创新实践报告-刘金明.doc

创 新 实 践 报 告实践名称： 创新性研究与设计 系部名称： 测绘工程学院 专业班级： 测绘工程11-1班 学生姓名： 刘金明 学 号： 20110386 指导教师： 王强 霍春玲 黑龙江工程学院教务处制实践项目基于TM遥感影像的森林生物量估算研究实践日期2014-2015（1）17-20周实践地点黑龙江工程学院同组人数1实践类型 传统 R 现代 其 他 验证性 综合性 R 设计性 其 他R 自立式 合作式 研究式 其 他一、 创新实践研究的背景及意义森林生态系统作为陆地生态系统的主体，在维护全球气候系统、调节全球碳平衡、减缓大气温室气体浓度上升等方面具有不可替代的作用。森林及其变化对陆地生物圈及其它地表过程有着重要影响。因此，推算森林生物量便成为生态学和全球变化研究的重要内容之一。森林生物量，指各种森林在一定的年龄、一定的面积上所生长的全部干物质的重量，反应了森林生态系统最基本数量特征，是森林固碳能力的重要标志，也是森林生态系统研究的基础。当前，森林生物量的测定及未来变化趋势研究已成为森林生态系统研究的热点。随着遥感和地理信息系统技术的发展，传统的生物量估算方法已经不能很好的克服一些不利因素和满足技术要求，利用遥感信息和 GIS 技术进行森林生物量估算已经成为一种全新手段。其基本原理是利用遥感影像的信息与实测生物量，建立完整的数学模型，利用这些数学模型来测定林分生物量，实现更大尺度森林生物量研究。不少人就某些森林类型的全国尺度的生物量也分别进行了估算 ，这些研究大大推进了我国森林生物量及相关的生态系统生态学和全球变化研究的开展， 也为后来系统研究中国的森林植被碳库及其变化打下了基础。二、实践仪器设备硬件设备：电脑一台；软件设备：CAJViewer 7.2 PDF阅读器三、实践内容、成果及参考文献1、 实践内容： 论文课题：基于TM遥感影像的森林生物量估算研究 （1）阅读20篇以上参考文献（其中至少2篇外文文献），学习并积累论文研究方法、数据处理过程等相关知识，为自己论文编写打下基础。 （2）翻译一篇外文文献。（见附表）2、 成果：目前，国内不少学者在不同尺度上对森林生物量的研究较多，为中国的森林生态系统碳循环研究提供了基础资料。根据森林生物量的研究历史，国内外森林生物量研究方法主要包括实测法、模型法和遥感法。目前，大多数基于遥感技术的森林生物量估算，仅建立在生物量与遥感数据之间的线性相关分析上，缺乏对机理模型的研究，融合机理模型的遥感生物量估算能为生物量估算提供新的方法，也将是今后遥感建模的主要方向。森林生态系统是陆地生态系统的主体，森林生物量是森林生态系统最基本的数量特征，也是森林生态系统结构和功能变化的重要指标，森林生物量研究对森林生态系统研究具有重要意义。中国对森林生物量的研究起步较晚，但发展迅速，未来森林生物量研究的主要发展方向为：a) 研究尺度扩大，在空间尺度上，随着 RS、GIS、GPS 等技术的发展，精度不断提高，区域、全球尺度的森林生物量定量研究越来越适合当前的发展趋势，在时间尺度上，由地面监测站和遥感数据相结合，实现森林生物量的实时动态监测，充分反映森林生态系统的变化趋势；b) 研究结果精确化，随着经验模型的发展与机理模型在森林生物量研究中的广泛应用，可更为精确地估算森林生物量，反映森林生态系统的内部特征，对林业生产经营管理具有一定的指导意义；c) 随着生物量研究现状监测及研究方法的进步，将逐步实现森林生物量时空动态的模拟，解决现有气候条件及不同人为干扰下，未来几十年甚至上百年后森林生物量的不确定性。3、 参考文献：1.张昌顺.刘爱兵.谢高地.陈龙.刘春兰 基于森林资源清查的迪庆州森林生物量及生产力研究期刊论文 - 资源科学2011(11)2.田勇燕.秦飞.吴静.李亚丽.梁波.关庆伟 徐州市森林生物量的估算期刊论文 - 江苏林业科技 2011(3)3.张俊.孙玉军 森林生态系统碳循环研究方法概述期刊论文 - 林业资源管理 2007(1)4.何红艳.郭志华.肖文发 遥感在森林地上生物量估算中的应用期刊论文 - 生态学杂志 2007(8)5.沈泽昊.刘增力.方精云 贡嘎山海螺沟冷杉群落物种多样性与群落结构随海拔的变化期刊论文 - 生物多样性2004(2)6.沈月琴.王枫.张耀启.朱臻.王小玲 中国南方杉木森林碳汇供给的经济分析期刊论文 - 林业科学 2013(9)7.郑德祥.廖晓丽.李成伟.叶倩玲.陈平留 福建省森林碳储量估算与动态变化分析期刊论文 - 江西农业大学学报2013(1)8.毛学刚.李明泽.范文义.姜欢欢 近30年来小兴安岭地区生物量变化及地统计分析期刊论文 - 地理研究 2011(6)9.谢立红.张荣涛 黑龙江省森林碳汇估算及潜力分析期刊论文 - 国土与自然资源研究 2011(4)10.李江.孟梦.邱琼.朱宏涛.翟明普.陈宏伟.郭永清.冯弦.刘永刚 思茅松中幼龄人工林生物量及生产力动态期刊论文 - 东北林业大学学报 2010(8)11.张亮.林文欢.王正.余娜.陈红跃 广东省森林植被碳储量空间分布格局期刊论文 - 生态环境学报 2010(6)12.唐建维.庞家平.陈明勇.郭贤明.曾荣 西双版纳橡胶林的生物量及其模型期刊论文 - 生态学杂志 2009(10)13.张茂震.王广兴.刘安兴 基于森林资源连续清查资料估算的浙江省森林生物量及生产力期刊论文 - 林业科学2009(9)14.魏安世.林寿明.李志洪 基于TM数据的森林植物碳储量估测方法研究期刊论文 - 中南林业调查规划 2006(4)15.孙志虎.王庆成 采用地统计学方法对水曲柳人工纯林表层根量的估计期刊论文 - 生态学报 2005(4)16.黄燕平.陈劲松 基于SAR数据的森林生物量估测研究进展期刊论文 - 国土资源遥感 2013(3)17.钱逸凡.伊力塔.张超.余树全.沈露.彭冬琴.郑超超 浙江省中部地区公益林生物量与碳储量期刊论文 - 林业科学2013(5)18.田稼穑.铁牛.李铁牛.苏兴权.韩永清 内蒙古大兴安岭兴安落叶松林碳储量研究期刊论文 - 林业资源管理 2013(3)19.巨文珍.王新杰.孙玉军 长白落叶松林龄序列上的生物量及碳储量分配规律期刊论文 - 生态学报 2011(4)20Dixon R K, Brwn S B, Houghton R A,et al. Carbon pools and flux of global forest ecosystem.Science, 1994,263: 185190.21Wang B S(王伯荪), Peng S L(彭少麟).Vegetation EcologyCommunities and Ecosystems(in Chinese). Beijing: Chinese Environmental Science Press, 1997. 340353.22 YangH X,Wu B, Zhang JT,etal. Progress of research into carbon fixation and storage of forest ecosystems. Journal ofBeijingNormalUniversity (NaturalScience), 2005, 41(2): 172-177.23 Brown S, Sathaye J, CanellM,etal. Mitigation of carbon emmision to atmosphere by forestmanagement. Com ForReb, 1996, 75: 80-91.24 XiangW H, TianD L, YanW D. Review ofresearcheson forestBiomass and productivity. CentralSouth Forest Inventory and Planning, 2003, 22(3): 57-64.25 kimura. Themethods for terrestrialplant communities productivitymeasurision. JiangR, ChenNQ Translated. Beijing, Science Press, 1981. 