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森林资源综合监测相关抽样技术理论与应用研究
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摘要
中国林业正处在全面实施以生态建设为主的林业发展战略,加速推进传统林业向现代林业转变,着力构建林业生态和产业体系,促进林业又快又好发展的重要时期。我国林业监测体系中不同的监测项目是在一定的社会经济和需求条件下产生和发展壮大的,各自具有其自身的特点,每种监测都有自已明确的目标、监测内容、监测方法、监测周期。根据社会形势对监测工作的需求,在不同尺度上,如区域(省)级及经营单位级层次上,如何整合现有的监测项目,如何构建统一的森林资源综合监测指标,建立与监测对象相适应的地面样地布设方案,其中抽样设计是关键。同时,针对不同指标匹配抽样调查方法,对改进和完善现有的监测体系有重要理论研究价值与实践意义。本论文是在国家“十一五”科技支撑课题“森林资源综合监测指标与技术体系研究(编号2006BAD23B01)”资助下进行的,其工作在于:
     (1)从核心概念出发,比较国内外森林资源清查体系特点,同时分析了世界主要林业发达国家森林资源综合监测现状。在分析我国森林资源监测现状与存在问题的基础上,针对我国森林资源监测的实际情况与社会形势对监测工作的需求,指出我国森林资源综合监测的发展趋势和建设目标为监测内容多样化、监测周期年度化、监测技术标准化、监测手段一体化、监测信息共享化。
     (2)抽样调查技术是森林资源综合监测关键技术。简略阐述抽样调查的历史背景。详细进行抽样理论的分类与比较。在分析国内外森林资源综合监测抽样调查体系的基础上,根据Michael et al.的抽样分类系统,重点综述了不同抽样方法在我国森林资源综合监测当中的应用现状。为实现我国现阶段监测的新需求,对如何改进和完善我国现有的抽样调查体系进行探讨。
     (3)提出了综合监测指标体系框架,总结了用于遥感监测分类方法的最近邻域分类法(k-NN分类法)和改进最近邻域分类法(ik-NN分类法),同时,提出了三阶段分层抽样设计,为森林资源综合监测提供抽样理论基础。
     (4)在介绍临界距离、角规常数、样地半径系数等概念基础上,对点抽样的总量估计、胸高断面积估计、总体大小估计、均值估计、方差和方差估计以及乘积估计进行系统建模。同时,对双重点抽样方法进行系统推导,可为建立经营单位级森林资源监测提供抽样技术指导。
     (5)为实现我国现阶段监测的新需求,对不同类型线截抽样(包括直线型、L型、Y型)的总体总值、密度、均值的条件和无条件估计,以及方差、估计区间进行系统推导,为森林资源综合监测提供抽样技术指导。
     (6)建立了直线型线截抽样的倒木蓄积总值、密度、均值、长度,以及估计精度、方差、估计区间等计算公式。在检查法的基础上,根据现有森林生态系统监测需求,应用直线型线截抽样原理对Ⅰ大区4小区进行了倒木调查的实验研究。分析表明:截线长度是影响精度的主要因子,不能把所有截线长度累加成用一条等长度的截线进行调查,这样严重影响样本选取的概率,对统计估计不利。
     (7)分析了倒木调查因子与截线长度的关系。分析表明:倒木蓄积总量的估计精度随截线的长度增加而增加;倒木总条数估计值和倒木蓄积密度随截线的长度增加先增大后减少,当截线长度区间为[50,100]m时,倒木总条数估计值和倒木蓄积密度相对稳定变化。从而确定直线型线截抽样的最优截线长度为50 m,此时,实验区倒木蓄积总量为536.85 m3,蓄积密度为27.53 m3·hm-2,倒木总条数为5887条,抽样精度达90.05%(可靠性为95%),为森林资源综合监测地面样地设计提供理论与实践依据。
     (8)对世界主要国家的森林资源清查地面样地设计进行了述评,包括样本单元的构成、样地大小、形状、样本单元中树木的选择方法、调查因子和监测周期。发现样本单元多采用群团样地或样地群,圆形样地应用较多,调查因子除森林资源外,还包括森林健康、生物多样性、碳和土壤等。建议在现有清查体系样地的基础上,布设小样方或样圆、样线等,扩展新的调查因子。
     (9)在假定没有样地边缘效应基础上,对基于固定面积嵌套样地抽样的资源现状进行理论探讨,在检查法的基础上,对Ⅰ大区4小区的系统样地内嵌套布设单个天然更新小样地(10 m×10 m),并进行幼苗幼树更新调查。利用所建数学模型,对研究区域的天然更新株数进行统计估计,其结果为:区域内云杉、冷杉、其它树种及总体幼苗幼树更新公顷株数无偏估计分别为420株·hm2、2251株·hm2、4501株·hm2、7178株·hm2,从而为天然林数量化经营提供了一个新的视角。同时,在假定全部固定样地的基础上,对净增量一般化表达、方差及其估计,分子总体净增量的估计、方差及方差估计进行推导,为森林资源综合监测提供理论依据。
     总之,所建立的三阶段分层抽样方法、点抽样方法、线截抽样方法以及基于固定面积嵌套样地的资源动态估计对改进我国森林资源监测体系提供科学理论依据。
Forestry in China is implementing its developing strategy which focuses on ecological construction in an all-around way, accelerating the transform from the traditional forestry to a modern one. It is experiencing an important period when efforts are concentrated on the construction of forestry ecology and industrial system for a rapid and steady progress. Various monitoring projects belonging to the forestry monitoring system have come into being and developed under certain social economic and demanding conditions, having their own characteristics. Every monitoring project has its own clear aim, content, method and period. According to the demand of social situation for the monitoring work, at different scale (e.g. the scale of an area or a province and its management unit), sampling design is crucial for the unification of the present monitoring projects, the construction of a universal and comprehensive monitoring indicators of forest resources and the creation of the ground plot scheme appropriate to the monitoring objects. At the same time, matching different indicators with their corresponding sampling methods has important research value and practical significance as well. This paper is aimed at:
