植被理化参数反演的尺度效应与敏感性分析
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摘要
植被为包括人类在内的几乎所有生物的生存提供了物质和能量来源,是生态系统中重最的组成成分之一。许多有关植物物质能量交换的生态过程如光合作用、蒸腾作用、呼吸作用、初级生产力和分解作用等都与植被的理化参数密切相关,因此准确地估算植被的理化参数含量具有重要意义。遥感技术是在一系列空间和时间尺度上监测地球生物圈和植被动态变化的十分有效的工具,尤其是近年来高光谱成像技术的快速发展,使得通过遥感技术定量反演植被理化参数成为研究的热点。
遥感技术的迅速发展,为地物信息的提取提供了各种不同空间尺度和光谱尺度的遥感数据,随之而来的是遥感应用中所面临的各种尺度问题,植被所具有的独特尺度特征,使得系统地分析理化参数反演的尺度效应问题显得尤为重要。本文的研究目的在于以机载AISA高光谱影像为数据源,将野外实测数据与数值模拟相结合,系统、全面地对植被理化参数反演过程中存在的尺度效应进行定量分析,并在此基础上进一步研究理化参数敏感性以及理化参数反演精度的尺度变化规律,探讨不同尺度上理化参数反演方法的选择,以期为植被理化参数的高精度定量反演提供充足的理论依据。
基于野外实测数据,分别在叶片尺度和冠层尺度上分析叶绿素含量与反射率、反射率导数以及多种光谱指数的相关关系。同时,以反演叶片叶绿素含量为例,将因子分析方法和分形分维理论应用于植被理化参数的提取中。结果表明,反射率、反射率导数以及光谱指数与叶绿素含量的关系在叶片和冠层尺度上都有很大的差异,在一定程度上说明了理化参数反演中尺度问题的存在。基于降维思想的因子分析方法和能够综合反映光谱曲线变化的分形分维理论作为具有潜在应用价值的方法可以较好地用于植被理化参数的反演。
提出植被理化参数反演中的三个尺度：叶片尺度、冠层尺度和像元尺度,从获取地物反射率或光谱指数和进行反演两个步骤展开对理化参数反演中尺度效应问题的分析。利用叶片光学模型PROSPECT-5及其与冠层辐射传输模型4SAIL耦合的PROSAIL模型,结合机载AISA高光谱遥感影像数据,分析三个尺度上植被反射率、植被指数的尺度变化规律；在像元尺度上,以叶绿素含量为例定量分析不同混合像元组合类型、不同植被覆盖度及不同空间分辨率对理化参数反演的影响。结果表明,植被反射率从叶片尺度到冠层尺度的变化受LAI的影响最为强烈,从冠层尺度到像元尺度植被覆盖度是影响反射率变化的主要因素；随着植被覆盖度和空间分辨率的变化,反射率和线性植被指数具有尺度不变的特征；而非线性指数则具有明显的尺度变化特征,并且离散林地相对于连续林地尺度的变化特征更为明显；叶绿素反演值和真实值的关系与像元中地物的组成类型以及像元内植被所占的面积比例都有密切的关系,随着空间分辨率的降低,叶绿素反演值与真实值的差值逐渐减小
基于模型的模拟数据,采用EFAST全局敏感性分析方法,定量分析叶片尺度、冠层尺度和像元尺度下植被理化参数如叶绿素含量、类胡萝卜素含量、含水量、干物质含量、叶片结构参数、LAI、叶倾角以及土壤背景等对植被反射率的敏感性变化。结果显示,叶片尺度上,叶片反射率对叶绿素含量、含水量和叶片结构参数最为敏感；冠层尺度上,LAI较低时,LAI是冠层反射率最为敏感的理化参数,LAI较高时,叶绿素、干物质和含水量成为分别影响可见光、近红外和短波红外冠层反射率的主要理化参数；像元尺度上,像元内植被所占的面积是影响像元反射率变化的最主要因素,其他参数如LAI、叶绿素、含水量、干物质、冠层的土壤背景信息等对像元反射率变化的贡献都较小。进一步分析理化参数对光谱指数的敏感性随尺度的变化规律,选择了在三个尺度上用于反演叶绿素含量、类胡萝卜素含量、含水量、干物质含量和叶面积指数的最佳高光谱指数和多光谱指数。
在上述研究的基础上,论文还对植被理化参数遥感反演研究当前存在的问题和发展趋势进行了分析和展望。
As one of the most important components of the ecosystem, vegetation provides matter and energy for almost all organism including humans. Many ecological processes about the exchange in matter and energy, like photosynthesis, evapotranspiration, respiratory, primary productivity, decomposition and so on, are closely related to vegetation biophysical and biochemical variables, so estimating these vegetation variables has a vital significance. Remote sensing data plays a crucial role in monitoring earth biosphere and dynamic change of vegetation. With the development of hyperspectral imaging techniques, devising vegetation biophysical and biochemical variables quantitatively becomes a research hotspot.