58-105.26 PanW C, LiLC, GaoZH,etal. The biomass and the productivity of the ecosystem of Chinese Firplantation.Central South Forestry Science and Technologies, 1978, (2): 1-14.4、 实践中存在的问题、解决方法及进一步的想法等本次实践整体来说内容不是很多，但是对阅读量有很大的要求，因此需要仔细的阅读和理解。在整个实践过程中最难的地方就是对于新知识的理解和吸收。在生物量研究中的各种方法以及许多专业名词都很陌生，所以吸收起来比较慢。面对这种情况我只有反复阅读、理解，参考各种资料，加深对其的理解和掌握，这样才能达到学习的目的和要求，在别人研究的基础上加以总结和创新，为以后的毕业设计打下坚实的基础。5、 实践心得体会本次实践主要是锻炼学生自主查阅资料以及自主学习的的能力，还有就是对外文文献的阅读、翻译水平的检验。对于外文的翻译中对于人名和出版社的缩写比较难，出版社可以查，但是人名就不知道怎么对应了，只能大概的翻译其中的一些人名。因此需要加强对该学科前沿研究的了解，学习更加先进、优秀的知识，不断的提高自己。 推算森林生物量和净生产对我国研究森林植被的生物生产力提供重要依据。虽然该方面的研究比较新鲜，但我不能因此而止步，我会迎难而上，尽自己最大的努力来完成该课题的研究，让自己达到另外一个高度。六、教师评语成 绩指导教师签字： 年 月 日注：1、此报告为参考格式，各栏项目可根据实际情况进行调整；2、 实验成绩以优（90100）、良（8089）、中（7079）、及格（6069）、不及格（60以下）五个等级评定。附录1 外文参考文献原文Influencing Factors on Forest Biomass Carbon Storage in Eastern China A Case Study of Jiangsu ProvinceJiameng Yang, a,b Runying Xu, a Zhijian Cai, b Jun Bi, a and Haikun WangForest vegetation plays a crucial role in improving the ecological environment and maintaining the regional ecological balance. However, most studies pay little attention to the factors that can impact forest biomass carbon storage (FBCS). This research estimated the FBCS by combining relevant forest inventory data and models of continuous functions for biomass expansion factor. A modeling equation was then established and applied to examine the impact of socioeconomic factors on FBCS in Jiangsu, a coastal province in Eastern China, as a case study. The results showed that Jiangsus FBCS increased by 20.28 Tg from 2005 to 2010, showing a prominent carbon sink effect but with spatial imbalance among the changes in carbon storage. Jiangsus FBCS is significantly affected by land use factors (e.g., forest area and cultivated area), population factors (e.g., population density and urbanization), and economic development factors (e.g., GDP). Relatively speaking, the forest area and GDP had positive effects on FBCS, while cultivated area, population density, and urbanization had significant negative effects.Keywords: Forest biomass carbon storage; Forest inventory data; Social-economic impact; JiangsuContact information: a: State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, P.R. China; b: College of Economic and Management, Nanjing Forestry University, Nanjing 210037, P.R. China; Corresponding author: (H. Wang)INTRODUCTIONThe Earths atmosphere, oceans, and terrestrial biosphere are three reservoirs of artificial sources of CO 2 (Fang and Guo 2007). As the largest part of the carbon stock in terrestrial ecosystems, the forest carbon pool stores nearly 2/3 of the terrestrial carbon (Ceulemans et al. 1999; Dixon et al. 1994), and plays an important role in stabilizing CO 2 concentration in the atmosphere. According to the estimation of the Intergovernmental Panel on Climate Change (IPCC 2007), carbon storage in the global terrestrial ecosystem is 2221 to 2477 Pg (1 Pg =10 15 g), of which about 20% is derived from vegetation and 80% originates from the soil. Forest vegetation, which covers 27.6% of the global land mass, accounts for about 77% of the whole vegetation carbon storage, and the forest ecosystem carbon storage per unit