基于遥感模型和地面观测的河口湿地碳通量研究
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摘要
陆地生态系统碳循环是全球气候变化研究的核心内容之一。河口湿地是陆地生态系统碳库的重要组成部分,同时作为一种重要的湿地类型,具有其独特的物质循环和能量收支方式,并且对人为或自然的干扰极为敏感。相对于其它陆地生态系统,有关河口湿地碳循环的研究还比较少,对其过程以及驱动因子还了解甚少。涡度协方差技术(eddy covariance)可以提供长期连续的观测数据,但其观测结果本身却只能代表特定生态系统在特定环境中的碳通量特征,不能直接应用在其它区域。遥感是目前进行生态系统变化的大尺度、连续和定量观测的唯一可行的手段,遥感数据为河口湿地碳通量的模拟及尺度推绎提供了可能。本研究基于崇明东滩建立的长期定位野外观测站,采用涡度协方差技术对长江河口湿地的二氧化碳通量进行直接监测,为河口湿地生态系统的碳通量及其变化提供精确估计。本研究尝试融合涡度协方差技术和遥感技术,评估应用遥感模型估算河口湿地碳通量的可行性,并尝试建立一种适合河口湿地生态系统特点的碳通量估算模型,同时揭示河口湿地的潮汐过程对生态系统的碳收支平衡和净生态系统碳交换的影响。主要结论如下:
1)通过基于MODIS计算的光谱指数与微气象因子[如水汽压亏缺(VPD)、土壤含水量(VWC)、土壤温度等]之间关系的分析,研究发现遥感光谱指数能较好地反映研究区内主要微气象因子的时间动态,并准确地捕获了许多微气象因子在通量站点间的空间差异。这些空间差异主要是由站点的植被组成和覆盖不同及离海距离的远近所导致的。在近海站点,地表水分指数(LSWI)和增强植被指数(EVI)被用来解释主要微气象因子季节变化,相对于距离海水较远的站点,LSWI被用来解释主要微气象因子的时间变化。本研究提示,用遥感数据对地表参数的反演需要根据生态系统的空间异质性选用不同的遥感参数。
2)通过对从大坝到近海的沿东—西横断面上6个样地的EVI和LSWI的时间序列分析,研究发现EVI、LSWI以及二者之间的差值(DVEL)均能反映出横断面上的植物群落类型和分布,及地表水分的变化特征,并可判断卫星过境前后各样地受潮水淹没状况。然而,遥感数据监测的难度会随着背景复杂性的增加而增大(如从近海到大坝)。鉴于河口湿地土壤背景变化多样,EVI能用于分析植被,但却难于扣除水分的影响。因此,本文构建一个融合植被信息和水分信息的综合湿地指数,并发现年均综合湿地指数可以更好地反映湿地的植被演替过程,例如当基于EVI的综合湿地指数(EVI/LSWI)小于0.25时,该区域处在演替发展前期,表现为无任何植被覆盖,经常被潮水淹没;当综合湿地指数介于0.25-0.75之间时,该区域则处在演替先锋阶段,表现为稀疏的植被覆盖和大面积的裸地等。
3)受潮汐作用的影响,河口湿地生态系统有着独特的碳循环过程,即横向碳交换以及湿地土壤在厌氧条件下甲烷气体的释放。可见,用于非湿地的经典遥感模型(光能利用效率(LUE)模型)不能直接应用于河口湿地初级生产力(GPP)的估算。本研究结果表明LUE模型估算的GPP和碳通量塔观测的GPP之间存在的差异无法完全用观测误差来进行解释,模型的拟合度也很低(R~2=0.55),这远远低于文献报导的关于该模型在森林、草原等生态系统中估算GPP的准确性(R~2在0.85以上)。由此,我们将潮汐作用所可能导致的横向碳交换及湿地土壤由于厌氧环境下产生的甲烷气体释放的影响加入到模型中,并选择地表水分指数(LSWI)、蒸发蒸腾量(ET)和潮高(TH)作为其解释变量,模型的拟合度得到明显改进。改进后的模型估算的GPP与碳通量塔观测的GPP之间匹配良好(R~2=0.88)。
4)为了分析遥感数据估算NEE及模拟环境梯度上NEE变化特征的可行性,本研究结合通量塔测量的NEE数据和遥感数据,采用分段回归模型估算NEE,其中模型的解释因子为地表反射率(7个波段)和光谱指数[NDVI、EVI、LSWI及LSWI(2100)],这些解释因子均来自MODIS的8d合成数据。结果显示模型估算的NEE与实测的NEE之间匹配良好(R~2=0.78),这说明用MODIS数据来估算NEE是可行的,而且模型估算的NEE值能从整体上反映研究范围内的NEE时空变异性以及受不同驱动因子而呈现的NEE变化特征。高潮带体现了由植被季节性变化而驱动的碳循环过程,而低潮带体现了因潮汐活动等因子驱动的碳循环过程。
5)本研究结合基于涡度协方差技术的通量塔测量的数据,全面探讨了遥感数据在河口湿地碳循环的影响因子分析以及碳通量估算中的可行性及研究方法上的改进。研究表明本研究提出的模型在河口湿地GPP和NEE估算以及其季节性动态的定量评价上具有一定的潜力,这为全球变化背景下的河口湿地碳通量闭合研究提供了借鉴。
The carbon cycle of terrestrial ecosystem is an important part of global climate change study. Estuarine wetland, as an important type of terrestrial ecosystems, has its unique material cycle and energy budget and plays an important role in the carbon cycle. Estuarine wetland also shows high sensitivity to global climate change and is vulnerable to natural or anthropogenic disturbance. Compared to other terrestrial ecosystems, rare research has been done on carbon cycle of the estuarine wetland, especially on its processes and driving factors. Nowadays, eddy covariance (EC) technology has provided continuous measurement on carbon exchange at ecosystem level. However, the measurement can only represent the carbon flux at scale of tower footprint. On the other hand, remote sensing technology provides the real-time surface information and shows potential on estimating the ecological parameters and carbon flux at regional or