森林分层随机抽样调查

本文首先介绍了分层随机抽样的基本概念和基本原理,其次又给出了关于总体平均数分层随机抽样的方法和分层随机抽样的公式,例如,绝对误差限、相对误差限、精度和抽样单元数目,特别是引进了最优配置和效率的概念。最后,本文又列举了如何利用分层随机抽样的实例,该例具有一定的参考价值。总之,分层随机抽样可以提高估计精度,减少抽样误差。     

城市街道绿化树种结构量化研究方法

本文主要从调查方法选择和群落结构特征参数计算两方面阐述了城市街道绿化树种结构量化研究方法。其中着重分析了在城市街道绿化树种结构研究中运用分层随机抽样调查法应如何进行分层与如何确定合适的样本量 ;同时介绍了城市街道绿化树种各类结构特征参数的具体计算公式 ,重点分析了用Shannon wiener指数和Simpson指数计算城市街道绿化树种多样性指数的差异性 ,强调了城市街道绿化树木物种重要值不需计算频度     

对分层抽样技术应用的回顾和理解——与《事后分层抽样技术在森林资源二类调查中的应用》作者商榷

回顾了抽样技术在森林资源调查中的应用．我国在20世纪80年代就在森林资源抽样控制调查中采用了先抽样后分层的抽样技术,指出Sh^2、Sh、Syh-的错误名称和正确叫法．     

Spatial variability and optimal sampling strategy of soil respiration

Soil respiration is the second largest flux of carbon between terrestrial ecosystems and the atmosphere and is affecting climate sensitivity and vulnerability of the terrestrial carbon stock. Monitoring soil carbon dioxide efflux is a complex task, due to the high spatial and temporal variability of the fluxes. For this reason, more than 30 sampling points are required to attain reliable estimates of ecosystem soil respiration. However, the number of sampled points is often limited by labour, time and budget constraints. Stratified sampling is an alternative to random sampling as a method to reduce the number of sampling points when an effective proxy variable is available for the definition of the strata.     

基于混合像元分解方法的康保县植被覆盖度估测

以康保县为研究区,使用Landsat8 OLI数据,结合134个野外样地调查数据,通过像元二分法、完全约束最小二乘法和随机森林三种方法来进行混合像元的分解,探讨混合像元分解方法对提取地表植被覆盖度的可行性。结果显示:基于随机森林的混合像元分解方法结果最优,F为0.664,RMSE为0.127;三种方法都能较为精确地估测出康保县的植被覆盖情况,且与实测数据较为拟合,因此采用混合像元分解的方法进行植被覆盖度估测是完全可行的。     

林地面积空间抽样方法研究

论文采用空间简单随机抽样、空间分层抽样和三明治空间抽样模型,利用两种布样方式,随机和分层布样。通过对总体样本量和空间相关性的估算,进行样本分配和空间分层,结果表明：三明治空间抽样模型的抽样精度最高,其次是空间分层抽样、简单随机抽样;布样时采用分层抽样方式,将地理区域上分布的对象依照相似的属性值划分到不同的区域里,可以明显提高抽样调查效率及估计精度。     

基于遥感技术的宜良县云南松蓄积量反演

以云南省宜良森林二类调查数据为样本,基于GoogleEarthEngine云平台Landsat 8 OLI影像,结合植被因子、纹理特征以及K-T变化为自变量,构建了多元线性回归和随机森林的建模方法,建立了森林蓄积量反演模型。以宜良县云南松为研究对象,运用Landsat8 OLI遥感影像数据结合地面角规控制样地调查数据,建立了多元线性回归和随机森林估测模型。结果表明：多元线性回归模型精度一般,其R²和RMSE分别为0.259、34.5579,随机森林模型精度极高,其R²和RMSE分别为0.887、1.1954。运用Landsat 8 OLI影像数据进行森林地上蓄积量估测的不确定因素较多,随机森林估测模型可作为云南松及其他树种地上蓄积量遥感估测的一种对比方法,为今后森林蓄积量估测提供参考。     

空间平衡抽样及其在森林资源调查中的应用

我国现行的森林资源调查方法存在着空间关联性强、适应性差的缺陷.空间平衡抽样(SBS)强调样本点抽取的随机等概和空间上的均衡分布,通过包含概率栅格层的过滤运算,极大地减少了无反应样本单元现象的发生,在森林资源调查中具有较大的潜在应用价值.文章在简要回顾基于模型的抽样、基于设计的抽样两大类森林资源调查方法特点,分析4种传统概率抽样方法(简单随机抽样、系统抽样、分层抽样、整群抽样)弊端的基础上,介绍了基于通用随机方格分层算法(GRTS)的空间平衡抽样的原理和步骤,最后以紫金山国家森林公园为研究对象进行了风景林蓄积量调查空间平衡抽样案例研究.研究表明,空间平衡抽样在降低调查成本、减少空间关联性强方面明显优于简单随机抽样;但在提高抽样精度方面没有表现出明显的优势,只有当样本容量大于或等于理论计算容量时,空间平衡抽样才表现出一定的抽样精度优势.     

