东北森林碳循环日步长模型与遥感综合应用研究
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摘要
森林是主要的陆地生态系统碳库,准确评估森林碳通量和碳储量是估算未来大气CO2浓度,预测未来气候变化对陆地生态系统影响的关键。目前,森林碳循环是全球气候变化研究的焦点之一,而森林生产力的模拟是碳循环究的重点。我国东北森林是世界上3大块温带森林之一,占全国森林面积和森林蓄量积的1/3以上,在我国和全球碳循环和生态环境建设中起着重要的作用。然而东北森林碳循环的研究尚不全面,在我国和全球碳循环模拟、估算和预测中还急需来自该区域的研究成果。
本文以遥感过程机理模型为主要研究手段对2007年东北地区森林和黑龙江省森林生产力进行了定量估算,并对时空格局变化及其影响因子(LAI、气象等)的响应进行了深入分析,旨在通过研究探索东北森林生态系统碳收支时空格局及其对气候变化的响应机制,形成采用遥感机理模型定量估算东北森林碳循环完善的方法体系。依据这一研究目的,本研究以lkm分辨率的MODIS土地覆盖类型数据、MODIS的LAI数据、气象数据、土壤数据为模型输入数据,采用修正的BEPS遥感机理模型估算东北森林的净初级生产力(NPP)、总初级生产力(GPP)和植物自养呼吸(Ra),并用获得NPP与通量数据进行对比分析,用迭代的方法进行植物生理参数优化,直到获得适合于东北森林的植物生理参数,然后利用100m分辨率的Landsat数据、气象数据、土壤数据,采用多种优化方法获得的植物生理参数估算了黑龙江省森林净初级生产力(NPP)、总初级生产力(GPP)和植物自养呼吸(Ra),并对模型输出的两种分辨率净初级生产力(NPP)、总初级生产力(GPP)和植物自养呼吸(Ra)数据进行了时空变化对比以及影响因子分析。
针对上述问题展开了如下研究并得出相应的主要结论：
1、MODIS和TM遥感数据的预处理。TM遥感数据的预处理包括多光谱数据组建、几何校正、辐射定标、大气校正等过程。为了减少遥感数据预处理的工作量,本研究采用IDL语言开发了遥感图像预处理程序(辐射定标程序、太阳高度角校正程序、辐射归一化程序等)。对MODIS三种数据产品进行预处理,包括图像的镶嵌、裁切、投影变化等,重点研究了应用LACC算法程序,对46幅MODIS2007年东北地区的LAI数据进行处理,并取得了很好的效果。
2、TM遥感图像的分类。本研究采用较成熟的最大似然分类器对2007年黑龙江省TM遥感影像中的森林进行分类,分类系统为针叶林、阔叶林和针阔混交林。采用TM波段和增加不同辅助信息(NDVI、DEM、VDVI+DEM)四种方法,分别对三个区域TM遥感影像进行分类并进行精读比较分析,采用分类精度最高的方法,最终小兴安岭地区和大兴安岭地区采用6B分类方法,长白山采用6B+DEM的分类方法,大小兴安岭和长白山分类精度分别为：68.4%,77.7%、79.1%,并把分类结果与MODIS土地覆盖类型产品进行了比较。
3、LAI定量反演。本研究以时间集中在8月中旬的TM遥感数据为基础,采用基于4-Scale几何光学模型和指数统计模型相结合的混合方法,对黑龙江省三大林区(大兴安岭、小兴安岭、长白山)分别估算了LAI,并对三个区域的LAI反演结果进行了常规统计和空间对比分析,结果为：黑龙江省林区阔叶林的LAI平均值约为3.1,针阔混交林的LAI平均值约为2.3,针叶林的LAI平均值约为2.3,整个黑龙江省森林的LAI平均值约为2.56。
4、建立了土壤数据库。本研究土壤数据(AWC)由土壤质地数据确定,以东北地区土壤类型数据为基础,利用土壤质地和土壤类型的转换关系获得了土壤质地图,并应用DeJong和Loebe表格模型,获得了1km分辨率的黑龙江省土壤有效含水量栅格数据文件。
5、建立了东北地区气象数据库。根据国家气象信息中心提供的东北三省96个和内蒙古10个站点2007年的气象数据,对站点气象数据进行了日变化分析,分析结果为2007年为正常年份无干旱和涝现象。以此数据为基础,采用克吕格插值的方法获得了每日的1km的栅格气象数据。该数据库包含了日最低温度、日最高温度、日太阳总辐射、日降水量、日相对湿度五种气象数据,共1825个栅格文件,数据量为15 G。由于气象数据处理的工作量特别的大,本项研究中编写了批量气象数据处理的程序进行气象数据处理。
6、模型的参数优化及算法的改进。应用BEPS模型首先考虑的问题就是模型适应性的问题,本研究采用四种方法对BEPS模型的植物生理参数进行了优化：实测法、文献法、遥感反演法、迭代法,最终使BEPS模型的植物生理参数适合了东北地区森林NPP,GPP的估算,同时在模型算法上进行了优化,不仅提高了模型的运行速度,而且是使模型能够计算不同空间分辨率的输入数据。
7、可视化BEPS模型运行系统的开发。应用Microsoft Visual C++ 6.0平台,开发了功能完善、算法先进的可视化BEPS模型运行系统,实现了LAI数据、逐日气象数据的智能读取和NPP、GPP、Ra的选择输出,并且输入输出数据的格式与ENVI的标准格式完全兼容。非专业人员获得输入数据之后,便可应用该系统对研究区的NPP.GPP.Ra进行估算,有利于BEPS模型的推广和应用。
8、NPP、GPP和Ra的估算和时空分析。应用GIS和统计的方法,对采用MODIS数据估算的东北森林的净初级生产力(NPP)、总初级生产力(GPP)、植物自养呼吸(Ra)进行了空间和时间上分布规律研究,整个东北森林2007年GPP平均值为897.33gC·m-2·a-1、最大值为1294.89gC·m-2·a-1、最小值为494.51gC·m-2·a-1。2007年NPP平均值为369.92gC·m-2·a-1、最大值为632.82gC·m-2·a-1、最小值为75.17gC·m-2·a-1。2007年Ra平均值为296.86gC·m-2·a-1、最大值为529.51gC·m-2·a-1、最小值为80.35gC·m-2·a-1。本研究以小兴安岭为例对两套方案获取的不同空间分辨率NPP数据进行了年、季节、月和日多种时间分辨率上的全方位的的对比分析。从对比结果来看,采用两套分类数据(1km分辨率MODIS分类数据或100m分辨率TM分类数据)获得的不同空间分辨率2007年小兴安岭地区森林NPP差别不大。
9、多种NPP验证方法研究。NPP的研究结果与森林资源清查固定样地数据、MODIS的NPP数据产品进行验证和对比,并获得了一致性结论：本研究的NPP模拟结果比较符合实际。获得的NPP数据还与其他研究者的研究成果进行了对比分析,本文的模拟值大小兴安岭NPP的平均值为369.92gC·m-2·a-1、最大值为632.82gC·m-2·a-1、最小值为75.17gC·m-2·a-1,模拟值都在取值范围内,说明BEPS模型模拟森林NPP较为合理可靠,并做了估算结果的不确定分析阐明了产生误差的原因。
10、NPP影响因子及敏感性分析。本研究应用SPSS13.0软件,采用线性回归模型对小兴安岭地区的年NPP影响因子(LAI、气温、降水、太阳辐射、纬度)进行了回归分析,结果表明,LAI和NPP的关系极其显著,其相关系数为0.515,其原因LAI直接决定太阳短波辐射能量的植被吸收。其次为纬度相关系数为0.197。NPP与降水和温度呈正相关关系,相关系数比较低。选取植被参数(LAI)、气象因子(包括气温、降水、太阳辐射)等主要的影响因子,模拟不同的影响因子增减变化后NPP的结果,进行影响因子敏感性分析,统计该影响因子变化后NPP的变化量,并对不同影响因子的变化量进行分析。
Forests are the major terrestrial carbon pool, an accurate assessment of forest carbon balance and storage is the key to estimate future atmospheric CO2 concentrations, climate changes and its impact on terrestrial ecosystems. Currently, forest carbon cycle is one of the focuses on global climate change research, and the simulation of forest ecosystem productivity is the key to carbon cycle study. Northeast China forest is one of the three largest temperate forests in the world, accounting more than 1/3 on the national total forest area and the storage capacity, also it plays an important role in our country and global carbon cycle, forestry and ecological environment construction. However, the study on forest carbon cycle in Northeast area is not comprehensive, research results from the region in China and the global carbon cycle assessment, modeling and forecasting are also urgently needed.
In this paper, we conducted a quantitative estimate of foresty productivity in Northeast China and Heilongjiang province in 2007 by using remote sensing mechanism model, and made a deep analysis on temporal and spatial pattern change and its impact factors (LAI, weather, etc.), producing a impeccable method system to quantitative estimate the carbon cycle of northeast forest by remote sensing mechanism model through studying the temporal and spatial pattern of northeast forest ecological system carbon budget and its response mechanism of climate change. Based on the purpose of study, the model input data includes MODIS land cover type of lkm resolution, LAI data of MODIS, meteorological data and soil data,then estimate the net ptimary productivity (NPP)of the Northeast forest, gross primary productivity (GPP), plant autotrophic respiration (Ra) in northeast by corrected remote sensing mechanism model, compare the obtain NPP with the flux data, using iterative method to optimize the vegetation physiological parameters until getting the parameters that fitting northeast forest. An estimate of forest NPP,GPP and Ra through vegetation physiological parameters by kinds of optimize method using 100m resolution Landsat data, meteorological data and soil data, and made an analysis of temporal and spatial pattern change and impact factor on this three types data of two resolutions in the model.
Aim at the above issues address the following research and draws the main conclusions of the corresponding:
1、Preprocessing MODIS and TM data. TM data preprocess contains Multi-spectral data form, geometric correction, radiometric calibration, atmospheric correction, etc. In order to reduce the workload of remote sensing data preprocessing, this study developed a remote sensing image IDL language preprocessor (radiometric calibration procedures, calibration procedures for the solar elevation angle, radiation normalization procedures, etc.). The preprocessing on three MODIS data products included image mosaic, crop, projection change and so on. Focus on the application of LACC algorithm procedures, achieved very good results on processing the 46 MODIS LAI data of Northeast in 2007.
2、TM remote sensing image classification. This study used a more sophisticated maximum likelihood classifier for forest classification of TM remote sensing images in Heilongjiang Province in 2007, classification system is coniferous forest, broadleaf forest and conifer, respectively, of the three regional TM remote sensing image classification and comparative analysis of the intensive through TM band and increase using different four auxiliary information (NDVI, DEM, VDVI+DEM) methods, using the highest classification accuracy method, finally, Daxing'anling and Xiaoxing'anling region were classify by 6B, using the 6B+ DEM classification method in Changbai Mountain. The classification accuracy in the three areas:68.4%,77.7%,79.1%. And the classification results were compared with the MODIS land cover products.
3、LAI quantitative inversion. In this paper, base on mid-Auguest TM remote sensing data, made use of geometric optics model 4-Scale and exponential statistical model combining mixed methods, estimated LAI in the three major forest areas in Heilongjiang Province (Daxing'anling, Xiaoxing'anling, Changbai Mountaint), compared conventional and spatial statistical analysis on LAI inversion results of the three regions.The results were:in Heilongjiang forest, broad-leaved forest LAI average was about 3.1, Mixed forest LAI average was about 2.3, coniferous forest LAI average was about 2.3, regardless of forest type in the entire forest area of Heilongjiang Province, the average LAI was about 2.56.
4、Establish the soil database. The soil data (AWC) data is determine by the soil texture, we can obtain soil texture map soil type data in the Northeast region by using the conversion relationships based on soil types, also obtained lkm of Heilongjiang soil water raster data files by applying the forms models of De Jong and Loebe.