area is 1.9 to 5 times that of agricultural land. The Food and Agriculture Organization (FAO 2001) estimates that aboveground biomass per unit forest area is 109 Mg/ha and that global forest aboveground biomass reaches 422 Pg. Many scholars have explored the size, distribution, potential, estimation methods, etc., of forest vegetation carbon storage (Fang et al. 2001a; Liu et al. 2000; Luo et al. 2009; Wang et al. 2001; Wu et al. 2008; Zhou et al. 2008), which has laid a good foundation for studying forest biomass carbon storage (FBCS) in China.Reducing carbon emissions and increasing carbon storage are the fundamental ways to respond to global climate change for all countries and areas. As it is known, there are many natural factors influencing FBCS, such as temperature, rainfall, conflagration, etc. (Bradford et al. 2013; Chen et al. 2013; Saunders et al. 2014; Wamelink et al. 2009; Zhang et al. 2010). The impact of socioeconomic factors, such as population, urbanization, GDP, and energy consumption, on carbon emissions has also received much attention (Kaya 1990; Ma et al. 2011; Schaffer 2008; Zhang et al. 2012; Zhang and Yang 2013). Regarding ways to increase carbon storage, some scholars calculate the carbon fixing capacity of forest vegetation at the national scale (Fang and Chen 2001b; Schimel et al. 2000), while other scholars estimate the carbon storage of relevant forest types at regional or provincial scales, such as the mangrove forest in Yingluo Bay, Guangdong Province (Wang et al. 2013), the ecological service forest in Zhejiang Province (Zhang et al. 2007), and the subalpine coniferous forest in Western Sichuan (Xian et al. 2009). Meanwhile some scholars analyze the impacts of a large-scale reforestation program and urbanization on carbon storage dynamics in Guangdong province (Zhou et al. 2008) and Xiamen City (Ren et al. 2011a), respectively, and assess the influence of tree species, forest age, and ownership changes on vegetation carbon storage in Fujian Province (Ren et al. 2011b). However, there have been few investigations that have comprehensively studied the impact of socioeconomic factors on FBCS for various regions of China. To enhance forest carbon sequestration, the Chinese government made a promise at the United Nations Climate Change Conference in September 2009 to increase forest coverage by 40 million ha and forest stock volume by 1.3 billion m 3 by 2020 from the 2005 levels. The realization of this goal depends on scientific decision-making and effective implementation by local governments with respect to forestry resources, policies, and technology.There are four coastal provinces in Eastern China: Shandong, Jiangsu, Shanghai, and Zhejiang (see Fig. 1). These provinces accounted for 31.28% of Chinas GDP and 23.84% of the national forestry output value in 2010 (NBSC 2011; SFA 2011). However, the forest area and stock of these provinces are only 4.87% and 1.98%, respectively, of Chinas total (SFA 2010). Jiangsu Province has a total land area of 102.6 thousand km 2 and a total population of 78.66 million people, which are 1.06% and 5.87% of the countrys totals, respectively (PGJ 2011).As one of the most developed provinces of China, Jiangsus per capita GDP exceeded US $10,000 in 2012 and is ranked first at the provincial level nationwide (NBSC 2013). By contrast, Jiangsus forest coverage (15.29%) is below the average level for China (20.36%) (SFA 2011), and its economic development is facing great ecological and environmental pressures. Geographically, a notion that has gained favor is a three-fold division of Jiangsu Province into the south (Sunan), the central (Suzhong), and the north (Subei) (Fig. 1). Using Jiangsu as a case study, this investigation aims to estimate the changes of the FBCS, to illustrate the potential impacts of socio-economic factors on FBCS, and to offer recommendations to enhance FBCS for Jiangsu and other coastal provinces in eastern China that have developed economies and poor forest resources.Fig. 1. Regions in Jiangsu Province, ChinaMETHODOLOGYEstimation Method of FBCSAverage biomass, average expansion factor, and continuous functions for biomass expansion factor (BEF) are the three principal methods to calculate regional-scale forest biomass (Fang et al. 2002). Here, a function expressed as BEF = a + b/x was used to obtain a variable BEF value for each forest type, where x (unit: m 3 /ha) is the timber volume, and a (unit: Mg/m 3 ) and b (unit: Mg) are the corresponding constants for an arbor species (Fang and Guo 2007; Liu et al. 2000). The BEFs of major species of Jiangsus arbor forest were calculated based on the Seventh and Eighth Forest Resource Inventory of the Jiangsu Province (JFB 2010, 2011a) (Table 1). The Eighth Forest Inventory of Jiangsu was organized by Jiangsu Forestry Bureau and implemented by Jiangsu Monitoring Center for Forest, which kept identical with the Seventh with respect to the sample plot range, quantities, shape, area and localization manner of sample trees. The investigation results comprehensively reflected the present situation, characteristics and change of forest resource, and the ecosystem in Jiangsu Province. Table 1. BEFs of Major Tree Species in Jiangsu Arbor ForestsForest typeConstantsStock per hectare X(m3/ha)BEF (Mg/m3)a(Mg/m3)b(Mg)2005201020052010Broad-leavedforestPopulus deltoides0.496926.97347.8962.181.0600.931Mied broadleaf0.97885.376428.0725.341.1701.191Lignum cinnamomi camphorae1.03578.059118.02 1.483Quercus1.3288 -3.8999 76.5 1.278Other Quercus1.1453 8.5473 52.48 53.03 1.308 1.306Ulmus pumila 0.97885.3764 35.12 1.132Paulownia 0.4158 41.3318 14.69 3.229 Melia azedarach 0.97885.376414.05 1.361 Sapium sebiferum 0.9788 5.376427.9 1.172Broussonetia papyrifera1.1783 2.5585 30.81 1.261Ginkgo biloba 1.17832.5585 11.39 21.601.403 1.297Celtis sinensis1.1783 2.5585 40.171.242Salix babylonica Linn.0.9788 5.376430.3619.701.1561.252Robinia pseudoacacia1.1783 2.5585 22.9 14.761.2901.352Pterocarya stenoptera0.97885.3764 36.1425.831.128 1.187Hardwood1.1783 2.558523.61 22.90 1.2871.290Other soft broadleaf0.7554 5.092829.789.010.926 1.679ConiferousforestMetasequoia glyptostroboides0.415841.331882.96108.960.9140.795Cunninghamia lanceolata0.4652 19.14149.8957.090.8490.800Taxodium ascendens0.465219.14171.430.733Cupressus funebris Endl.0.8893 7.396534.0741.261.1061.069Pinus elliottii0.5292 25.08752.491.007Pinus massoniana0.5034 20.547 44.2330.860.9681.169Mixed conifer 0.813618.46619.2940.501.7711.270Cedrus deodara0.5292 25.08715.33 2.166Pinus thunbergii Parl.0.5292 25.08723.7222.611.5871.639Pinus densiflora Sieb.et Zucc0.572316.4897.794.502.689 