global scale. Based on the continuous measurement of three carbon flux towers established in Dongtan of Chongming Island since 2004, the impact factors of carbon cycle (micrometeorological factors and tidal flooding) and the estimation of carbon flux were explored by using remote sensing. Therefore, the aims of this dissertation is to 1) examine the relations between main micrometeorological factors and vegetation indices (VIs) estimated from remotely sensed data, and develop the inversion models of these parameters; 2) detect the spatiotemporal changes of tidal flooding inundation on estuarine wetland and evaluate the vegetation successional stages using remotely sensed time series data; 3) close the carbon budget of estuarine wetlands by coupling tower-based measurements and remotely sensed data; 4) to estimate the net ecosystem carbon exchange (NEE) along the tidal inundation gradient and explore the effect of tidal flooding on the ecosystem carbon cycle. The major findings are summarized as follows:
1) We analyzed the correlations between VIs and micrometeorological factors at the three towers in 2005 and found that the surface spectral reflectance and VI reflected the spatiotemporal characteristics of micrometeorological factors very well in our study area, and succeeded in capturing the spatial heterogeneity at three towers (site D, M and S). VIs are potential for the inversion of the micrometeorological factors (e.g. VPD, VWC, soil temperature) and different VIs are chosen for each site due to the difference in vegetation composition and cover, and the distance from the sea. For example, the enhanced vegetation index (EVI) and the surface water index (LSWI) is the main explanatory variables at the offshore site (site S), and LSWI is the main explanatory variable at the site far from sea (site D). Our result suggests that the different VIs should be considered to estimate micrometeorological factors at different sites when remotely sensed data are used.
2) A wavelet analysis was performed on time series VIs derived from remotely sensed data to investigate the spatiotemporal dynamic of the tidal flooding inundation in tidal marsh. The results show that MODIS is suitable for monitoring tidal flooding inundation from the dam to the sea front. However, due to the complex background of estuarine wetland, EVI can well detect the vegetation dynamics but cannot exclude the effect of water content, and LSWI can well detect the water content but cannot exclude the effect of vegetation. Therefore, on the basis of the time series analysis of vegetation indices, vegetation-water index (VWI) that merges vegetation and water information was developed to track the vegetation succession in estuarine wetland and the yearly value of VWI is nore more effective. For example, at the primary successional stage, VWR is less than 0.25; at the pioneer stage; VWR is between 0.25 and 0.75; at development stage, VWR is between 0.5 and 2.0.
