历史调查数据参与森林调查因子遥感定量估测的研究

森林调查因子估测是森林资源遥感调查的主要内容之一。本文以多元数量化回归估测模型Ⅰ为基础,综合考虑历史调查资料(主要为前期调查因子值)和遥感数据等信息,建立了以小班为估测单元,保留小班平均直径、平均树高、每公顷蓄积量的遥感定量估测模型。经检验,所建立模型具有良好的估测效果。     

基于RS和GIS的毛乌素沙地荒漠化程度定量估测

选取内蒙古自治区毛乌素沙地东北部伊金霍洛旗为研究区域,以少量野外定位调查数据与其对应的RS和GIS信息为基础, 利用主成分分析、因子分析和多元线性回归分析方法,建立以像元为单位的植被盖度和生物量的定量估测模型.通过对数学模型的分析,得到影响植被盖度和生物量的主要指标(因子).在对荒漠化程度量化分级后,建立以植被盖度和生物量2个重要指标为影响因子的荒漠化程度定量估测模型,实现区域性荒漠化程度的定量估测.     

森林蓄积量遥感估测研究的现状及展望

论述了森林蓄积量遥感估测的研究现状,介绍了森林蓄积量遥感估测已经取得的进展,以及近年出现的新的蓄积量遥感估测方法。随着改进型的神经网络模型的优化,人工神经网络模型在森林蓄积定量估测领域应用的研究将会是森林蓄积量遥感定量估测的发展趋势。     

森林调查中基本的回归估测、比估测和双重抽样方法

现代森林调查中,引进了多种多样的数理统计方法。这些方法通过不同的条件、途径来提高抽样估测效率。分层抽样就是通过对所调查林区进行一系列分类来达到缩小变动、提高精度的目的。与此相同,回归估测、比估测和双重抽样则都是利用对林区中某些辅助调查因子的资料,并充分利用它们和估测因子(蓄积量)之间的回归关系来提高估测精度。但应明确指出,比估测、回归估测是两种相近的估测方法,而双重抽样则是一种具有两重样本的采样方法。众所周知,抽样调查是由采样和估测两个环节组成,分层抽样就是分层采样和分层估测的总称;如果条件具备,分层采样同样可以利用回归估测或多种方法的比估测来进行     

基于GF-1的森林蓄积量遥感估测

森林蓄积量是评价森林资源数量的一个重要指标。结合遥感影像和地面调查数据估测森林蓄积量受遥感影像、遥感因子、预处理方法、估测方法等多方面的影响。为研究国产GF-1遥感影像估测森林蓄积量的最佳遥感因子组合方式和较优估测方法,并绘制森林蓄积量空间分布图,为我国森林蓄积量的研究提供理论基础和科学依据。为研究GF-1遥感影像估测森林蓄积量的遥感因子和估测方法,以湖南省醴陵市为研究对象,以国产GF-1遥感影像为数据源,通过对遥感图像预处理,获取光谱信息、纹理因子、植被指数作为特征变量,结合同时期的二类调查样地数据,从GF-1遥感影像像元与样地不匹配角度出发,应用移动窗口的方法解决像元与样地的对应关系,采用多元逐步回归、偏最小二乘回归和随机森林模型对研究区森林蓄积量进行估测,采用建模精度和估测精度进行分析评价。实验结果表明:1)3个模型选择的因子都包含了NDVI、 Band2、DI3、CO1和DVI等5个遥感因子,说明其对森林蓄积量的估测比较敏感;2)随机森林模型优于偏最小二乘回归和多元逐步回归,其决定系数R2为0.73、估测精度为83.69%。利用GF-1遥感影像结合随机森林模型应用于森林蓄积量的估测结果趋于真实分布,效果较理想;采用移动窗口法,利用国产GF-1遥感影像并结合随机森林进行森林蓄积量估测具有较好的应用前景。     