不跨越边界基于Horvitz-Thompson估计量的分层自适应群团抽样

提出一种新的不跨越边界的基于Horvitz-Thompson估计量的分层自适应群团抽样方法,给出其估计量公式,并证明其无偏性。将9种抽样方法(简单随机抽样、分层简单随机抽样、基于修正Hansen-Hurwitz估计量的ACS、基于修正Horvitz-Thompson估计量的ACS、跨越边界基于Hansen-Hurwitz估计量的ACSI、跨越边界基于Hansen-Hurwitz估计量的ACSII、不跨越边界基于修正Hansen-Hurwitz估计量的ACS、跨越边界基于修正Horvitz-Thompson估计量的ACS、不跨越边界基于修正Horvitz-Thompson估计量的ACS)应用于中国乌兰布和沙漠边缘植被花棒密度调查,并对比9种抽样方法精度。结果表明:不跨越边界的基于Horvitz-Thompson估计量的分层自适应群团抽样的效果最佳;分层的抽样方法比不分层的抽样方法效率要高。     

林分平均胸径100倍圆法测定每公顷胸高断面积的理论初探(之三)——方法与特点

提出了林分平均胸径100倍圆法的测树方法、一般操作步骤与技术要求。通过100Dg圆公式含义设计了100倍圆法在生产上可实行的林分平均胸径实测方法,即100Dg圆圆内控制检尺法、100Dg圆随机检尺法、100Dg圆快速估测法、100Dg圆快速估测修正等4种方法,并进行了讨论。     

贮木场原木到材预报模型的研究

本文论述了应用动态系统分析方法建立贮木场原木到材生产线性随机预报模型的原理和方法，并给出了计算实例。     

枣步曲幼虫空间分布型及抽样技术的研究

通过ＩｗａｏＸ－Ｘ回归测定，枣步曲幼虫的空间分布为奈曼－Ａ型分布或波松－二项分布，分布的基本成分为致密的个体群，个体群间及个体群内的分布均为随机分布，个体群的面积小于或等于１株树，对于估值抽样提出了两种方案，即整体抽样和层抽样，前者以随机抽样和棋盘式抽样为优，并给出了理论抽样公式，后者以最下层抽样代替整株抽样估计种群密度，可以提高抽样效率，降低抽样费用，且估计精度能达到科研与生产的要求，最后给出Ｉ     

DNA fingerprinting of Populus trichocarpa clones using RAPD markers

Nine trees from a single, natural population of black cottonwood (Populus trichocarpa Torr. et Gray) in Alaska were screened for randomly amplified polymorphic DNA (RAPD) markers with ten different 10-base random oligonucleotide primers in order to evaluate the use of RAPD analysis for distinguishing black cottonwood clones. Nine primers amplified the genomic DNA targets; two primers were able to differentiate all clones. Eight clones were distinguished among the nine tree samples assayed. Two trees showed identical banding patterns with all primers used; therefore it is suggested that these trees are from the same clone. The RAPD fingerprinting method is simple and powerful-one primer can distinguish different clones, while the use of multiple primers reduces fingerprint similarity and resolves discrepancies.     

杉木种源对断面积模型参数影响的验证

对杉木种源所设5个种源基因库以及四川省珙县洛表、民胜及邛崃所设种源试验区的8个种源试验调查材料，用随机效应检查方法检验同一地区不同种源对断面积生长模型参数的影响。结果证明了“同一地区不同种源的断面积模型参数相同”这一结论可靠。     

杉木种源对立地指数模型的影响

以鸡公山、洪雅、大岗山、柳州、六峰山５个杉木种源基因库以及四川省珙县洛表、珙县民胜、邛崃所所设杉木种源试验区的调查材料,用随机效应检查方法检验同一地区不同种源对立地指数模型的影响。结果表明,同一地区种源对立地指数模型的斜率无显著影响,立地指数有显著差异;不同地区的立地指数模型斜率有显著差异。     

