北京西南山地森林绿量遥感反演的研究
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摘要
论文提出既能表现植被三维的特征,同时又要能够反映植被动态变化规律,本质上具有绿色色度内涵,能够更为真实地反映植被现实绿量,且适用于遥感影像数据的绿量表征指标；基于遥感影像资料结合同步样点实测数据,构建绿量经验反演模型,探寻能够达到精度要求,具有实用价值的基于像元信息分解的绿量反演模型,应用所构建的经验反演模型和研究区相关遥感影像资料,确定北京西南山地不同植被类型,不同坡向,不同海拔高度的森林绿量,分析北京西南山地森林绿量的空间分布和季节动态规律。研究结果如下：
1.本研究提出采用叶面积指数(LAI)与叶绿素含量指数(CVI)的乘积形成耦合指标来表征绿量(vegetation quantity, VQ),即VQ=LAI×CVI。其本质上是具有绿色色度内涵的叶面积指数,能够较好的反映植被的三维空间特征,且对叶绿素含量高度灵敏。实际应用表明本文所界定的绿量指标能够在遥感影像中更为真实地反映植被的现实绿量。
2.回归模型对比分析、绿量模拟值与实测值散点图分析和森林绿量季节动态监测表明,TSAVI作为自变量的乘幂回归方程适合作为绿量反演模型输入参数(R2=0.918, RMSE=1.534),绿量经验反演模型为：VQ=14.892×TSAVI0.779
3.叶面积指数实测值与反演值之间的拟合精度检验表明,基于像元信息分解的绿量反演模型能够达到精度要求,可以采用像元分析法进行叶面积指数遥感反演,对于大尺度绿量反演和动态监测具有实用价值。
4.北京西南山地森林绿量空间分布差异明显,绿量主要分布在海拔500m-1500m范围的山地。绿量分布格局上,房山和门头沟交界的山地以及门头沟区和房山区与河北交界山区等区域山地森林为高绿量分布区域。北京西南山地森林绿量总体平均值为8.378,其中,阔叶林8.028、针叶林8.354、灌木林8.905。绿量分布格局受林分类型变化影响明显。坡向对绿量存在一定影响,绿量平均值阴坡9.015,阳坡7.986,相同林分类型在4个海拔梯度均表现为阴坡绿量值大于阳坡。森林平均绿量随着海拔显著升高,不同海拔区平均绿量为海拔500m以下6.764,海拔500-1000m8.54,海拔1000-1500m9.318,海拔1500m以上为11.64,可以明显的看出在海拔500m以下的森林容易遭到破坏和干扰导致绿量降低,且阴坡和阳坡绿量均值差异较大。
5.北京西南山地森林绿量变化规律与物候相关联密切。森林绿量平均值季节变化表现为：3月1.479,5月3.789,8月7.129,11月1.629,12月0.966；生长盛期(8月)绿量明显高于其他月份；阴坡和阳坡森林绿量季节变化均呈单峰曲线,在8月和12月分别达到极大值和极小值。4个海拔梯度上的植被绿量均在生长盛期(8月)达到最高值,生长停滞期(12月)达到最低值。
本研究所提出的绿量指标,能够在遥感影像中更为真实地反映植被的现实绿量。采用本文的绿量指标,基于遥感影像资料和样点实测数据,对北京西南山区森林绿量的研究结果,可为北京城市不同的功能区的绿化系统评价提供技术参数,可供北京城市森林生态效益监测和评价参考。
This dissertation proposed vegetation quantity index that was capable of describing the 3-dimensional characteristics and dynamics of vegetation, with consideration of chromaticity of greenness and accurate estimation of vegetation quantity, and at the same time applicable for remote sensing data. Based on the remote sensing imagery and synchronized measurements from sample plots, empirical retrieval model of vegetation quantity was built, and effort was made on building practical and accurate retrieval vegetation quantity models based on pixel decomposition. Using the built retrieval models and related remote sensing imagery in the research area, a variety of forest types, slope aspects, and forest vegetation quantities at different altitudes in mountain area of southwestern Beijing were identified, and also analysis the spatial distribution and seasonal dynamics of forest vegetation quantity in mountain area of south-western Beijing. The results were as follows.
(1) This study proposed using the product of leaf area index (LAI) and chlorophyll volume index (CVI) to describe the vegetation quantity, i.e. VQ=LAI X CVI. This was essentially an index with consideration of chromaticity of greenness, capable of better describing 3-dimensional characteristics of vegetation with high sensitivity on chlorophyll volume. Application showed that this index can estimate vegetation quantity more accurately from remote sensing imagery.