5、The establishment of the Northeast regional meteorological databases. A daily meteorological data change analysis was made on 96 sites of the three northeastern provinces and 10 sites of Inner Mongolia meteorological data from National Meteorological Information Center, as the results for the year 2007 without the normal phenomenon of drought or floods. Based on the data, produced the daily grid 1km data by Kruger interpolation. The database contains five meteorological data 1825 grid file included the daily minimum temperature, maximum temperature, daily solar radiation, daily precipitation, relative humidity, the data capacity of 15 G.. The workload of Meteorological Data Processing was particularly large, so we write a batch preparation procedure for meteorological data processing in this study.
6、Model parameter optimization and algorithm improvements. The first consideration of BEPS model application is the issue on model adaptation, this study optimized the vegetation physiological parameters of BEPS by four methods:measurement method, literature, remote sensing method, iterative method, and finally made the vegetation Physiological parameters of BEPS model fit the estimates of Northeast forest NPP and GPP. So the model algorithm has been optimized at the same time, not only improve the model speed, but also make the model to calculate input data of different spatial resolutions.
7、The development of visual BEPS model running systems. Applications Microsoft Visual C++ 6.0 platform, developed a comprehensive, advanced visualization algorithms to run BEPS model system,then realized the intelligence read of LAI data, daily weather data and the choice output of NPP, GPP and Ra, also, input and output data format was fully compatible with the ENVI standard format. Subsystems after the non-professional staff to obtain input data, the system can be applied in estimating NPP, GPP and Ra of the study area, and it is benefit to BEPS model extension and application.
8、Estimation and temporal and spatial analysis of NPP, GPP and Ra. Apply GIS and statistical methods, data estimated by MODIS net primary productivity of the Northeast (NPP), gross primary productivity (GPP), plant autotrophic respiration (Ra) for the spatial and temporal distribution of the entire Northeast Forest. Average GPP in the whole northeast forest was 897.33gC·m-2·a-1 in 2007, the maximum was 1294.89gC·m-2·a-1, and the minimum was 494.51gC·m-2·a-1. Average NPP was 369.92gC·m-2·a-1 in 2007, the maximum was 632.82gC·m-2·a-1, and the minimum was 75.17gC·m-2·a-1. Average Ra was 296.86 gC·m-2·a-1 in 2007, the maximum was 529.51gC·m-2·a-1 and the minimum was 80.35gC·m-2·a-1. In case of Xiaoxing'anling, this paper performing a variety of comprehensive comparative analysis on different NPP of different spatial resolution at a year, a season, a month and a day, respectively. From the comparison results, it had little relationship using two sets of disaggregated data (classification data of 1km resolution MODIS and 100m resolution TM) to obtain NPP of different spatial resolutions in Xiaoxing'anling forest in 2007.
9、Variety of authentication methods.Compared the consistency conclusions by using permanent plots of forest resources inventory data, MODIS NPP products and studies of other comparison model for NPP with obtained NPP simulation results of this study,it was more realistic. MODIS NPP data with other researchers to obtain research results of a comparative analysis, this paper simulated the average NPP was 369.92gC·m-2·a-1 in Daxing' anling and Xiaoxing' anling, the maximum was 632.82 gC·m-2·a-1 while the minimum was 75.17gC·m-2·a-1, all simulated values were within the range, indicating that BEPS model NPP simulation is more reasonable and reliable in Daxing'anling and Xiaoxing'anling.Also made the uncertainty analysis of the estimation results to clarify the cause of the errors.
10、NPP impact factors and sensitivity analysis. In this paper we apply SPSS13.0 software, using a linear regression model region of small impact factor of NPP (LAI, temperature, precipitation, solar radiation, dimensions).The regression analysis showed that, the relationship between LAI and NPP was extremely significant, The correlation coefficient was 0.515, the reason solar radiation LAI directly determine the energy absorption of vegetation. Second, the dimension correlation coefficient was 0.197. The relationship of NPP and precipitation and temperature was positively correlated with a low correlation coefficient.Selected the vegetation parameters (LAI), meteorological factors (including temperature, precipitation and solar radiation) and other major factors to simulate the impact of different factors increase or decrease the results after the NPP, the impact ty analysis, statistics, after changes in the impact factor the change in NPP, and analyzed the variation of different factors.
本文以遥感过程机理模型为主要研究手段对2007年东北地区森林和黑龙江省森林生产力进行了定量估算,并对时空格局变化及其影响因子(LAI、气象等)的响应进行了深入分析,旨在通过研究探索东北森林生态系统碳收支时空格局及其对气候变化的响应机制,形成采用遥感机理模型定量估算东北森林碳循环完善的方法体系。依据这一研究目的,本研究以lkm分辨率的MODIS土地覆盖类型数据、MODIS的LAI数据、气象数据、土壤数据为模型输入数据,采用修正的BEPS遥感机理模型估算东北森林的净初级生产力(NPP)、总初级生产力(GPP)和植物自养呼吸(Ra),并用获得NPP与通量数据进行对比分析,用迭代的方法进行植物生理参数优化,直到获得适合于东北森林的植物生理参数,然后利用100m分辨率的Landsat数据、气象数据、土壤数据,采用多种优化方法获得的植物生理参数估算了黑龙江省森林净初级生产力(NPP)、总初级生产力(GPP)和植物自养呼吸(Ra),并对模型输出的两种分辨率净初级生产力(NPP)、总初级生产力(GPP)和植物自养呼吸(Ra)数据进行了时空变化对比以及影响因子分析。
针对上述问题展开了如下研究并得出相应的主要结论：
1、MODIS和TM遥感数据的预处理。TM遥感数据的预处理包括多光谱数据组建、几何校正、辐射定标、大气校正等过程。为了减少遥感数据预处理的工作量,本研究采用IDL语言开发了遥感图像预处理程序(辐射定标程序、太阳高度角校正程序、辐射归一化程序等)。对MODIS三种数据产品进行预处理,包括图像的镶嵌、裁切、投影变化等,重点研究了应用LACC算法程序,对46幅MODIS2007年东北地区的LAI数据进行处理,并取得了很好的效果。
2、TM遥感图像的分类。本研究采用较成熟的最大似然分类器对2007年黑龙江省TM遥感影像中的森林进行分类,分类系统为针叶林、阔叶林和针阔混交林。采用TM波段和增加不同辅助信息(NDVI、DEM、VDVI+DEM)四种方法,分别对三个区域TM遥感影像进行分类并进行精读比较分析,采用分类精度最高的方法,最终小兴安岭地区和大兴安岭地区采用6B分类方法,长白山采用6B+DEM的分类方法,大小兴安岭和长白山分类精度分别为：68.4%,77.7%、79.1%,并把分类结果与MODIS土地覆盖类型产品进行了比较。
3、LAI定量反演。本研究以时间集中在8月中旬的TM遥感数据为基础,采用基于4-Scale几何光学模型和指数统计模型相结合的混合方法,对黑龙江省三大林区(大兴安岭、小兴安岭、长白山)分别估算了LAI,并对三个区域的LAI反演结果进行了常规统计和空间对比分析,结果为：黑龙江省林区阔叶林的LAI平均值约为3.1,针阔混交林的LAI平均值约为2.3,针叶林的LAI平均值约为2.3,整个黑龙江省森林的LAI平均值约为2.56。
4、建立了土壤数据库。本研究土壤数据(AWC)由土壤质地数据确定,以东北地区土壤类型数据为基础,利用土壤质地和土壤类型的转换关系获得了土壤质地图,并应用DeJong和Loebe表格模型,获得了1km分辨率的黑龙江省土壤有效含水量栅格数据文件。
5、建立了东北地区气象数据库。根据国家气象信息中心提供的东北三省96个和内蒙古10个站点2007年的气象数据,对站点气象数据进行了日变化分析,分析结果为2007年为正常年份无干旱和涝现象。以此数据为基础,采用克吕格插值的方法获得了每日的1km的栅格气象数据。该数据库包含了日最低温度、日最高温度、日太阳总辐射、日降水量、日相对湿度五种气象数据,共1825个栅格文件,数据量为15 G。由于气象数据处理的工作量特别的大,本项研究中编写了批量气象数据处理的程序进行气象数据处理。
6、模型的参数优化及算法的改进。应用BEPS模型首先考虑的问题就是模型适应性的问题,本研究采用四种方法对BEPS模型的植物生理参数进行了优化：实测法、文献法、遥感反演法、迭代法,最终使BEPS模型的植物生理参数适合了东北地区森林NPP,GPP的估算,同时在模型算法上进行了优化,不仅提高了模型的运行速度,而且是使模型能够计算不同空间分辨率的输入数据。
7、可视化BEPS模型运行系统的开发。应用Microsoft Visual C++ 6.0平台,开发了功能完善、算法先进的可视化BEPS模型运行系统,实现了LAI数据、逐日气象数据的智能读取和NPP、GPP、Ra的选择输出,并且输入输出数据的格式与ENVI的标准格式完全兼容。非专业人员获得输入数据之后,便可应用该系统对研究区的NPP.GPP.Ra进行估算,有利于BEPS模型的推广和应用。
8、NPP、GPP和Ra的估算和时空分析。应用GIS和统计的方法,对采用MODIS数据估算的东北森林的净初级生产力(NPP)、总初级生产力(GPP)、植物自养呼吸(Ra)进行了空间和时间上分布规律研究,整个东北森林2007年GPP平均值为897.33gC·m-2·a-1、最大值为1294.89gC·m-2·a-1、最小值为494.51gC·m-2·a-1。2007年NPP平均值为369.92gC·m-2·a-1、最大值为632.82gC·m-2·a-1、最小值为75.17gC·m-2·a-1。2007年Ra平均值为296.86gC·m-2·a-1、最大值为529.51gC·m-2·a-1、最小值为80.35gC·m-2·a-1。本研究以小兴安岭为例对两套方案获取的不同空间分辨率NPP数据进行了年、季节、月和日多种时间分辨率上的全方位的的对比分析。从对比结果来看,采用两套分类数据(1km分辨率MODIS分类数据或100m分辨率TM分类数据)获得的不同空间分辨率2007年小兴安岭地区森林NPP差别不大。
9、多种NPP验证方法研究。NPP的研究结果与森林资源清查固定样地数据、MODIS的NPP数据产品进行验证和对比,并获得了一致性结论：本研究的NPP模拟结果比较符合实际。获得的NPP数据还与其他研究者的研究成果进行了对比分析,本文的模拟值大小兴安岭NPP的平均值为369.92gC·m-2·a-1、最大值为632.82gC·m-2·a-1、最小值为75.17gC·m-2·a-1,模拟值都在取值范围内,说明BEPS模型模拟森林NPP较为合理可靠,并做了估算结果的不确定分析阐明了产生误差的原因。
10、NPP影响因子及敏感性分析。本研究应用SPSS13.0软件,采用线性回归模型对小兴安岭地区的年NPP影响因子(LAI、气温、降水、太阳辐射、纬度)进行了回归分析,结果表明,LAI和NPP的关系极其显著,其相关系数为0.515,其原因LAI直接决定太阳短波辐射能量的植被吸收。其次为纬度相关系数为0.197。NPP与降水和温度呈正相关关系,相关系数比较低。选取植被参数(LAI)、气象因子(包括气温、降水、太阳辐射)等主要的影响因子,模拟不同的影响因子增减变化后NPP的结果,进行影响因子敏感性分析,统计该影响因子变化后NPP的变化量,并对不同影响因子的变化量进行分析。
Forests are the major terrestrial carbon pool, an accurate assessment of forest carbon balance and storage is the key to estimate future atmospheric CO2 concentrations, climate changes and its impact on terrestrial ecosystems. Currently, forest carbon cycle is one of the focuses on global climate change research, and the simulation of forest ecosystem productivity is the key to carbon cycle study. Northeast China forest is one of the three largest temperate forests in the world, accounting more than 1/3 on the national total forest area and the storage capacity, also it plays an important role in our country and global carbon cycle, forestry and ecological environment construction. However, the study on forest carbon cycle in Northeast area is not comprehensive, research results from the region in China and the global carbon cycle assessment, modeling and forecasting are also urgently needed.