     (1) From the core concept, the inventory and monitoring methods were introduced, and characteristics of national forest inventory at home and abroad was compared. At the same time, the current condition of forest resources monitoring in mainly developed forestry countries was analyzed. Based on the current condition and existing problem in China, according to the actual condition in forest resources monitoring and the monitoring need from the social practice, the development trend and construction goal of forest resources comprehensive monitoring in China was pointed out, that is, the diversification of monitoring content, annually monitoring period, the standardization of monitoring technology, the integration of monitoring measures and the sharing of monitoring information.
     (2) Sampling survey technique is key to forest resources comprehensive monitoring. The historical of sampling survey was briefly introduced. Classification and comparison of sampling theory were carried out in detail. Based on the analysis of forest resources comprehensive monitoring at home and abroad, according to the sampling classification of Michael Kohl, et al., the application condition of different sampling method on forest resources comprehensive monitoring was reviewed. In order to meet the new need of the current monitoring, improving the sampling survey system in China was discussed.
     (3) The indicators system framework of comprehensive monitoring was put forward. The classification method(k-NN, k-Nearest Neighbor and ik-NN, improved k-Nearest Neighbor) of remote sensing was summarized. At the same time, three-stage sampling for stratification was put forward, it would provide the theoretical basis for comprehensive forest resources monitoring.
     (4) Based on the some concepts, such as limiting distance, basal area factor, plot radius factor, estimation of population total, basal area, population size, mean, variances and product of point sampling was systematically built. At the same time, the method of double point sampling was systematically deduced, it would provide the sampling guidance for setting up the forest resources monitoring at the management sacle.
     (5) To meet the new need at present, the conditional estimation and unconditional estimation of population total, density and mean, at the same time, variance, estimation interval and precision of various kinds of line intersect sampling(including straight-line, Ell-shaped transects and Y-shaped transects) was systematically deduced so that it can provide the scientific proof for forest resources comprehensive monitoring.
     (6) The formula of calculation of total volume, density, mean and total number of log, estimation precision, variance and estimation interval was systematically deduced. Based on the method control, according to the need of the forest ecological system at present, the straight-line intersect sampling was applied in the inventory of log in the fourth small section of I section. The results show that the lengthen of transect is a main factor impacting the precision, A inventory could't be carried out by one transect with the length equivalent to the total length of all transects, because the probility of selecting sample will be affected too much, and it was not good for statistical estimation.
