利用ETM多光谱数据与SPOT5全色数据相融合进行森林资源调查

以ETM多光谱数据与SPOT5全色数据融合的遥感图像为对象,利用遥感图像和地面调查相结合的方法对吉林省东部山区进行森林资源调查,既可节约成本,又可提高调查精度,为遥感技术应用于森林资源调查设计探索了经验。     

Combining harvest sample data with inventory data to estimate forest biomass

Forest biomass databases which go beyond stem volume are needed for carbon balance calculations and also for forest monitoring, forest damage inventories and the solution of other related problems. In this article, it is shown how recursive multiple regression analysis can combine forest inventory data with biomass harvest data. The stand‐level conversion factor derived for Pinus sylvestris L. branch biomass for the “Severka”; Forest Farm shows a reasonable goodness of fit, with a coefficient of determination of R² = 0.87. Statistically significant coefficients were achieved by including stand density and the quadratic mean diameter in the regression. Equations developed for Switzerland, using Burger's Picea abies (L.) Karst. branch and needle conversion factors for individual trees, show R² = 0.87 and R ² = 0.91, achieved by taking h _dom.50, elevation, diameter at breast height and age into account.     

基于数据仓库理论的林业信息数据模型的设计

本文应用数据仓库理论,分析了林业信息数据仓库模型的建立及建立数据环境模型中的关键技术和解决方案,并在林业数据环境设计中得到了应用。     

Goodness-of-fit analysis for lumber data

Summary Four different probability distributions were studied to evaluate their relative goodness-of-fit in describing the modulus of rupture (MOR) and modulus of elasticity (MOE) of populations of dimension lumber. The distributions under consideration were the normal, lognormal, Weibull and Johnson's S_B. The populations of lumber consisted of 96 data sets of various species groups, mechanical properties, sizes, structural grades and growth regions. The goodness-of-fit criteria selected in this study were the log likelihood, Kimball and Kolmogorov-Smirnov (K-S) tests. The K-S statistic was also calculated at the value of the random variable associated with the lower five percent exclusion limit of the empirical cumulative distribution. This value indicated the degree of goodness-of-fit at the lower tail of the distribution. The results indicated that the S_B distribution generally provided the best fit to the data. The maximum likelihood test overwhelmingly recommended the S_B distribution. The Kimball and Kolmogorov-Smirnov tests gave milder endorsements of the S_B distribution. No distribution proved to be superior to the others in modeling the lower five percent exclusion limit of the populations.The author would like to thank the Engineering Data Management (EDM) Inc. of Fort Collins, Colorado, for the use of their parameter evaluation software, STAtistical Data MANager     

监理内业资料的管理

本通过多年的工程实践证明，强有力的监理工作，是工程投资、进度、质量控制的重要保证，是工程建设的核心主题。     

森林立地数据库的应用

立地质量分类与评价是林业的基础性工作 ,只有把林地的立地质量搞清楚 ,才能采取相应的经营管理措施 ,获取最大的收获量 ,取得较高的经济效益。如何利用已建立的森林立地数据库中的数据 ,采用先进的技术和方法 ,直接做出立地质量评价 ,仍然是当前需要解决的问题之一 ,因此 ,在这方面做了一些有益的尝试。1　数据准备1 1　数据分析数据库的生成在做数据分析时 ,根据其分析的目的要求 ,常常需要几个数据库中的某些数据。为了便于数据处理 ,须把几个数据库中的这些字段抽引出来重新组合 ,生成一个专门用于数据分析的临时性数据库。这样做不但数…     

基于ALOS数据的遥感植被分类研究

以广西平南县的植被覆盖为研究对象,以ALOS为遥感数据源,并对其进行了数据处理,运用信息量、相关系数及OIF方法,分析数据的波段特征;采用决策树分类算法,根据各种植被光谱特征建立知识库,提出基于光谱信息的植被分类方法,并将其与传统的监督分类方法进行了比较。结果表明:基于光谱特征的植被遥感信息提取方法分类总体精度显著提高,Kappa系数达到0.877;采用Landsat—5 TM遥感影像对该方法进行推广,分类总体精度为86.09%,Kappa系数为0.83。结果表明该分类方法能有效地对植被进行分类与识别,并对不同数据源的植被分类有一定的普适性,为实现植被的自动化提取提供了理论依据和方法。     

森林资源数据更新

在描述一般森林资源动态过程的基础上,定义了数据更新。阐述了非最小单位数据更新的两种模式,并为FRMII(Forest Resource Management Information Infrastructure)软件系统的“数据更新”模块,选择了数据更新方案。重点分析了最小单位森林资源状态因子及其变化规律,提出并实现了数据更新算法。     