3) Compared to other ecosystems, estuarine wetland shows distinct carbon flux dynamics—the lateral carbon flux incurred by tidal flooding and methane generation under anaerobic conditions of wetland soils. In this study, we estimated the 2005's annual carbon budget of an estuarine wetland on Chongming Island and partitioned the losses of carbon due to lateral tidal dynamics and anaerobic methane production using innovative technique. The average GPP calculated from eddy covariance between March and November was 5.65 g C m~(-2) day~(-1), whereas that from the LUE model was 1.27 g C m~(-2) day~(-1). The correlation coefficient between GPP simulated from the LUE model and that calculated from flux tower data was low in the growing season (R~2=0.55). We further hypothesized that tidal activities and uncounted methane release were responsible for the discrepancy, which partly can be predicted from measurements of LSWI, evapotranspiration (ET) and tide height (TH). We developed an integrated GPP model by combining the LUE model and an autoregression model. The average GPP from the integrated model increased to 5.69 g C m~(-2) day~(-1), and R~2 for the correlation between the simulated and calculated data increased to 0.88, demonstrating the potential of our technique for GPP estimation and quantification of seasonal variation in estuarine ecosystems.
4) To find how the spatial pattern of NEE varies along tidal inundation gradient, we coupled MODIS data and the tower-based NEE measurement to develop a NEE estimation model by using a piecewise regression model. The results show that the model gives a fairly good prediction of NEE (R~2=0.78, p<0.01). We then applied the model to estimate NEE for each 500x500m cell across the transect covering 500 m×3000 m in 2005～2006. Our empirical model captures the expected spatiotemporal patterns of NEE along the tidal inundation gradient. The variations of NEE near the island-side are mainly caused by seasonal shift and yearly cycle of vegetation, whereas in the ocean-side, NEE is more influenced by tidal activities, and in the middle area, NEE is subjected to both phenological conditions of vegetation and tidal cycles. We estimated that the average NEE varies from -2.4 to -1.8 g C m~(-2) d~(-1) along the tidal inundation gradient from island-side to ocean-side. In conclude, this study illustrates that our NEE estimation model derived from MODIS and tower-base flux data is effective for estimating NEE in the similar ecosystem, and the estimates are useful for analyzing the spatiotemporal pattern of NEE and the impacts of climate variability and disturbances on estuarine wetland carbon fluxes.
5) Coupling the observed data of carbon flux towers, the comprehensive research was conducted to analyze the feasibility of remote sensing data in the evaluation and estimation of the carbon flux in the estuarine wetland, and then the improved models were developed and demonstrated the great potential for correcting unavoidable errors when estimating carbon budget of the estuarine wetland. Our study can be used for reference to study the carbon flux in the similar ecosystem as the estuarine wetland in the context of the global climate change.
1)通过基于MODIS计算的光谱指数与微气象因子[如水汽压亏缺(VPD)、土壤含水量(VWC)、土壤温度等]之间关系的分析,研究发现遥感光谱指数能较好地反映研究区内主要微气象因子的时间动态,并准确地捕获了许多微气象因子在通量站点间的空间差异。这些空间差异主要是由站点的植被组成和覆盖不同及离海距离的远近所导致的。在近海站点,地表水分指数(LSWI)和增强植被指数(EVI)被用来解释主要微气象因子季节变化,相对于距离海水较远的站点,LSWI被用来解释主要微气象因子的时间变化。本研究提示,用遥感数据对地表参数的反演需要根据生态系统的空间异质性选用不同的遥感参数。
2)通过对从大坝到近海的沿东—西横断面上6个样地的EVI和LSWI的时间序列分析,研究发现EVI、LSWI以及二者之间的差值(DVEL)均能反映出横断面上的植物群落类型和分布,及地表水分的变化特征,并可判断卫星过境前后各样地受潮水淹没状况。然而,遥感数据监测的难度会随着背景复杂性的增加而增大(如从近海到大坝)。鉴于河口湿地土壤背景变化多样,EVI能用于分析植被,但却难于扣除水分的影响。因此,本文构建一个融合植被信息和水分信息的综合湿地指数,并发现年均综合湿地指数可以更好地反映湿地的植被演替过程,例如当基于EVI的综合湿地指数(EVI/LSWI)小于0.25时,该区域处在演替发展前期,表现为无任何植被覆盖,经常被潮水淹没;当综合湿地指数介于0.25-0.75之间时,该区域则处在演替先锋阶段,表现为稀疏的植被覆盖和大面积的裸地等。