基于岭回归和人工神经网络估测森林可燃物负荷量

选取东北林业大学帽儿山实验林场为研究区域,以少量野外定位调查数据及与其对应的遥感和GIS信息为基础,利用岭回归和人工神经网络分析方法,对森林可燃物负荷量估测模型及其影响因子进行系统研究。结果表明:对于TM3、TM(4×3)/7、TM4/3、海拔等10个影响可燃物负荷量估测的主要因子,利用岭回归方法可以克服变量间由于存在复共线性关系对求解待定参数所造成的不利影响。建立以像元为单位的岭回归和岭回归与神经网络组合估测模型,模型平均绝对百分比误差分别为17.6%和11.7%,2种方法可用于实现特定林场尺度森林可燃物负荷量的定量估测,其中组合模型效果较好。     

介绍一种数量化方法

回归估测在森林调查中应用很广,但要求自变量是可以定量表示的,即为数量化的因子。而林木生长受许多因子综合影响,其中许多因子未必是数量化因子,如坡向,坡位,立地条件等等,而这些因子对林木生长也有很重     

森林蓄积量估测模型建立优化与应用

通过分析森林蓄积量与遥感因子、环境因子、林分特征及其他因子之间的相关关系，论述了森林蓄积量估测模型建立优化和应用方法，总结了模型成功应用的实例，提出了影响估测精度的因素及模型应用需要把握的关键因子。     

森林蓄积量定量遥感估测模型研究

采用印度遥感卫星数据,以云南省高黎贡山自然保护区阔叶林为主要研究对象,设置可能影响森林蓄积量估测的遥感信息因子和可通过GIS获取的因子为自变量,通过逐步回归分析的方法建立了研究区域阔叶林蓄积量估测模型.结合小班面积估测值,计算了各小班相应的蓄积量.     

基于CEBERS-WFI遥感数据的森林生物量估测方法研究

以中巴卫星CEBERS-WFI遥感数据为基础,结合东北三省的地理、气象因子及森林资源连续清查固定样地信息,构建BP人工神经网络森林生物量估测模型,对我国东北三省的森林生物量进行估测,并反演了森林生物量的空间分布图像。结果表明,基于CEBERS-WFI遥感数据的BP人工神经网络应用于森林生物量估测简单实用,是一种快捷、有效的估测方法。     

基于RS、GIS 的呈贡县总体活立木蓄积量分层抽样控制方法

在传统的森林资源调查中,总体活立木蓄积量抽样控制法所需的样地数多,需耗费大量的人力物力.利用SPOT 5卫星数据,基于RS和GIS将呈贡县森林资源先分为A层(有疏林地层)和B层(其它地类),在各层内机械抽样初步确定样本数为1 816个,再根据样地SAVI值和DNNIR值确定各层最终样本数为424个.可极大地减少外业工作量.     

Estimating the volume of forest growing stock using auxiliary information derived from relascope or ocular assessments

The aim of this study is to demonstrate the potential of integrating probabilistic sampling and estimation with the conventional technique referred to as forest inventory by compartments. The objective of this paper is to propose two strategies for the assessment of growing stock volume using two-phase sampling, namely: (i) relascope basal area estimation performed on first-phase sampling points followed by volume estimation performed on a sub-sample of points selected in the second phase; (ii) ocular evaluation of growing stock volume performed on first-phase sampling plots of fixed size followed by volume estimation performed on a sub-sample of plots selected in the second phase. The effectiveness of using the auxiliary information gathered in the first phase is assessed by comparing the double-expansion estimator of total volume which depends solely on the second-phase sample with the two-phase ratio estimator which instead calibrates the double-expansion estimator on the basis of first-phase information. Conservative estimators of sampling variances and confidence intervals are derived for both the estimators. As is usual in forest inventories, first-phase sampling is assumed to be performed on a systematic random grid while three different schemes are considered for drawing the second-phase sample: simple random sampling without replacement, stratified sampling and 3-P sampling. The performance of double-expansion and ratio estimators under the three schemes adopted in the second phase is empirically checked by means of a simulation study performed on a real compartment in a beech forest of Central Italy. Simulation results show that the use of auxiliary information generated in the first phase constitutes a very effective way of increasing the accuracy of volume estimation at the compartment level, with a moderate increase of fieldwork.     