4.237Pinus abroad0.5723 16.48947.55 0.919 Other Pinus 0.529225.0876.694.279Mixed coniferous-broadleaf forest0.801912.2799 26.2639.78 1.2701.111Notes: “”stand for 0 or no data.The arbor forest biomass (AFB) was calculated from the inventory data of the regional forest resource using the method of continuous functions for BEF, （1）where, for the ith arbor forest species, A i is the forest area (unit: ha), X i is the corresponding timber volume per unit area (unit: m 3 /ha), and BEF i is the biomass expansion factor (unit: Mg/m 3 ).Then, the FBCS for each region was estimated using the following equation, （2）where q is the carbon coefficient of the forest biomass and adapted as 0.5 (Fang et al. 2002); A j (for j = 1, 2, 3) represents the area (unit: ha) of the economic forest, the bamboo forest (calculated by plant number), and the shrubbery, respectively; B j (for j = 1, 2, 3) is the per unit biomass (unit: Mg/ha) of the economic forest, the bamboo forests, and the shrubbery, respectively.When calculating the FBCS of prefectural-level cities, the average biomass of Chinas economic forest (23.7 Mg/ha) was taken as the economic forest biomass per unit area (B 1 ), and the average biomass of the shrubbery in the south of the Qingling Mountain Range and Huaihe River in China (19.76 Mg/ha) (Fang et al. 1996) was taken as the shrubbery per unit area (B 3 ). Per plant biomass of the bamboo forest varies from 0.02235 Mg to 0.02262 Mg, with the bamboo density (stand density) of 2788 to 4545 plant per hectare (Nie 1994).The bamboo forest area accounts for 69.5%, and the average density is 4182 plants per hectare in Jiangsu Province, so bamboo biomass (B 2 ) was estimated in accordance with an average of 0.0225 Mg per plant. According to the similar wood density, the parameters of Pterocarya stenoptera were calculated with reference to mixed broadleaf trees, those of Broussonetia papyrifera, Ginkgo biloba, Celtis sinensis, and Robinia pseudoacacia were calculated in accordance with the hardwood class, and those of Pinus elliottii, Cedrus deodara, and Pinus thunbergii parl, which are generally found in coniferous forests, were all calculated in reference to other pines and coniferous forests.Socioeconomic Model for Factors Influencing FBCSFBCS is closely related to the quantity and quality of forest and is influenced by a variety of socioeconomic factors. However, there is usually limited knowledge of the specific forces driving these impacts. One key limitation to a precise understanding of these impacts is the absence of refined analytic tools. In past decades, some analytic tools, such as IPAT (Environmental impact (I) = Population (P)*Affluence (A)*Technology (T) and STIRPAT (stochastic impacts by regression on population, affluence, and technology), were established to analyze the impacts of human behavior on carbon emissions (York et al. 2003). However, the model is deficient with respect to identifying the socioeconomic impacts on FBCS. Here, an equation is proposed to analyze the impacts of human behavior on FBCS, (3)where LA is the land area; POP is the population; and FA is the forest area. Then, POP/LA, FA/POP, and FBCS/FA, respectively define population density (PD), per capita forest area (PCFA), and carbon density (CD).To reduce the heteroscedasticity of the data and to elimi