Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest ...Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.展开更多
快速、可靠、精确的评估植被冠层结构参数在大气—植被相互作用的研究中起着举足轻重的作用。从冠层结构参数的反演原理、冠层间隙度的提取、冠层结构的反演模型和丛生指数3个方面论述了冠层结构参数即叶面积指数(LAI,leaf area index)...快速、可靠、精确的评估植被冠层结构参数在大气—植被相互作用的研究中起着举足轻重的作用。从冠层结构参数的反演原理、冠层间隙度的提取、冠层结构的反演模型和丛生指数3个方面论述了冠层结构参数即叶面积指数(LAI,leaf area index)和叶倾角分布(LAD,leaf angle distribu-tion)的反演方法,并从鱼眼像片的采集、分析和模型的假设等方面分析影响冠层结构参数反演精度的原因,指出未来鱼眼影像技术虽然是LAI和LAD间接测量的理想手段,但是受观测环境、相机光学特性和冠层本身的影响,反演结果需要通过验证来消除不确定因素。展开更多
聚集指数CI(Clumping Index)是植被冠层的一个重要结构参数,对植被冠层的辐射截获,以及全球碳、水循环的研究均有重要作用。现有星载CI产品的估算主要是基于CI-NDHD(Normalized Difference between Hotspot and Dark spot)线性模型方法...聚集指数CI(Clumping Index)是植被冠层的一个重要结构参数,对植被冠层的辐射截获,以及全球碳、水循环的研究均有重要作用。现有星载CI产品的估算主要是基于CI-NDHD(Normalized Difference between Hotspot and Dark spot)线性模型方法,由于针叶林和阔叶林在叶片尺度上存在聚集层级的差异,该模型对它们分别采用了不同的模型系数。但是,该模型对中粗分辨率的针阔混交林像元通常采用阔叶林的CI反演系数,因此,理论上会导致该类型CI的高估。为此,本文提出了一种动态选取混交林像元端元CI组分的方法,以改进针阔混交林植被聚集指数的估算精度。首先,通过国际地圈—生物圈计划(IGBP)的地表类型和描述二向性反射分布函数BRDF(Bidirectional Reflectance Distribution Function)特征的地表各向异性平整指数AFX(Anisotropic Flat Index)进行双重约束,逐像元地计算端元CI值;然后,结合高分辨率的土地覆盖分类数据确定端元在像元中的面积比例,并估算MODIS针阔混交林像元的聚集指数MFCI(Mixed Forest CI);最后,将方法应用于研究区MODIS数据的MFCI估算,并通过地面实测数据进行精度评价。结果表明:目前的MODIS产品算法高估了针阔混交林像元的CI值,而MFCI估算方法在CI-NDHD算法的基础上,可以较显著地改善该类型聚集指数的估算精度,当针叶林树种成数达到60%时,精度改善可达28.03%,其中,改进结果的均方根误差(RMSE)和偏差(Bias)各降低约84%和175%。研究表明,MFCI方法对针阔混合像元的端元组分的变化敏感,在高分辨率地表分类已知的条件下,MFCI方法为针阔混交林CI产品生产和精度提高提供了可行的解决方案。展开更多
叶面积指数LAI(Leaf Area Index)是表征植被冠层结构特征的一个重要参数,已经成为多个对地观测系统的陆表参数标准产品,也是定量遥感模型的重要输入参数。快速、准确地获取植被LAI对于开展遥感产品验证、促进遥感模型的发展具有极为重...叶面积指数LAI(Leaf Area Index)是表征植被冠层结构特征的一个重要参数,已经成为多个对地观测系统的陆表参数标准产品,也是定量遥感模型的重要输入参数。快速、准确地获取植被LAI对于开展遥感产品验证、促进遥感模型的发展具有极为重要的意义。随着传感器性能与应用软件功能扩展,智能手机已经成为植被LAI测量的新选择。然而,由于手机成像传感器窄视场角的限制,现有算法依赖于叶倾角分布函数为球型分布的假设,即G函数(单位叶面积在垂直于观测天顶角的平面上的投影)恒等于0.5。