(2) Comparisons among regression models, estimated vegetation quantities, scatter plots of measured values, and monitoring of seasonal forest vegetation dynamics showed that the power equation with TSAVI as the independent variable can be a good estimator of vegetation quantity (R2=0.918. RMSE=1.534). The empirical retrieval vegetation quantity model is VQ=14.892×TSAVI0.779.
(3) Based on the validation of prediction accuracy using measured values of leaf area index and retrieved values, it was shown that retrieval vegetation quantity model based on pixel decomposition, which retrieves leaf area index by pixel decomposition, can meet the demand of accuracy. Thus it is useful in large-size vegetation quantity retrieval and dynamic monitoring.
(4) There was significant heterogeneity in the spatial distribution of forest vegetation quantity in mountain area of south-western Beijing, with vegetation concentrated between 500m-1500m in altitude. In terms of the pattern of vegetation quantity distribution, forests in the mountain areas between Fangshan and Mentougou as well as between Fangshan and Hebei had high intensity of vegetation quantity. The average vegetation quantity in mountain area of southwestern Beijing was 8.378, with averages for broad-leaved forest, coniferous forest, and shrubbery being 8.028,8.354, and 8.905 respectively. The distribution patterns of vegetation quantity were significantly affected by the types of forest stand. Slope aspect had some effect on vegetation quantity, with averages at shady slope and sunny slope being 9.015 and 7.986 respectively. Shady slope had higher vegetation quantities than sunny slope at all four altitudes with the same type of forest stand. Average forest vegetation quantity increased significantly with altitude, with average vegetation quantities being:6.764 below 500m,8.54 between 500-1000m,9.318 between 1000-1500m, and 11.64 above 1500m. It was obvious that average vegetation quantity below altitude of 500m was low because forests there were vulnerable to destruction and disturbance, with significant difference between shady slope and sunny slope.
(5) The dynamics of vegetation quantity in mountain area of southwestern Beijing were highly correlated with climate. The average forest vegetation quantities in different months were: March 1.47、May 3.789, August 7.129, November 1.629, December 0.966; growth season (August) had significantly higher value than other months; the seasonal dynamics of vegetation quantity at shady and sunny slopes can be both described by parabolic curves, with maximum and minimum at August and December respectively. The vegetation quantities at four altitudes all achieved their maximums in growth season (August) and their minimums in stagnation season (December).
The vegetation quantity index proposed in this study can better describe the actual vegetation quantity from remote sensing imagery. Using the index proposed by this dissertation, the results from the research on forest vegetation quantity in mountain area of southwestern Beijing based on remote sensing imagery and data measured at sample sites, can provide technical support for the assessment of ecosystems in districts of different functions in Beijing, serving as references for monitoring and assessment of the benefit of forest ecosystems in Beijing.
1.本研究提出采用叶面积指数(LAI)与叶绿素含量指数(CVI)的乘积形成耦合指标来表征绿量(vegetation quantity, VQ),即VQ=LAI×CVI。其本质上是具有绿色色度内涵的叶面积指数,能够较好的反映植被的三维空间特征,且对叶绿素含量高度灵敏。实际应用表明本文所界定的绿量指标能够在遥感影像中更为真实地反映植被的现实绿量。
2.回归模型对比分析、绿量模拟值与实测值散点图分析和森林绿量季节动态监测表明,TSAVI作为自变量的乘幂回归方程适合作为绿量反演模型输入参数(R2=0.918, RMSE=1.534),绿量经验反演模型为：VQ=14.892×TSAVI0.779
3.叶面积指数实测值与反演值之间的拟合精度检验表明,基于像元信息分解的绿量反演模型能够达到精度要求,可以采用像元分析法进行叶面积指数遥感反演,对于大尺度绿量反演和动态监测具有实用价值。
4.北京西南山地森林绿量空间分布差异明显,绿量主要分布在海拔500m-1500m范围的山地。绿量分布格局上,房山和门头沟交界的山地以及门头沟区和房山区与河北交界山区等区域山地森林为高绿量分布区域。北京西南山地森林绿量总体平均值为8.378,其中,阔叶林8.028、针叶林8.354、灌木林8.905。绿量分布格局受林分类型变化影响明显。坡向对绿量存在一定影响,绿量平均值阴坡9.015,阳坡7.986,相同林分类型在4个海拔梯度均表现为阴坡绿量值大于阳坡。森林平均绿量随着海拔显著升高,不同海拔区平均绿量为海拔500m以下6.764,海拔500-1000m8.54,海拔1000-1500m9.318,海拔1500m以上为11.64,可以明显的看出在海拔500m以下的森林容易遭到破坏和干扰导致绿量降低,且阴坡和阳坡绿量均值差异较大。
5.北京西南山地森林绿量变化规律与物候相关联密切。森林绿量平均值季节变化表现为：3月1.479,5月3.789,8月7.129,11月1.629,12月0.966；生长盛期(8月)绿量明显高于其他月份；阴坡和阳坡森林绿量季节变化均呈单峰曲线,在8月和12月分别达到极大值和极小值。4个海拔梯度上的植被绿量均在生长盛期(8月)达到最高值,生长停滞期(12月)达到最低值。
本研究所提出的绿量指标,能够在遥感影像中更为真实地反映植被的现实绿量。采用本文的绿量指标,基于遥感影像资料和样点实测数据,对北京西南山区森林绿量的研究结果,可为北京城市不同的功能区的绿化系统评价提供技术参数,可供北京城市森林生态效益监测和评价参考。