3)受潮汐作用的影响,河口湿地生态系统有着独特的碳循环过程,即横向碳交换以及湿地土壤在厌氧条件下甲烷气体的释放。可见,用于非湿地的经典遥感模型(光能利用效率(LUE)模型)不能直接应用于河口湿地初级生产力(GPP)的估算。本研究结果表明LUE模型估算的GPP和碳通量塔观测的GPP之间存在的差异无法完全用观测误差来进行解释,模型的拟合度也很低(R~2=0.55),这远远低于文献报导的关于该模型在森林、草原等生态系统中估算GPP的准确性(R~2在0.85以上)。由此,我们将潮汐作用所可能导致的横向碳交换及湿地土壤由于厌氧环境下产生的甲烷气体释放的影响加入到模型中,并选择地表水分指数(LSWI)、蒸发蒸腾量(ET)和潮高(TH)作为其解释变量,模型的拟合度得到明显改进。改进后的模型估算的GPP与碳通量塔观测的GPP之间匹配良好(R~2=0.88)。
4)为了分析遥感数据估算NEE及模拟环境梯度上NEE变化特征的可行性,本研究结合通量塔测量的NEE数据和遥感数据,采用分段回归模型估算NEE,其中模型的解释因子为地表反射率(7个波段)和光谱指数[NDVI、EVI、LSWI及LSWI(2100)],这些解释因子均来自MODIS的8d合成数据。结果显示模型估算的NEE与实测的NEE之间匹配良好(R~2=0.78),这说明用MODIS数据来估算NEE是可行的,而且模型估算的NEE值能从整体上反映研究范围内的NEE时空变异性以及受不同驱动因子而呈现的NEE变化特征。高潮带体现了由植被季节性变化而驱动的碳循环过程,而低潮带体现了因潮汐活动等因子驱动的碳循环过程。
5)本研究结合基于涡度协方差技术的通量塔测量的数据,全面探讨了遥感数据在河口湿地碳循环的影响因子分析以及碳通量估算中的可行性及研究方法上的改进。研究表明本研究提出的模型在河口湿地GPP和NEE估算以及其季节性动态的定量评价上具有一定的潜力,这为全球变化背景下的河口湿地碳通量闭合研究提供了借鉴。
The carbon cycle of terrestrial ecosystem is an important part of global climate change study. Estuarine wetland, as an important type of terrestrial ecosystems, has its unique material cycle and energy budget and plays an important role in the carbon cycle. Estuarine wetland also shows high sensitivity to global climate change and is vulnerable to natural or anthropogenic disturbance. Compared to other terrestrial ecosystems, rare research has been done on carbon cycle of the estuarine wetland, especially on its processes and driving factors. Nowadays, eddy covariance (EC) technology has provided continuous measurement on carbon exchange at ecosystem level. However, the measurement can only represent the carbon flux at scale of tower footprint. On the other hand, remote sensing technology provides the real-time surface information and shows potential on estimating the ecological parameters and carbon flux at regional or global scale. Based on the continuous measurement of three carbon flux towers established in Dongtan of Chongming Island since 2004, the impact factors of carbon cycle (micrometeorological factors and tidal flooding) and the estimation of carbon flux were explored by using remote sensing. Therefore, the aims of this dissertation is to 1) examine the relations between main micrometeorological factors and vegetation indices (VIs) estimated from remotely sensed data, and develop the inversion models of these parameters; 2) detect the spatiotemporal changes of tidal flooding inundation on estuarine wetland and evaluate the vegetation successional stages using remotely sensed time series data; 3) close the carbon budget of estuarine wetlands by coupling tower-based measurements and remotely sensed data; 4) to estimate the net ecosystem carbon exchange (NEE) along the tidal inundation gradient and explore the effect of tidal flooding on the ecosystem carbon cycle. The major findings are summarized as follows:
1) We analyzed the correlations between VIs and micrometeorological factors at the three towers in 2005 and found that the surface spectral reflectance and VI reflected the spatiotemporal characteristics of micrometeorological factors very well in our study area, and succeeded in capturing the spatial heterogeneity at three towers (site D, M and S). VIs are potential for the inversion of the micrometeorological factors (e.g. VPD, VWC, soil temperature) and different VIs are chosen for each site due to the difference in vegetation composition and cover, and the distance from the sea. For example, the enhanced vegetation index (EVI) and the surface water index (LSWI) is the main explanatory variables at the offshore site (site S), and LSWI is the main explanatory variable at the site far from sea (site D). Our result suggests that the different VIs should be considered to estimate micrometeorological factors at different sites when remotely sensed data are used.
