The purpose of this paper is to research with the development of urban and regional change and lead to the change of urban vegetation, for the purpose of ecological environmental quality assessment, urban planning and urban ecological security assessment.

In this study, the Longyangxia Reservoir was taken as the research area and the chlorophyll-a and suspended matter concentration data of 20 measured sample points and the simultaneous landsat8 remote sensing image data were used. Firstly, remote sensing images were preprocessed, and then 15 sample data were randomly selected from 20 data points. Through correlation analysis of the 15 sample data and landsat OLI data, bands sensitive to chlorophyll-a and solid suspended matter concentration were found. Using the detected sensitive bands, an inversion model of the relevant concentrations of chlorophyll-a and solid suspended matter was constructed. Finally, the remaining 5 sample data are used to calculate the relevant precision index. By using the calculated determination coefficient, root mean square error and overall relative error, the precision of the inversion model is comprehensively evaluated, so as to find a universal model. Through the established inversion formula, ENVI/IDL and ArcGIS software were used to complete the mapping of chlorophyll-a and suspended matter concentration of Longyangxia Reservoir in 2013 and 2018, providing a reference for the comprehensive treatment of Longyangxia Reservoir.

In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inversion models of partial least squares regression (PLSR), back propagation neural network (BPNN) and support vector machines (SVM) with remote sensing data to predict the TN content in Datong County, Xining City, Qinghai Province, China. The results showed that the average TN content was 1.864 g/kg, and the coefficient of variation (CV) was 30.596%. The prediction accuracy of the SVM model (R2 = 0.676, RMSE = 0.296) among the three inversion models was higher than that of the BPNN model (R2 = 0.560, RMSE = 0.305) and the PLSR model (R2 = 0.374, RMSE = 0.334). The model with the highest accuracy predicted the spatial distribution of TN, and TN content showed a spatial distribution trend which was high in the northwest and low in the southeast, and gradually decreased from north to south. This study provides reference basis and support for soil fertility evaluations and sustainable agricultural development.

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