2018 to 2020, respectively) in the detection of time-series land surface changes in the study area. Overall, the method proposed was a basic method for remote sensing big database construction, near real-time disturbance identification and monitoring of land surface ecological information and other technologies, which can provide technical support and decision-making support for the investigation and monitoring of ecological protection and restoration in national land space and assessment and early warning.