THE EARLY WARNING METHOD OF ECOLOGICAL ENVIRONMENT MONITORING AND ENVIRONMENTAL BIG DATA-A CASE STUDY OF THE MOUNTAINOUS AREAS IN NORTHERN FUJIAN

At present, China is facing ecological and environmental problems such as soil erosion, grassland degradation, sharp decline of forests, and reduction of biodiversity. The above -mentioned problems often involve a large span of time and space. complex processes, and many driving factors, which are difficult to deal with traditional methods. Faced with the monitoring, evaluation, and early warning of a wide range of ecological environment conditions, based on remote sensing technology and big data technology, this study obtains a wide range of long-term series of rich monitoring data sources, and provides systematic data information for regional ecological environment early warning evaluation. Taking the mountainous areas of northern Fujian as an example, thc entropy method is used to determine thc index weights, reduce human interference, and consider thc index relationships between different timc periods in the time series, providing an operable evaluation method for realizing regional ecological environment big data early warning. Based on the BP neural network, the model is constructed, and thc early warning and prediction of thc ecological environment are performed in the mountainous arca of northern Fujian based on the index quality and timc series length data. By using thc BP neural network model to predict the ecological environment of the study area, the R2 between the measured data value and the predicted value is 0.990. which can ensure the prediction accuracy. The ecological environment early warning prediction method based on the BP neural network model achieves a high degree of fitting to the vegetation coverage in the northern Fujian mountainous area, and the overall prediction value is slightly lower than the actual by 5%. The prediction results can basically reflect the degree of vegetation coverage change, providing suggestions for regional development and environmental protection.