因而,传统算法无法解决植被叶倾角分布未知的情况。本文提出了一种基于形状匹配的G函数估算方法,基于有限长度方法和多幅影像间隙率,计算样方内的植被冠层聚集指数,利用泊松分布模型分别得到了植被冠层有效叶面积指数(LAI_(eff))和真实叶面积指数(LAI_(tru)),并用黑龙江海伦农场两种农作物类型(玉米和大豆)的破坏性测量得到的时间序列真实LAI数据(LAI_(des))对算法进行了验证。结果表明,算法改进之前的均方根误差(RMSE)分别是0.84(垂直拍摄)和1.33(倾斜57°拍摄),改进后LAI_(eff)(有效LAI)和LAI_(tru)(真实LAI)的RMSE为分别为0.58(垂直拍摄)和0.56(垂直拍摄)。新算法得到的LAI值在时间序列变化趋势上与实测值更为一致。本文算法扩展了农作物LAI测量方法,为从智能手机影像中快速、准确提取植被LAI提供了可能。后续研究将会从分析外部光照环境变化对测量结果的影响和增加不同植被类型的验证数据两个方向进一步开展工作。展开更多
叶面积指数(leaf area index,LAI)是陆地生态系统最重要的结构参数之一,遥感和基于冠层孔隙率模型的光学仪器观测是快速获取LAI的有效方法,但由于植被叶片的聚集效应,这些方法通常只能获取有效叶面积指数(effectiveLAI,LAIe).本文以东...叶面积指数(leaf area index,LAI)是陆地生态系统最重要的结构参数之一,遥感和基于冠层孔隙率模型的光学仪器观测是快速获取LAI的有效方法,但由于植被叶片的聚集效应,这些方法通常只能获取有效叶面积指数(effectiveLAI,LAIe).本文以东北林业大学帽儿山实验林场为研究区,利用LAI2000观测森林冠层LAIe,并结合TRAC观测的叶片聚集度系数估算了森林冠层LAI,并通过分析基于Landsat5-TM数据计算的不同植被指数与LAIe之间的关系,建立了该区森林LAI遥感估算模型.结果表明:研究区阔叶林的LAI和LAIe基本相当,而针叶林的LAI比LAIe大27%;减化比值植被指数(reduced simple ratio,RSR)与该区LAIe的相关性最好(R2=0.763,n=23),最适合该区LAI的遥感提取.当海拔<400 m时,LAI随海拔高度的上升而快速增大;当海拔在400～750 m时,LAI随海拔高度的上升缓慢增大;当海拔>750 m时,LAI呈下降趋势.研究区森林冠层LAI与森林地上生物量存在显著的正相关关系.展开更多
文摘植被聚集指数(clumping index,CI(Ω))是表征植被冠层聚集程度的重要结构参数,由于其定量化研究起步较晚,导致对CI季相变化特征的研究不充分,结论争议较大.为此,本文基于长时间序列的MODIS CI产品,从北半球中高纬度植被物候特征敏感区,在13个国际地圈-生物圈计划(IGBP)类型中,优选了84个高质量的代表性像元,开展典型像元CI季相变化特征的案例研究.以归一化植被指数(normalized difference vegetation index,NDVI)作为对比,提出改进的动态阈值法,结合离散Fourier变换方法,分别提取不同地类的生长季开始时间(start of season,SOS)及生长季结束时间(end of season,EOS),最终建立北半球中高纬度各地类生长季与休眠期的经验Ω.结果表明:CI具有较为明显的物候变化规律及季节变化特征,甚至能够识别出耕地的一年两熟迹象,但相对于NDVI相对稳定的季相变化特征,大部分地类的CI表现出较大的变化和不确定性,其中,SOS和EOS多分别在第100和第300天左右变化,生长季则多维持在200d左右;提取物候特征参数的最佳阈值随提取时期、地物类别的变化而变化,其中提取SOS和EOS的最佳阈值多集中在40%~80%和80%~90%;经验Ω呈现出针叶林的聚集效应最强,耕地的聚集效应最弱的特征.本研究对于揭示不同地类CI季相特征及相关应用研究提供了有用的证据和参考.
基金the National Science Foundation of China(Grant Nos.41871233,41371330 , 41001203).
文摘Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.