Remote sensing technique provides a series of remote sensing data with different spatial and spectral resolution, and the scale issues in remote sensing application follow. The unique scale characteristics make the scale issues in vegetation physicochemical parameters inversion more prominent. Based on the field experiment, airborne AISA hyperspectral data as well as PROSAIL model, we researched systemically the scale issues in vegetation physicochemical parameter inversion and the sensitivity change of vegetation reflectance to vegetation physicochemical parameter, which were wished to provide adequate theoretical basis for accurately inversing vegetation physicochemical parameters.
Based on the field experiment, the relationships between chlorophyll content and vegetation reflectance, derivative of the reflectance and vegetation indices in leaf and canopy scale were analyzed quantitatively. In addition, the factor analysis method and the fractal dimension were used in this paper to retrieve vegetation physicochemical parameters. The results show that there are great differences of relationships between leaf scale and canopy scale, which illustrate the scale problems in the physicochemical parameters inversion to a certain extent. The results also prove that the factor analysis method reducing dimension and the fractal dimension reflecting synthetic variations of vegetation reflectance have potential applications on vegetation physicochemical parameters inversion.
In this paper, leaf scale, canopy scale and pixel scale were proposed as three consecutive scales in vegetation physicochemical parameters inversion, and the scale effect was analyzed from two steps of obtaining landmark reflectance or spectral index and inversing parameters. Based on the AISA hyperspectral data and the simulated data from PROSPECT model and PROSAIL model, we analyzed the spatial scale effects of vegetation reflectance and vegetation indices at leaf, canopy and pixel scale, and the effect of different combination type in the pixel, different vegetation coverage and different spatial resolution on the physicochemical parameters inversionat pixel scale. The results show that from leaf scale to canopy scale, the scale variation of vegetation reflectance is most strongly impacted by LAI, while by vegetation coverage from canopy scale to pixel scale. At pixel scale, the linear vegetation index DVI shows a scale-invariant feature, while the nonlinear index NDV1shows significant scale variation, which is more obvious in the continuous woodland. The relationship between the inversed chlorophyll content and the true content has close related to the element type and the vegetation area in the pixel. With the reduced spatial resolution, the difference between the inversed chlorophyll content and the true content gradually decreases.
Based on the simulated data from PROSPECT and PROSAIL model, the sensitivity of vegetation reflectance at leaf, canopy and pixel scale to vegetation physiochemical parameters (chlorophyll content, carotenoid content, water content, dry matter content,leaf structure parameter, LAI, leaf angle and soil background) were analyzed adopting the EFAST global sensitivity analysis method. The results show that at leaf scale, chlorophyll, water content and leaf structure parameter are the most sensitive variables to leaf reflectance. At canopy scale, LAI is the most important variable to canopy reflectance in lower foliage cover (LAI<3), and as LAI increased, chlorophyll a+b, dry matter content, and water content control the variation of canopy reflectance in VIS, NIR and SWIR regions respectively. At pixel scale, the vegetation area in the pixel is the most important parameter to the reflectance variety, and the other variables like LAI, chlorophyll, water content, dry matter content, soil background and son on have no significant contribution to the variation of pixel reflectance. In addition, we analyzed the sensitivity of physicochemical parameters to spectral indices with scale changing, and the best hyperspectral indices and multispectral indices used to retrieve chlorophyll content, carotenoid content, water content, dry matter content and LAI are chose at different scale, by calculating and comparing the correlation coefficient between vegetation indices and vegetation physiochemical variables.
