黑龙江省森林植被净初级生产力遥感估算研究
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摘要
森林净初级生产力(NPP)是森林生态系统的基本数量特征之一,其大小反映了某类森林生态系统自身的生物学特性及系统结构特征,同时也反映了环境因子对森林生长的综合影响。NPP不仅是碳循环原动力,而且是判定碳源、碳汇以及调节生态过程的主要因子。
本文以黑龙江省的森林资源为研究对象,以地理信息系统和遥感为技术手段,利用ETM+遥感数据、DEM数据和气象数据等资料,以C-FIX估算模型为基础,通过开发森林植被NPP估算系统,估算黑龙江省2000年5～9月的森林植被NPP,并定量分析黑龙江省森林NPP空间格局,以及与立地类型因子的关系,为预测未来黑龙江省陆地生态系统碳汇能力提供理论依据。
主要研究结论如下:
(1)黑龙江省2000年5～9月林地的总NPP为69.75×10~(12)gC,其中每月的NPP总量最大值出现在6月份,占5～9月林地NPP总量的27.23%。6～8月是NPP的主要积累月份。5月NPP高值区主要集中在大兴安岭北部、小兴安岭南部和西北部、张广才岭和老爷岭南部。9月NPP高值区主要集中在小兴安岭南侧和张广才岭。其中有林地NPP总量为60.99×10~(12)gC,占全省5～9月份林地NPP总量的87.44%,灌木林地占10.41%,疏林地占1.63%,其它林地占0.52%。
(2)通过提取各土类的分布,并对全省2000年5～9月NPP总量数据进行掩膜处理,分别得到六种土类对应的林地NPP。结果表明,植被NPP总量为暗棕壤＞棕色针叶林土＞沼泽土＞草甸土＞白浆土＞黑土。植被NPP平均值为暗棕壤＞棕色针叶林土＞黑土＞草甸土＞沼泽土＞白浆土。NPP平均值排序也基本反映了黑龙江省该六种森林土壤类型的综合肥力。
(3)分析了森林植被NPP与经纬度、高程、温度、太阳辐射、坡度、坡向和土壤类型的关系,建立了黑龙江省5～9月森林植被NPP估算模型,其中高程和太阳总辐射等因子与NPP密切相关。
(4)本研究以C-FIX模型为基础,以ERDAS IMAGE为平台,通过空间建模语言(SML)和ERDAS宏语言(EML),开发了黑龙江省森林植被NPP定量估算系统。估算系统能够根据遥感影像的分类数据,完成对研究区各种地类的NPP数据的估算、提取,实现森林植被NPP估算的定量化、自动化。
论文的创新点:
(1)在黑龙江省省域尺度上,利用TM影像对森林植被5～9月的NPP进行遥感定量估算,为研究省域尺度NPP提供了新的思路和方法。
(2)利用多元线性回归方法,建立了黑龙江省5～9月森林植被NPP估算模型,该模型可以分析NPP与经纬度、高程、温度、太阳辐射、坡度、坡向和土壤类型的关系。
(3)以ERDAS IMAGE为平台,开发了黑龙江省森林植被NPP定量估算系统。该估算系统能够根据遥感影像的分类数据和相关因子,实现森林植被NPP估算的定量化、自动化。
Forest net primary productivity(NPP) is one of the basic quantitative characters of forest ecosystem.NPP represents the biologic and systematic properties for a specific kind of forest ecosystem;besides,it also reflects the impacts of various environmental factors on forest growth.NPP acts not only as the driving force of the carbon cycle,but also as a primary factor to investigate the carbon source and sink,and to adjust the ecosystem processes.
Using the ETM+ remote sensing data,DEM data and meteorological data,and developing the NPP estimate system of forest vegetation that based on the C-FIX estimate model to study the forest resources of Heilongjiang Province by GIS and RS,the NPP of forest vegetation of Heilongjiang Province from May to September in 2000 was estimated, and the spatial pattern of forest NPP in Heilongjiang Province and the relationship between NPP and site type factors were quantitative analyzed,which provided a theoretical foundation for further prediction of carbon sink ability of terrestrial ecosystem in Heilongjiang Province.