基于遥感分层技术的平南县森林蓄积抽样调查方法研究

采用遥感数据辅助分层可解决分层抽样在大范围森林资源调查中分层面积不准确的缺点.以ALOS数据为基础,将平南县的森林资源分为A层（有林地、疏林地层）和B层（其它地类层）.在各层内机械预布设样地,比较预布样地缓冲区（角规控制检尺所能绕测到的最大范围）的SAVI值、DNN IR值及对明显地物的目视解译,确定各样地缓冲区的地类,A层样地数有578个,B层有978个.根据分层抽样各层所需样本数,在确定好地类的样地中,随机抽取各层所需样本数并调查其蓄积量.结果表明,抽样的估计精度为91.5%,全县森林蓄积量为5 900 186.7 m3.     

Effect of cluster configuration and auxiliary variables on the efficiency of local pivotal method for national forest inventory

ABSTRACT

Planning a forest inventory comprises making decisions related to the sampling strategy: cluster configuration, sample size and sample allocation within the survey area. Cluster configuration includes deciding on the number of sample plots within the cluster and distances between them. Available resources set the limit for field work in terms of man-days. If the time consumption for measurements is known, the sample size can be determined under the constraint. In this study, we simulated the second phase of inventory sampling with fixed time resources by replicating sample selection with a spatially balanced sampling utilizing local pivotal method (LPM) for different cluster configurations to find the most efficient. As a result, the temporary cluster configuration was changed from 9 to 5-sample plot configuration in a pilot inventory. Further, the sample selection was performed with LPM having total growing stock volume and broadleaf volume proportion as auxiliary information. The pilot results were aligned with the time series in respect to forest area and total growing stock volume, but in tree species groups deviations were observed in growing stock volume. A more comprehensive optimization should include the travelling routes, the plot-to-plot distances and the plot design. In any case, the result is region specific.     

用SPOT资料对大兴安岭特大火灾林火行为研究及对阿穆尔林业局蓄积损失估计

本项研究以SPOT卫星标准假彩色合成影像和NOAA-AVHRR影像为主要信息源,采用影像的目视判读和局部地区计算机图像增强处理的方法,配合地形和气象资料对1987年5月6日至6月2日大兴安岭特大森林火灾的林火行为进行了研究。在SPOT影像判读勾绘的基础上,采用分层抽样技术并配合适量地面样地资料,对阿穆尔林业局森林火灾面积、强度及立木蓄积损失情况进行了估计。结果表明,应用SPOT和NOAA卫星资料研究森林火灾过程、林火行为和火灾损失估计,可快速、经济地获得所需信息并完全达到要求的精度。     

广西森林资源连续清查角规样地体系评价

广西森林资源连续清查(以下简称"广西连清)"角规样地体系,是我国唯一的以点抽样理论为基础,以固定角规样地为监测载体的省(区)级森林资源连续清查体系。广西连清第7次复查,除了增设的方形样地调查以外,还对原有的角规样地进行了复查,因此本文得以用同时进行调查的方形样地调查结果作为参照对象,对角规样地体系的优点和存在的问题进行定性和定量相结合的综合分析评价。分析评价结果表明,与方形样地比较,角规样地除了具有显著的隐蔽性外,外业工作量只相当于方形样地的53%,两套体系的活立木蓄积量差异仅为3.16%,角规样地和方形样地体系总蓄积量抽样精度分别为94.47%与94.57%,均达到国家森林资源连续清查技术规定要求(≥90%)。角规样地复位率大于规定的98%,样木复位率远大于规定的95%,达97%以上,能满足林木蓄积生长量和消耗量监测的要求。角规样地体系的不足主要是由于漏测木和进测木的存在,导致森林资源的现状估计值偏低,且动态估计精度明显低于方形样地体系。     

应用中分辨率遥感数据和地理信息系统改进森林材积和生物量评估策略(英文)