In this paper, we conducted a quantitative estimate of foresty productivity in Northeast China and Heilongjiang province in 2007 by using remote sensing mechanism model, and made a deep analysis on temporal and spatial pattern change and its impact factors (LAI, weather, etc.), producing a impeccable method system to quantitative estimate the carbon cycle of northeast forest by remote sensing mechanism model through studying the temporal and spatial pattern of northeast forest ecological system carbon budget and its response mechanism of climate change. Based on the purpose of study, the model input data includes MODIS land cover type of lkm resolution, LAI data of MODIS, meteorological data and soil data,then estimate the net ptimary productivity (NPP)of the Northeast forest, gross primary productivity (GPP), plant autotrophic respiration (Ra) in northeast by corrected remote sensing mechanism model, compare the obtain NPP with the flux data, using iterative method to optimize the vegetation physiological parameters until getting the parameters that fitting northeast forest. An estimate of forest NPP,GPP and Ra through vegetation physiological parameters by kinds of optimize method using 100m resolution Landsat data, meteorological data and soil data, and made an analysis of temporal and spatial pattern change and impact factor on this three types data of two resolutions in the model.
Aim at the above issues address the following research and draws the main conclusions of the corresponding:
1、Preprocessing MODIS and TM data. TM data preprocess contains Multi-spectral data form, geometric correction, radiometric calibration, atmospheric correction, etc. In order to reduce the workload of remote sensing data preprocessing, this study developed a remote sensing image IDL language preprocessor (radiometric calibration procedures, calibration procedures for the solar elevation angle, radiation normalization procedures, etc.). The preprocessing on three MODIS data products included image mosaic, crop, projection change and so on. Focus on the application of LACC algorithm procedures, achieved very good results on processing the 46 MODIS LAI data of Northeast in 2007.
2、TM remote sensing image classification. This study used a more sophisticated maximum likelihood classifier for forest classification of TM remote sensing images in Heilongjiang Province in 2007, classification system is coniferous forest, broadleaf forest and conifer, respectively, of the three regional TM remote sensing image classification and comparative analysis of the intensive through TM band and increase using different four auxiliary information (NDVI, DEM, VDVI+DEM) methods, using the highest classification accuracy method, finally, Daxing'anling and Xiaoxing'anling region were classify by 6B, using the 6B+ DEM classification method in Changbai Mountain. The classification accuracy in the three areas:68.4%,77.7%,79.1%. And the classification results were compared with the MODIS land cover products.
3、LAI quantitative inversion. In this paper, base on mid-Auguest TM remote sensing data, made use of geometric optics model 4-Scale and exponential statistical model combining mixed methods, estimated LAI in the three major forest areas in Heilongjiang Province (Daxing'anling, Xiaoxing'anling, Changbai Mountaint), compared conventional and spatial statistical analysis on LAI inversion results of the three regions.The results were:in Heilongjiang forest, broad-leaved forest LAI average was about 3.1, Mixed forest LAI average was about 2.3, coniferous forest LAI average was about 2.3, regardless of forest type in the entire forest area of Heilongjiang Province, the average LAI was about 2.56.
4、Establish the soil database. The soil data (AWC) data is determine by the soil texture, we can obtain soil texture map soil type data in the Northeast region by using the conversion relationships based on soil types, also obtained lkm of Heilongjiang soil water raster data files by applying the forms models of De Jong and Loebe.
5、The establishment of the Northeast regional meteorological databases. A daily meteorological data change analysis was made on 96 sites of the three northeastern provinces and 10 sites of Inner Mongolia meteorological data from National Meteorological Information Center, as the results for the year 2007 without the normal phenomenon of drought or floods. Based on the data, produced the daily grid 1km data by Kruger interpolation. The database contains five meteorological data 1825 grid file included the daily minimum temperature, maximum temperature, daily solar radiation, daily precipitation, relative humidity, the data capacity of 15 G.. The workload of Meteorological Data Processing was particularly large, so we write a batch preparation procedure for meteorological data processing in this study.
6、Model parameter optimization and algorithm improvements. The first consideration of BEPS model application is the issue on model adaptation, this study optimized the vegetation physiological parameters of BEPS by four methods:measurement method, literature, remote sensing method, iterative method, and finally made the vegetation Physiological parameters of BEPS model fit the estimates of Northeast forest NPP and GPP. So the model algorithm has been optimized at the same time, not only improve the model speed, but also make the model to calculate input data of different spatial resolutions.
7、The development of visual BEPS model running systems. Applications Microsoft Visual C++ 6.0 platform, developed a comprehensive, advanced visualization algorithms to run BEPS model system,then realized the intelligence read of LAI data, daily weather data and the choice output of NPP, GPP and Ra, also, input and output data format was fully compatible with the ENVI standard format. Subsystems after the non-professional staff to obtain input data, the system can be applied in estimating NPP, GPP and Ra of the study area, and it is benefit to BEPS model extension and application.
8、Estimation and temporal and spatial analysis of NPP, GPP and Ra. Apply GIS and statistical methods, data estimated by MODIS net primary productivity of the Northeast (NPP), gross primary productivity (GPP), plant autotrophic respiration (Ra) for the spatial and temporal distribution of the entire Northeast Forest. Average GPP in the whole northeast forest was 897.33gC·m-2·a-1 in 2007, the maximum was 1294.89gC·m-2·a-1, and the minimum was 494.51gC·m-2·a-1. Average NPP was 369.92gC·m-2·a-1 in 2007, the maximum was 632.82gC·m-2·a-1, and the minimum was 75.17gC·m-2·a-1. Average Ra was 296.86 gC·m-2·a-1 in 2007, the maximum was 529.51gC·m-2·a-1 and the minimum was 80.35gC·m-2·a-1. In case of Xiaoxing'anling, this paper performing a variety of comprehensive comparative analysis on different NPP of different spatial resolution at a year, a season, a month and a day, respectively. From the comparison results, it had little relationship using two sets of disaggregated data (classification data of 1km resolution MODIS and 100m resolution TM) to obtain NPP of different spatial resolutions in Xiaoxing'anling forest in 2007.
9、Variety of authentication methods.Compared the consistency conclusions by using permanent plots of forest resources inventory data, MODIS NPP products and studies of other comparison model for NPP with obtained NPP simulation results of this study,it was more realistic. MODIS NPP data with other researchers to obtain research results of a comparative analysis, this paper simulated the average NPP was 369.92gC·m-2·a-1 in Daxing' anling and Xiaoxing' anling, the maximum was 632.82 gC·m-2·a-1 while the minimum was 75.17gC·m-2·a-1, all simulated values were within the range, indicating that BEPS model NPP simulation is more reasonable and reliable in Daxing'anling and Xiaoxing'anling.Also made the uncertainty analysis of the estimation results to clarify the cause of the errors.
10、NPP impact factors and sensitivity analysis. In this paper we apply SPSS13.0 software, using a linear regression model region of small impact factor of NPP (LAI, temperature, precipitation, solar radiation, dimensions).The regression analysis showed that, the relationship between LAI and NPP was extremely significant, The correlation coefficient was 0.515, the reason solar radiation LAI directly determine the energy absorption of vegetation. Second, the dimension correlation coefficient was 0.197. The relationship of NPP and precipitation and temperature was positively correlated with a low correlation coefficient.Selected the vegetation parameters (LAI), meteorological factors (including temperature, precipitation and solar radiation) and other major factors to simulate the impact of different factors increase or decrease the results after the NPP, the impact ty analysis, statistics, after changes in the impact factor the change in NPP, and analyzed the variation of different factors.
引文
[1]王业迎.生物圈的第一性生产力.北京：科学出版社.1985.
[2]Liu J, Chen J M, Chen W. Net prmary produetivity distribution in the BOREAS region from a proeess model using satellite and surfaee data [J]. Journalof Geophysieal Researeh, 1999,104(D22),27735-27754.
[3]Walker, B.and Steffen, W.A synthesis of GCTE and related researeh. Ii:IGBP Seienee NO1. Stoekholm:IGBP Secretariat.1997:23.
[4]方精云,朴世龙,赵淑清.CO2失汇与北半球中高纬度陆地生态系统的碳汇.植物生态学报2001,25(5)：594-602.
[5]沈文清,马钦彦,刘允芬.森林生态系统碳收支状况研究进展[J].江西农业大学学报，2006,28(2)：312-317.
[6]杨洪晓,吴波,张金屯,等.森林生态系统的固碳功能和碳储量研究进展[J].北京师范大学学报(自然科学版),2005,41(2)：172-177.
[7]Baldoeehi D, Falge E, GuvL, erval.Fluxnet:a new tool to study the temporal and spatial variability of ecosystem-seale carbon dioxide, water vapor, and energy flux densities [J].Bulletin of the Ameriean Meteorological Soeiety,2001,82(16):2415-2434.
[8]Baldocchi D D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems:past, present and future [J]. Global Change Biology,2003,9: 479-492.
[9]Aubinet M, A.Grelle, A.Ibrom, et al. Estimates of the annual net carbon and water exchange of forests:The Euro Flux Methodology [J]. Advances in Ecological Research, 2000, (30):113-175.
[10]冯险峰,刘高焕,陈述彭,等.陆地生态系统净第一性生产力过程模型研究综述[J].自然资源学报,2004,19(5)：369-378.
[11]方精云,陈安平.中国森林植被碳库的动态变化及其意义[J].植物学报,2001,43(9)：967-973.
[12]Yang X, Wang M X, huang Y.The climatic-induced net carbon sink by terrestrial biosphere over 1901-1995 [J]. Advances in Atmospheric Sciences(大气科学进展),2001,18(6):1192-1206.
[13]Lieth, H. Modelling the Primary Production of the World, in Primary Production of the Biosphere [M], H. Lieth and R.H.Whittaker, Editors. NewYork:SPringer-Verlag,1975: 237-263.
[14]Lieth, H, Box E O X. Evapotranspiration and Primary Productivity:C. W. Thornthwaite Memorial Model, Publications in Climatalogy,1972.25 (2):37-46
[15]Uchijima, Z, Seino, H.Agroelimatic Evaluation of Net Primary Productivity of Natural Vegetation.(1)Chikugo Model for Evaluating Primary Produetivity.Journal of Aqricultural Meteorology,1985:40:343-352.
[16]Running SW, Hunt ER.Generalization of a forest ecosystem processmodel for other biomes. BIOME-BGC and an application for global-scale models. In:Ehleringer JR, Field C B, eds Scaling Physiological Processes Leaf toGlobe. San Diego Academic Press,1993.141-158.
[17]Sellers P J, Mintz Z, Sud YC, et al.A simple biosphere model (SiB) for use within general circulation models [J]. J. Atmos. Sci,1986,43:505-531.
[18]Liu J, Chen J. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment,1997,62:158-175.
[19]Chen WJ, Chen JM, Liu J and Cihlar J. Approaehes for redueing uneertainties in Regional forest earbon balanee. Global Biogeoehem. Cyeles,2000,14,827-838.
[20]Potter, CS, JT Randerson. Terrestrial Ecosystem Production:A Process Model Baseson Global Satellite and Surface Data [J]. Global Biogeochemical Cycles,1993,7(4):811-814.
[21]Foley JA. An equilibrium model of the terrestrial carbon budget. Tellus,1995,47(B):310-319.
[22]Lloyd I, Taylor JA. On the temperature dependence of soil respiration [J]. Functional Ecology,1994,8:315-323.