     (7) Relationship between the inventory factors of logs and the length of transect was analysed. The analysis shows that:the precision of the total volume of logs increased with the increase length of transect; Both the total number of logs and the density of volume firstly increased and then decreased with the increase length of transect. When the interval of the length of transect arrived at [50,100] m, they relatively stable. In the end, the best transect length was determined 50 meter, then the total volume of logs in the research area was 536.85 m3, the density of volume was 27.53 m3·hm-2, the total number of logs was 5887 and the sampling precision gained 90.05%(reliability was 95%), it can provide the scientific and practical proof of ground plot design for forest resources comprehensive monitoring.
     (8) Ground Plot Design techniques for National Forest Inventory of major countries in the world are reviewed. They cover sample unit, plot configuration, plot size, selection of trees in sample unit, and investigation variables and interval. Clustered circular plots are commonly adapted. Forest health, biomass, biodiversity and soil carbon are also included in the investigation variables besides normal ones. Based on the plots of national forest inventory, Small plots including rectangle, circle, line, and so on should be configurated to get more information.
     (9) On the hypotheses of no edge effect of plots, estimation following fixed area plot sampling was carried out. Singular and small plots(10 m x 10 m) were systematically nested in the plots of the method control, and regeneration survey was carried out. According to the builded model, the number of seedings and small trees was estimated. The unbiased estimation of the number per hectare of spruce, fir, other tree species and total seedlings was 420,2252,4501,7178 respectively.
     At the same time, on the basis of all the fixed plots, the general expression of increment, variance and its estimation were built, as well as the increment of different domains. It has provided theoretical evidences for the comprehensive monitoring of forest resource.
     In summary, three-stage sampling for stratification, point sampling, line intersect sampling and dynamical estimation of resources based on fixed and nested plot was buildt, they will benefit to improve the forest resources monitoring system.
引文
[I]Affleck, D.L.R., Gregoire, T.G., Valentine, H.T.. Design unbiased estimation in line intersect sampling using segmented transects [J]. Environmental and Ecological Statistics,2005,12(2):139-154.
    [2]Anthonie Van laar, AkCaA.. Forest Mensuration[M]. Springer,2007.234.
    [3]Battles, J.J., Dushoff, J.G., Fahey, T.J.. Line intersect sampling of forest canopy gaps[J]. Forest Science,1996,42:131-138.
    [4]Bechtold W.A., Patterson P.L.. The enhanced forest inventory and analysis Program-national sampling design and estimation procedures. USDA,2005, General Technical Report SRS-80.
    [5]Bell, J.F., Alexander, L.B.. Application of the variable plot method of sampling forest stands [R]. Oregon:Oregon State Board of Forestry,1957.30.
    [6]Bohl, J., Brandli, U. B.. Deadwood volume assessment in the third Swiss National Forest Inventory: methods and first results [J]. European Journal of Forest Research,2007,126(3):449-457.
    [7]Brasse, P., Lischke, H.. Swiss National Forest Inventory:Methods and Models of the Second Assessment. WSL Swiss Federal Research Institute, CH-8903 Birmensdorf,2001.
    [8]Brown T.K.. A plannar intersect method for sampling fuel volume and surface area[J]. Forest Science, 1971,17(1):96-102.
    [9]Canfield, R.H.. Application of the line interception method in sampling range vegetation[J]. Journal of Forestry,1941,39:34-40.
    [10]Chevrou, R.. Inventaire des haies[J]. Rev.For.Franc,1973,25:47-53.
    [11]Esseen, P.A., Jansson, K.U., Nilsson, M.. Forest edge quantification by line intersect sampling in aerial photographs [J]. Forest Ecology and Management,2006,230:32-42.
    [12]FAO. National forest inventory field manual template. Forest Resources Assessment Programme Working Paper 94/E, Rome,2004.
    [13]Federal Ministry of Food, Agriculture and Consumer Protection. Survey instructions for the 2nd National Forest Inventory(2001-2002). Bonn:Bundesministerium fur Verbraucherschutz, Ernahrung und Landwirtschaft.2006.107.
    [14]Gillis, M.D., Omule, A.Y., Brierley, T.. Monitoring Canada's forests:The National Forest Inventory[J]. The Forestry Chronicle,2005,81(2):214-221.
    [15]Gillis, M.D., Stephen, L.G., Clarke, D., et al.. Canada's National Forest Inventory:What can it tell us about old growth? [J]. The Forestry Chronicle,2003,79(3):421-428.
