基于波段比模型的地形调节植被指数组合算法构建与验证

发布时间：2018-01-22 23:00

本文关键词： 地形植被光谱分析地形影响植被指数组合算法　出处：《农业工程学报》2017年05期 　论文类型：期刊论文

【摘要】：为消除山区植被遥感监测中的地形影响,该文根据山区主要地物波谱曲线特征和波段比模型等基本原理,构建地形调节植被指数(topography-adjusted vegetation index,TAVI)组合算法。首先,提出TAVI研究思路。其次,利用山区Landsat8多光谱遥感影像分析山区主要地物波谱曲线特征,阐释TAVI光谱原理。接着,用红光波段数据构建新的阴影植被指数(shady vegetation index,SVI),并优选比值植被指数(ratio vegetation index,RVI)与SVI形成TAVI组合算法,再结合地形调节因子"极值优化"算法计算TAVI结果。最后,采用目视比较、统计分析和差值分析证明TAVI组合算法达到经大气加地形校正后遥感影像计算的NDVI的削减地形影响的效果,其与太阳入射角余弦值一元线性回归方程斜率降至0.035,相关系数降至0.075。TAVI组合算法可应用于山区植被信息和有关参数的遥感监测与估算。
[Abstract]:In order to eliminate the topographic influence in remote sensing monitoring of vegetation in mountainous areas, this paper bases on the basic principles of spectral curve characteristics and wave band ratio model of main features in mountainous areas. A topography-adjusted vegetation vegetation TAVI (topography-adjusted vegetation index) combination algorithm is constructed. Put forward the idea of TAVI research. Secondly, using Landsat8 multispectral remote sensing image of mountain area to analyze the characteristics of spectral curve of main features in mountain area, explain the principle of TAVI spectrum. A new shady vegetation index (SVI) was constructed from the red light band data. The ratio of vegetation index and vegetation index (RVI) and SVI were selected to form a combination algorithm of TAVI. Combined with the terrain adjustment factor "extremum optimization" algorithm to calculate the TAVI results. Finally, visual comparison. Statistical analysis and difference analysis show that the combined TAVI algorithm can reduce the topographic effect of NDVI calculated by atmospheric and topographic correction. The slope of the linear regression equation with the cosine value of the solar incident angle decreases to 0.035. The correlation coefficient is reduced to 0.075. TAVI combined algorithm can be applied to the remote sensing monitoring and estimation of vegetation information and related parameters in mountainous areas.
【作者单位】：福州大学地理空间信息技术国家地方联合工程研究中心空间数据挖掘与信息共享教育部重点实验室海西政务大数据应用协同创新中心;中国科学院遥感与数字地球研究所;
【基金】：国家科技支撑计划项目(2013BAC08B00) 国家自然科学基金项目(41401488) 福建省自然科学基金项目(2017J01658)
【分类号】：TP79
【正文快照】： 江洪,何国金,黄海明,曹小杰,汪小钦,张兆明.基于波段比模型的地形调节植被指数组合算法构建与验证[J].农业工程学报,2017,33(5):156-161.doi:10.11975/j.issn.1002-6819.2017.05.023 http://www.tcsae.orgJiang Hong,He Guojin,Huang Haiming,Cao Xiaojie,Wang Xiaoqin,Zhang

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