Vegetation Dynamic Monitoring And Temperature Data Generation In Chuzhou City Based On Remote Sensing

Vegetation is an important link between soil,atmosphere,hydrology and other ecological elements.It is an important part of terrestrial ecosystem.Defining the characteristics of vegetation change and exploring the relationship between vegetation change and climate change can enrich the regional research of global change,and at the same time,to understand the trend of regional ecological environment change,Therefore,it has certain reference value for formlating reasonable ecological environment protection policies and measures.To sum up,the study of vegetation change is of great significance.In view of the above significance,based on the data of the first environmental satellite image for 2009-2017,and the temperature data of 7 meteorological stations in Chuzhou City,Anhui Province,the methods of pixel dichotomy model,mean value method and partial least square regression are used in this paper.The temporal and spatial dynamic characteristics of vegetation NDVI and its relationship with temperature in Chuzhou City of Anhui Province in recent nine years are analyzed.The main contents and achievements of this paper are as follows:Based on the HJ star summer data from 2009-2017,using pixel dichotomy model and ENVI remote sensing software,the acquisition of NDVI data is completed,and the vegetation coverage change in these nine years is studied.Based on the HJ star data from April to October 2013,the variation of vegetation coverage and vegetation spectral curve in vegetation growth cycle was studied by using pixel dichotomy model.The experimental results show that with the change of vegetation growth cycle,the vegetation coverage in Chuzhou has a process of first increasing and then decreasing,and the maximum vegetation coverage is up to 81%.At the same time,the spectral characteristic curve of vegetation also reflects this problem.Based on the NDVI data of HJ star from April to October of 2009-2013 and the temperature data of Chuzhou city in this time period,the partial least square regression is used to establish the relationship model between the two,and the average relative error is used to evaluate the accuracy of the model,at the same time,The temperature prediction of the region in 2017 is carried out by using the constructed model.and the prediction results are compared with the actual results to verify the predictability of the model.The experimental results show that the average relative error of the model is less than 2,and the relative error between the 2017 data prediction and the actual data is less than 6.The results show that the constructed relational model is more effective.Stable and predictable.