    [16]Gillis, M.D.. Canada's national forest inventory[J]. Environmental Monitoring and Assessment,2001, 67:121-129.
    [17]Gregoire, T.G., Harry, T.V.. Line intersect sampling:EⅡ-shaped transects and multiple intersections [J]. Environmental and Ecological Statistics, 2003,10:263-279.
    [18]Gregoire, T.G., Harry, T.V.. Sampling strategies for natural resources and the environment[M]. New
    York:Chapman&Hall/CRC,2008.223; 238; 247-270; 279-326.
    [19]Gregoire, T.G., Scott, C.T.. Altered selection probabilities caused by avoiding the edge in field surveys[J]. Journal of Agricultural, Biological and Environmental Statistics,2003,8:36-47.
    [20]Gregoire, T.G.. The unbiasedness of the mirage correction procedure for boundary overlap[J]. Forest science,1982,28(3):504-508.
    [21]Gruijter, J.D., Brus, D., Bierkens M., et al.. Sampling for Natural Resource Monitoring[M]. Netherlands:Springer.2006.15.
    [22]Hansen, M.H.. Line intersect sampling of wooded strips[J]. Forest Science,1985,31:282-288.
    [23]Harry, T.V., Jeffrey, H.Gove, Timothy G.Gregoire. Monte Carlo approaches to sampling forested tracts with lines or points[J]. Canadian Journal of Forest Research,2001,31:1410-1424.
    [24]http://www.fsi.nic.in/forest_inventory.htm
    [25]Huang, S.M., Yang, Y.Q., Heidt, J.. A proposed framework for developing an integrated growth and yield monitoring system for Alberta[J]. The Forestry Chronicle,2004,80(1):114-126.
    [26]International Union of Forestry Research Organization. International Guidelines for Forest Monitoring[R]. Vienna:IUFRO World Series,1994.
    [27]IPCC. Good practice guidance for land use, land use change and forestry. In Penman J, Gytarsky M, Hiraishi M et al. Eds. IPCC/OECD/IEA/IGES. Hayama, Japan.2003.
    [28]Kaiser, L.. Unbiased estimation in line-intercept sampling[J]. Biometrics,1983,39:965-976.
    [29]Kangas A., Maltamo M.. Forest Inventory Methodology and Applications[M]. Netherlands:Springer, 2006.3; 195-224; 301.
    [30]Katila, M., Tomppo, E.. Selecting estimation parameters for the Finnish multisource National Forest Inventory[J]. Remote Sensing of Environment,2001,76:16-32.
    [31]Kim, S.. NFI in Korea. In:Mainstreaming Forestry-Training Workshop on Broadening, Harmonization and Cross-Sectoral Integration of National Forest Inventories in Latin American Region Valdivia, Chile.24-28, September,2007.
    [32]Kleinn, C., Vilckoa, F.. A new empirical approach for estimation in k-tree sampling. Forest Ecology and Management[J],2006,237:522-533.
    [33]LaBau, V.J., Bones, J.T., Kingsley, N.P., et al.. A history of the forest survey in the United States: 1830-2004[R]. FS-877. Washington, DC:U.S. Department of Agriculture, Forest Service,2007.
    [34]Lehmann, E.L.. "Student" and small-sample theory[J]. Statistical Science,1999,4:418-426.
    [35]Lucas, H.A., Seber, G.A.F.. Estimating coverage and particle density using the line intercept method[J]. Biometrics,1977,64:618-622.
    [36]Mandallaz, D.. Samplin;g techniques for forest inventories[M]. New York:Chapman&Hall/CRC, 2007.177-185.
    [37]Matern, B.. A method of estimating the total length of roads by means of a line survey[J]. Stuf.For.Suec,1964,18:68-70.
    [38]Matney, T.G, Parker, R.C.. Stand and stock tables from double-point samples[J]. Forest Science, 1991,37(6):1605-1613.
    [39]McRoberts, R.E, Tomppo, E.. Remote sensing for national forest inventories[J]. Remote Sensing of Environment,2007,110:412-419.
    [40]McRoberts, R.E., Bechtold, W.A., Patterson, P.L., et al.. The enhanced forest inventory and analysis program of the USDA Forest Service:Historical perspective and announcement of statistical documentation [J]. Journal of Forestry,2005,103(6):304-308.