文摘快速、可靠、精确的评估植被冠层结构参数在大气—植被相互作用的研究中起着举足轻重的作用。从冠层结构参数的反演原理、冠层间隙度的提取、冠层结构的反演模型和丛生指数3个方面论述了冠层结构参数即叶面积指数(LAI,leaf area index)和叶倾角分布(LAD,leaf angle distribu-tion)的反演方法,并从鱼眼像片的采集、分析和模型的假设等方面分析影响冠层结构参数反演精度的原因,指出未来鱼眼影像技术虽然是LAI和LAD间接测量的理想手段,但是受观测环境、相机光学特性和冠层本身的影响,反演结果需要通过验证来消除不确定因素。
文摘聚集指数CI(Clumping Index)是植被冠层的一个重要结构参数,对植被冠层的辐射截获,以及全球碳、水循环的研究均有重要作用。现有星载CI产品的估算主要是基于CI-NDHD(Normalized Difference between Hotspot and Dark spot)线性模型方法,由于针叶林和阔叶林在叶片尺度上存在聚集层级的差异,该模型对它们分别采用了不同的模型系数。但是,该模型对中粗分辨率的针阔混交林像元通常采用阔叶林的CI反演系数,因此,理论上会导致该类型CI的高估。为此,本文提出了一种动态选取混交林像元端元CI组分的方法,以改进针阔混交林植被聚集指数的估算精度。首先,通过国际地圈—生物圈计划(IGBP)的地表类型和描述二向性反射分布函数BRDF(Bidirectional Reflectance Distribution Function)特征的地表各向异性平整指数AFX(Anisotropic Flat Index)进行双重约束,逐像元地计算端元CI值;然后,结合高分辨率的土地覆盖分类数据确定端元在像元中的面积比例,并估算MODIS针阔混交林像元的聚集指数MFCI(Mixed Forest CI);最后,将方法应用于研究区MODIS数据的MFCI估算,并通过地面实测数据进行精度评价。结果表明:目前的MODIS产品算法高估了针阔混交林像元的CI值,而MFCI估算方法在CI-NDHD算法的基础上,可以较显著地改善该类型聚集指数的估算精度,当针叶林树种成数达到60%时,精度改善可达28.03%,其中,改进结果的均方根误差(RMSE)和偏差(Bias)各降低约84%和175%。研究表明,MFCI方法对针阔混合像元的端元组分的变化敏感,在高分辨率地表分类已知的条件下,MFCI方法为针阔混交林CI产品生产和精度提高提供了可行的解决方案。
文摘叶面积指数LAI(Leaf Area Index)是表征植被冠层结构特征的一个重要参数,已经成为多个对地观测系统的陆表参数标准产品,也是定量遥感模型的重要输入参数。快速、准确地获取植被LAI对于开展遥感产品验证、促进遥感模型的发展具有极为重要的意义。随着传感器性能与应用软件功能扩展,智能手机已经成为植被LAI测量的新选择。然而,由于手机成像传感器窄视场角的限制,现有算法依赖于叶倾角分布函数为球型分布的假设,即G函数(单位叶面积在垂直于观测天顶角的平面上的投影)恒等于0.5。因而,传统算法无法解决植被叶倾角分布未知的情况。本文提出了一种基于形状匹配的G函数估算方法,基于有限长度方法和多幅影像间隙率,计算样方内的植被冠层聚集指数,利用泊松分布模型分别得到了植被冠层有效叶面积指数(LAI_(eff))和真实叶面积指数(LAI_(tru)),并用黑龙江海伦农场两种农作物类型(玉米和大豆)的破坏性测量得到的时间序列真实LAI数据(LAI_(des))对算法进行了验证。结果表明,算法改进之前的均方根误差(RMSE)分别是0.84(垂直拍摄)和1.33(倾斜57°拍摄),改进后LAI_(eff)(有效LAI)和LAI_(tru)(真实LAI)的RMSE为分别为0.58(垂直拍摄)和0.56(垂直拍摄)。新算法得到的LAI值在时间序列变化趋势上与实测值更为一致。本文算法扩展了农作物LAI测量方法,为从智能手机影像中快速、准确提取植被LAI提供了可能。后续研究将会从分析外部光照环境变化对测量结果的影响和增加不同植被类型的验证数据两个方向进一步开展工作。