应用遥感技术、地理信息系统和野外观测数据,评估了热带森林环境下地上生物量和木材蓄积量。用于模拟森林属性的这些数据具有地理特异性和高度的不确定性,因此,这方面需要开展更多的研究工作。选取了16个试样地带1460个样地,测定树木胸径及其他用于评估生物量的其他森林属性。本实验在印尼加里曼丹东部的热带雨林开展。应用现有的胸径-生物量公式来评估地上生物量密度。估测值在研究区修正的GIS地图上重叠显示,计算各种地被物的生物量密度。用样品数据子集表达遥感方法来形成地上生物量和材积线性方程模型。皮尔森相关统计检验采用ETM条带反射率、植被指数、图像变化图层、主成分分析条带、缨帽变换、灰度共生矩阵纹理特征和DEM数据作为预报值。在显著的遥感数据中形成了两个线性模型。为了分析每块地被物总的生物量和材积量,对2000年到2003年卫星ETM图进行了预处理、最大似然估计法分类和主体分析过滤。遥感方法获得的结果表明:材积量为(158±16)m3·hm-2,地上生物量为(168±15)t·hm-2;而野外测定和地理信息系统估计的结果分别是材积量为(157±92)m3·hm-2、地上生物量为(167±94)t·hm-2。用多个瞬间ETM数据评估了从2000年到2003年间的生物量丰富度动态,结果发现这一时期总生物量呈略微的下降趋势。遥感技术评估的生物量丰富度低于地理信息系统和野外测定的结果。前一种测定方法估计2000年和2003年总生物量分别是10.47Gt和10.3Gt,而后一种则估计11.9Gt和11.6Gt。还发现,灰度共生矩阵纹理特征与材积量和生物量之间存在较强的相关性。图7表9参43。     

Estimation of aboveground tree carbon stock using SPOT-HRG data (a case study: Darabkola forests)

Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon stocks. Data sets from forest monitoring using advanced satellite imagery are now used in international policy agreements. Data sets enable tracking of emissions of CO₂ into the atmosphere caused by deforestation and other types of land-use changes. The aim of this study is to determine the capability of SPOT-HRG Satellite data to estimate aboveground carbon stock in a district of Darabkola research and training forest, Iran. Preprocessing to eliminate or reduce geometric error and atmospheric error were performed on the images. Using cluster sampling, 165 sample plots were taken. Of 165 plots, 81 were in natural habitats, and 84 were in forest plantations. Following the collection of ground data, biomass and carbon stocks were quantified for the sample plots on a per hectare basis. Nonparametric regression models such as support vector regression were used for modeling purposes with different kernels including linear, sigmoid, polynomial, and radial basis function. The results showed that a third-degree polynomial was the best model for the entire studied areas having an root mean square error, bias and accuracy, respectively, of 38.41, 5.31, and 62.2; 42.77, 16.58, and 57.3% for the best polynomial for natural forest; and 44.71, 2.31, and 64.3% for afforestation. Overall, these results indicate that SPOT-HRG satellite data and support vector machines are useful for estimating aboveground carbon stock.     

森林二类调查中蓄积量遥感估测方法应用实例

探讨了用在ＴＭ数据中与地面对应的样点上测得的密度值和波段比，并加上定性因子与地面样地中分别测得的蓄积量进行多元回归，从而估测森林二类调查中所需的林业局、场、林班之蓄积量的方法，并与实测结果进行了对比，证明这一方法是可行的，为遥感在二类调查中的应用提供了必要依据。     

Double sampling for stratification in periodic inventories—Infinite population approach

Double sampling for stratification is a sampling design that is widely used for forest and other resource inventories in forest ecosystems. It is shown that this sampling design can be adapted to repeated inventories including estimators of net change, even for non-proportional allocation of second-phase units and periodically updated stratification. The method accounts for the transition of sampling units among strata. Moreover, it may outperform classical single phase designs if sample plots are appropriately allocated to strata with respect to predefined target variables, here: volume per ha of bigger trees of the main tree species. The latter requires a clear definition of predominant aims of the inventory and an appropriate optimization method. Access to inventory data of a state forest district from two occasions allowed for an optimization of the design based on the first occasion, which proved to be still advantageous on the following occasion. Estimators are developed under the infinite population approach, which is generally deemed more appropriate for forest inventories.