    [41]Michael, K., Magnussen, S., Marchetti, M.. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory[M]. Berlin-Heidelberg:Springer-Verlag,2006.81.
    [42]Moeur, M., Stage, A.R.. Most similar neighbor:an improved sampling inference procedure for natural resource planning[J]. Forest Science,1995,41:337-359.
    [43]Nelson, R., Short, A., Valenti, M.. Measuring biomass and carbon in Delaware using an airborne profiling LID AR[J]. Scandinavian Journal of Forest Research,2004,19(6):500-511.
    [44]Oderwald, R.G.. Stock and stand tables for point, double sampling with a ratio of means estimator[J]. Canadian Journal of Forest Research,1994,24(12):2350-2352.
    [45]Pal, S. K, Bandyopahayay, S., Murthy, C.A.. A Genetic Classifiers for Remotely Sensed Images: Comparison with Standard Methods[J]. International Journal of Remote Sensing,2001,22: 1423-1439.
    [46]Pieter G.de Vries. Sampling theory for forest inventory[M]. Berlin:Springer-Verlag,1986.242-279.
    [47]Reese, H., Nilsson M., Pahlen, T.G., et al.. Countrywide estimates of forest variables using satellite data and field data from the national forest inventory[J]. Ambio,2003,32(8):542-549.
    [48]Remi TEISSIER DU CROS, Lopez, S.. Preliminary study on the assessment of deadwood volume by the French national forest inventory[J]. Annals of forest science,2009,66(302):1-10.
    [49]Schmid, V.P.. Stichproben am waldtrand[R]. Schweizerische Anstalt fur das Forstliche Versuchswesen,1969,45(3):234-303.
    [50]Smith, W.B.. Forest inventory and analysis:a national inventory and monitoring programm[J]. Environmental Pollution,2002,116:233-242.
    [51]Tarmo K. R., Csillag, F., Mitchell, S., et al.. Integration of forest inventory and satellite imagery: a Canadian status assessment and research issues[J]. Forest Ecology and Management,2005,207: 405-428.
    [52]Tokola, T., Pitkanen, J., Partinen, S., et al.. Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials[J]. International Journal of Remote Sensing, 1996,17:2333-2351.
    [53]Tokola, T., Shrestra, S.M.. Comparison of cluster-sampling techniques for forest inventory in southern Nepal[J]. Forest Ecology and Management,1999,116:219-231.
    [54]Tomppo, E., Gagliano, C., De Natale F, et al.. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery[J]. Remote Sensing of Environment, 2009,113(3):500-517.
    [55]Tomppo, E., Halme, M.. Using coarse scale forest variables as ancillary information and weighting of variables in k-nn estimation:a genetic algorithm approach[J]. Remote Sensing of Environment, 2004,92:1-20.
    [56]Tomppo, E. Satellite imagery-based national forest inventory of Finland[J]. Proceedings of the symposium on global and environmental monitoring, techniques and impacts[C]. Victoria, British Columbia, Canada:International Archives of Photogrammetry and Remote Sensing,1991,28: 419-424.
    [57]Valentine, H.T., Ducey, M.J., Gove, J.H., et al.. Corrections for cluster plot slop[J]. Forest science, 2006,52:55-66.
    [58]Vries. Multi-stage line intersect sampling[J]. Forest Science,1974,20(2):129-133.
    [59]Warren, W.G., Olsen, P.F.. A line intersect technique for assessing logging waste[J]. Forest Science, 1964, (10):267-276.
    [60]Woldendorp, G, Keenan, R.J., Barry, S., et al.. Analysis of sampling methods for coarse woody debris[J]. Forest Ecology and Management,2000,198:133-148.
    [61]Wood, M.S., Keightley, E.K., Lee, A, et al.. Continental forest monitoring framework, technical report-design and pilot study. National Forest Inventory, Bureau of Rural Sciences, Canberra.2006.
    [62]艾建林.改进云南省森林资源连续清查体系的探讨[J].云南林业调查规划设计,1998,23(3):32-35.
    [63]岑巨延,李巧玉,曾伟生,等.广西森林资源连续清查角规样地体系评价[J].中南林业调查规划,2007,26(3):8-13.