[23]Rodle, H.A comparison of the contribution of various grasses to the greenhouse effect.Seiencc,1990,248:1217-1225.
[24]Keeling, CD, Whorf, TP. Aimospherie CO2 reeords from sites in the SIO air sain Pling network.In Trends:A ComPendium of Data on Global Change, oak Ridge, TN:Carbon Dioxide Information Analysis Center,2004.
[25]Tao S, Chen P, Riehes MR, and Wang WC. Greenhouse effete and elimate change. Beijing:China Oeean Press,1999:345-356.
[26]Riehes MR, Wang WC, and Chen RQ. Reeent Progress in the joint agreements on "Global and Regional Climate Change"study between the United States and the PeoPle's RePublic of China. Bulletin of the American Meteorological soeiety,2000,81(3):491-499.
[27]周广胜,王玉辉,蒋延玲,杨利民.陆地生态系统转变与碳循环.植物生态学报,2002,26(2)：250-254.
[28]Grow M, Hall J and Susank, W. Strategies to estimate national forest earbon stoeks from inventory data:The 1980 New Zealand baseline. Global Change Boil.1978,7:389-403.
[29]CooPer CF. Carbon storage in managed forests. Can.J. For. Res.1983,3:155-166.
[30]Raich JW and Nadelhoffer, KJ. Below gound carbon alloeation in forest ecosystems: global trends. Ecology,1989,70:1346-1354.
[31]Post, WM., Emanuel WR and Zinke, PJ. Soil carbon Pools and life zones. Nature.1982, 298:156-159.
[32]Adams JM, Valentini R and Oechel W. Strategies for measuring and modeling carbon dioxide and water vapor fluxes over terrestrial ecosystems. Global Change Biol,1990,2: 159-168.
[33]Apps G., Ball JT and Grivet C. Physiological and environmental regulation of stomatal eonductanee, photosynthesis and transpiration:a model that ineludes a lamina boundary layer. Agriculture and Forest Meteorology,1990,54:107-136.
[34]Fang JY, Wang, G.G. and Liu, G.H.Forest biomass of China:A estimated based on the biomass-volume relationship. Eeologieal Applieation,1998,8(4):1084-1091.
[35]IPCC, Climate Change 1990, The IPCC scientific assessment, Cambridge University Press. 1990.
[36]Hibbard K and Pollard D. A giobal elimate model (GENESIS) with a land surface transfer seheme (LSX). Part 1:Present day cllinate. Cllinate,2002,8:732-761.
[37]Keeling CD. Climate change and cathon dioxide:An introduction National Academy of Seienee,2005,94:8273-8274.
[38]Watson RT and Verardo DJ. Land-use change and forestry. CambridgeUniversity Press, 2005.
[39]Woflsy SC and Pollard D.A global elimate model (GENESIS) with a land surfaee Transfer scheme (LSX). Part2:CO2 sensitivity.J.Climate,2004,8:1104-1121.
[40]曲建升,孙成权.全球变化研究中的“科学政治化”倾向—以美国气候政策为例.气候 与环境研究,2005,6(2):228-233.
[41]Foley JA, Levis S and Prentiee, IC. Coupling dynamic model of climate and vegetation. Global Change Biology,1998,4:561-579.
[42]Li KY, Coe MT. and Ramankut N.Regional hydrological modeling in Africa:Calibration and peiformance test of IBIS. GeoPhysical Research Abstraets,20035:13006.
[43]刘辉志,董文杰,符涂斌,等.半干早地区吉林通榆“干旱化和有序人类活动”长期观测实验.气候与环境研究,2004,9(2)：378-389.
[44]Falkowski P, SCholes, RJ and Boyle E. The Global Carbon Cyele:A test of Our Knowledge of Earth as a System.Seienee,2004,290:291-296.
[45]Budyko ML. The Earth's Climate Past and Future. New York, Academic Press,2004.
[46]于贵瑞,李海涛,王绍强.全球变化与陆地生态系统碳循环与碳蓄积[M].北京：气象出版社,2003.
[47]Apps MJ, WA Kurz.The role of Canadian forests in the global carbon budget[C]. Carbon balance of the world's forest ecosystems:towards a global assessnent.Helsinki: Publication of Academy of Finlad.1994,14-39.
[48]Turner DP, G.J Koerper, ME Harmon, et al. A carbon budget for forests of the conterminous United States [J]. Ecological Applications.1995, (5):421-436.
[49]Kauppi PE, K Miehkainen, K Kuusela. Biomass and carbon budget of European forests.1971-1990 [J]. Science,1992, (256):70-74.
[50]Spiecker H, K Mielikainen, M Kohl, et al. Growth trends in European forests [M]. Berlin: Springer-Vedlag,1996.
[51]Kolchugina TP, TS Vinson. Carbon sources and sinks in forest biomes of the former Soviet Union [J]. Global Biogeochemical Cycles,1993, (7):291-304.
[52]Dixon RK, Solomon AM, Brown S, et al. Carbon pool and flux of global forest eco.systems [J]. Science,1994,263(5144):185-190.
[53]Houghton RA.Terrestrial sources and sinks of carbon inferred from terrestrial data [J].Tellus,1996, (48B):419-432.
[54]Aubinet MA, Grelle A, Ibrom, et al. Estimates of the annual net carbon and water exchange of forests:The EuroFlux Methodology [J]. Advances in Ecological Research, 2000, (30):113-175.
[55]肖复明,张群,范少辉.中国森林生态系统碳平衡研究[J].世界林业研究,2006,19(1)：53-57.
[56]Lindroth A, A Grelle, AS Moren.Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity [J].Global Change Biology,1998, (4):443-450.
[57]Black TA, G.den Hartog, H Neumann, et al. Annual cycles of CO2 and water vapor fluxes above and within a boreal aspenstand [J]. Global Change Biology,1996, (2):219-230.
[58]Lloyd J, KOLLE O, Shibistova O, et al. The carbon balance of a central Siberian forest[C].Proceedings of International workshop for Advanced Flux Network and Flux Evaluation.Sapporo:ASAHI Printing CO., Ltd.2001,39-45.
[59]Running SW, Baldocchi DD, Turner DP, et al. A global terrestrial monitoring network integrating tower fluxes, Flask sampling, Ecosystem modeling and EOS satellite data [J]. Remote Sensing of Environment,1999, (70):108-127.
[60]Running SW. A blueprint for improved global change monitoring of the terrestrial biosphere. NASA Earth Observation,1998,10(1):8-11.
[61]Chen J, Chen W, Liu J, et al.Annual carbon balance of Canada's forests during 1859-1996 [J]. Global Biogeochemical Cycles,2000,14(3):827-838.
[62]Soojeong Myeong, David J.Nowak, Michael J. Duggin.A temporal analysis of urban forestcarbon storage using remote sensing [J]. Remote Sensing of Environment,2006, (101):277-282.
[63]Tatea KR, Scotta NA, Parshotama A, et al.A multi-scale analysis of a terrestrial carbon budget:IsNew Zealand a source or sink of carbon [J]. Agriculture, Ecosystems and Environment,2000,82(1-3):229-246.
[64]Potter CS, Klooster S, de Carvallo CR, et al. Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region [J]. J Geophys Res,2001, 106(d10):10423-10446.
[65]Zamolodchikov D, Dmitriv Karelin. An empirical model of carbon fluxes in Russian tundra [J]. Global Change Biology,2001,7(2):147-161.
[66]Soegaard H.Fluxes of carbon dioxide, water vapor and sensible heat in a boreal agricultural area of Sweden-scaled from canopy to landscape level [J]. Agricultural and Forest Meteorology.1999, (98-99):463-478.
[67]Collatz G.J, Ball JT, Crivet C, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpirations model that includes a laminar boundary layer [J]. Agriculture and Forest meteorology.1991, (54):107-136.
[68]汪业勖,赵士洞.陆地碳循环研究中的模型方法.应用生态学报,1998,9(6)：658-665.
[69]刘洪杰M iami模型的生态学应用[J].生态利学.1997,16(1)52-55.
[70]赵士洞,罗天祥.区域尺度陆地生态系统生物生产力研究方法[J].资源利学.1998,20(1)：23-34.
[71]周广胜,张新时.自然植被净第性生产力模型初探[J].植物生态学报.1995,19(3)：193-200.
[72]Running SW, Coughlan JC. A general model of forest ecosystem processes for regional applications:1. Hydrological balance, canopy gas exchange and primary production processes [J]. Ecol Model,1988,42:125-154.
[73]董文娟,齐晔,李惠民等.植被生产力的空间分布研究—以黄河小花间卢氏以上流域为例[J].地理与地理信息科学.2005.21(3)：105-108.
[74]Potter CS, Randerson JT and Field CB. Terrestrial ecosystem Produetion, a Proeess model based on global satellite and surface data. Global Biogeoehemical Cydes,1993,7:811-841.
[75]Potter CS, Matson PA. Vitousek PM. Evaluation of soil database attributes in aGlobal earbon eyele model:implications for global ehange researeh. Ii:Environmental Information management and analysis:ecosystem to global seales (ed.W.Miehener) Taylor and Francis. London, UK,1994:281-302.
[76]Monteith JL. Solar Radiation and productivity in Tropical ecosystems [J]. Journal of Applied Ecology,1972, (9):747-766.
[77]Potter, CS. And Klooster, SA. Global model estimates of earbon and nitrogen storage in litter and soil pools:response to ehanges in vegetation quality and biomass allocation Tellus,1997,49(B):1-17.
[78]Chen JM., Liu J, Cihlar J, et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications [J]. Ecological Modeling,1999,124:99-119.
[79]Liu J, Chen JM., Cihlar J, et al.Net primary productivity mapped for Canada at 1-km resolution [J]. Global Ecology & Biogeography,2002, Ⅱ:115-129.
[80]冯险峰,刘高焕,陈述彭等.陆地生态系统净第一性生产力过程模型研究综述[J].自然资源学报,2004,19(3)：369-378.
[81]松下文经,杨翠芬,陈晋等.广域空间尺度上植被净初级生产力的精确计算[J].地理学报,2004,59(1)：80-87.
[82]Bunkei Matsushita, Masayuki Tamura.Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment, 2002,81:58-66.
[83]刘明亮.中国土地利用／土地覆盖变化与陆地生态系统植被碳库和生产力研究,北京：中科院遥感应用研究所,2001.
[84]李文华,李飞.中国森林资源研究.北京：中国林业出版社,1996：25-36.
[85]冯宗炜.森林生态系统生物量和碳储存量的研究历史.见：王如松,方精云,高林,等.现代生态学的热点问题研究.北京：中国科学技术出版社,1999：335-347.
[86]罗天祥.中国主要森林类型生物生产力格局及其数学模型.1996.
[87]罗天祥.区域尺度陆地生态系统生物生产力研究方法.资源科学,1998,20(1)：23-34.
[88]莫兴国,陈丹,林忠辉,等.不同水分条件麦田能量和CO2通量变化特征研究.中国生态农业学报,2003,11(4)：77-81.
[89]方精云,徐高龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5)：497-508.
[90]温学发,于贵瑞，孙晓敏.基于涡度相关技术估算植被/大气间净CO2交换量中的不确定性.地球科学进展,2004,19(4)：658-663.
[91]刘世荣等.中国暖温带森林生物多样性研究.北京：中国林业出版社,1998,37-44.
[92]张宪洲.我国自然植被第一性生产力的估算与分布.自然资源,1993,(1)：15-21.
[93]周广胜,张新时.自然植被净第一性生产力模型初探.植物生态学,1995,19(3)：193-200.
[94]张永强,沈彦俊,刘昌明,等.华北平原典型农田水、热与CO2通量的测定.地理学报,2002,57(3)：333-342.
