基于不同立地质量的香格里拉高山松林郁闭度遥感反演

以香格里拉市为研究区,基于2006年TM遥感影像和2006年森林资源规划设计调查数据,以小班各遥感因子为自变量,每个小班的郁闭度为因变量,对香格里拉高山松林按区分立地质量等级和不区分地位级2种策略建立郁闭度神经网络遥感反演模型,并进行精度评价。基于小班平均高和平均年龄建立的地位级表将立地质量等级划分为好、中、差3种类型。研究结果表明,红外、近红外、植被指数和第一主成分等遥感因子对郁闭度的解释能力较强;从模型独立样本验证结果得出,不区分地位级郁闭度估测精度为67.64%,区分地位级后,好、中、差3种立地类型的郁闭度估测精度分别为74.14%、75.32%、72.38%,区分立地质量类型模型的精度优于不区分地位级模型的精度。     

广西隆安县马尾松地位级生长模型建立与应用研究

利用广西隆安县森林资源规划设计调查数据,通过改进常用生长模型结构,建立了马尾松地位级生长模型,并编制了隆安县马尾松地位级表。研究结果显示:①利用森林资源规划设计调查数据资料,不必增加额外的外业调查工作,即能建立性能良好、预估精度高的地位级平均树高生长模型;②通过改进Korf生长函数模型结构获得的地位级生长模型(H=34.862012×exp(-5.27302×exp(-0.543543×A))-0.052398×A×S)最优良,总相对误差小于3%,预估精度达98%以上;③地位级模型(表)在立地质量评价、林木生长过程和收获量预测、营林树种布局和结构调整等森林经营管理工作中可发挥积极作用,且比地位指数表的使用更简便、高效。     

四川巨桉人工林地位级表的编制

在泸洲、宜宾、富顺、洪雅、眉山、丹棱、沙湾、夹江、沐川等地的不同立地条件和不同年龄的林分中，调查了巨桉人工林标准地165块。通过比较选出了H＝0.20871＋2.92447A-0.1192A^2作为巨桉人工林地位级导向曲线的最优模型，用等分法编制了地位级表，并对其精度进行了检验，精度可靠，能满足生产和科研的需要。     

不同地位级雷竹竹材的物理力学性质比较

测定了不同地位级不同竹龄雷竹竹材的物理力学性质,结果表明:不同地位级雷竹竹材的基本密度和力学性质都随竹龄的增大而提高;径向、弦向和体积全干缩率随竹龄增大而减少,地位级Ⅱ的竹材的基本密度和力学性质高于地位级Ⅰ的,径向、弦向和体积全干缩系数小于地位级Ⅰ;不同地位级的竹材基本密度、顺纹抗压强度、顺纹抗拉强度和弦向抗弯强度有显著差异.     

辽东山区黄檗人工林环剥经营技术研究

以辽东山区黄檗人工林为研究对象进行环剥试验,确定了辽东山区黄檗人工林环剥起始年龄Ⅲ地位级15年,Ⅱ地位级与Ⅳ地位级相应提前和延后1年;最佳环剥季节为5～6月,其树皮再生成功率均值达到95.83%;阴而无雨的天气为环剥最佳天气条件,树皮再生成功率均值可达到95.85%;测算出30年生黄檗单株鲜皮总收获量均值为33.53 kg。     

塞罕坝地区樟子松生长规律研究

为了了解塞罕坝地区樟子松的生长规律,对塞罕坝地区不同立地条件樟子松人工林进行了调查,并建立了樟子松胸径、树高、材积的最优生长模型。研究结果表明,胸径生长的最优模型为对数模型,树高和材积生长的最优模型为Richards模型;Ⅰ地位级樟子松胸径、树高及材积的总生长量、连年生长量和平均生长量都明显高于Ⅱ地位级,Ⅰ地位级樟子松数量成熟龄大于Ⅱ地位级,Ⅰ地位级标准木和优势木数量成熟龄分别为83 a和70 a左右,Ⅱ地位级则分别在79 a和57 a左右,均超过当前技术规程中确定的采伐年龄。     