    [64]陈朝土川.台湾森林调查体系之探讨.林金树.2007两岸森林经营学术研讨会论文集[C].台湾:国立嘉义大学森林暨自然资源学系,2007.17-38.
    [65]陈火春.论森林资源监测在森林经理中的作用[J].林业调查规划,2002,27(1):1-3.
    [66]陈雪峰.试论国家森林资源连续清查体系的建设[J].林业资源管理,2000,(2):3-8.
    [67]党永峰,滕晓华.森林资源连续清查中固定样地和判读样地不匹配对遥感判读结果的影响及解决方法[J].内蒙古林业调查设计,2003,26(增刊):48-49.
    [68]丁洪美.国家林业局一重点科研项目通过专家评审[N].中国绿色时报,2006-10-17(第A01版).
    [69]丁洪美.综合监测—新时期的使命[N].中国绿色时报,2006-10-19(第A03版).
    [70]杜子芳.抽样技术及其应用[M].北京:清华大学出版社,2005.28-55.
    [71]樊鸿康.抽样调查技术[M].天津:南开大学出版社,1995.1-23.
    [72]冯士雍,倪加勋,邹国华.抽样调查理论与方法.北京:中国统计出版社,2004.1-30.
    [73]冯士雍,施锡铨.抽样调查—理论、方法与实践[M].上海:上海科学技术出版社,1994.1-18.
    [74]傅宾领,古育平,聂祥永.新时期加强森林资源与生态状况监测的对策措施[J].林业资源管理,2007,(1):25-28.
    [75]葛宏立,韦希勤.两相抽样地类合并后蓄积估计方法探讨[J].林业资源管理,2001,(1):43-45.
    [76]葛宏立,周国模,张国江,等.遥感、地面三相抽样及其在森林资源年度监测面积估计中的应用[J].林业科学,2007,43(6):77-82.
    [77]郭晋平,周志翔.景观生态学[M].北京:中国林业出版社,2007.8;14;22.
    [78]国家林业局.国家森林资源连续清查技术规定[S].2004.
    [79]侯平,潘存德.森林生态系统中的粗死木质残体及其功能[J].应用生态学报,2001,12(2):309-314.
    [80]黄力平,张小平,赵直.新疆森林资源动态监测体系的构想[J].新疆师范大学学报(自然科学版),2005,24(3):136-140.
    [81]江泽慧.中国森林资源与可持续发展[M].北京:科学出版社,2007.
    [82]姜东涛.固定小班与不等长样带的森林调查技术[J].东北林业大学学报,2003,31(5):18-23.
    [83]姜秀英.抽样调查中最优轮换率的确定[J].东北林业大学学报,2001,29(1):96-97.
    [84]蒋有绪.努力建设我国森林生态环境长期监测体系[N].中国绿色时报,2005-4-12.
    [85]亢新刚.森林资源经营管理[M].北京:中国林业出版社,2001.1;161;226;270.
    [86]雷渊才,唐守正.适应性群团抽样技术在森林资源清查中的应用[J].林业科学,2007,43(11):132-137.
    [87]李晖,管远保.湖南省森林资源与生态状况综合监测初步探讨[J].林业资源管理,2007,(1):29-33.
    [88]李炳凯.全国森林资源清查和规划设计调查结果的比较及其使用[J].浙江林业科技,2005,25(6):37-39.
    [89]李具来,刘炳义,左维秋.日本的可持续森林经营监测评价[J].林业勘查设计,2004,132(4):16-21.
    [90]李明阳,吴文浩,何燕洁,等.空间平衡抽样及其在森林资源调查中的应用[J].林业调查规划,2008,33(4):1-6.
    [91]李土生.浙江省公益林森林资源与生态状况综合监测方案[J].林业资源管理,2006,(1):43-46.
    [92]李芝喜,曹宁湘,王维勤,等.利用遥感多阶不等概抽样清查森林资源[J].北京林学院学报,1985,(2):70-75.
    [93]林进,叶荣华.关于建设国家森林资源和生态环境综合监测评价体系的几点设想[J].林业资源管理,1998(3):14-19.
    [94]林材生.多目标分层次复合抽样设计研究[D].厦门大学博士论文,2006.6.
    [95]林昌庚,彭世揆,刘世荣,等.用点抽样进行森林连续清查的研究[J].南京林产工业学院学报,1981, (1): 1-14.