[95]李迪强,等.中国潜在植被生产力的分布与模拟.植物学报,1998,40(6)：560-566.
[96]康惠宁,马钦彦,袁嘉祖.中国森林C汇功能基本估计[J].应用生态学报,1996,7(3)：230-234.
[97]陈育峰.气候—森林响应过程敏感性的初步研究—以四川西部紫果云杉群落为例.地理学报,1996,51(增刊)：58-65.
[98]陈育峰,李克让.应用林窗模型研究全球气候变化对森林群落的可能影响—以四川西部紫果云杉群落为例.地理学报,1996,51(增刊)：73-80.
[99]方精云,刘国华,徐篙龄.中国陆地生态系统的碳循环及其全球意义.中国环境科学出版社,1996：129-139.
[100]张冬有.黑龙江省森林植被净初级生产力遥感估算研究[D].北京林业大学博士学位论文,2009.
[101]国志兴,王宗明,张柏,等.2000年-2006年东北地区植被NPP的时空特征及影响因素分析[J].资源科学,2008,30(8)：1226-1235.
[102]王萍.基于IBIS模型的东北森林净第一性生产力模拟[J].生态学报,2009,29(6)：3213-3220.
[103]王绍强,周成虎,刘纪远,等.东北地区陆地碳循环平衡模拟分析[J].地理学报,2001,56(1)：390-398.
[104]张峰,周广胜.中国东北样带植被净初级生产力时空动态遥感模拟[J].植物生态学报2008,32(4)：798-809.
[105]赵俊芳,延晓冬,贾根锁.东北森林净第一性生产力与碳收支对气候变化的响应[J].生态学报,2008,28(1)：92-100.
[106]张娜,于贵瑞,于振良,等.基于景观尺度过程模型的长白山净初级生产力空间分布影响因素分析[J].应用生态学报,2003,14(5)：659-664.
[107]王维芳,王琪,李国春,等.基于气象模型的黑龙江省植被净第一性生产力[J].东北林业学报,2009,37(4)：27-29.
[108]赵敏,周广胜.中国北方林生产力变化趋势及其影响因子分析[J].西北植物学报,2005,25(3)：466-471.
[109]李秀芬,郑有飞,王晨轶,等.黑龙江省森林NPP的遥感反演[J].中国农业气象,2009,31(1)：88-92.
[110]王琪.黑龙江省植被净第一性生产力的估算及空间特征分析[D].东北林业大学硕士学位论文,2009.
[111]毛留喜,孙艳玲,延晓冬.陆地生态系统碳循环模型.应用生态学报,2006,17(11)：2189-2195.
[112]程肖侠,延晓冬.气候变化对中国东北大兴安岭森林演替的影响.生态学杂志,2007,26(8)：1277-1278.
[113]Li KY and Coe MT, Ramankutty N. Modeling the hydrological impact of land-use Change in West Africa. Journal of Hydrology,2007,337:258-268.
[114]赵敏,周广胜.中国森林生态系统的植物碳贮量及其影响因子分析[J].地理科学,2004,24(1)：50-54.
[115]Fang JY,AP Chen,CH Peng SQ.Zhao&LJ Ci.Change in forest biomass carbon storage in China between 1949 and 1998[J].Science,2001,(292):2320-2322.
[116]王效科,冯宗炜,欧阳志云.中国森林生态系统的植物碳储量和碳密度研究[J].应用生态学报,2001,12(1)：13-16.
[117]Pacala SW, Hurtt GC, Baker D, et al.Consistent land-and atmpsphere-based US carbon sink estimates [J]. Science,2001,292(5525):2316-2320.
[118]Schimel DS, House JI.Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems [J]. Nature,2001, (414):169-172.
[119]邬建国.景观生态学—格局、过程、尺度与等级[M].北京：高等教育出版社,2000,190-197.
[120]王培娟,孙睿,朱启疆.陆地植被二氧化碳通量尺度扩展研究进展[J].遥感学报,2005,9(6)：751-759.
[121]Greenough JA, Apps MJ, Kurz WA.Influence of methodology and assumptions on reportednational carbon flux inventories:An illustration from Canadian forest sector [J]. Mitigation and Adaptation Strategies for Global Change,1997, (2):267-283.
[122]李世华,牛铮,李壁成.植被净第一性生产力遥感过程模型研究[J].水土保持研究,2005,12(3)：126-128.
[123]周蕾,王绍强,陈镜明,等.1991年至2000年中国陆地生态系统蒸散时空分布特征[J].资源科学,2009,31(6)：962-972.
[124]王培娟,谢东辉,张佳华,朱启疆.长白山森林植被NPP主要影响因子的敏感性分析[J].地理研究,2008,27(2)：323-330.
[125]王培娟,孙睿,朱启疆,谢东辉,陈镜明.复杂地形条件下提高BEPS模型模拟能力的途径[J].中国图象图形学报,2008,11(7)：1017-1024.
[126]王秋凤,牛栋,于贵瑞,任传友,温学发.长白山森林生态系统CO2和水热通量的模拟研究[J].中国科学D辑地球科学2004,34(增刊Ⅱ)：131-140.
[127]郑凌云.基于卫星遥感与BEPS生态模式的藏北草地变化及NPP动态研究[D].中国气象科学研究院硕士学位论文,2008.
[128]刘志斌,刘茂松,徐驰,阮尉琳,刘小钊.江阴市植被净初级生产力及碳汇价值分析[J].南京林业大学学报(自然科学版),2007,31(3)：139-142.
[129]王军邦.中国陆地净生态系统生产力遥感模型研究[D].浙江大学博士学位论,2008.
[130]张小全,侯振宏.森林、造林、再造林和毁林的定义与碳计量问题[J].林业科学,2003,39(2)：146-152.
[131]高志强,刘纪远,曹明奎,等.土地利用和气候变化对区域净初级生产力的影响[J].地理学报,2004,59(4)：581-591.
[132]Ji JJ. Adimate-vegetation interaction model:simulating physical and biologieal processes at the surface.Joumal of Biogeography,1995,22:445-451.
[133]程肖侠,延晓冬.气候变化对中国东北主要森林林型的森林影响.生态学报.2008,28(2)：534-543.
[134]延晓冬,赵士洞,符涂斌,等.气候变化背景下小兴安岭天然林的模拟研究.自然资源学报.1999,14(4)：374-376.
[135]延晓冬.林窗模型的几个基本问题的研究1.模拟样地面积的效应.应用生态学报,2001,12(1)：17-22.
[136]李慧.福建省森林生态系统NPP和NEP时空模拟研究.福建师范大学博士论文,2008：61-85.
[137]陈雄文,王凤友.林窗模型BKPF模型模拟红松林阔叶混交林群落对气候变化的潜在反应.植物生态学报,2000,24(3)：327-331.
[138]夏朝宗,熊利亚,庄大方,等.基于MODis的陆地植被光合过程参数反演研究.地理科学进展,2004,23(4)：10-18.
[139]赵敏,周广胜.中国北方林生产力变化趋势及其影响因子分析.西北植物学报,2005,25(3)：466-471.
[140]Chen JM, Deng F, Chen MZ, Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter [J]. IEEE Trans Geosci Remote Sens,2006,44(8):2230-2237.
[141]Farquhar GD, Caemmerer SV, Berry JA. A biochemicalmodel of photosynthetic CO2 assimilation in leaves of C-3 Species [J]. Planta,1980,149:78-90.
[142]Allen RG, Pereira LS, Raes D, et al.Crop evapotranspiration-Guidelines for computing crop water requirements [M]. FAO Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations, Rome,1998.
[143]Chen JM, Chen XY, Ju WM, et al. A remote sensing-driven distributed hydrological model:mapping evapotranspiration in a forested watershed [J]. J Hydrology,2005,305: 15-39.
[144]Liu J, Chen J M, Cihlar J. Mapping Evapotranspiration Based on Remote Sensing:An Application to Canada's Landmass [J]. Water Resources Research,2003,39:1189-1200.
[145]Liu J, Chen J M, Cihlar J, et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs [J]. Remote Sens Environ,1997,62:158-175.
[146]Liu J, Chen J M, Cihlar J, et al. Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data [J]. J Geophys Res,1999, 104(27):735-754.
[147]Chen JM, Liu J, Cihlar J, et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications [J]. Ecological Modelling,1999,124:99-119.
[148]Norman JM. Simulation of microclimates. In:Hatfield, J.L., Thomason, I.J. (Eds.), Biometeorology in Integrated Pest Management. Academic Press, New York,1982.
[149]Amthor JS, Chen JM, Clein JS, et al. Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models:intermodel comparisons and relationships to field measurements [J]. J Geophys Res,2001,106(33):623-648.
[150]Jarvis, PG., Morison JIL. Stomatal control of transpiration and photosynthesis. In:Jarvis, P.G., Mansfield, T.A. Ⅲ (Eds.), Stomatal Physiology. Cambridge University Press, New York,1981.247-279.
[151]Running, SW, Coughlan JC. A general model of forest ecosystem processes for regional applications I. Hy-drological balance, canopy gas exchange and primary pro-duction processes. Ecol. Model.1988,42,125-154.
[152]刘纪远.中国资源环境遥感宏观调查与动态研究.北京：中国科学技术出版社,1996
[153]喻锋,李晓兵,王宏,等.基于NDVI-Ts特征空间的中国土地覆盖分类研究植物[J].生态学报,2005,29(6)：934-944.
[154]邓书斌ENVI遥感图像处理方法.北京：科学出版社,130-132.
[155]J.M. Chen and S. Leblanc. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing.1997,35:1316-1337.
[156]X.Li and A.H. Strahler. Modeling the gap probability of discontinuous vegetation canopy. IEEE Transactions on Geoscience and Remote Sensing.1988,26:161-170.
[157]X.Li, A.H. Strahler and C.E. Woodcock. A Hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinous canopies. IEEE Transactions on Geoscience and Remote Sensing.1995,33:466-480.
[158]张时煌,彭公炳,黄玫.基于地理信息系统技术的土壤质地分类特征提取与数据融合[J].气候与环境研究,2004,9(1)：65-78.
[159]Bruand, AD, Bajze M, Hardy Prediction of water retention properties of clay soil validity of relationships using a single soil characteristic [J]. Soil Use and Managent.1994,10(1): 99-103.
[160]翁笃鸣.中国辐射气候研究[M].北京：气象出版社,1997.
[161]何洪林,于贵瑞,牛栋.复杂地形条件下的太阳资源辐射计算方法研究[J].资源科学,2003,25(1)：78-85.
[162]许吟隆,Richard J利用ECMWF再分析数据验证PRECIS对中国区域气候的模拟能力[J].中国农业气象,2004,25(1)：5-9.
[163]Kimball, JS, Thornton PE, White MA, Running, SW. Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region.Tree Physiol.1997,17, 589-599.
[164]Dang QL, Margolis HA, Coyea MR, Sy M, Collatz G.J. Regulation of branch-level gas exchange ofboreal trees:roles of shoot water potential and vapourpressure difference. Tree Physiol.1997,17,521-535.
[165]Bonan, G.B. Land-atmosphere CO2 exchange simu-lated by a land surface process model coupled to anatmospheric general circulation model. J. Geophys. Res.1995,100,2817-2831.
[166]Cramer W, Field CB.Comparing global models of terrestrial net primary productivity (NPP):introduction. Global Change Biology 5(supp 1):ⅲ-ⅳ (1924). Angstrom A. Solar and terrestrial radiation. Q. J. R.Meteorol.Soc.1999,50:121-126.
[167]张茂震,王广兴.浙江省森里生物量动态[J].生态学报,2008,28(11)：5665-5674.
[168]Brown S, Gillespie AJR, Lugo AE. Biomass estimation methods for tropical forests with application to forest inventory data. Forest Science,1989,35:881-902.
