Results of using geoinformation and statistical analysis methods to study spectral reflectance characteristics of agricultural crops of Belarus

Alena V. Kaziak, Y. Davidovich, Mikita A. Shastakou
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引用次数: 0

Abstract

The results of using geoinformation and statistical analysis methods to study spectral reflectance characteristics of the nine most typical agricultural crops of Belarus are presented. Spectral brightness coefficients and normalised difference vegetation index (NDVI) values were extracted from Landsat-8 multispectral satellite images in the software package ENVI (version 5.2) and analysed based on the methods of zonal statistics in the software complex ArcGIS (version 10.2) and mathematical and statistical analysis in the program Statistica (version 10). The verification of satellite data with the corresponding field measurements was carried out on the basis of correlation analysis, namely, a reliable strong positivelinear relationship between the measured in the field by a specialised GreenSeeker instrument NDVI values and the calculated by Landsat-8 satellite data NDVI values was established. The character of the distribution of spectral brightness coefficients and average NDVI values depending on the type of agricultural crop was assessed using a dispersion analysis, which allowed revealing patterns hidden in the spectral data. In particular, after applying the procedure of multiple comparisons using post hoc tests, it was established which types of crops significantly differ from each other and for which dates these differences were observed. The obtained scientific results were systematised and presented in the form of correspondingtables. The data contained in the tables made it possible to improve the methodology of automated recognition of the crops considered in the study.
利用地理信息和统计分析方法研究白俄罗斯农作物光谱反射率特征的结果
介绍了利用地理信息和统计分析方法研究白俄罗斯9种最典型农作物光谱反射率特征的结果。在ENVI软件包(5.2版)中,从Landsat-8多光谱卫星图像中提取光谱亮度系数和归一化差异植被指数(NDVI)值,并根据ArcGIS软件包(10.2版)中的区域统计方法和Statistica程序(10版)的数学和统计分析方法进行分析。在相关分析的基础上,对卫星数据与相应的现场测量进行了验证,即,在专业GreenSeeker仪器现场测量的NDVI值与Landsat-8卫星数据计算的NDVI之间建立了可靠的强正线性关系。使用分散分析评估了光谱亮度系数和平均NDVI值随农业作物类型的分布特征,从而揭示了光谱数据中隐藏的模式。特别是,在使用事后测试应用多重比较程序后,确定了哪些作物类型彼此之间存在显著差异,以及在哪些日期观察到了这些差异。所获得的科学结果被系统化,并以相应表格的形式呈现。表格中包含的数据使研究中考虑的作物的自动识别方法得以改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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