基于森林清查和遥感的城市森林净初级生产力估算

城市森林作为陆地生态系统的组成成分之一,研究其在全球碳循环中的地位和作用有很重要的意义.文章以上海城市森林为研究对象,开展基于基于森林清查数据和遥感技术的城市森林净初级生产力(NPP)估算研究.首先,根据选取上海市典型的森林植被类型,设置森林植被样方,测量反映植被生物学特性的特征参数,包括林龄、胸径、树高和叶面积指数(LAI)等,采用能反映林龄和蓄积量共同影响的生产力回归模型估算了样方NPP,建立了基于LAI的样方NPP回归模型;其次,利用一景相近时相的SPOT5影像,经进行几何纠正和辐射定标后,计算出能较好地反映植被特征和消除土壤背景影响的修正土壤调节植被指数(MSAVI),建立了基于MSAVI的区域森林LAI遥感估测模型;最后,根据建立的样方NPP回归模型以及区域LAI遥感估测模型,进行尺度化转换,估算出区域尺度上的上海城市森林净初级生产力.通过比较与前人运用传统方法研究估算的NPP,精度可达到89%,且本模型简单可行.因此本研究可为快速定量评估城市森林碳储量提供依据.     

神经网络模型森林生物量遥感估测方法的研究

森林生物量的估测是全球变化研究的基础,而遥感宏观、综合、动态、快速的特点决定了基于遥感的生物量模型为森林生物量估测的发展方向,目前的遥感生物量估测方法大多基于回归分析,需要预先假设、事后检验,仅为经验性的统计模型。神经网络的分布并行处理、非线性映射、自适应学习和容错等特性,使其具有独特的信息处理和计算能力,在机制尚不清楚的高维非线性系统体现出强大优势,可以用于遥感生物量估测。文章在野外调查的基础上,尝试应用BP网络和RBF网络技术,建立广州TM遥感影像数据与森林样方生物量实测数据之间的神经网络模型,通过训练和仿真,与生物量实测数据进行比较。结果表明,在独立样地估测中,人工神经网络估测的相对误差均小于15.18%,获得了满意的效果。而RBF网络与BP网络相比,在识别精度上、稳定性、速度上,均优于BP网络,其最大相对误差不超过10.12%,平均相对误差为4.76%。可见应用神经网络方法的“黑箱”操作虽然难以归纳出指导性规律,但可以获得很高的精度。尤其RBF网络,在训练完成后,可以应用该模型进行大区域生物量估算,对于森林的规划及管理具有深远意义。     

基于PCA的森林生物量遥感信息模型研究

森林生物量和遥感多光谱数据、植被指数及地学因子存在相关关系,但这些因子间可能存在着多重相关性,如利用这些因子直接建模估测森林生物量,则可能出现病态模型。因此,文章采用主成分分析方法,提取遥感及地学因子的主成分,再建立主成分与生物量多元线性回归模型,估测森林生物量,达到既可保留多个遥感及地学因子的主要信息,又可避免因子间共线性的问题,以及降维,简化模型的作用。文章以高黎贡山自然保护区常绿阔叶林为研究对象,利用地面样地胸径每木调查数据,结合生物量相对生长式,得样地生物量。利用2006年印度卫星(IRS)数据,包括B2、B3、B4、B5四个波段,提取DVI、NDVI、PVI、RVI、VI3、SLAVI六种植被指数,利用DEM提取海拔、坡度、坡向值共13个遥感及地学因子。在此基础上,提取13个因子的主成分,第一主成分至第五主成分的累计贡献率达98.7%。以前5个主成分值作自变量,建立主成分与地面生物量的回归模型,模型经方差分析及相关性检验,达到显著相关水平,相关系数R=0.7129,可用于森林生物量估测。     

