关键词: 关中地区, 杏树, GF1-WFV, 最佳时相, 辨识方法, 光谱指数, 决策树分类

Abstract: The aim is to find out the best phase and the optimal method for identifying apricot trees by using the monthly GF1-WFV images collected in 2014,2017 and 2018(a total of 43 images),and to provide a theoretical basis for remote sensing monitoring of apricot trees in Guanzhong area and even all fruit regions of China.Firstly,the images of each period were preprocessed.Then,the identification efficiencies of six kinds of identification methods(comparison of reflectance spectra of ground objects in the same period of image,bands difference or ratio analysis in the same period of image,calculation and analysis of spectral index in the same period of image,spectral index change tracking among the adjacent periods of images divided into the same set,images compounding and analysis of the multiple indices combined using)were tested separately by using ROI (Region of interest) data collected from the sample plots of various fruit trees in different periods of images. Finally,the identification efficiency of exploration results was verified in global image.The conclusions were as follows:Compared with other fruit tree species,apricot trees had lower VI1 value at full flowering stage,so the thresholds of VI1 value had better identification efficiency for identifying apricot trees in this period;In the images of apricot trees blooming,the overall classification accuracy had been improved by using dual thresholds of NDVI(Normalized difference vegetation index) and VI1;Apricot trees could also be well identified by the band ratios between full flowering and pre-blooming period images(same serial number);The accuracy of identifying apricot trees was more ideal by using quadruple thresholds of NDVI,VI1,R_b1/R_b1 Pre-flowering and R_b3/R_b3 Pre-flowering ,the classification accuracy of apricot trees could reach 83.14%,and overall classification accuracy could reach 80.93%;The flowering period was the best time to identify apricot trees.

Key words: Guanzhong region, Apricot tree, GF1-WFV, The best phase, Identification method, Spectral index, Decision tree classification