关中地区梨树遥感辨识的最佳时相与方法  

Optimal phase and method for identifying pear trees in Guanzhong area via remote sensing

作　　者：邢东兴 白萌 王明军[2] 焦俏 封建民[1] 杨波[1] XING Dongxing;BAI Meng;WANG Mingjun;JIAO Qiao;FENG Jianmin;YANG Bo(College of Resources and Environment,Xianyang Normal University,Xianyang 712000,Shaanxi,China;College of Physics and Electronic Engineering,Xianyang Normal University,Xianyang 712000,Shaanxi,China)

机构地区：[1]咸阳师范学院资源环境学院,陕西咸阳712000 [2]咸阳师范学院物理与电子工程学院,陕西咸阳712000

出　　处：《果树学报》2022年第8期1490-1502,共13页Journal of Fruit Science

基　　金：陕西省教育厅专项科研项目(20JK0971);国家自然科学基金项目(61771385);陕西省教育厅哲学社科重点基地项目(17JZ079)。

摘　　要：【目的】利用22景GF6-WFV影像,探寻关中地区梨树(砀山酥梨与早酥梨)遥感辨识的最佳时相与方法。【方法】首先对各景影像进行了预处理;随后基于12种果树与3种农作物样地的ROI(感兴趣区)数据对8类探试性方法(即红边参数法、光谱距离法、图像增强处理与分析法、影像差值与比值法、反射光谱及其波段差分法、光谱指数及其变化分析、辨识方法优化组合)的辨识效能进行了逐一测试,并优选出较佳方法及其对应时相;最后采用全域影像对较佳方法及其最佳组合的辨识精度以及坚实性进行了验证。【结果】(1)梨树的最佳辨识时相为盛花期;(2)RGB分量阈值法对盛花期梨树具有较强的辨识效能,并且其辨识效能具有一定的坚实性;(3)R_(710)阈值法对盛花期梨树也具有较强的辨识效能,其辨识的总体精度高于常见的植被指数[如R_(710)-R_(425)、MSR_(red-edge)=(R_(750)-R_(425))(/R_(710)+R_(425))、IRECI=(R_(830)-R_(660))/(R_(710)/R_(750))、NDVI=(R_(830)-R_(660))(/R_(830)+R_(660))、SR_(red-edge)=R_(830)/R_(710)、CL_(red-edge)=R_(830)/R_(710)-1、mNDVI_(red-edge)=(R_(750)-R_(710))(/R_(750)+R_(710)-2×R_(425))、NDVI_(red-edge)=(R_(750)-R_(710))(/R_(750)+R_(710))等];(4)仅采用梨树盛花期影像无法将梨树与李树精确区分,而采用梨树盛花期与李树盛花期两期影像中的红边1波段的差值(即R_(710-apr)-R_(710-mar))的阈值可将梨树与李树予以精确区分;(5)RGB分量、R_(710)与R_(710-apr)-R_(710-mar)三种阈值法之间具有一定的互补性,由其组合构建的决策树对梨树的辨识效果最佳,梨树类正确率可达92.91%,非梨类正确率可达97.53%,总体精度可达97.19%。【结论】采用梨树盛花期与李树盛花期两期影像,并基于RGB分量、R_(710)与R_(710-apr)-R_(710-mar)3种阈值法组建的决策树可将研究区内的梨树予以高精度辨识。【Objective】In the present study,22 GF6-WFV images were used to explore the optimal phase and method for identification of pear trees(including Dangshansu pear trees and Zaosu pear trees)by remote sensing in Guanzhong area,in order to provide a method for monitoring of pear trees using remote sensing.【Methods】Firstly,each image was preprocessed(including image space clipping,image radiation calibration,image atmospheric radiation correction,image geometry correction,image mean filtering,etc).Then,the identification efficiency of eight methods(including red edge parameter method,spectral distance method,image enhancement processing and analysis method,image difference and ratio method,reflection spectrum and bands difference method,spectral indices and their change analysis method,and optimal combination of identification methods)were tested based on the region of interest(ROI)data of sample plots of 15 crops.The better methods and their corresponding application phases were optimized.Finally,the identification accuracy and solidity of these methods and their optimal combination were verified by using global images.【Results】(1)The best identification phase to identify pear trees was the full flowering stage,and the identification accuracy at fruit ripenin stage and other phases was not ideal.(2)The RGB component threshold method had strong identification effect on pear trees in full blooming period(the RGB component threshold method refers to that the images of each phase were processed by false color synthesis;then,the obtained false color synthesis images were processed by the method of grayscale stretch,and the stretched results were stored as 24 bites RGB images;Finally,the differences of RGB component vales between pear trees sample pixels and those of non pear crops were compared and analyzed).The RGB component values of sample pixels of pear trees and the other crops were of great differences when the extreme values of R,G and B component of pear sample pixels were used as thresholds(i.e.R compon

关 键 词：梨 关中地区 GF6-WFV影像 遥感辨识 最佳时相 

分 类 号：S661.2[农业科学—果树学]