Based on the study mentioned above, we analyzed the problems existed in vegetation physicochemical parameters diversion at present, and discussed the overview of its trends.
遥感技术的迅速发展,为地物信息的提取提供了各种不同空间尺度和光谱尺度的遥感数据,随之而来的是遥感应用中所面临的各种尺度问题,植被所具有的独特尺度特征,使得系统地分析理化参数反演的尺度效应问题显得尤为重要。本文的研究目的在于以机载AISA高光谱影像为数据源,将野外实测数据与数值模拟相结合,系统、全面地对植被理化参数反演过程中存在的尺度效应进行定量分析,并在此基础上进一步研究理化参数敏感性以及理化参数反演精度的尺度变化规律,探讨不同尺度上理化参数反演方法的选择,以期为植被理化参数的高精度定量反演提供充足的理论依据。
基于野外实测数据,分别在叶片尺度和冠层尺度上分析叶绿素含量与反射率、反射率导数以及多种光谱指数的相关关系。同时,以反演叶片叶绿素含量为例,将因子分析方法和分形分维理论应用于植被理化参数的提取中。结果表明,反射率、反射率导数以及光谱指数与叶绿素含量的关系在叶片和冠层尺度上都有很大的差异,在一定程度上说明了理化参数反演中尺度问题的存在。基于降维思想的因子分析方法和能够综合反映光谱曲线变化的分形分维理论作为具有潜在应用价值的方法可以较好地用于植被理化参数的反演。
提出植被理化参数反演中的三个尺度：叶片尺度、冠层尺度和像元尺度,从获取地物反射率或光谱指数和进行反演两个步骤展开对理化参数反演中尺度效应问题的分析。利用叶片光学模型PROSPECT-5及其与冠层辐射传输模型4SAIL耦合的PROSAIL模型,结合机载AISA高光谱遥感影像数据,分析三个尺度上植被反射率、植被指数的尺度变化规律；在像元尺度上,以叶绿素含量为例定量分析不同混合像元组合类型、不同植被覆盖度及不同空间分辨率对理化参数反演的影响。结果表明,植被反射率从叶片尺度到冠层尺度的变化受LAI的影响最为强烈,从冠层尺度到像元尺度植被覆盖度是影响反射率变化的主要因素；随着植被覆盖度和空间分辨率的变化,反射率和线性植被指数具有尺度不变的特征；而非线性指数则具有明显的尺度变化特征,并且离散林地相对于连续林地尺度的变化特征更为明显；叶绿素反演值和真实值的关系与像元中地物的组成类型以及像元内植被所占的面积比例都有密切的关系,随着空间分辨率的降低,叶绿素反演值与真实值的差值逐渐减小
基于模型的模拟数据,采用EFAST全局敏感性分析方法,定量分析叶片尺度、冠层尺度和像元尺度下植被理化参数如叶绿素含量、类胡萝卜素含量、含水量、干物质含量、叶片结构参数、LAI、叶倾角以及土壤背景等对植被反射率的敏感性变化。结果显示,叶片尺度上,叶片反射率对叶绿素含量、含水量和叶片结构参数最为敏感；冠层尺度上,LAI较低时,LAI是冠层反射率最为敏感的理化参数,LAI较高时,叶绿素、干物质和含水量成为分别影响可见光、近红外和短波红外冠层反射率的主要理化参数；像元尺度上,像元内植被所占的面积是影响像元反射率变化的最主要因素,其他参数如LAI、叶绿素、含水量、干物质、冠层的土壤背景信息等对像元反射率变化的贡献都较小。进一步分析理化参数对光谱指数的敏感性随尺度的变化规律,选择了在三个尺度上用于反演叶绿素含量、类胡萝卜素含量、含水量、干物质含量和叶面积指数的最佳高光谱指数和多光谱指数。
在上述研究的基础上,论文还对植被理化参数遥感反演研究当前存在的问题和发展趋势进行了分析和展望。
As one of the most important components of the ecosystem, vegetation provides matter and energy for almost all organism including humans. Many ecological processes about the exchange in matter and energy, like photosynthesis, evapotranspiration, respiratory, primary productivity, decomposition and so on, are closely related to vegetation biophysical and biochemical variables, so estimating these vegetation variables has a vital significance. Remote sensing data plays a crucial role in monitoring earth biosphere and dynamic change of vegetation. With the development of hyperspectral imaging techniques, devising vegetation biophysical and biochemical variables quantitatively becomes a research hotspot.