    [96]刘安兴.森林资源监测技术发展趋势[J].浙江林业科技,2005,25(4):70-76.
    [97]刘安兴.森林资源年度监测理论与方法研究-以浙江省为例[D].南京林业大学博士学位论文.2006,131.
    [98]刘凤阁,陈桂云,赵厚平.小样本估计(自助法)在森林抽样调查中的应用的研究[J].林业勘查设计,2003,128(4):32-34.
    [99]刘惠英,张思玉,吉霞.粗死木质残体的水土保育功能[J].世界林业研究,2004,17(3):25-28.
    [100]刘素青,洪家胜,彭世揆.林分蓄积量调查的线截抽样原理[J].东北林业大学学报,1999,27(2):60-64.
    [101]刘素青,洪家胜,彭世揆.林分蓄积量调查的线截抽样原理[J].东北林业大学学报,1999,27(2):60-64.
    [102]刘素青,洪家胜,彭世揆.线截抽样与线状物体调查原理研究[J].江西农业大学学报,1998,20(3):376-380.
    [103]刘素青.林分株数线截抽样估计原理研究[J].甘肃农业大学学报,1998,33(1):68-72.
    [104]刘羿,向新年,刘安兴.控制二类调查蓄积的方法研究[J].浙江林学院学报,2005,22(5):535-539.
    [105]陆元昌.森林健康状态监测技术体系综述[J].世界林业研究,2003,16(1):20-25.
    [106]罗仙仙,亢新刚,杨华.我国森林资源综合监测抽样理论研究综述[J].西北林学院学报,2008,23(6):187-193.
    [107]罗仙仙,亢新刚.森林资源综合监测研究综述[J].浙江林学院学报,2008,25(6):803-809.
    [108]马建维,李长胜,孙玉军,等.森林调查学[M].哈尔滨:东北林业大学出版社,1995.1-23;95-108:284-300;368;384-398.
    [109]马茂江,张 文,万国礼,等.德国与我国森林资源调查监测对比分析[J].四川林勘设计,2008,(3):48-49.
    [110]梅青.我国启动森林资源和生态状况综合监测体系建设框架研究[N].中国绿色时报,2004-12-17(第T00版).
    [111]梅安新,彭望(?),秦其明,等.遥感导论[M].北京:高等教育出版社,2006.197.
    [112]孟宪宇.测树学(第2版)[M].北京:中国林业出版社,1996.244;252-260.
    [113]倪金生,李琦,曹学军.遥感与地理信息系统基本理论和实践[M].北京:电子工业出版社,2006.7-12:116-117.
    [114]聂祥永.森林资源与生态状况监测信息资源整合架构探析[J].林业资源管理,2006,(2):51-56.
    [115]庞新生.多目标双重事后分层抽样中辅助变量的选择[J].统计与论坛,2001,16(2):63-65.
    [116]秦家鼎,周洪泽.利用陆地卫星产品的森林多阶抽样调查方法[J].东北林学院学报,1984,12(1):26-32.
    [117]闰恩荣,王希华,黄建军.森林粗死木质残体的概念及其分类[J].生态学报,2005,25(1):158-167.
    [118]佘光辉,林国忠,温小荣,等.森林二类调查方法的改进及角规抽样监测体系的建立[J].南京林业大学学报(自然科学版),2007,31(5):11-14.
    [119]舒清态,唐守正.国际森林资源监测的现状与发展趋势[J].世界林业研究,2005,18(3):33-37.
    [120]宋新民,李金良.抽样调查技术[M].北京:中国林业出版社(第2版),2007.53;75;180-184;207;211.
    [121]孙山泽.抽样调查[M].北京:北京大学出版社,2005.1-11;163.
    [122]孙玉军.资源环境监测与评价[M].北京:高等教育出版社,2007.127;142.
    [123]谭 军,吴乔明.序贯抽样决策中的抽样数量[J].南京林业大学学报(自然科学版),2002,26(3):53-55.
    [124]王海霞.谈二阶抽样调查在林业资源监测中的应用[J].华东森林经理,2003,17(3):31-33.
    [125]王小平,曹立明.遗传算法—理论、应用与软件实现[M].西安:西安交通大学出版社,2002.1-50.
