Study On Ecological Environmental Health Assessment And Quantitative Remote Sensing Inversion Method Of Key Parameters

This paper studies the problem of ecological environment health evaluation and theremote sensing inversion methods involved in this problem. In this paper, we try toimprove remote sensing inversion so that we can obtain the information fast, accuratelyand repeatedly that replied in the research of ecological environment health.On the other hand, with the introduction of information entropy model ofrelationship between time and space, we fully excavate the intrinsic relationshipbetween time and space distribution information of ecological parameters andecological environment health, and then analysis the characterization of the former tothe latter to put forward an improved comprehensive evaluation method that is moreobjective and accurate based on the improved entropy weight comprehensive evaluationmodel, which will provide scientific basis in maintaining and improving the ecologicalenvironment health.In the study of remote sensing inversion of ecological environment parameters, thisarticle focuses on the inversion of land surface temperature and land surface soil watercontent. For the inversion of land surface temperature, this paper presents anapproximate analytical solution based on multi-band remote sensing data. This methodhas avoided the iterative initial value selection problem and the iterative stabilityproblem that caused by numerical methods. Verified with the model data and the actualremote sensing data, the method is proved to be a better inversion method with theaccuracy, speed and stability.For the inversion of land surface soil water content, after the comparison of theexisting methods, this paper used the vegetation index-land surface temperature featurespace inversion method which is the best. Through the test in the test area, the methodproved to have highly feasibility in the inversion of land surface soil water content.In the study of ecological environment evaluation, this paper presents distributionindicators based on temporal relationship information entropy instead of the traditionalstate indicators. Furthermore, this paper not only determined indicators that covered bythe comprehensive evaluation model for the difficulty to obtain and the relationship between the combination of indicators and the target selection but also improvescomprehensive evaluation model of entropy weight to make the parameters’ weight tobe determined by ecological environment health-related information quantity instead ofall the information quantity. The test results show that the distribution indicators makeup for the lack of state indicators and get rid of the dependence on the characteristic ofstudied parameters.The new indicators selection method and the improved entropy weightcomprehensive evaluation model have improved the objective accuracy of theecological environment health evaluation which was worthy of further promotion.