The primary conclusions were listed as below:
(1) The gross NPP of forest in Heilongjiang Province from May to September in 2000 was 69.75×10~(12)gC,the maximum NPP value for month appeared in June;shared 27.23% of the gross forest NPP from May to September.June,July and August were the major months for NPP to accumulate.In May,high levels of NPP distributed among the northern of Great Xingan Mountains,the southern and north-western of Xiao xing'an Mountains, and the southern of Zhangguangcai Range and Laoye Range.In September,high level of NPP mainly centered around southem of Xiao xing'an Mountains and Zhangguangcai Range.Forest land owned gross forest NPP of 60.99×10~(12)gC,which accounted for 87.44%of total forest NPP from May to September,while shrub land 10.41%,open forest land 1.63%,and other types of land accounted for 0.52%of the gross forest NPP.
(2) Got the forest NPP that respectively corresponding to the six categories soil by mask processing the gross NPP of Heilongjiang Province from May to September in 2000. It showed that the order of Vegetation NPP level from high to low was dark brown forest soil,brown taiga soil,swamp,meadow soil,planosol and black soil,meanwhile,the average forest NPP from high to low was dark brown forest soil,brown taiga soil,black soil,meadow soil,swamp and planosol.The order of average NPP basically reflected the integrated soil fertility of these six soil types in Heilongjiang Province.
(3) The monthly NPP estimation model of Heilongjiang Province from May to September in 2000 was build by analyzing the relationship between vegetation NPP of forest and the latitude and longitude,elevation,temperature,solar radiation,slope gradient, slope direction and soil type.It concluded that the forest NPP in Heilongjiang was closely related to the elevation and solar radiation.
(4) Based on the C-FIX model,taking ERDAS IMAGE as the platform for the development,the quantitative estimate system of forest vegetation NPP in Heilongjiang Province was realized through spatial Modeling Language(SML) and the ERDAS Macro Language(EML).The system could accomplish the evaluation and extraction of NPP data for various soil types according to the classified remote sensing image data,and achieve the goal of estimating forest NPP quantitatively and automatically.
The innovation of this dissertation:
(1) The forest vegetation NPP from May to September was quantitatively estimated using TM images in the scale of Heilongjiang Province,which provided an idea and method to the NPP study of province scale.
(2) A monthly forest NPP estimation model was built using multivariate linear regression method,which analysed the relationship of NPP and the latitude and longitude, elevation,temperature,solar radiation,slope gradient,slope direction and soil type in Heilongjiang Province from May to September in 2000.
(3) The quantitative estimate system of forest vegetation NPP in Heilongjiang Province was developed and realized,taking ERDAS IMAGE as a platform.This estimate system realized the quantification and automation of forest vegetation NPP estimation that based on the classified remote sensing image data and related factors.
本文以黑龙江省的森林资源为研究对象,以地理信息系统和遥感为技术手段,利用ETM+遥感数据、DEM数据和气象数据等资料,以C-FIX估算模型为基础,通过开发森林植被NPP估算系统,估算黑龙江省2000年5～9月的森林植被NPP,并定量分析黑龙江省森林NPP空间格局,以及与立地类型因子的关系,为预测未来黑龙江省陆地生态系统碳汇能力提供理论依据。
主要研究结论如下:
(1)黑龙江省2000年5～9月林地的总NPP为69.75×10~(12)gC,其中每月的NPP总量最大值出现在6月份,占5～9月林地NPP总量的27.23%。6～8月是NPP的主要积累月份。5月NPP高值区主要集中在大兴安岭北部、小兴安岭南部和西北部、张广才岭和老爷岭南部。9月NPP高值区主要集中在小兴安岭南侧和张广才岭。其中有林地NPP总量为60.99×10~(12)gC,占全省5～9月份林地NPP总量的87.44%,灌木林地占10.41%,疏林地占1.63%,其它林地占0.52%。
(2)通过提取各土类的分布,并对全省2000年5～9月NPP总量数据进行掩膜处理,分别得到六种土类对应的林地NPP。结果表明,植被NPP总量为暗棕壤＞棕色针叶林土＞沼泽土＞草甸土＞白浆土＞黑土。植被NPP平均值为暗棕壤＞棕色针叶林土＞黑土＞草甸土＞沼泽土＞白浆土。NPP平均值排序也基本反映了黑龙江省该六种森林土壤类型的综合肥力。
(3)分析了森林植被NPP与经纬度、高程、温度、太阳辐射、坡度、坡向和土壤类型的关系,建立了黑龙江省5～9月森林植被NPP估算模型,其中高程和太阳总辐射等因子与NPP密切相关。
(4)本研究以C-FIX模型为基础,以ERDAS IMAGE为平台,通过空间建模语言(SML)和ERDAS宏语言(EML),开发了黑龙江省森林植被NPP定量估算系统。估算系统能够根据遥感影像的分类数据,完成对研究区各种地类的NPP数据的估算、提取,实现森林植被NPP估算的定量化、自动化。
论文的创新点:
(1)在黑龙江省省域尺度上,利用TM影像对森林植被5～9月的NPP进行遥感定量估算,为研究省域尺度NPP提供了新的思路和方法。
(2)利用多元线性回归方法,建立了黑龙江省5～9月森林植被NPP估算模型,该模型可以分析NPP与经纬度、高程、温度、太阳辐射、坡度、坡向和土壤类型的关系。
(3)以ERDAS IMAGE为平台,开发了黑龙江省森林植被NPP定量估算系统。该估算系统能够根据遥感影像的分类数据和相关因子,实现森林植被NPP估算的定量化、自动化。
Forest net primary productivity(NPP) is one of the basic quantitative characters of forest ecosystem.NPP represents the biologic and systematic properties for a specific kind of forest ecosystem;besides,it also reflects the impacts of various environmental factors on forest growth.NPP acts not only as the driving force of the carbon cycle,but also as a primary factor to investigate the carbon source and sink,and to adjust the ecosystem processes.