This dissertation proposed vegetation quantity index that was capable of describing the 3-dimensional characteristics and dynamics of vegetation, with consideration of chromaticity of greenness and accurate estimation of vegetation quantity, and at the same time applicable for remote sensing data. Based on the remote sensing imagery and synchronized measurements from sample plots, empirical retrieval model of vegetation quantity was built, and effort was made on building practical and accurate retrieval vegetation quantity models based on pixel decomposition. Using the built retrieval models and related remote sensing imagery in the research area, a variety of forest types, slope aspects, and forest vegetation quantities at different altitudes in mountain area of southwestern Beijing were identified, and also analysis the spatial distribution and seasonal dynamics of forest vegetation quantity in mountain area of south-western Beijing. The results were as follows.
(1) This study proposed using the product of leaf area index (LAI) and chlorophyll volume index (CVI) to describe the vegetation quantity, i.e. VQ=LAI X CVI. This was essentially an index with consideration of chromaticity of greenness, capable of better describing 3-dimensional characteristics of vegetation with high sensitivity on chlorophyll volume. Application showed that this index can estimate vegetation quantity more accurately from remote sensing imagery.
(2) Comparisons among regression models, estimated vegetation quantities, scatter plots of measured values, and monitoring of seasonal forest vegetation dynamics showed that the power equation with TSAVI as the independent variable can be a good estimator of vegetation quantity (R2=0.918. RMSE=1.534). The empirical retrieval vegetation quantity model is VQ=14.892×TSAVI0.779.
(3) Based on the validation of prediction accuracy using measured values of leaf area index and retrieved values, it was shown that retrieval vegetation quantity model based on pixel decomposition, which retrieves leaf area index by pixel decomposition, can meet the demand of accuracy. Thus it is useful in large-size vegetation quantity retrieval and dynamic monitoring.
(4) There was significant heterogeneity in the spatial distribution of forest vegetation quantity in mountain area of south-western Beijing, with vegetation concentrated between 500m-1500m in altitude. In terms of the pattern of vegetation quantity distribution, forests in the mountain areas between Fangshan and Mentougou as well as between Fangshan and Hebei had high intensity of vegetation quantity. The average vegetation quantity in mountain area of southwestern Beijing was 8.378, with averages for broad-leaved forest, coniferous forest, and shrubbery being 8.028,8.354, and 8.905 respectively. The distribution patterns of vegetation quantity were significantly affected by the types of forest stand. Slope aspect had some effect on vegetation quantity, with averages at shady slope and sunny slope being 9.015 and 7.986 respectively. Shady slope had higher vegetation quantities than sunny slope at all four altitudes with the same type of forest stand. Average forest vegetation quantity increased significantly with altitude, with average vegetation quantities being:6.764 below 500m,8.54 between 500-1000m,9.318 between 1000-1500m, and 11.64 above 1500m. It was obvious that average vegetation quantity below altitude of 500m was low because forests there were vulnerable to destruction and disturbance, with significant difference between shady slope and sunny slope.
(5) The dynamics of vegetation quantity in mountain area of southwestern Beijing were highly correlated with climate. The average forest vegetation quantities in different months were: March 1.47、May 3.789, August 7.129, November 1.629, December 0.966; growth season (August) had significantly higher value than other months; the seasonal dynamics of vegetation quantity at shady and sunny slopes can be both described by parabolic curves, with maximum and minimum at August and December respectively. The vegetation quantities at four altitudes all achieved their maximums in growth season (August) and their minimums in stagnation season (December).
The vegetation quantity index proposed in this study can better describe the actual vegetation quantity from remote sensing imagery. Using the index proposed by this dissertation, the results from the research on forest vegetation quantity in mountain area of southwestern Beijing based on remote sensing imagery and data measured at sample sites, can provide technical support for the assessment of ecosystems in districts of different functions in Beijing, serving as references for monitoring and assessment of the benefit of forest ecosystems in Beijing.
引文
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