京津冀MODIS长时序增强型植被指数拟合重建方法适用性研究
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摘要
长时间序列植被指数拟合重建的结果可为植被变化动态监测及物候信息提取、生物量信息提取、农作物产量预测及面积估算、生态质量评价及生态系统碳循环研究等提供更精准、更可靠的数据来源,从而有效反映生态环境质量。MODIS EVI数据虽经过最大值合成(MVC)处理,但仍存在云、冰雪、气溶胶等噪声。该文基于Timesat软件中非对称高斯函数拟合法(AG)、双Logistic函数拟合法(DL)、SG滤波法(SG)3种方法对京津冀2001—2015年MODIS EVI时间序列数据进行拟合重建,从时间序列、空间格局两个维度,并结合数理统计方法,对比分析了不同方法的拟合效果。结果表明:噪声比与拟合重建的方法无明显相关关系。在去噪效果和保真性、拟合优度等方面,AG拟合和DL拟合整体无明显差异,在部分像元点上AG拟合表现出更好的重建效果。SG方法可以更有效的保留原始植被特征。3种方法重建后的效果表现出与地类空间分布相关的差异性。对于京津冀地区长时序数据,AG拟合在人类扰动较小的草地、森林和灌木地区域表现出更好的重建效果,SG方法在人类活动干扰更强的耕地区域重建效果更优。
The results of fitting and reconstruction of long-term series vegetation index data can provide more accurate information and more reliable data source for vegetation dynamic monitoring,biomass information extraction,crop yield prediction and area estimation,vegetation phenological information extraction,ecological quality assessment and ecosystem carbon cycle research,which can effectively reflect the quality of ecological environment.After extensive research and verification,it is found that different fitting reconstruction methods have different suitability for different geographical environments.A large number of studies on comparative analysis methods mainly focus on qualitative analysis based on sample curve analysis and visual comparison,and quantitative analysis based on root mean square error,correlation coefficient,Akaike information criterion and Bayesian information standard.However,the evaluation indexes that quantitative analyses adopt are mostly the mean value,the maximum value,and the minimum value,which ignore the influence of abnormal values and spatial pattern differences on reconstruction result.In this study,the unchanging areas of cultivated land,forests,grasslands and shrubs in the Beijing-Tianjin-Hebei region were extracted through the spatial analysis tool in ArcGIS.Then,weights to all pixels were assigned in combination with the quality reliability of VI pixel.Next,the fitting reconstruction of the time series data of MDOSI EVI 16 d in Beijing-Tianjin-Hebei region from 2001 to 2015 were finished by asymmetric Gaussian function fitting method(AG),double logistic function fitting method(DL),and SG filtering method(SG).Before analyzing the fitting results,the fitted and original vegetation growth curves of Beijing station in 2006 were firstly extracted,then the start and end time of growing season were extracted by the dynamic threshold method.Verification was made combined with China National Specimen Information Infrastructure data and typical plant phenological observation dataset of Chinese phenological observation network.The results illustrated that the fitted vegetation growth curve by the three methods was consistent with the field observation data.The fitting result of the sampling point curve in the past 15 years was analyzed based on the analysis result of the relationship between noise ratio and fitting method.Combined with correlation coefficient,root mean square error,Akaike information criterion,Bayesian information standard,the spatial pattern of fitting result was analyzed.Finally,the method of mathematical statistics was used to quantitatively analyze the fitting result.The results showed that there was no significant difference between AG fitting and DL fitting in the denoising result.AG fitting showed better fitting reconstruction result at some pixel points,while SG filtering can preserve the original vegetation features more effectively.The reconstruction results of the three methods showed the difference related to the spatial distribution of land types.For the long-term time series data of Beijing-Tianjin-Hebei region,AG fitting showed better reconstruction result in grassland,forest and shrub areas with less human disturbance,and the result of SG filtering was better in the reconstruction of cultivated areas with stronger human activities.This study can provide reference for the fitting of time series data of vegetation in Beijing-Tianjin-Hebei region,and provide more objective and clear method support for the evaluation of the result of fitting reconstruction of time series data.
The results of fitting and reconstruction of long-term series vegetation index data can provide more accurate information and more reliable data source for vegetation dynamic monitoring,biomass information extraction,crop yield prediction and area estimation,vegetation phenological information extraction,ecological quality assessment and ecosystem carbon cycle research,which can effectively reflect the quality of ecological environment.After extensive research and verification,it is found that different fitting reconstruction methods have different suitability for different geographical environments.A large number of studies on comparative analysis methods mainly focus on qualitative analysis based on sample curve analysis and visual comparison,and quantitative analysis based on root mean square error,correlation coefficient,Akaike information criterion and Bayesian information standard.However,the evaluation indexes that quantitative analyses adopt are mostly the mean value,the maximum value,and the minimum value,which ignore the influence of abnormal values and spatial pattern differences on reconstruction result.In this study,the unchanging areas of cultivated land,forests,grasslands and shrubs in the Beijing-Tianjin-Hebei region were extracted through the spatial analysis tool in ArcGIS.Then,weights to all pixels were assigned in combination with the quality reliability of VI pixel.Next,the fitting reconstruction of the time series data of MDOSI EVI 16 d in Beijing-Tianjin-Hebei region from 2001 to 2015 were finished by asymmetric Gaussian function fitting method(AG),double logistic function fitting method(DL),and SG filtering method(SG).Before analyzing the fitting results,the fitted and original vegetation growth curves of Beijing station in 2006 were firstly extracted,then the start and end time of growing season were extracted by the dynamic threshold method.Verification was made combined with China National Specimen Information Infrastructure data and typical plant phenological observation dataset of Chinese phenological observation network.The results illustrated that the fitted vegetation growth curve by the three methods was consistent with the field observation data.The fitting result of the sampling point curve in the past 15 years was analyzed based on the analysis result of the relationship between noise ratio and fitting method.Combined with correlation coefficient,root mean square error,Akaike information criterion,Bayesian information standard,the spatial pattern of fitting result was analyzed.Finally,the method of mathematical statistics was used to quantitatively analyze the fitting result.The results showed that there was no significant difference between AG fitting and DL fitting in the denoising result.AG fitting showed better fitting reconstruction result at some pixel points,while SG filtering can preserve the original vegetation features more effectively.The reconstruction results of the three methods showed the difference related to the spatial distribution of land types.For the long-term time series data of Beijing-Tianjin-Hebei region,AG fitting showed better reconstruction result in grassland,forest and shrub areas with less human disturbance,and the result of SG filtering was better in the reconstruction of cultivated areas with stronger human activities.This study can provide reference for the fitting of time series data of vegetation in Beijing-Tianjin-Hebei region,and provide more objective and clear method support for the evaluation of the result of fitting reconstruction of time series data.
引文
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