[169]Brown S, Lugo AE. Biomass of tropical forests:A new estimate based on forest volumes. Science,1984,223:1290-1293.
[170]Kauppi PE, Mielik IK, Kuusela K. Biomass and carbon budget of European forests,1971 to 1990. Science,1992,256(5053):70-74.
[171]Birdsey RA, Plantinga AG, Heath LS. Past and prospective carbon storage in United States forests. For Ecol Man,1993,59:33-40.
[172]Brown S, Lugo AE. Above ground biomass estimates for tropical moist forests of Brazilian Amazon. Interciencia,1992,17:8-18.
[173]Fang J Y, Chen A P, Peng C H, et al. Changes in forest biomass carbon storage in China between 1949 and 1998.Science,2001,292:2320-2322.
[174]方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产力[J].生态学报,1996,16(4)：497-508.
[175]方精云,陈安平,赵淑清,慈龙骏.中国森林生物量的估算：对Fang等Science一文(Science,2001,291:2320～2322)的若干说明[J].植物生态学报,2002,26(2)：243-249.
[176]唐守正,李希菲,孟昭和.林分生长模型研究的进展[J].林业科学研究,1993,29(6)：672-679.
[177]Borders BE, Patterson WD. Projeeting stand tables:A comparison of the w ibull diameter distribution method, a Percentile-based projection method, and a basal area growth projeetion method [J]. ForSci,1990,36:413-424.
[178]杨洪晓,吴波,张金屯,林德荣,常顺利.森林生态系统的固碳功能和碳储量研究进展[J].北京师范大学学报(自然科学版),2005,41(2)：172-177.
[179]Raich, JW, EB Rastetter, JM Melillo, et al. Potential Net Primary Productivity in South Ameriea:Application of a Global Model.Eogical Applications,1991,1(4):399-429.
[180]Scurlock JM., W Cramer, RJ Olson, et al. Terrestrial NPP:Toward a Consitent Data Set for Global Model Evaiuatio.1999,9(3):913-919.
[181]国志兴,王宗明,张柏,刘殿伟,杨桄,宋开山,李方.2000年-2006年东北地区植被NPP的时空特征及影响因素分析[J].资源科学,2008,30(8)：1226-1235.
[182]何勇,董文杰,季劲均,等.基于AVIM的中国陆地生态系统净初级生产力模拟[J].地球科学进展,2005,20(3)：345-349.
[183]朴世龙,方精云,郭庆华.利用CASA模型估算我国植被净第一性生产力[J].植物生态学报,2001,25(5)：603-608.
[184]陶波,李克让,邵雪梅,等.中国陆地净初级生产力时空特征模拟[J].地理学报,2003,58(3)：372-380.
[185]朱文泉,潘耀忠,张锦水.中国陆地植被净初级生产力遥感估算[J].植物生态学报,2007, 31(3)：413-424.
[186]刘明亮.中国土地利用/土地覆被变化与陆地生态系统植被碳库和生产力研究[D].北京：中国科学院遥感应用研究所,2001.
[187]孙睿,朱启疆.中国陆地植被净第一性生产力及季节变化研究[J].地理学报,2000,55(1)：36-45.
[188]王绍强,周成虎,刘纪远,等.东北地区陆地碳循环平衡模拟分析[J].地理学报,2001,56(4)：390-400.
[189]冯宗炜,王效科,吴刚.中国森林生态系统的生物量和生产力.北京：科学出版社.1999：226-227.
[190]Lacaze R, Roujean JL.G-function and HOt SpoT (GHOST) reflectance model-Application to multi-scale airborne POLDER measurements. Remote Sensing of Environment,2001,76(1):67-80.
[191]Chen JM, Menges CH, Leblanc SG. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment,2005,97,447-457.
[192]Lacaze R, Chen JM, Roujean JL; Leblanc SGRetrieval of vegetation clumping index using hot spot signatures measured by POLDER instrument. Remote Sensing of Environment,2002:79(1):84-95.
[193]Chen JM, Cihlar J.A hotspot function in a simple bidirectional reflectance model for satellite applications. Jouranl of Geophysical Research,1997,102:226-227.
[2]Liu J, Chen J M, Chen W. Net prmary produetivity distribution in the BOREAS region from a proeess model using satellite and surfaee data [J]. Journalof Geophysieal Researeh, 1999,104(D22),27735-27754.
[3]Walker, B.and Steffen, W.A synthesis of GCTE and related researeh. Ii:IGBP Seienee NO1. Stoekholm:IGBP Secretariat.1997:23.
[4]方精云,朴世龙,赵淑清.CO2失汇与北半球中高纬度陆地生态系统的碳汇.植物生态学报2001,25(5)：594-602.
[5]沈文清,马钦彦,刘允芬.森林生态系统碳收支状况研究进展[J].江西农业大学学报，2006,28(2)：312-317.
[6]杨洪晓,吴波,张金屯,等.森林生态系统的固碳功能和碳储量研究进展[J].北京师范大学学报(自然科学版),2005,41(2)：172-177.
[7]Baldoeehi D, Falge E, GuvL, erval.Fluxnet:a new tool to study the temporal and spatial variability of ecosystem-seale carbon dioxide, water vapor, and energy flux densities [J].Bulletin of the Ameriean Meteorological Soeiety,2001,82(16):2415-2434.
[8]Baldocchi D D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems:past, present and future [J]. Global Change Biology,2003,9: 479-492.
[9]Aubinet M, A.Grelle, A.Ibrom, et al. Estimates of the annual net carbon and water exchange of forests:The Euro Flux Methodology [J]. Advances in Ecological Research, 2000, (30):113-175.
[10]冯险峰,刘高焕,陈述彭,等.陆地生态系统净第一性生产力过程模型研究综述[J].自然资源学报,2004,19(5)：369-378.
[11]方精云,陈安平.中国森林植被碳库的动态变化及其意义[J].植物学报,2001,43(9)：967-973.
[12]Yang X, Wang M X, huang Y.The climatic-induced net carbon sink by terrestrial biosphere over 1901-1995 [J]. Advances in Atmospheric Sciences(大气科学进展),2001,18(6):1192-1206.
[13]Lieth, H. Modelling the Primary Production of the World, in Primary Production of the Biosphere [M], H. Lieth and R.H.Whittaker, Editors. NewYork:SPringer-Verlag,1975: 237-263.
[14]Lieth, H, Box E O X. Evapotranspiration and Primary Productivity:C. W. Thornthwaite Memorial Model, Publications in Climatalogy,1972.25 (2):37-46
[15]Uchijima, Z, Seino, H.Agroelimatic Evaluation of Net Primary Productivity of Natural Vegetation.(1)Chikugo Model for Evaluating Primary Produetivity.Journal of Aqricultural Meteorology,1985:40:343-352.
[16]Running SW, Hunt ER.Generalization of a forest ecosystem processmodel for other biomes. BIOME-BGC and an application for global-scale models. In:Ehleringer JR, Field C B, eds Scaling Physiological Processes Leaf toGlobe. San Diego Academic Press,1993.141-158.
[17]Sellers P J, Mintz Z, Sud YC, et al.A simple biosphere model (SiB) for use within general circulation models [J]. J. Atmos. Sci,1986,43:505-531.
[18]Liu J, Chen J. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment,1997,62:158-175.
[19]Chen WJ, Chen JM, Liu J and Cihlar J. Approaehes for redueing uneertainties in Regional forest earbon balanee. Global Biogeoehem. Cyeles,2000,14,827-838.
[20]Potter, CS, JT Randerson. Terrestrial Ecosystem Production:A Process Model Baseson Global Satellite and Surface Data [J]. Global Biogeochemical Cycles,1993,7(4):811-814.
[21]Foley JA. An equilibrium model of the terrestrial carbon budget. Tellus,1995,47(B):310-319.
[22]Lloyd I, Taylor JA. On the temperature dependence of soil respiration [J]. Functional Ecology,1994,8:315-323.
[23]Rodle, H.A comparison of the contribution of various grasses to the greenhouse effect.Seiencc,1990,248:1217-1225.
[24]Keeling, CD, Whorf, TP. Aimospherie CO2 reeords from sites in the SIO air sain Pling network.In Trends:A ComPendium of Data on Global Change, oak Ridge, TN:Carbon Dioxide Information Analysis Center,2004.
[25]Tao S, Chen P, Riehes MR, and Wang WC. Greenhouse effete and elimate change. Beijing:China Oeean Press,1999:345-356.
[26]Riehes MR, Wang WC, and Chen RQ. Reeent Progress in the joint agreements on "Global and Regional Climate Change"study between the United States and the PeoPle's RePublic of China. Bulletin of the American Meteorological soeiety,2000,81(3):491-499.
[27]周广胜,王玉辉,蒋延玲,杨利民.陆地生态系统转变与碳循环.植物生态学报,2002,26(2)：250-254.
[28]Grow M, Hall J and Susank, W. Strategies to estimate national forest earbon stoeks from inventory data:The 1980 New Zealand baseline. Global Change Boil.1978,7:389-403.
[29]CooPer CF. Carbon storage in managed forests. Can.J. For. Res.1983,3:155-166.
[30]Raich JW and Nadelhoffer, KJ. Below gound carbon alloeation in forest ecosystems: global trends. Ecology,1989,70:1346-1354.
[31]Post, WM., Emanuel WR and Zinke, PJ. Soil carbon Pools and life zones. Nature.1982, 298:156-159.
[32]Adams JM, Valentini R and Oechel W. Strategies for measuring and modeling carbon dioxide and water vapor fluxes over terrestrial ecosystems. Global Change Biol,1990,2: 159-168.
[33]Apps G., Ball JT and Grivet C. Physiological and environmental regulation of stomatal eonductanee, photosynthesis and transpiration:a model that ineludes a lamina boundary layer. Agriculture and Forest Meteorology,1990,54:107-136.
[34]Fang JY, Wang, G.G. and Liu, G.H.Forest biomass of China:A estimated based on the biomass-volume relationship. Eeologieal Applieation,1998,8(4):1084-1091.
[35]IPCC, Climate Change 1990, The IPCC scientific assessment, Cambridge University Press. 1990.
[36]Hibbard K and Pollard D. A giobal elimate model (GENESIS) with a land surface transfer seheme (LSX). Part 1:Present day cllinate. Cllinate,2002,8:732-761.
[37]Keeling CD. Climate change and cathon dioxide:An introduction National Academy of Seienee,2005,94:8273-8274.
[38]Watson RT and Verardo DJ. Land-use change and forestry. CambridgeUniversity Press, 2005.
[39]Woflsy SC and Pollard D.A global elimate model (GENESIS) with a land surfaee Transfer scheme (LSX). Part2:CO2 sensitivity.J.Climate,2004,8:1104-1121.
[40]曲建升,孙成权.全球变化研究中的“科学政治化”倾向—以美国气候政策为例.气候 与环境研究,2005,6(2):228-233.
[41]Foley JA, Levis S and Prentiee, IC. Coupling dynamic model of climate and vegetation. Global Change Biology,1998,4:561-579.
[42]Li KY, Coe MT. and Ramankut N.Regional hydrological modeling in Africa:Calibration and peiformance test of IBIS. GeoPhysical Research Abstraets,20035:13006.
[43]刘辉志,董文杰,符涂斌,等.半干早地区吉林通榆“干旱化和有序人类活动”长期观测实验.气候与环境研究,2004,9(2)：378-389.
[44]Falkowski P, SCholes, RJ and Boyle E. The Global Carbon Cyele:A test of Our Knowledge of Earth as a System.Seienee,2004,290:291-296.
[45]Budyko ML. The Earth's Climate Past and Future. New York, Academic Press,2004.
[46]于贵瑞,李海涛,王绍强.全球变化与陆地生态系统碳循环与碳蓄积[M].北京：气象出版社,2003.