2) A wavelet analysis was performed on time series VIs derived from remotely sensed data to investigate the spatiotemporal dynamic of the tidal flooding inundation in tidal marsh. The results show that MODIS is suitable for monitoring tidal flooding inundation from the dam to the sea front. However, due to the complex background of estuarine wetland, EVI can well detect the vegetation dynamics but cannot exclude the effect of water content, and LSWI can well detect the water content but cannot exclude the effect of vegetation. Therefore, on the basis of the time series analysis of vegetation indices, vegetation-water index (VWI) that merges vegetation and water information was developed to track the vegetation succession in estuarine wetland and the yearly value of VWI is nore more effective. For example, at the primary successional stage, VWR is less than 0.25; at the pioneer stage; VWR is between 0.25 and 0.75; at development stage, VWR is between 0.5 and 2.0.
3) Compared to other ecosystems, estuarine wetland shows distinct carbon flux dynamics—the lateral carbon flux incurred by tidal flooding and methane generation under anaerobic conditions of wetland soils. In this study, we estimated the 2005's annual carbon budget of an estuarine wetland on Chongming Island and partitioned the losses of carbon due to lateral tidal dynamics and anaerobic methane production using innovative technique. The average GPP calculated from eddy covariance between March and November was 5.65 g C m~(-2) day~(-1), whereas that from the LUE model was 1.27 g C m~(-2) day~(-1). The correlation coefficient between GPP simulated from the LUE model and that calculated from flux tower data was low in the growing season (R~2=0.55). We further hypothesized that tidal activities and uncounted methane release were responsible for the discrepancy, which partly can be predicted from measurements of LSWI, evapotranspiration (ET) and tide height (TH). We developed an integrated GPP model by combining the LUE model and an autoregression model. The average GPP from the integrated model increased to 5.69 g C m~(-2) day~(-1), and R~2 for the correlation between the simulated and calculated data increased to 0.88, demonstrating the potential of our technique for GPP estimation and quantification of seasonal variation in estuarine ecosystems.
4) To find how the spatial pattern of NEE varies along tidal inundation gradient, we coupled MODIS data and the tower-based NEE measurement to develop a NEE estimation model by using a piecewise regression model. The results show that the model gives a fairly good prediction of NEE (R~2=0.78, p<0.01). We then applied the model to estimate NEE for each 500x500m cell across the transect covering 500 m×3000 m in 2005～2006. Our empirical model captures the expected spatiotemporal patterns of NEE along the tidal inundation gradient. The variations of NEE near the island-side are mainly caused by seasonal shift and yearly cycle of vegetation, whereas in the ocean-side, NEE is more influenced by tidal activities, and in the middle area, NEE is subjected to both phenological conditions of vegetation and tidal cycles. We estimated that the average NEE varies from -2.4 to -1.8 g C m~(-2) d~(-1) along the tidal inundation gradient from island-side to ocean-side. In conclude, this study illustrates that our NEE estimation model derived from MODIS and tower-base flux data is effective for estimating NEE in the similar ecosystem, and the estimates are useful for analyzing the spatiotemporal pattern of NEE and the impacts of climate variability and disturbances on estuarine wetland carbon fluxes.
5) Coupling the observed data of carbon flux towers, the comprehensive research was conducted to analyze the feasibility of remote sensing data in the evaluation and estimation of the carbon flux in the estuarine wetland, and then the improved models were developed and demonstrated the great potential for correcting unavoidable errors when estimating carbon budget of the estuarine wetland. Our study can be used for reference to study the carbon flux in the similar ecosystem as the estuarine wetland in the context of the global climate change.
引文
*http://www.fsl.orst.edu/larse/bigfoot/index.html
*http://ipcc～wgl.ucar.edu/wgl-report.html
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