融合无人机光谱信息与纹理信息的冬小麦生物量估测

Biomass Estimation in Winter Wheat by UAV Spectral Information and Texture Information Fusion

【目的】生物量是表征植被生命活动的重要参数,对植被长势监测、产量预测有重要意义。以无人机为平台的高光谱遥感技术,具有机动灵活、成本低、空间覆盖广的优势,能够及时准确地估测植被生物量,已成为遥感估算研究的热点之一。由于光谱特征反演生物量存在饱和问题,因此,本研究尝试结合纹理特征与植被指数构建一种"图-谱"融合指标,探究"图-谱"融合指标的抗饱和能力及生物量估测能力。【方法】首先,利用无人机高光谱影像,提取其光谱信息和纹理信息,分别基于植被指数和纹理特征构建生物量模型。其次,针对光谱特征存在的饱和问题,将植被指数与对生物量敏感的纹理指标相乘或相除两种形式构建"图-谱"融合指标,分析"图-谱"融合指标的饱和性,并基于"图-谱"融合指标构建生物量估算模型。最后,对比不同指标构建的生物量模型的估测效果,来分析"图-谱"融合指标估测生物量的能力。【结果】(1)植被指数多在LAI=5时出现饱和现象,而"图-谱"融合指标VI×sm658,VI/ent658,VI/dis658,VI/con658,VI/dis514,VI/con514,VI/var514,VI×con802,VI×dis802均在LAI>5时才出现饱和现象,相比之下,这些"图-谱"融合指标一定程度上改善了饱和问题;(2)与植被指数相比(除了GNDVI、NDVI之外),抗饱和能力提高的"图-谱"融合指标VI×sm658、VI/ent658、VI/dis658、VI/con658、VI/dis514、VI/con514、VI/var514、VI×con802、VI×dis802,其与生物量的相关性也相对提高,所构建的生物量模型精度较高(R2=0.81,RMSE=826.02 kg·hm-2)。(3)对比单一植被指数、纹理特征,将纹理特征与光谱特征相结合的"图-谱"融合指标估算小麦生物量的能力相对最强,模型精度明显高于单一植被指数(R2=0.69)和单一纹理特征(R2=0.71)构建的生物量模型。【结论】"图-谱"融合指标的抗饱和能力明显提高,其构建的生物量模型精度也有效提高,实现了结合光谱信息和纹理信息的冬小麦生物量遥感估测,为生物量定量反演提供一种新思路。

【Objective】Biomass,an important parameter to characterize vegetation activities,is of great significance for plant growth monitoring and yield forecasting.Hyperspectral remote sensing technology based on the unmanned aerial vehicle(UAV)has the advantages of flexibility,non-destructive and wide coverage,and could also timely and accurately estimate vegetation biomass,so it has become one attention topic in remote sensing application.Since saturation problem existed in the inversion of biomass by spectral features,the objective of this study was to propose a'image and spectrum'fusion index by integrating the biomass-related texture feature into vegetation index.【Method】In this study,the extracted spectral indices and texture features from UAV hyperspectral imagery were used to first construct biomass models,respectively.Secondly,the'image and spectrum'fusion indices by combining(multiplying or dividing)the biomass-sensitive vegetation index and texture feature were established to solve the saturation problem by spectral information and texture information fusion and to construct biomass model.Finally,the estimation effect of the biomass model constructed by different indices was compared,and then analyze the ability of the'image and spectrum'fusion indices to estimate biomass.【Result】(1)The vegetation index was almost saturated when LAI was no larger than 5,while these'image and spectrum'fusion indices,VI×sm658,VI/ent658,VI/dis658,VI/con658,VI/dis514,VI/con514,VI/var514,VI×con802,VI×dis802,began to perform saturation when at LAI was larger than 5.Compared with the vegetation index,the anti-saturation ability of the'image and spectrum'fusion index was improved obviously.(2)Compared with the vegetation index(excepting for GNDVI、NDVI),the anti-saturation ability of the'image and spectrum'fusion indices(VI×sm658,VI/ent658,VI/dis658,VI/con658,VI/dis514,VI/con514,VI/var514,VI×con802,VI×dis802)improved effectively,and their correlations with biomass improved as well.Meanwhile the biomass model based on the'image and spectrum'fusion indices performed well,with R^2 and RMSE values of 0.81 and 826.02 kg·hm^-2,respectively.(3)Compared with spectral index and texture feature,biomass model accuracy by'image and spectrum'fusion index(R^2=0.81)was significantly higher than that of the vegetation index(R^2=0.69)and texture feature(R^2=0.71).【Conclusion】Results showed that both the anti-saturation ability and the accuracy of biomass model constructed by the'image and spectrum'fusion index were effectively improved,which indicated that spectral information and texture information fusion could achieve a great estimation of winter wheat biomass.The research provided a new way for quantitative inversion of biomass.