    [126]王彦辉,唐守正.德国等欧洲国家的森林受害及监测[C].面向21世纪的林业论文集.北京:中国农业科技出版社,1998.
    [127]王彦辉,肖文发,张星耀.森林健康监测与评价的国内外现状和发展趋势[J].林业科学,2007,43(7):78-85.
    [128]王忠仁,韩爱惠.德国奥地利森林资源监测与经营管理的特点及启示[J].林业资源管理,2007,(3):103-108.
    [129]韦希勤.国家级森林资源监测体系中的地面样地设计[J].世界林业研究,1996,(3):24-28.
    [130]肖兴威,姚昌恬,陈雪峰,等.美国森林资源清查的基本做法和启示[J].林业资源管理,2005,(2):27-33;42.
    [131]肖兴威.中国森林资源和生态状况综合监测研究[M].北京:中国林业出版社,2008.16.
    [132]肖兴威.中国森林资源清查[M].北京:中国林业出版社,2005.4;47.
    [133]肖兴威.中国森林资源与生态状况综合监测体系建设的战略思考[J].林业资源管理,2004,(3):1-5.
    [134]谢邦昌(原著).张尧庭,董麓(改编).抽样调查的理论及其应用方法[M].北京:中国统计出版社,1998.1-11.
    [135]徐新良,曹明奎.森林生物量遥感估算与应用分析[J].地球信息科学,2006,8(4):122-128.
    [136]叶荣华,周卫东,黄国胜,等.国家森林资源和生态环境综合监测及体系评价的一个技术方案[J].林业资源管理,2000(3):17-21.
    [137]叶荣华.美国国家森林资源清查体系的新设计[J].林业资源管理,2003,(3):65-68.
    [138]叶荣华.瑞士的国家森林资源清查[J].世界林业研究,1995,17(4):39-43.
    [139]于峰,张彬,代启光.简述系统抽样在三类调查中的应用[J].林业勘查设计,2003,126(2):41-42.
    [140]于政中.森林经理学(第二版)[M].北京:中国林业出版社,1993.1-6;226-234.
    [141]余肖生,周宁,张芳芳.基于KNN的图像自动分类模型研究[J].中国图书馆学报,2007,(1):74-76.
    [142]曾伟生,周佑明.森林资源一类和二类调查存在的主要问题与对策[J].中南林业调查规划,2003,22(2):8-11.
    [143]曾伟生.角规复合样地的动态估计方法研究[J].中南林业调查规划,2005,24(2):1-4.
    [144]张会儒.德国森林资源和环境监测技术体系及其借鉴[J].世界林业研究,2002,15(2):63-70.
    [145]张煜星,王祝雄.遥感技术在森林资源清查中的应用研究[M].北京:中国林业出版社,2007.1-13:19.
    [146]赵良平,叶建仁,曹国江,等.森林生态健康理论与病虫害可持续控制—对美国林业考察的思考[J].南京林业大学学报(自然科学版),2002,26(1):5-9.
    [147]赵宪文.林业遥感定量估测[M].北京:中国林业出版社,1997.1-9;97;120.
    [148]赵义民.河南森林资源连续清查体系研究[J].河南农业大学学报,2005,39(4):402-405.
    [149]赵玉涛,余新晓,程根伟,等.粗死木质残体的水文生态功能[J].山地学报,2002,20(1):12-18.
    [150]郑小贤.德国、奥地利和法国的多目的森林资源监测述评[J].北京林业大学学报,1997,19(3):79-84.
    [151]郑小贤.瑞典、瑞士和芬兰的多目的森林环境监测[J].世界林业研究,1997,19(2):58-64.
    [152]中国科学技术协会.2006—2007学科发展报告综合卷[M].北京:中国科学技术出版社,2007.3.
    [153]周光辉,曾伟生,陈雪峰.我国森林资源和生态状况监测存在的问题与对策[J].中南林业调查规划,2006,25(4):1-9.
    [154]周科松.抽样调查中复合样地设计方法研究[J].中南林业调查规划,2001,20(2):9-12.
    [155]朱胜利.国外森林资源调查监测的现状和未来发展特点[J].林业资源管理,2001,(2):21-26.
    [156]朱维凡,韦希勤.PR抽样在森林蓄积量调查中的应用[J].内蒙古林业调查设计,1999,(1):26-27.

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