延边地区萌生柞木林地位级表编制与生长规律的探讨

由于过去沿用的地位级表、立地指数表对萌生柞木林一般都偏高,不适应生产需要。为此,我们试编了《延边地区萌生柞木林地位级表》,并分别地位级探讨了林木生长规律。一、地位级表的编制地位级表是根据林分平均高及其标准差随年龄的变化规律编制的。 1.标准地来源:供编表用的标准地共316块,其来源见表1。     

规划设计调查数据在林分生长模型中的应用初探

采用森林资源规划设计调查小班数据编制了硬阔叶林分的地位级指数表,并建立了硬阔叶林分的生长模型。检验表明,所建模型达到了一定的精度,基本能满足森林经营的要求。     

云南松天然林立木高径比的研究

<正> 分别地位级、林龄、疏密度对云南松天然林立木高径比(H/D)的研究,尚不多见。本文拟在年龄相同地位级不同,地位级相同年龄不同的条件上,对立木高径比进行研究,找出立木高径比随年龄、直径的变化规律。根据多年在云南全省各地收集鮑标准地样木、标准木资料,选取不同林龄的标准地74块,样木1085株,选取90年生的标准地31块,其中:Ⅰ地位级标准地1块,样木274株,Ⅱ地位级标准地18块,样木109株;Ⅲ地位级标准地12块,样木76株。共计105块标准地,1544株样木。现将研究结果分述如下:     

对编制地位级表及其方法的探讨

一、编制地位级表的必要性“地位级是说明森林立地条件好坏的因子,并且也是决定立地条件对培育森林适用程度的因子。”这是苏联著名的林学家阿努钦教授给地位级下的定义。它阐明了地位级的真正含义。它告诉我们地位级不仅是说明森林立地条件的指标,而且对于生长在不同地位级的立地条件下的森林,在进行森林经营时,应当采取不同的经营措施。在很多有关森林经理的书籍和中苏两国的“森林经理及调查规程”中都明确地规定,应当分别不同地他     

THE STUDY ON SITE QUALITY EVALUATION FOR DAHURIAN LARCH(LARIX GMELINII RUPR.)

Site quality of larch(Larix gmelinii Rupr.)in Da Xingan Mountain in InnerMongolia was studied systematically with about 1000 temporary plots and computersoftware.The nature of site class and site index curve was discussed in view of generalmathematical model of height growth.The height growth of dominant tree of four divisionsof forest stands were studied and site index tables were constructed respectively.In orderto unify the comparing standard and convenience for management in this region,site indextable was constucted too.Based on discussing the action and accuracy of forest type,aseries of site quality evaluation(forest type-site index class-site index)was suggested.     

基于不同立地质量的杉木生物量遥感估测

[目的]研究不同立地质量对杉木生物量遥感估测精度的影响,为进一步提高和完善森林生物量遥感监测体系提供一种新的思路和方法。[方法]以2007年建德市森林资源二类调查数据和TM影像为研究材料,采用蓄积量—生物量换算因子连续函数法计算杉木林生物量和地位级法评价立地质量等级,比较杉木立地质量好、中等、差和不分地位等级4种生物量遥感估测模型,并进行精度检验。[结果]表明:(1)以TM遥感影像主成分分析中第一主成分为自变量的模型拟合效果最好,决定系数R2均在0.69以上,最高0.855。(2)利用预留独立样本对模型精度进行验证,不分地位级总体估测精度为87.78%,分立地质量等级好、中、差3种类型总体估测精度分别为97.37%、95.82%、98.23%。分不同立地质量类型可以提高杉木生物量遥感估测精度。[结论]研究结果为森林生物量遥感估测提供一种改进的思路,且为提高森林生物量和碳储量遥感估测精度提供一种参考方法。     

The study on site quality evaluation for dahurian larch (Larix gmelinii Rupr.)

Site quality of larch (Larix gmelinii Rupr.) in Da Xingan Mountain in Inner Mongolia was studied systematically with about 1000 temporary plots and computer software. The nature of site class and site index curve was discussed in view of general mathematical model of height growth. The height growth of dominant tree of four divisions of forest stands were studied and site index tables were constructed respectively. In order to unify the comparing standard and convenience for management in this region, site index table was constucted too. Based on discussing the action and accuracy of forest type, a series of site quality evaluation (forest type-site index class-site index) was suggested. This subject of study is supported by the National Natural Scientific Fund (No. 39270549).     

