The Carya Cathayensis Yield Estimation Model Based On Remote Sensing In Lin'an

Carya cathayensis is an important economic species in Lin'an of Zhejiang Province, is the mainsource of income for local farmers. So it was greatly significant important to understand the plantingarea, growth and yield for the policy development, macro-control of price. The remote sensing modelfor Carya cathayensis yield estimation was to be established in Lin'an, Zhejiang Province based oncanopy spectrum and main agriculture parameter. The main achievements are as follows:1. Planting areaTwo scenes of the CBERS-02data in the summer and winter and field survey data were dealedwith principal component analysis and supervised classification methods, the spatial distribution andarea information of Carya cathayensis in Linan of Zhejiang Province were extracted.2. Yield estimation modelAfter analysis of the relevance of vegetation index (NDVI, PVI, RVI, DVI), meteorologicalfactors and Carya cathayensis yield in the Carya cathayensis different growing periods, NDVI is thebest estimation of the remote sensing factor in the growing period,flowering period is the bestestimation of the growing period,among18meteorological factors,10meteorological factors has thegood relevance with the Carya cathayensis yield,average precipitation in July ten-day period have thegood relevance with the Carya cathayensis yield.In the optimum period of Carya cathayensis yieldestimation,established the Carya cathayensis prediction model.With a regression analysis of thevegetation index NDVI,meteorological factors and Carya cathayensis yield.Comparison with analysisof statistical data,using the correlation coefficient evaluation and the root mean square error(RMSE)evaluation method to build the model accuracy。Through research,the following conclusions of this paper:1.The overall classification accuracy of83.71%,Carya cathayensis planting area of3.11millionhectares. Comparison with analysis of statistical data,the total precision error of1.3%.The resultsshowed that using the differences of winter and summer images,the extraction of area information isfeasible by remote sensing-based dynamic monitoring.2.The regeression analysis of vegetation index and meteorological factors,attained themulti-dimensional linear regression model is better,predicting values of0.851,the accuracy of fittingof0.820,F of52.536,RMSE of7.78,the maximum relative prediction error can be up to14.78%,and the smallest relative predicition error is5.67%.The results showed that the model is easy to use,andthe model stability is better,which can be used an effective tool for prediction of Carya cathayensisproduction.