Using the ETM+ remote sensing data,DEM data and meteorological data,and developing the NPP estimate system of forest vegetation that based on the C-FIX estimate model to study the forest resources of Heilongjiang Province by GIS and RS,the NPP of forest vegetation of Heilongjiang Province from May to September in 2000 was estimated, and the spatial pattern of forest NPP in Heilongjiang Province and the relationship between NPP and site type factors were quantitative analyzed,which provided a theoretical foundation for further prediction of carbon sink ability of terrestrial ecosystem in Heilongjiang Province.
The primary conclusions were listed as below:
(1) The gross NPP of forest in Heilongjiang Province from May to September in 2000 was 69.75×10~(12)gC,the maximum NPP value for month appeared in June;shared 27.23% of the gross forest NPP from May to September.June,July and August were the major months for NPP to accumulate.In May,high levels of NPP distributed among the northern of Great Xingan Mountains,the southern and north-western of Xiao xing'an Mountains, and the southern of Zhangguangcai Range and Laoye Range.In September,high level of NPP mainly centered around southem of Xiao xing'an Mountains and Zhangguangcai Range.Forest land owned gross forest NPP of 60.99×10~(12)gC,which accounted for 87.44%of total forest NPP from May to September,while shrub land 10.41%,open forest land 1.63%,and other types of land accounted for 0.52%of the gross forest NPP.
(2) Got the forest NPP that respectively corresponding to the six categories soil by mask processing the gross NPP of Heilongjiang Province from May to September in 2000. It showed that the order of Vegetation NPP level from high to low was dark brown forest soil,brown taiga soil,swamp,meadow soil,planosol and black soil,meanwhile,the average forest NPP from high to low was dark brown forest soil,brown taiga soil,black soil,meadow soil,swamp and planosol.The order of average NPP basically reflected the integrated soil fertility of these six soil types in Heilongjiang Province.
(3) The monthly NPP estimation model of Heilongjiang Province from May to September in 2000 was build by analyzing the relationship between vegetation NPP of forest and the latitude and longitude,elevation,temperature,solar radiation,slope gradient, slope direction and soil type.It concluded that the forest NPP in Heilongjiang was closely related to the elevation and solar radiation.
(4) Based on the C-FIX model,taking ERDAS IMAGE as the platform for the development,the quantitative estimate system of forest vegetation NPP in Heilongjiang Province was realized through spatial Modeling Language(SML) and the ERDAS Macro Language(EML).The system could accomplish the evaluation and extraction of NPP data for various soil types according to the classified remote sensing image data,and achieve the goal of estimating forest NPP quantitatively and automatically.
The innovation of this dissertation:
(1) The forest vegetation NPP from May to September was quantitatively estimated using TM images in the scale of Heilongjiang Province,which provided an idea and method to the NPP study of province scale.
(2) A monthly forest NPP estimation model was built using multivariate linear regression method,which analysed the relationship of NPP and the latitude and longitude, elevation,temperature,solar radiation,slope gradient,slope direction and soil type in Heilongjiang Province from May to September in 2000.
(3) The quantitative estimate system of forest vegetation NPP in Heilongjiang Province was developed and realized,taking ERDAS IMAGE as a platform.This estimate system realized the quantification and automation of forest vegetation NPP estimation that based on the classified remote sensing image data and related factors.
引文
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