基于遥感技术的人工速生林二氧化碳吸收量估测

选择宁夏中卫市中冶·美利纸业集团的林纸一体化人工速生杨基地作为研究对象,以CBERS/CCD影像和同步实测样地数据为基础,利用相关分析方法筛选出相关系数分别为0.939和0.936的NDVI和RVI两个变量,构建了基于NDVI、RVI及NDVI和RVI的3个人工速生林地叶面积遥感回归估测模型,R2分别为0.882、0.877和0.885,并通过相关检验,估算出研究区不同林龄速生杨林地的叶面积。结合样地的实测净光合速率（PN,Net Photosynthetic Rate,μmol·m-2·s-1）,推算出不同林龄的速生杨单位叶面积的年二氧化碳净吸收量,最终估算出整个研究区的年固定碳（CO2）的净增量分别为326 648.66、315 688.73和322 509.04 t。通过与常规方法测得结果的比较,表明遥感结合地面实测数据估算林木固碳是可行的;从建立模型的R2值分析,根据不同植被指数建立的多元模型的精度要优于单一植被指数建立的一元模型;根据估测结果,随着林龄的增长,林木的固碳能力不断提高,但受不同生境的影响,增长速度存在空间差异。     

基于多元线性模型、支持向量机(SVM)模型和地统计方法的地表水溶解性总固体(TDS)估算及其精度对比——以艾比湖流域为例

地表水溶解性总固体(TDS)是地表水各组分浓度的总指标,是地表水水化学特性长期演变的最终结果,也是表征水文地球化学作用过程的重要参数,TDS的高低直接影响地表水的含盐量.本研究以艾比湖流域为研究对象,结合实测地表水TDS数据;选用准同步的Landsat OLI数据,首先,利用光谱诊断指数选取与地表水TDS相关性较高的波段,其次,利用地统计方法、多元线性回归模型和支持向量机(SVM)模型对TDS进行预测,并对其结果进行精度比较.结果表明,SVM模型为最优估测模型,拟合决定系数R2为0.97,均方误差(RMSE)为50.59;多元线性回归模型的精度与SVM模型精度较为接近,拟合决定系数R2为0.9,RMSE为66.55;地统计克里格插值法预测精度最低,拟合决定系数R2为0.87,RMSE为95.73.遥感估测SVM模型预测值在大区域能较好地反映出艾比湖流域TDS的总体特征.该模型在水质遥感领域的应用中具有良好的可行性和有效性,其预测结果也与艾比湖流域水体TDS的实际分布相吻合,因此遥感估测SVM模型在水质估测中具有一定的应用潜力.     

生物环境因子对树干呼吸时空变异的影响

树干呼吸是解释森林生态系统生产力变化的关键过程之一.测定树干呼吸速率,理解其生物环境调控机制,有助于构建森林碳循环模型,提供森林碳收支的估测精度.本文比较分析了树干呼吸3种测定方法,指出活体测定法与质量平衡方法的结合使用,可以更深入地进行树十呼吸机理研究.针对树十呼吸速率时空变异,着重探讨温度、树干液流的重要影响机制,并比较分析了不同森林生态系统的维持呼吸年通量,维持呼吸年通量与林龄、维度、年均温和降水量呈显著线性相关.表4参84     

基于遥感估算的上海城市森林碳储量空间分布特征

城市森林在固碳释氧、应对气候变化方面发挥着重要作用,对其碳储量的估算为中国城市做好碳达峰、碳中和工作提供重要数据基础。以上海城市森林为研究对象,采用样地调查数据与Landsat OLI遥感影像,分别计算样地碳储量和遥感参数变量(波段数据、植被指数数据和纹理数据),构建基于多元逐步回归模型和普通克里格残差矫正相结合的估算模型,分析区域尺度城市森林碳储量和碳密度的空间分布特征。结果表明,(1)81个样地的碳储量范围为0.09—7.10 t,均值为2.14 t,数据有右偏分布和瘦尾特征,变异系数为0.75,样地类型多样;上海城市森林总碳储量为2.87 Mt,碳密度主要集中在13—40 t·hm~(-2)之间,均值为25.09 t·hm~(-2),整体呈现中部较低,东西部较高的态势,与土地利用强度及城市森林分布有关。(2)主城区碳储量为0.37 Mt,碳密度为1—69 t·hm~(-2),均值为21.77 t·hm~(-2);非主城区碳储量为2.50 Mt,占上海城市森林碳储量的87.11%,碳密度范围为0—89 t·hm~(-2),均值为25.66 t·hm~(-2)。(3)在选择波段反射率和遥感植被指数的同时,提取影像纹理特征并纳入模型,提高了影像的分类精度;采用多元逐步回归模型结合残差矫正的方法估算城市森林碳储量,使估算结果的均方根误差降低了10.29%,平均绝对误差降低了5.5%,提高了估算精度。     

