NDVI协同下森林生物量定量估算研究
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摘要
为了实现森林生物量的定量估计。本文以2010年7-8月北京首钢松林公园树木的测量数据,北京市2010年夏季SPOT5遥感影像为模型研究的数据。以北京首钢松林公园为研究区,区内105个采样点的(地上)森林生物量为研究对象,以ArcGis9.3为平台,应用Geostatistical analyst、空间分析工具的Features to Raster模块和SPSS多元统计软件,借助外挂式分析工具Hawth Tools,基于空间统计学的协同克里格法,找出了实测生物量与NDVI之间的协同变异函数,利用NDVI和地面实测森林生物量建立协同克里格模型进行生物量空间插值,形成生物量空间分布图,对森林生物量进行估量。结果为：该研究区的森林生物量估计值是96.6Mg/hm2,标准误差在小于1.422379～1.426711的范围。也就是当可信度P=0.95,真实的生物量值在93.75Mg／hm2—99.45Mg／hm2之间。说明NDVI协同下森林生物量定量估计的方法具有一定的实际价值。
创新点：
研究建立较为精确的森林生物量估测模型属于森林经理学、生态学和林业遥感技术相结合的较为有开创性的研究内容。协同克里格模型要比其他传统模型更精确、也更符合实际。论文结合地面实测蓄积量转换成的生物量和SPOT遥感影像提取的NDVI,找出了实测生物量与NDVI之间的协同变异函数,利用NDVI和地面实测森林生物量建立协同克里格模型进行生物量空间插值,形成生物量空间分布图,对森林生物量进行估量,在估算生物量的方法上有一定的创新性。
该文在研究地面森林生物量的采样方式、插值方法方面有新的思路和见解。在利用高分辨率卫星数据对森林蓄积即生物量估计尤其对其分布与变动的分析方面有自己独到的见解与新意。
充分利用遥感数据的丰富、易测的优点,在进一步提高对地面数据的的估测精度的同时,提高森林生物量定量估计的快捷、完整、有效性是林学、生态学等学科研究的重点,属于学科前沿领域,对于大尺度开展森林生物量估算有理论意义和实用价值。该文的研究对进一步开展遥感估测地面生物量的研究,扩大空间统计方法的应用与借鉴有较好的意义。
To estimate the forest biomass, the105plots were selected for individual measurement, which were located in Shougang Pinewood Park of Beijing and acquired in July to Aug,2010. After combination with simultaneous SPOT5RS images, the data were used for modeling. With applying the Geostatistical Analyst model in ArcGIS9.3and SPSS18.0. Using the RS data or not, kriging prediction model was applied to analyze the above-biomass. Although two methods also carried out the prediction value and standard error, the Kriging covariance model (KCM) using remote sensing data was a better remote sensing inversion model for forest biomass, which improving prediction accuracy because of synergy between KCM and NDVI. The later method was more rapid, effective, comprehensive and precise for forest biomass prediction. The results showed:in this study field, the biomass was96.6mg/hm2which standard error was below1.426711. Innovation:
(1) The research established more accurate forest biomass estimation model, which combined forest management, forestry ecology and remote sensing technology. Covariance Kriging model was more accuracy and practical than traditional methods. With identifying the relationship between VDV1and forest biomass, the method confirmed that the collaborative variation function existed between the field data (forest biomass) and NDVI. So, it was possible to use this method for forest biomass prediction. With this reliable model, the forest biomass distribution map can be draw. Above-mentioned method was used to estimate biomass by best linear minimum bias estimator, which may effectively reduce evaluated error. Although neither of two methods achieve implemented requirement, the research idea was novel.
(2) This method was the base for special sampling and predicting the forest biomass. The variation of NDVI was analyzed in the study area. The spatial statistics sampling was better than random sampling, systematic sampling methods and comprehensive sampling especially. The method has its own unique insights in this study area.
(3) By making full use of the features of abundant and easy to measure, remote sensing data improved the availability prediction, precision and effectiveness of forest biomass. This is very important for forest, ecology and environment science. Further research of using remote sensing to estimate biomass and statistical method has application and reference significance.
创新点：
研究建立较为精确的森林生物量估测模型属于森林经理学、生态学和林业遥感技术相结合的较为有开创性的研究内容。协同克里格模型要比其他传统模型更精确、也更符合实际。论文结合地面实测蓄积量转换成的生物量和SPOT遥感影像提取的NDVI,找出了实测生物量与NDVI之间的协同变异函数,利用NDVI和地面实测森林生物量建立协同克里格模型进行生物量空间插值,形成生物量空间分布图,对森林生物量进行估量,在估算生物量的方法上有一定的创新性。
该文在研究地面森林生物量的采样方式、插值方法方面有新的思路和见解。在利用高分辨率卫星数据对森林蓄积即生物量估计尤其对其分布与变动的分析方面有自己独到的见解与新意。
充分利用遥感数据的丰富、易测的优点,在进一步提高对地面数据的的估测精度的同时,提高森林生物量定量估计的快捷、完整、有效性是林学、生态学等学科研究的重点,属于学科前沿领域,对于大尺度开展森林生物量估算有理论意义和实用价值。该文的研究对进一步开展遥感估测地面生物量的研究,扩大空间统计方法的应用与借鉴有较好的意义。
To estimate the forest biomass, the105plots were selected for individual measurement, which were located in Shougang Pinewood Park of Beijing and acquired in July to Aug,2010. After combination with simultaneous SPOT5RS images, the data were used for modeling. With applying the Geostatistical Analyst model in ArcGIS9.3and SPSS18.0. Using the RS data or not, kriging prediction model was applied to analyze the above-biomass. Although two methods also carried out the prediction value and standard error, the Kriging covariance model (KCM) using remote sensing data was a better remote sensing inversion model for forest biomass, which improving prediction accuracy because of synergy between KCM and NDVI. The later method was more rapid, effective, comprehensive and precise for forest biomass prediction. The results showed:in this study field, the biomass was96.6mg/hm2which standard error was below1.426711. Innovation:
(1) The research established more accurate forest biomass estimation model, which combined forest management, forestry ecology and remote sensing technology. Covariance Kriging model was more accuracy and practical than traditional methods. With identifying the relationship between VDV1and forest biomass, the method confirmed that the collaborative variation function existed between the field data (forest biomass) and NDVI. So, it was possible to use this method for forest biomass prediction. With this reliable model, the forest biomass distribution map can be draw. Above-mentioned method was used to estimate biomass by best linear minimum bias estimator, which may effectively reduce evaluated error. Although neither of two methods achieve implemented requirement, the research idea was novel.
(2) This method was the base for special sampling and predicting the forest biomass. The variation of NDVI was analyzed in the study area. The spatial statistics sampling was better than random sampling, systematic sampling methods and comprehensive sampling especially. The method has its own unique insights in this study area.
(3) By making full use of the features of abundant and easy to measure, remote sensing data improved the availability prediction, precision and effectiveness of forest biomass. This is very important for forest, ecology and environment science. Further research of using remote sensing to estimate biomass and statistical method has application and reference significance.
引文
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