Remote sensing technique provides a series of remote sensing data with different spatial and spectral resolution, and the scale issues in remote sensing application follow. The unique scale characteristics make the scale issues in vegetation physicochemical parameters inversion more prominent. Based on the field experiment, airborne AISA hyperspectral data as well as PROSAIL model, we researched systemically the scale issues in vegetation physicochemical parameter inversion and the sensitivity change of vegetation reflectance to vegetation physicochemical parameter, which were wished to provide adequate theoretical basis for accurately inversing vegetation physicochemical parameters.
Based on the field experiment, the relationships between chlorophyll content and vegetation reflectance, derivative of the reflectance and vegetation indices in leaf and canopy scale were analyzed quantitatively. In addition, the factor analysis method and the fractal dimension were used in this paper to retrieve vegetation physicochemical parameters. The results show that there are great differences of relationships between leaf scale and canopy scale, which illustrate the scale problems in the physicochemical parameters inversion to a certain extent. The results also prove that the factor analysis method reducing dimension and the fractal dimension reflecting synthetic variations of vegetation reflectance have potential applications on vegetation physicochemical parameters inversion.
In this paper, leaf scale, canopy scale and pixel scale were proposed as three consecutive scales in vegetation physicochemical parameters inversion, and the scale effect was analyzed from two steps of obtaining landmark reflectance or spectral index and inversing parameters. Based on the AISA hyperspectral data and the simulated data from PROSPECT model and PROSAIL model, we analyzed the spatial scale effects of vegetation reflectance and vegetation indices at leaf, canopy and pixel scale, and the effect of different combination type in the pixel, different vegetation coverage and different spatial resolution on the physicochemical parameters inversionat pixel scale. The results show that from leaf scale to canopy scale, the scale variation of vegetation reflectance is most strongly impacted by LAI, while by vegetation coverage from canopy scale to pixel scale. At pixel scale, the linear vegetation index DVI shows a scale-invariant feature, while the nonlinear index NDV1shows significant scale variation, which is more obvious in the continuous woodland. The relationship between the inversed chlorophyll content and the true content has close related to the element type and the vegetation area in the pixel. With the reduced spatial resolution, the difference between the inversed chlorophyll content and the true content gradually decreases.
Based on the simulated data from PROSPECT and PROSAIL model, the sensitivity of vegetation reflectance at leaf, canopy and pixel scale to vegetation physiochemical parameters (chlorophyll content, carotenoid content, water content, dry matter content,leaf structure parameter, LAI, leaf angle and soil background) were analyzed adopting the EFAST global sensitivity analysis method. The results show that at leaf scale, chlorophyll, water content and leaf structure parameter are the most sensitive variables to leaf reflectance. At canopy scale, LAI is the most important variable to canopy reflectance in lower foliage cover (LAI<3), and as LAI increased, chlorophyll a+b, dry matter content, and water content control the variation of canopy reflectance in VIS, NIR and SWIR regions respectively. At pixel scale, the vegetation area in the pixel is the most important parameter to the reflectance variety, and the other variables like LAI, chlorophyll, water content, dry matter content, soil background and son on have no significant contribution to the variation of pixel reflectance. In addition, we analyzed the sensitivity of physicochemical parameters to spectral indices with scale changing, and the best hyperspectral indices and multispectral indices used to retrieve chlorophyll content, carotenoid content, water content, dry matter content and LAI are chose at different scale, by calculating and comparing the correlation coefficient between vegetation indices and vegetation physiochemical variables.
Based on the study mentioned above, we analyzed the problems existed in vegetation physicochemical parameters diversion at present, and discussed the overview of its trends.
引文
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