[47]Apps MJ, WA Kurz.The role of Canadian forests in the global carbon budget[C]. Carbon balance of the world's forest ecosystems:towards a global assessnent.Helsinki: Publication of Academy of Finlad.1994,14-39.
[48]Turner DP, G.J Koerper, ME Harmon, et al. A carbon budget for forests of the conterminous United States [J]. Ecological Applications.1995, (5):421-436.
[49]Kauppi PE, K Miehkainen, K Kuusela. Biomass and carbon budget of European forests.1971-1990 [J]. Science,1992, (256):70-74.
[50]Spiecker H, K Mielikainen, M Kohl, et al. Growth trends in European forests [M]. Berlin: Springer-Vedlag,1996.
[51]Kolchugina TP, TS Vinson. Carbon sources and sinks in forest biomes of the former Soviet Union [J]. Global Biogeochemical Cycles,1993, (7):291-304.
[52]Dixon RK, Solomon AM, Brown S, et al. Carbon pool and flux of global forest eco.systems [J]. Science,1994,263(5144):185-190.
[53]Houghton RA.Terrestrial sources and sinks of carbon inferred from terrestrial data [J].Tellus,1996, (48B):419-432.
[54]Aubinet MA, Grelle A, Ibrom, et al. Estimates of the annual net carbon and water exchange of forests:The EuroFlux Methodology [J]. Advances in Ecological Research, 2000, (30):113-175.
[55]肖复明,张群,范少辉.中国森林生态系统碳平衡研究[J].世界林业研究,2006,19(1)：53-57.
[56]Lindroth A, A Grelle, AS Moren.Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity [J].Global Change Biology,1998, (4):443-450.
[57]Black TA, G.den Hartog, H Neumann, et al. Annual cycles of CO2 and water vapor fluxes above and within a boreal aspenstand [J]. Global Change Biology,1996, (2):219-230.
[58]Lloyd J, KOLLE O, Shibistova O, et al. The carbon balance of a central Siberian forest[C].Proceedings of International workshop for Advanced Flux Network and Flux Evaluation.Sapporo:ASAHI Printing CO., Ltd.2001,39-45.
[59]Running SW, Baldocchi DD, Turner DP, et al. A global terrestrial monitoring network integrating tower fluxes, Flask sampling, Ecosystem modeling and EOS satellite data [J]. Remote Sensing of Environment,1999, (70):108-127.
[60]Running SW. A blueprint for improved global change monitoring of the terrestrial biosphere. NASA Earth Observation,1998,10(1):8-11.
[61]Chen J, Chen W, Liu J, et al.Annual carbon balance of Canada's forests during 1859-1996 [J]. Global Biogeochemical Cycles,2000,14(3):827-838.
[62]Soojeong Myeong, David J.Nowak, Michael J. Duggin.A temporal analysis of urban forestcarbon storage using remote sensing [J]. Remote Sensing of Environment,2006, (101):277-282.
[63]Tatea KR, Scotta NA, Parshotama A, et al.A multi-scale analysis of a terrestrial carbon budget:IsNew Zealand a source or sink of carbon [J]. Agriculture, Ecosystems and Environment,2000,82(1-3):229-246.
[64]Potter CS, Klooster S, de Carvallo CR, et al. Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region [J]. J Geophys Res,2001, 106(d10):10423-10446.
[65]Zamolodchikov D, Dmitriv Karelin. An empirical model of carbon fluxes in Russian tundra [J]. Global Change Biology,2001,7(2):147-161.
[66]Soegaard H.Fluxes of carbon dioxide, water vapor and sensible heat in a boreal agricultural area of Sweden-scaled from canopy to landscape level [J]. Agricultural and Forest Meteorology.1999, (98-99):463-478.
[67]Collatz G.J, Ball JT, Crivet C, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpirations model that includes a laminar boundary layer [J]. Agriculture and Forest meteorology.1991, (54):107-136.
[68]汪业勖,赵士洞.陆地碳循环研究中的模型方法.应用生态学报,1998,9(6)：658-665.
[69]刘洪杰M iami模型的生态学应用[J].生态利学.1997,16(1)52-55.
[70]赵士洞,罗天祥.区域尺度陆地生态系统生物生产力研究方法[J].资源利学.1998,20(1)：23-34.
[71]周广胜,张新时.自然植被净第性生产力模型初探[J].植物生态学报.1995,19(3)：193-200.
[72]Running SW, Coughlan JC. A general model of forest ecosystem processes for regional applications:1. Hydrological balance, canopy gas exchange and primary production processes [J]. Ecol Model,1988,42:125-154.
[73]董文娟,齐晔,李惠民等.植被生产力的空间分布研究—以黄河小花间卢氏以上流域为例[J].地理与地理信息科学.2005.21(3)：105-108.
[74]Potter CS, Randerson JT and Field CB. Terrestrial ecosystem Produetion, a Proeess model based on global satellite and surface data. Global Biogeoehemical Cydes,1993,7:811-841.
[75]Potter CS, Matson PA. Vitousek PM. Evaluation of soil database attributes in aGlobal earbon eyele model:implications for global ehange researeh. Ii:Environmental Information management and analysis:ecosystem to global seales (ed.W.Miehener) Taylor and Francis. London, UK,1994:281-302.
[76]Monteith JL. Solar Radiation and productivity in Tropical ecosystems [J]. Journal of Applied Ecology,1972, (9):747-766.
[77]Potter, CS. And Klooster, SA. Global model estimates of earbon and nitrogen storage in litter and soil pools:response to ehanges in vegetation quality and biomass allocation Tellus,1997,49(B):1-17.
[78]Chen JM., Liu J, Cihlar J, et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications [J]. Ecological Modeling,1999,124:99-119.
[79]Liu J, Chen JM., Cihlar J, et al.Net primary productivity mapped for Canada at 1-km resolution [J]. Global Ecology & Biogeography,2002, Ⅱ:115-129.
[80]冯险峰,刘高焕,陈述彭等.陆地生态系统净第一性生产力过程模型研究综述[J].自然资源学报,2004,19(3)：369-378.
[81]松下文经,杨翠芬,陈晋等.广域空间尺度上植被净初级生产力的精确计算[J].地理学报,2004,59(1)：80-87.
[82]Bunkei Matsushita, Masayuki Tamura.Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia. Remote Sensing of Environment, 2002,81:58-66.
[83]刘明亮.中国土地利用／土地覆盖变化与陆地生态系统植被碳库和生产力研究,北京：中科院遥感应用研究所,2001.
[84]李文华,李飞.中国森林资源研究.北京：中国林业出版社,1996：25-36.
[85]冯宗炜.森林生态系统生物量和碳储存量的研究历史.见：王如松,方精云,高林,等.现代生态学的热点问题研究.北京：中国科学技术出版社,1999：335-347.
[86]罗天祥.中国主要森林类型生物生产力格局及其数学模型.1996.
[87]罗天祥.区域尺度陆地生态系统生物生产力研究方法.资源科学,1998,20(1)：23-34.
[88]莫兴国,陈丹,林忠辉,等.不同水分条件麦田能量和CO2通量变化特征研究.中国生态农业学报,2003,11(4)：77-81.
[89]方精云,徐高龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5)：497-508.
[90]温学发,于贵瑞，孙晓敏.基于涡度相关技术估算植被/大气间净CO2交换量中的不确定性.地球科学进展,2004,19(4)：658-663.
[91]刘世荣等.中国暖温带森林生物多样性研究.北京：中国林业出版社,1998,37-44.
[92]张宪洲.我国自然植被第一性生产力的估算与分布.自然资源,1993,(1)：15-21.
[93]周广胜,张新时.自然植被净第一性生产力模型初探.植物生态学,1995,19(3)：193-200.
[94]张永强,沈彦俊,刘昌明,等.华北平原典型农田水、热与CO2通量的测定.地理学报,2002,57(3)：333-342.
[95]李迪强,等.中国潜在植被生产力的分布与模拟.植物学报,1998,40(6)：560-566.
[96]康惠宁,马钦彦,袁嘉祖.中国森林C汇功能基本估计[J].应用生态学报,1996,7(3)：230-234.
[97]陈育峰.气候—森林响应过程敏感性的初步研究—以四川西部紫果云杉群落为例.地理学报,1996,51(增刊)：58-65.
[98]陈育峰,李克让.应用林窗模型研究全球气候变化对森林群落的可能影响—以四川西部紫果云杉群落为例.地理学报,1996,51(增刊)：73-80.
[99]方精云,刘国华,徐篙龄.中国陆地生态系统的碳循环及其全球意义.中国环境科学出版社,1996：129-139.
[100]张冬有.黑龙江省森林植被净初级生产力遥感估算研究[D].北京林业大学博士学位论文,2009.
[101]国志兴,王宗明,张柏,等.2000年-2006年东北地区植被NPP的时空特征及影响因素分析[J].资源科学,2008,30(8)：1226-1235.
[102]王萍.基于IBIS模型的东北森林净第一性生产力模拟[J].生态学报,2009,29(6)：3213-3220.
[103]王绍强,周成虎,刘纪远,等.东北地区陆地碳循环平衡模拟分析[J].地理学报,2001,56(1)：390-398.
[104]张峰,周广胜.中国东北样带植被净初级生产力时空动态遥感模拟[J].植物生态学报2008,32(4)：798-809.
[105]赵俊芳,延晓冬,贾根锁.东北森林净第一性生产力与碳收支对气候变化的响应[J].生态学报,2008,28(1)：92-100.
[106]张娜,于贵瑞,于振良,等.基于景观尺度过程模型的长白山净初级生产力空间分布影响因素分析[J].应用生态学报,2003,14(5)：659-664.
[107]王维芳,王琪,李国春,等.基于气象模型的黑龙江省植被净第一性生产力[J].东北林业学报,2009,37(4)：27-29.
[108]赵敏,周广胜.中国北方林生产力变化趋势及其影响因子分析[J].西北植物学报,2005,25(3)：466-471.
[109]李秀芬,郑有飞,王晨轶,等.黑龙江省森林NPP的遥感反演[J].中国农业气象,2009,31(1)：88-92.
[110]王琪.黑龙江省植被净第一性生产力的估算及空间特征分析[D].东北林业大学硕士学位论文,2009.
[111]毛留喜,孙艳玲,延晓冬.陆地生态系统碳循环模型.应用生态学报,2006,17(11)：2189-2195.
[112]程肖侠,延晓冬.气候变化对中国东北大兴安岭森林演替的影响.生态学杂志,2007,26(8)：1277-1278.
[113]Li KY and Coe MT, Ramankutty N. Modeling the hydrological impact of land-use Change in West Africa. Journal of Hydrology,2007,337:258-268.
[114]赵敏,周广胜.中国森林生态系统的植物碳贮量及其影响因子分析[J].地理科学,2004,24(1)：50-54.
[115]Fang JY,AP Chen,CH Peng SQ.Zhao&LJ Ci.Change in forest biomass carbon storage in China between 1949 and 1998[J].Science,2001,(292):2320-2322.
[116]王效科,冯宗炜,欧阳志云.中国森林生态系统的植物碳储量和碳密度研究[J].应用生态学报,2001,12(1)：13-16.
[117]Pacala SW, Hurtt GC, Baker D, et al.Consistent land-and atmpsphere-based US carbon sink estimates [J]. Science,2001,292(5525):2316-2320.
[118]Schimel DS, House JI.Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems [J]. Nature,2001, (414):169-172.
[119]邬建国.景观生态学—格局、过程、尺度与等级[M].北京：高等教育出版社,2000,190-197.
[120]王培娟,孙睿,朱启疆.陆地植被二氧化碳通量尺度扩展研究进展[J].遥感学报,2005,9(6)：751-759.
[121]Greenough JA, Apps MJ, Kurz WA.Influence of methodology and assumptions on reportednational carbon flux inventories:An illustration from Canadian forest sector [J]. Mitigation and Adaptation Strategies for Global Change,1997, (2):267-283.