Two new density estimators for distance sampling

Two new density estimators for k-tree distance sampling are proposed and their performance is assessed in simulated distance sampling from 22 stem maps representing a wide range of natural to semi-natural forest tree stands with random to irregular (clustered) spatial distribution of trees. The new estimators are model-based. The first (Orbit) computes density as the inverse of the average of the areas associated with each of the k-trees nearest to a sample location. The area of the k-th tree is obtained as a prediction from a linear regression model while the area of the first is obtained via a Poisson probability integral. The second (GamPoi) is based on the expected distribution of distance to the k nearest tree in a forest where the local distribution of trees is random but the stem density varies from sample location to sample location as a gamma distribution. In a comprehensive assessment with 17 promising reference estimators, a subset composed of Morisita’s, Persson’s, Byth’s, Kleinn’s, Orbit, and GamPoi was significantly better, in terms of relative root mean square error (RRMSE), than average. GamPoi emerged as the better estimator for sample sizes larger than or equal to 30. For smaller sample sizes, both Kleinn’s and Morisita’s appear attractive.     

基于随机森林的大姚县TM遥感影像分类研究

随机森林(Random Forest)是一种组合多棵决策树分类器的新的分类算法。以楚雄州大姚县为例,采用Landsat-TM数据,通过最大似然、支持向量机、随机森林3种分类器进行分类对比研究。结果表明,支持向量机和随机森林的分类精度明显优于最大似然法,两者分类精度相差不大;在分类时间上,最大似然法明显比随机森林和支持向量机快,支持向量机最慢。综合分析,随机森林算法表现更优,它在保证分类精度的前提下,也能保证一定的时间效率,更适宜实际生产应用。     

草本植物格局研究的透视采点法

基于帽儿山调查样地数据,应用"3S"处理技术并与均匀度理论相结合,提出一种新的草本植物格局研究方法——透视采点法。这里以帽儿山夏季草本植物鹿蹄草属中的兴安鹿蹄草(Pyrola dahurica)为研究对象,通过透视采点法及"3S"技术处理扫描的兴安鹿蹄草图像,来计算其均匀度的方法用以研究兴安鹿蹄草的分布格局。结果表明:兴安鹿蹄草的格局分布大多为随机格局,说明该种植物的散布没有内部相关性,即个体间没有明显的他感和排斥作用。     

Adaptive cluster sampling for estimation of deforestation rates

National estimates of deforestation rates may be based on a survey. Precise estimation requires an efficient design. When deforestation rates are low (<1%) large sample sizes are required with traditional sampling designs to meet a precision target. This study explores the efficiency of adaptive cluster sampling (ACS) for this estimation problem. The efficiency is assessed by simulated ACS sampling from 18,200 × 200 km populations with 78–10,742 deforestation polygons (DFP) of different shape and size and average 10-year deforestation rates between 0.2% and 1.0%. Each population is composed of four million square 1 ha population units (PU) in a regular grid. Relative root mean square errors (RMSE) of ACS were, depending on sample size, 30–50% lower than comparable errors with simple random sampling (SRS) designs. ACS achieves this advantage by adaptively adding PUs to an initial SRS sample of size n. Realized ACS sample sizes were, on average, twice the nominal size (n). Three measures of ACS efficiency indicated that the costs of adaptively increasing the sample size are critical for the effectiveness of ACS. Population effects were manifest in all estimators. Estimates of the abundance, size, and shape of DFPs will allow a prediction of these effects. Populations dominated by a few large DFPs were clearly unsuited for ACS. The performance of ACS relative to that of SRS was similar across plot sizes of 1, 10, and 40 ha. The general conclusion of this study is that the lower RMSE of ACS remains attractive when the average cost of adaptively adding a PU to the initial sample is low relative to the average cost of sampling a PU at random.     

An assessment of windbreaks in Central Wisconsin

Soil erosion by wind is a persistent problem in central Wisconsin. The extent and effectiveness of windbreaks in erosion-prone areas are unknown. We investigated the potential effectiveness of windbreaks established for soil erosion control in Portage County, Wisconsin. Our objectives included quantification of their extent and condition, development of a routine method for field assessment, and compilation of a reference database of windbreak information. We used aerial photographs to identify the windbreak population, and a two-stage, stratified random sampling technique to obtain samples for field evaluation. Variables and attributes examined included species, number of gaps, height, width, porosity, spacing, live crown ratio, crown condition, and a condition rating. Methods included simple photo-interpretation techniques, field measurements, optical scanning techniques, and data manipulation in geographic information systems. We identified over 2600 windbreaks comprising a total extent of 834 km; only a small proportion of this may offer effective protection at critical periods. Collectively, field windbreaks protect a very small proportion only of the county‘s agricultural land area from wind erosion. These results imply a need for increased planting rates, explicit maintenance or renovation of existing barriers, and windbreak designs that are both sustainable and compatible with current agricultural production efforts. This revised version was published online in June 2006 with corrections to the Cover Date.