干旱区绿洲耕层土壤重金属铬含量的高光谱估测

土壤重金属铬含量的高光谱估测技术较传统测量方法具有无污染、方便快捷进行动态监测的优势。对新疆渭干河-库车河三角洲绿洲耕层土壤98个样品的原始光谱反射率R分别进行倒数1/R、对数lg(R)、倒数对数lg(1/R)以及微分变换。不同处理结果与实测土壤重金属铬含量进行相关分析从而筛选出具有极显著相关的特征波段(P0.001)。以不同变换处理下的特征波段反射率作为自变量,土壤铬含量为因变量,采用多元线性逐步回归、偏最小二乘回归、BP神经网络和随机森林回归方法构建土壤重金属铬含量的高光谱估测模型,并对最优估测结果进行克里格空间插值。结果表明,(1)原始光谱反射率的微分变换处理可有效提升光谱与土壤重金属铬含量之间的敏感性,其中经微分变换后的土壤光谱反射率与铬含量的相关系数由0.487显著提高到0.669(P0.001)。(2)综合比较各模型的训练集和验证集估测结果,经倒数对数一阶微分[lg(1/R)]′处理后的BP神经网络模型具有较高的估测精度和很强的稳定性,可作为研究区土壤重金属铬含量的最优估测模型,其决定系数(Rd~2)在0.8以上,均方根误差(RMSE)小于6.5,相对分析误差(RPD)大于2。(3)研究区土壤重金属铬含量具有中等空间变异性,低含量区域主要分布在新和县和沙雅县的外缘地带,而位于库车市东北部区域的耕层土壤铬含量达到最高水平。受人类活动影响该绿洲的土壤污染问题日趋严重,耕层土壤重金属铬含量呈现出较高含量的空间分布。     

基于ETM+图像的植被覆盖度遥感估算模型

植被覆盖度(VFC)的定量遥感是多种地表过程研究的迫切需要.文章选用南京市一幅Landsat 7 ETM 图像,经大气校正后提取了归一化植被指数(NDVI),与地面实测的植被覆盖度进行回归分析,建立了1～4次多项式关系模型.结果表明,NDVI与VFC呈极显著的正相关关系(r = 0.874, P < 0.001).在NDVI-VFC的1～4次多项式关系模型中,模型幂次越高,拟合程度越好.综合考虑模型的精度和稳定性,3次多项式模型作为最优模型推荐使用:VFC = -1.3438 NDVI 3 0.9774 NDVI 2 0.9988 NDVI 0.1507 (R2 = 0.7961, RMSE = 0.1094),该模型精度在植被中等密集区域(VFC＝0.4～0.8)最高,植被稀疏区域(VFC < 0.4)最低,植被密集区域(VFC > 0.8)居中.模型可直接用于全图像的VFC计算,并可通过植被指数的校准,进行推广使用.     

神经网络法应用于酚类化合物对青海弧菌毒性的预测

青海弧菌对有毒酚类化合物具有强烈的敏感性,为建立酚类衍生物对青海弧菌毒性的定量结构-活性相关性(QSAR)模型,分析了16种酚类衍生物的分子结构与对青海弧菌毒性之间的相关关系,计算了酚类衍生物的分子连接性指数和分子形状指数,并优化筛选了分子连接性指数的1阶路径指数(~1χ)和分子形状指数的2阶特征指数(K_2)及1阶和2阶指数乘积值(K_4),用这3种指数与对青海弧菌的毒性进行多元回归分析,多元回归方程的决定系数R~2=0.971。为进一步提高预测精度,将这3种分子结构参数作为神经网络的输入变量,毒性值作为输出变量,采用3:2:1的网络结构,通过反向传播(BP)神经网络法获得满意的QSAR预测模型,总的相关系数r为0.996,计算得到的毒性预测值与实验值较为吻合,平均相对误差仅为1.98%,结果表明该模型具有良好的预测酚类衍生物毒性的能力,可以看出神经网络方法对酚类化合物发光菌毒性预测比多元线性回归方法的统计学意义更加明显。     

Using forest inventory data to assess fisher resting habitat suitability in California.

