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

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。

Construction and validation of combination model of topography-adjusted vegetation index based on band-ratio model

Abstract: A novel combination model of topography-adjusted vegetation index (TAVI) was developed based on the band-ratio model and spectral feature of land covers in a rugged terrain to reduce the topographic effect. Firstly, the topographic correction strategies in rugged terrain were introduced, including the traditional empirical statistical model based on digital elevation model (DEM), terrain radiative transfer model combined with DEM and band-ratio model. With the support of terrain radiative transfer model basic principle, the concept model of TAVI was proposed to eliminate the topographic effect. Secondly, the operational land imager (OLI) 7 bands of Landsat 8 on December 13th, 2014, in a sample within research area in Fuzhou city, China were illustrated. Meanwhile, the spectral features of 5 major land covers in the sample were also analyzed, such as the cement road, water, vegetation in flat, vegetation in shady slope and vegetation in sunny slope. After the illustration and analysis, the OLI red and near-infrared wavebands were selected to develop a new TAVI combination model. Thirdly, a novel shady vegetation index (SVI) was developed based on the band-ratio model and the physical feature of red band. The ratio vegetation index (RVI), as the basic band-ratio model, was selected to form the novel combination model of TAVI integrating with the SVI. Fourthly, the TAVI of research area was computed with the newly proposed combination model and the topographic adjustment coefficient optimization algorithm that is depended on the balance between the maximal TAVI values in shady and sunny slopes in rugged terrains. Then, three validation methods were adopted to verify the correction effect of new TAVI combination model, including the visual examination, statistics analysis and vegetation indices difference analysis. The statistics analysis were the comparisons the correlation coefficient and the inclination between the cosine of solar incidence and vegetation indices, including the TAVI calculated from the apparent reflectance directly, RVI and normalized different vegetation index (NDVI) computed from the correction models. These correction models include the atmospheric correction with the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) model in ENVI software, topographic correction with C-model based on the DEM and the correction with the second simulation of the satellite signal in the solar spectrum (6S) model combined with DEM. The verification results showed that the novel combination of TAVI achieved similar correction effect to that from the NDVI after the correction with 6S model combined with DEM, which achieved the best corrected result in these correction models. The correlation coefficient between the cosine of solar incidence and TAVI decreased to ±0.08, while the inclination of the linear regression equation between them reduced to ±0.04. These numbers showed that the topographic effect was successfully eliminated by TAVI. In summary, the novel combination model of TAVI, even without the DEM support, could achieve satisfactory result in elimination of topographic effect in rugged terrain, which amounts to nearly the same effect of atmospheric+topographic corrections. Therefore, the novel model of TAVI can be utilized to monitor vegetation information and retrieve bio-physical parameters in rugged terrains, while the topographic adjustment coefficient needs to be improved from the empirical method to physical or semi-physical model in the next step.