Site classification for Maiden's gum, Eucalyptus globulus subsp. Maidenii, in Rwanda

Soil A horizon and meteorological variables were used to derive a site index prediction equation and a site class discriminant rule for Maiden's gum planted in Rwanda. Quadratic discriminant analysis supplied a higher site class prediction accuracy from less variables than site index prediction equation. The most important discriminatory variables were P concentration, Mg stores, clay-plus-silt fraction, Al concentration, December rainfall, and Mn : Ca ratio.     

辽宁东部地区主要树种组林分平均胸径生长率模型的研究

该文采用超总体理论编制辽宁省东部地区主要树种组林分平均胸径生长率通用模型，以2005年森林资源二类调查资料为原始数据，涵盖辽宁东部7个市31个县（市、区）8个树种（组）在不同地理位置和不同立地条件的生长水平。建立4种模拟方程，通过拟合与优化及结果精度的检验选出了各树种组的平均胸径生长率模型。     

吉林省西部地区小叶杨人工林成熟龄的研究

通过对吉林省西部地区小叶杨林地地位质量的划分 ,编制了标准木树干解析表 ,人工林地位级表和立地条件分类表 ;并据此确定了小叶杨的各类成熟龄。为小叶杨近、成熟林地的经营利用提供了依据     

Modelling tree mortality across diameter classes using mixedeffects zero-inflated models

The mortality of trees across diameter class model is a useful tool for predicting changes in stand structure.Mortality data commonly contain a large fraction of zeros and general discrete models thus show more errors.Based on the traditional Poisson model and the negative binomial model,different forms of zero-inflated and hurdle models were applied to spruce-fir mixed forests data to simulate the number of dead trees.By comparing the residuals and Vuong test statistics,the zero-inflated negative binomial model performed best.A random effect was added to improve the model accuracy;however,the mixed-effects zero-inflated model did not show increased advantages.According to the model principle,the zeroinflated negative binomial model was the most suitable,indicating that the"0"events in this study,mainly from the sample"0",i.e.,the zero mortality data,are largely due to the limitations of the experimental design and sample selection.These results also show that the number of dead trees in the diameter class is positively correlated with the number of trees in that class and the mean stand diameter,and inversely related to class size,and slope and aspect of the site.     

用人工神经网络构建多形地位指数模型

以马尾松人工林132株优势木树干解析数据为训练样本,用145块标准地优势木平均高数据为检验样本,把林分年龄和地位指数或优势木平均高作为输入变量,将优势木平均高或地位指数作为输出变量,通过构建人工神经网络逆模型的途径,分别建立了多形地位指数曲线式和计算式模型。结果表明,多形地位指数曲线式的总体拟合精度为99.64％,总体预测精度达96％以上,比传统技术构建的多形地位指数模型能较真实地模拟各地位级的多形曲线;多形地位指数计算式的总体拟合精度为98.81％,用于计算地位指数,省去用迭代法计算地位指数的工作量。基于BP神经网络模型多形地位指数模型,对马尾松人工林地位指数测定提供指导作用,可为森林立地质量评价提供理论依据。     

Weibull and percentile models for lidar-based estimation of basal area distribution

Abstract

The aim of this study was to assess the accuracy of basal area distributions of sample plots in coniferous forest derived from small-footprint airborne laser scanner data, and to compare the accuracy of two methods for derivation of such distributions based on: (1) two percentiles of a two-parameter Weibull and parameter recovery, and (2) a system of 10 percentiles defined across the range of observed diameters. The 12 percentiles were derived from the empirical basal area distributions of 141 plots with size 300–400 m². Regression analysis was used to relate the percentiles to various canopy height and canopy density metrics derived from the laser data. On average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 7–91% of the variability. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of ?1.2 to 2.1% between predicted and ground-truth values of plot volume. The standard deviations of the differences between predicted and ground-truth values of plot volume were 15.1–16.4%. Neither bias nor standard deviation differed significantly between the two validated methods.