The fisher (Martes pennanti) is a forest-dwelling carnivore whose current distribution and association with late-seral forest conditions make it vulnerable to stand-altering human activities or natural disturbances. Fishers select a variety of structures for daily resting bouts. These habitat elements, together with foraging and reproductive (denning) habitat, constitute the habitat requirements of fishers. We develop a model capable of predicting the suitability of fisher resting habitat using standard forest vegetation inventory data. The inventory data were derived from Forest Inventory and Analysis (FIA), a nationwide probability-based sample used to estimate forest characteristics. We developed the model by comparing vegetation and topographic data at 75 randomly selected fisher resting structures in the southern Sierra Nevada with 232 forest inventory plots. We collected vegetation data at fisher resting locations using the FIA vegetation sampling protocol and centering the 1-ha FIA plot on the resting structure. To distinguish used and available inventory plots, we used nonparametric logistic regression to evaluate a set of a priori biological models. The top model represented a dominant portion of the Akaike weights (0.87), explained 31.5% of the deviance, and included the following variables: average canopy closure, basal area of trees <51 cm diameter breast height (dbh), average hardwood dbh, maximum tree dbh, percentage slope, and the dbh of the largest conifer snag. Our use of routinely collected forest inventory data allows the assessment and monitoring of change in fisher resting habitat suitability over large regions with no additional sampling effort. Although models were constrained to include only variables available from the list of those measured using the FIA protocol, we did not find this to be a shortcoming. The model makes it possible to compare average resting habitat suitability values before and after forest management treatments, among administrative units, across regions and over time. Considering hundreds of plot estimates as a sample of habitat conditions over large spatial scales can bring a broad perspective, at high resolution, and efficiency to the assessment and monitoring of wildlife habitat.     

太阳黑子与杉木生长关系

根据多层误差板传网络结构模型和三次设计发展了一种太阳黑子人工神经网络预报方法，以杉木胸径生长的年轮指数和太阳黑子自相关因子输入变量，应用改进的人工神经网络方法建立了太阳黑子相对数年平均值的预测模型，模型的模拟回归优度为93．3％，预测精度达到95．74％，并对网络模型中变量进行灵敏度分析，分析表明，杉木胸径生长的年轮指数对太阳黑子对相对数年平均值影响较平坦，而太阳黑子自相关因子Yt-4和Yt-2对太阳子相对数年平均值影响较灵敏，3个因子对太阳黑子相对数年平均值均在一定的影响。图2表5参19     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

吕梁山南段森林碳密度及其空间分布格局研究

基于2000年和2005年两期森林资源清查资料,利用双向指示种分析（ two-way indicator species analysis, TWINSPAN）方法,对森林植被进行群系划分;采用生物量换算因子法,对森林样地碳密度进行估算,对碳密度及其动态变化特征进行研究;基于地统计学原理对森林碳密度的空间分布格局进行分析,并对其影响因子进行探讨。结果表明,（1）吕梁山南段森林植被可分为9个群系,即臭椿群系、柳树群系、辽东栎-油松混交群系、辽东栎群系、辽东栎-枫树混交群系、辽东栎-白桦-山杨混交群系、白皮松-辽东栎混交群系、白皮松-侧柏混交群系和槐树群系。2000年各群系的碳密度值介于23.53 Mg·hm^-2和75.65 Mg·hm^-2之间,平均值为54.90 Mg·hm^-2;2005年的碳密度值介于24.16 Mg·hm^-2和78.91 Mg·hm^-2之间,平均值为57.20 Mg·hm^-2,5年间9个森林群系的碳密度增加了2.30 Mg·hm^-2。（2）森林碳密度呈现出自南向北、自西向东增加的趋势;球状模型能很好地反映森林植被碳密度的空间结构特征;碳密度分布主要受结构性因子影响,空间依赖性较强,在小尺度上没有明显规律,而在中尺度上有群团状分布的特点。（3）随着海拔的升高,森林碳密度先增后降,1600～1800 m最大;坡上部森林碳密度最高,其次为坡下部,山脊部最低;阴坡半阴坡森林碳密度一般高于阳坡和半阳坡,无坡向处最低;斜坡和平坡碳密度值明显高于其他坡地,急坡地最小。     

Multi-model analysis of tree competition along environmental gradients in southern New England forests.

