Developing a remote sensing-based ecological index based on improved biophysical features

Abstract

More and more human activities have caused varying degrees of interference with the ecosystem. As an effective quantitative means of eco-environmental quality, remote sensing has been widely used. The remote sensing ecological index (RSEI) is the most popular ecological evaluation model at present. However, the model is affected by the direction of the eigenvector, and two completely opposite results will appear in the calculation of the model. In the past, researchers often manually judged whether the two results met the expectations according to a priori knowledge to select the appropriate calculation results. With the development of remote sensing big data, applying this artificial discrimination method to eco-environmental monitoring under the background of big data is difficult. Therefore, we test the evolution of the RSEI model eigenvector in time series through large samples. It is found that the results of the two models are completely opposite in space. For any model, changing the order of input bands may result in RSEI also being the opposite. Through the large sample test in different time periods, the eigenvectors of each ecological factor affect the direction of the corresponding RSEI. Therefore, we propose an improved model to judge the characteristic contribution direction of the first principal component by selecting the wet factor, which is less affected by seasonal changes. The model direction can be automatically modified without the intervention of researchers according to subjective experience. The improved model can adapt different periods of RSEI calculation. At the same time, no matter how the input band order changes, the final result direction is correct. The improved model makes it possible to monitor and calculate the eco-environmental quality of remote sensing big data and provides a solid scientific basis for the development of the model through research on the mechanism of the model.