[122]李世华,牛铮,李壁成.植被净第一性生产力遥感过程模型研究[J].水土保持研究,2005,12(3)：126-128.
[123]周蕾,王绍强,陈镜明,等.1991年至2000年中国陆地生态系统蒸散时空分布特征[J].资源科学,2009,31(6)：962-972.
[124]王培娟,谢东辉,张佳华,朱启疆.长白山森林植被NPP主要影响因子的敏感性分析[J].地理研究,2008,27(2)：323-330.
[125]王培娟,孙睿,朱启疆,谢东辉,陈镜明.复杂地形条件下提高BEPS模型模拟能力的途径[J].中国图象图形学报,2008,11(7)：1017-1024.
[126]王秋凤,牛栋,于贵瑞,任传友,温学发.长白山森林生态系统CO2和水热通量的模拟研究[J].中国科学D辑地球科学2004,34(增刊Ⅱ)：131-140.
[127]郑凌云.基于卫星遥感与BEPS生态模式的藏北草地变化及NPP动态研究[D].中国气象科学研究院硕士学位论文,2008.
[128]刘志斌,刘茂松,徐驰,阮尉琳,刘小钊.江阴市植被净初级生产力及碳汇价值分析[J].南京林业大学学报(自然科学版),2007,31(3)：139-142.
[129]王军邦.中国陆地净生态系统生产力遥感模型研究[D].浙江大学博士学位论,2008.
[130]张小全,侯振宏.森林、造林、再造林和毁林的定义与碳计量问题[J].林业科学,2003,39(2)：146-152.
[131]高志强,刘纪远,曹明奎,等.土地利用和气候变化对区域净初级生产力的影响[J].地理学报,2004,59(4)：581-591.
[132]Ji JJ. Adimate-vegetation interaction model:simulating physical and biologieal processes at the surface.Joumal of Biogeography,1995,22:445-451.
[133]程肖侠,延晓冬.气候变化对中国东北主要森林林型的森林影响.生态学报.2008,28(2)：534-543.
[134]延晓冬,赵士洞,符涂斌,等.气候变化背景下小兴安岭天然林的模拟研究.自然资源学报.1999,14(4)：374-376.
[135]延晓冬.林窗模型的几个基本问题的研究1.模拟样地面积的效应.应用生态学报,2001,12(1)：17-22.
[136]李慧.福建省森林生态系统NPP和NEP时空模拟研究.福建师范大学博士论文,2008：61-85.
[137]陈雄文,王凤友.林窗模型BKPF模型模拟红松林阔叶混交林群落对气候变化的潜在反应.植物生态学报,2000,24(3)：327-331.
[138]夏朝宗,熊利亚,庄大方,等.基于MODis的陆地植被光合过程参数反演研究.地理科学进展,2004,23(4)：10-18.
[139]赵敏,周广胜.中国北方林生产力变化趋势及其影响因子分析.西北植物学报,2005,25(3)：466-471.
[140]Chen JM, Deng F, Chen MZ, Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter [J]. IEEE Trans Geosci Remote Sens,2006,44(8):2230-2237.
[141]Farquhar GD, Caemmerer SV, Berry JA. A biochemicalmodel of photosynthetic CO2 assimilation in leaves of C-3 Species [J]. Planta,1980,149:78-90.
[142]Allen RG, Pereira LS, Raes D, et al.Crop evapotranspiration-Guidelines for computing crop water requirements [M]. FAO Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations, Rome,1998.
[143]Chen JM, Chen XY, Ju WM, et al. A remote sensing-driven distributed hydrological model:mapping evapotranspiration in a forested watershed [J]. J Hydrology,2005,305: 15-39.
[144]Liu J, Chen J M, Cihlar J. Mapping Evapotranspiration Based on Remote Sensing:An Application to Canada's Landmass [J]. Water Resources Research,2003,39:1189-1200.
[145]Liu J, Chen J M, Cihlar J, et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs [J]. Remote Sens Environ,1997,62:158-175.
[146]Liu J, Chen J M, Cihlar J, et al. Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data [J]. J Geophys Res,1999, 104(27):735-754.
[147]Chen JM, Liu J, Cihlar J, et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications [J]. Ecological Modelling,1999,124:99-119.
[148]Norman JM. Simulation of microclimates. In:Hatfield, J.L., Thomason, I.J. (Eds.), Biometeorology in Integrated Pest Management. Academic Press, New York,1982.
[149]Amthor JS, Chen JM, Clein JS, et al. Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models:intermodel comparisons and relationships to field measurements [J]. J Geophys Res,2001,106(33):623-648.
[150]Jarvis, PG., Morison JIL. Stomatal control of transpiration and photosynthesis. In:Jarvis, P.G., Mansfield, T.A. Ⅲ (Eds.), Stomatal Physiology. Cambridge University Press, New York,1981.247-279.
[151]Running, SW, Coughlan JC. A general model of forest ecosystem processes for regional applications I. Hy-drological balance, canopy gas exchange and primary pro-duction processes. Ecol. Model.1988,42,125-154.
[152]刘纪远.中国资源环境遥感宏观调查与动态研究.北京：中国科学技术出版社,1996
[153]喻锋,李晓兵,王宏,等.基于NDVI-Ts特征空间的中国土地覆盖分类研究植物[J].生态学报,2005,29(6)：934-944.
[154]邓书斌ENVI遥感图像处理方法.北京：科学出版社,130-132.
[155]J.M. Chen and S. Leblanc. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing.1997,35:1316-1337.
[156]X.Li and A.H. Strahler. Modeling the gap probability of discontinuous vegetation canopy. IEEE Transactions on Geoscience and Remote Sensing.1988,26:161-170.
[157]X.Li, A.H. Strahler and C.E. Woodcock. A Hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinous canopies. IEEE Transactions on Geoscience and Remote Sensing.1995,33:466-480.
[158]张时煌,彭公炳,黄玫.基于地理信息系统技术的土壤质地分类特征提取与数据融合[J].气候与环境研究,2004,9(1)：65-78.
[159]Bruand, AD, Bajze M, Hardy Prediction of water retention properties of clay soil validity of relationships using a single soil characteristic [J]. Soil Use and Managent.1994,10(1): 99-103.
[160]翁笃鸣.中国辐射气候研究[M].北京：气象出版社,1997.
[161]何洪林,于贵瑞,牛栋.复杂地形条件下的太阳资源辐射计算方法研究[J].资源科学,2003,25(1)：78-85.
[162]许吟隆,Richard J利用ECMWF再分析数据验证PRECIS对中国区域气候的模拟能力[J].中国农业气象,2004,25(1)：5-9.
[163]Kimball, JS, Thornton PE, White MA, Running, SW. Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region.Tree Physiol.1997,17, 589-599.
[164]Dang QL, Margolis HA, Coyea MR, Sy M, Collatz G.J. Regulation of branch-level gas exchange ofboreal trees:roles of shoot water potential and vapourpressure difference. Tree Physiol.1997,17,521-535.
[165]Bonan, G.B. Land-atmosphere CO2 exchange simu-lated by a land surface process model coupled to anatmospheric general circulation model. J. Geophys. Res.1995,100,2817-2831.
[166]Cramer W, Field CB.Comparing global models of terrestrial net primary productivity (NPP):introduction. Global Change Biology 5(supp 1):ⅲ-ⅳ (1924). Angstrom A. Solar and terrestrial radiation. Q. J. R.Meteorol.Soc.1999,50:121-126.
[167]张茂震,王广兴.浙江省森里生物量动态[J].生态学报,2008,28(11)：5665-5674.
[168]Brown S, Gillespie AJR, Lugo AE. Biomass estimation methods for tropical forests with application to forest inventory data. Forest Science,1989,35:881-902.
[169]Brown S, Lugo AE. Biomass of tropical forests:A new estimate based on forest volumes. Science,1984,223:1290-1293.
[170]Kauppi PE, Mielik IK, Kuusela K. Biomass and carbon budget of European forests,1971 to 1990. Science,1992,256(5053):70-74.
[171]Birdsey RA, Plantinga AG, Heath LS. Past and prospective carbon storage in United States forests. For Ecol Man,1993,59:33-40.
[172]Brown S, Lugo AE. Above ground biomass estimates for tropical moist forests of Brazilian Amazon. Interciencia,1992,17:8-18.
[173]Fang J Y, Chen A P, Peng C H, et al. Changes in forest biomass carbon storage in China between 1949 and 1998.Science,2001,292:2320-2322.
[174]方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产力[J].生态学报,1996,16(4)：497-508.
[175]方精云,陈安平,赵淑清,慈龙骏.中国森林生物量的估算：对Fang等Science一文(Science,2001,291:2320～2322)的若干说明[J].植物生态学报,2002,26(2)：243-249.
[176]唐守正,李希菲,孟昭和.林分生长模型研究的进展[J].林业科学研究,1993,29(6)：672-679.
[177]Borders BE, Patterson WD. Projeeting stand tables:A comparison of the w ibull diameter distribution method, a Percentile-based projection method, and a basal area growth projeetion method [J]. ForSci,1990,36:413-424.
[178]杨洪晓,吴波,张金屯,林德荣,常顺利.森林生态系统的固碳功能和碳储量研究进展[J].北京师范大学学报(自然科学版),2005,41(2)：172-177.
[179]Raich, JW, EB Rastetter, JM Melillo, et al. Potential Net Primary Productivity in South Ameriea:Application of a Global Model.Eogical Applications,1991,1(4):399-429.
[180]Scurlock JM., W Cramer, RJ Olson, et al. Terrestrial NPP:Toward a Consitent Data Set for Global Model Evaiuatio.1999,9(3):913-919.
[181]国志兴,王宗明,张柏,刘殿伟,杨桄,宋开山,李方.2000年-2006年东北地区植被NPP的时空特征及影响因素分析[J].资源科学,2008,30(8)：1226-1235.
[182]何勇,董文杰,季劲均,等.基于AVIM的中国陆地生态系统净初级生产力模拟[J].地球科学进展,2005,20(3)：345-349.
[183]朴世龙,方精云,郭庆华.利用CASA模型估算我国植被净第一性生产力[J].植物生态学报,2001,25(5)：603-608.
[184]陶波,李克让,邵雪梅,等.中国陆地净初级生产力时空特征模拟[J].地理学报,2003,58(3)：372-380.
[185]朱文泉,潘耀忠,张锦水.中国陆地植被净初级生产力遥感估算[J].植物生态学报,2007, 31(3)：413-424.
[186]刘明亮.中国土地利用/土地覆被变化与陆地生态系统植被碳库和生产力研究[D].北京：中国科学院遥感应用研究所,2001.
[187]孙睿,朱启疆.中国陆地植被净第一性生产力及季节变化研究[J].地理学报,2000,55(1)：36-45.
[188]王绍强,周成虎,刘纪远,等.东北地区陆地碳循环平衡模拟分析[J].地理学报,2001,56(4)：390-400.
[189]冯宗炜,王效科,吴刚.中国森林生态系统的生物量和生产力.北京：科学出版社.1999：226-227.
[190]Lacaze R, Roujean JL.G-function and HOt SpoT (GHOST) reflectance model-Application to multi-scale airborne POLDER measurements. Remote Sensing of Environment,2001,76(1):67-80.
[191]Chen JM, Menges CH, Leblanc SG. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment,2005,97,447-457.
[192]Lacaze R, Chen JM, Roujean JL; Leblanc SGRetrieval of vegetation clumping index using hot spot signatures measured by POLDER instrument. Remote Sensing of Environment,2002:79(1):84-95.
[193]Chen JM, Cihlar J.A hotspot function in a simple bidirectional reflectance model for satellite applications. Jouranl of Geophysical Research,1997,102:226-227.