Robust predictions of competitive interactions among canopy trees and variation in tree growth along environmental gradients represent key challenges for the management of mixed-species, uneven-aged forests. We analyzed the effects of competition on tree growth along environmental gradients for eight of the most common tree species in southern New England and southeastern New York using forest inventory and analysis (FIA) data, information theoretic decision criteria, and multi-model inference to evaluate models. The suite of models estimated growth of individual trees as a species-specific function of average potential diameter growth, tree diameter at breast height, local environmental conditions, and crowding by neighboring trees. We used ordination based on the relative basal area of species to generate a measure of site conditions in each plot. Two ordination axes were consistent with variation in species abundance along moisture and fertility gradients. Estimated potential growth varied along at least one of these axes for six of the eight species; peak relative abundance of less shade-tolerant species was in all cases displaced away from sites where they showed maximum potential growth. Our crowding functions estimate the strength of competitive effects of neighbors; only one species showed support for the hypothesis that all species of competitors have equivalent effects on growth. The relative weight of evidence (Akaike weights) for the best models varied from a low of 0.207 for Fraxinus americana to 0.747 for Quercus rubra. In such cases, model averaging provides a more robust platform for prediction than that based solely on the best model. We show that predictions based on the selected best models dramatically overestimated differences between species relative to predictions based on the averaged set of models.     

扁刺栲在两种类型林分中的生长过程分析

通过对扁刺栲—华木荷林区针阔混交林、次生阔叶林的群落调查以及扁刺栲的树干解析．研究结果表明：(1)扁刺栲在针阔混交林与次生阔叶林中,胸径快速生长期分别在a8～12和a10～14之间,生长高峰值分别出现在a10和a12,最大值分别为1．07cm和0．85cm．(2)扁刺栲在针阔混交林与次生阔叶林中,树高快速生长期分别在a6～10和a10～14之间,生长高峰值分别出现在a8和a10,最大值分别为0．55m和0．56m．(3)在针阔混交林中,16a生扁刺栲单株材积达0．0134m^3,而在次生阔叶林中只有0．0103m^3．在分析不同林分中扁刺栲生长差异及其原因的基础上,建议对次生阔叶林经营应采用动态管理．     

编码输入法在PCB分配参数预测中的应用

根据Free-Wilson法中化合物结构表达的思想，采用两种简单的编码输入方法对58个多氯联苯(PCB)的结构进行表征，并基于模型简单性原则对多元线性回归(MLR)与误差反向传递(BP)人工神经网络、模拟退火(SA)人工神经网络和遗传算法(GA)人工神经网络PCB分配参数预测模型的预测能力进行了比较，试验证实，粗略考虑PCB结构对称性的简单编码输入规则可以简化PCB分配参数预测模型的数字形式，所获得的MLR模型具备广泛的应用前景。     

