Comprehensive growth index monitoring of desert steppe grassland vegetation based on UAV hyperspectral

The goal of this study was to establish a comprehensive growth index (CGI) of grassland vegetation for monitor the overall condition of the grassland. Taking the desert grassland in Otuoke Banner, Ordos City, Inner Mongolia as the research object, this study integrates five indicators. First, the optimal band of the unmanned aerial vehicle hyperspectral data is optimized using the correlation analysis, successive projection algorithm (SPA), optimum index factor method, and band combination index method. A dual-band spectral index in good correlation with the CGI is then constructed in the optimal band. Afterwards, a CGI characterization model is established in accordance with the partial least squares regression (PLSR) algorithm and its accuracy is analyzed. Finally, the CGI of the study area is estimated. The experimental results are as follows. 1) The R(2) of models built using the training samples of the spectral indices corresponding to the optimal spectra screened by the SPA method was 0.7835, RMSE was 0.0712, and RE was 6.89%, less than 10%. The R(2) of the Validation samples was 0.7698, RMSE was 0.0471, and RE was 6.36%, less than 10%, highest precision. 2) Models were built using the spectral indices corresponding to the optimal spectra screened by the SPA method, and the CGI mean values were inverted. A comparison of the mean measured CGI values of the sample quadrat of the test area showed that the mean relative error was 3.82%. The results show that the vegetation growth of desert-steppe grasslands can be adequately monitored, providing technical support for the rapid and accurate diagnosis of grassland conditions. However, there are still shortcomings in this study. 1) The research area for this study was mainly in the desert steppe in Otuoke Banner, Ordos, hence the relevance and universality of the findings need to be verified, and subsequent experiments need to be carried out on desert steppes in other regions or even other types of grasslands to test the universality of the model. 2) In this study, the influence of soil background and litter on the spectral reflectance is not considered in depth. In addition, the influence of sensor observation angle and solar elevation angle on the inversion model demands further investigation efforts.