川西高山森林生态系统林下生物量及其随林窗的变化特征

作为森林生态系统的重要组成部分,林下植被及其残体的分布受到林冠层的影响,但迄今有关林窗对林下植被和残体生物量的影响尚无研究报道.于2013 年8 月2 日至20 日,以海拔3 600 m 的川西岷江冷杉原始林林下植被为研究对象,根据区域内的坡向和林分组成等因素设置3 个100 m×100 m 的典型样地,调查其生物量及其随林窗的变化特征.在每个样地内选择3 个大林窗,在林窗、林缘和林下分别设置3 个20 m×20 m 的样方,调查粗木质残体长度或高度、大小头直径、枯立木记录胸径、腐烂等级等;在林窗、林缘和林下分别设置3 个5 m×5 m 的样方,采用“收获法”收集样方内直径在2.5-10 cm之间的细木质残体和灌木生物量;在林窗、林缘和林下分别设置3 个1 m×1 m 的样方来调查凋落物储量和草本生物量;在1m×1 m 的样方内随机选择1 个20 cm×20 cm 的小样方来调查地被植物生物量.结果表明,（1）川西高山森林生态系统总生物量为72.75 t·hm^-2,其中林下生物量为67.92 t·hm^-2,占生态系统生物量的95.17%.活体植被以灌木为主,其生物量为9.81t·hm^-2;残体部分以粗木质残体为主,其储量为53.00 t·hm^-2;（2）林窗对灌木、草本、地被植物的影响各不相同,且不同物种的灌木生物量表现出不同的分布规律;草本生物量表现出明显的“边缘效应”,在林缘显著高于林下;林窗和林缘的地被植物生物量相对较低;（3）粗木质残体储量从林下到林窗呈现减小的趋势,但总体储量仍然较大,林窗和林缘的细木质残体储量高于林下.这些结果为认识高山森林生态系统林下生物量及其格局,以及林窗在森林生态系统的重要作用提供了基础理论依据.     

Dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain)

In this study we developed a dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain). The data used to develop the model were obtained from a network of permanent plots, of between 10 and 55-year-old, which the Unidade de Xestión Forestal Sostible (Sustainable Forest Management Unit) of the University of Santiago de Compostela has set up in pure plantations of this species of pine in its area of distribution in Galicia. In this model, the initial stand conditions at any point in time are defined by three state variables (number of trees per hectare, stand basal area and dominant height), and are used to estimate stand volume, classified by commercial classes, for a given projection age. The model uses three transition functions expressed as algebraic difference equations of the three corresponding state variables used to project the stand state at any point in time. In addition, the model incorporates a function for predicting initial stand basal area, which can be used to establish the starting point for the simulation. This alternative should only be used when the stand is not yet established or when no inventory data are available. Once the state variables are known for a specific moment, a distribution function is used to estimate the number of trees in each diameter class, by recovering the parameters of the Weibull function, using the moments of first and second order of the distribution (arithmetic mean diameter and variance, respectively). By using a generalized height–diameter function to estimate the height of the average tree in each diameter class, combined with a taper function that uses the above predicted diameter and height, it is then possible to estimate total or merchantable stand volume.     

Modelling forest management within a global vegetation model—Part 2: Model validation from a tree to a continental scale

The construction of a new forest management module (FMM) within the ORCHIDEE global vegetation model (GVM) allows a realistic simulation of biomass changes during the life cycle of a forest, which makes many biomass datasets suitable as validation data for the coupled ORCHIDEE-FM GVM. This study uses three datasets to validate ORCHIDEE-FM at different temporal and spatial scales: permanent monitoring plots, yield tables, and the French national inventory data. The last dataset has sufficient geospatial coverage to allow a novel type of validation: inventory plots can be used to produce continuous maps that can be compared to continuous simulations for regional trends in standing volumes and volume increments. ORCHIDEE-FM performs better than simple statistical models for stand-level variables, which include tree density, basal area, standing volume, average circumference and height, when management intensity and initial conditions are known: model efficiency is improved by an average of 0.11, and its average bias does not exceed 25%. The performance of the model is less satisfying for tree-level variables, including extreme circumferences, tree circumference distribution and competition indices, or when management and initial conditions are unknown. At the regional level, when climate forcing is accurate for precipitation, ORCHIDEE-FM is able to reproduce most productivity patterns in France, such as the local lows of needleleaves in the Parisian basin and of broadleaves in south-central France. The simulation of water stress effects on biomass in the Mediterranean region, however, remains problematic, as does the simulation of the wood increment for coniferous trees. These pitfalls pertain to the general ORCHIDEE model rather than to the FMM. Overall, with an average bias seldom exceeding 40%, the performance of ORCHIDEE-FM is deemed reliable to use it as a new modelling tool in the study of the effects of interactions between forest management and climate on biomass stocks of forests across a range of scales from plot to country.