关于林业资源数据统计的思考

为提高林业资源数据统计的准确度与可信度，进一步搞好资源数据统计与分析工作，对多年资源数据统计工作中遇到的主要问题进行了分析，并提出了解决问题的对策。     

基于小波变换的ICESAT-GlAS波形处理

小波变换在信号处理方面的优势越来越明显,但是小波中的小波基不是唯一的,所以采用不同的小波基即使处理同一组数据其结果也会不同。为了确定那种小波基更适合于大光斑激光雷达,通过对MATLAB中常用的小波基参数特性的比较分析最终确定选用Daubechies小波基和Symlets小波基,并分别通过对ICESAT-GLAS波形进行小波分解、阈值处理和小波重构来实现波形去噪过程。结果表明:用Symlets小波基中的sym7小波基比用Daubechies小波基中的db1小波基去噪后波形信噪比要高,而均方根误差则比用Daubechies小波基中的db1小波基的低,由此看来Symlets小波基中sym7小波基更适合于处理大光斑激光雷达波形数据。     

地理信息系统中的拓扑数据结构

本以一个有代表性的图形件为例。阐述了地理信息系统中的一个重要概念－拓扑关系。并说明如何用关系数据来描述和表达图素的拓扑关系。以及拓扑关系的应用。     

Reconstructing harvesting diameter distribution from aggregate data

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Distribution of removed trees among species and diameter classes is usually used to characterize selection harvesting. This information is, however, rarely available when analysing past time series. The challenge is then to determine the minimal level of information required to characterize harvests.     

Using ALS raster data in forest planning

Raster type of forest inventory data with site and growing stock variables interpreted for small squareshaped grid cells are increasingly available for forest planning.In Finland,there are two sources of this type of lattice data:the multisource national forest inventory and the inventory that is based on airborne laser scanning(ALS).In both cases,stand variables are interpreted for 16 m×16 m cells.Both data sources cover all private forests of Finland and are freely available for forest planning.This study analyzed different ways to use the ALS raster data in forest planning.The analyses were conducted for a grid of 375×375 cells(140,625 cells,of which 97,893 were productive forest).The basic alternatives were to use the cells as calculation units throughout the planning process,or aggregate the cells into segments before planning calculations.The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan.In addition,allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas.The use of segments led to 5–9%higher growth predictions than calculations based on cells.In addition,the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used.The shape of the treatment blocks was more irregular in cell-based planning.Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.     

Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data

Abstract

Methods for small area estimations were compared for estimating the proportion of forest and growing stock volume of temperate mixed forests within a district of a member state (canton) in Switzerland. The estimators combine terrestrial data with remotely sensed auxiliary data. By using different model types, different sources of auxiliary data and different methods of processing the auxiliary data, the increase in estimation precision was tested. Using the canopy height derived from remote sensing data, the growing stock volume and the proportion of forest were estimated. The regression models used for the small area estimation provided a coefficient of determination of up to 68% for the timber volume. The proportion of plots correctly classified into forest and non-forest plots ranged between 0.9 and 0.98. Models calibrated over forest area only resulted in a maximal coefficient of determination of 37%. Even though these coefficients indicate a moderate model quality, the use of remote sensing data clearly improved the estimation precision of both the proportion of forest and the growing stock volume. Generally, Lidar data led to slightly higher estimates compared to data from aerial photography. It was possible to reduce the variance of the estimated proportion of forest to nearly one tenth compared with the variance based on the terrestrial measurements alone. Similarly, the variance of the growing stock volume could be reduced to one fourth as compared with the variance based solely on the terrestrial measurements.     

Estimating soil moisture content using laboratory spectral data

Monitoring soil moisture is important for agriculture and forestry and plays an essential role in land surface processes as well as providing feedback among the earth’s surface ecosystems. Large-scale regional soil moisture spatial data can be obtained with a reliable and operational approach using remote sensing. In this paper, we provide an operational framework for retrieving soil moisture using laboratory spectral data. The inverted Gaussian function was used to fit soil spectral data, and its feature parameters, including absorption depth (AD) and absorption area (AA), were selected as variables for a soil moisture estimate model. There was a significant correlative relationship between soil moisture and AD, as well as AA near 1400 and 1900 nm. A one-variable linear regression model was established to estimate soil moisture. The model was evaluated using the determination coefficients (R2), root mean square error and average precision.Four models were established and evaluated in this study. The determination coefficients of the four models ranged from 0.794 to 0.845. The average accuracy for soil moisture estimates ranged from 90 to 92%. The results prove that it is feasible to estimate soil moisture using remote sensing technology.     

《国家级公益林管理系统(县级版)》小班数据快速录入方法探析

该文以office办公软件中的excel和access为基础,深入研究国家级公益林管理系统的数据库文件,了解其数据库结构和代码组成,将小班数据在excel中处理成数据库文件需要的数据,然后将其导入access,实现小班数据的批量录入,大大提高了工作效率和录入数据的准确性。     

CHRIS数据几何校正精度改善研究(英文)

This paper deals with a new type of multi-angle remotely sensed data--CHRIS （the Compact High Resolution Imaging Spec- trometer）, by using rational function models （RFM） and rigorous sensor models （RSM）. For ortho-rectifying an image set, a rigorous sen- sor model-Toutin＇s model was employed and a set of reported parameters including across track angle, along track angle, IFOV, altitude, period, eccentricity and orbit inclination were input, then, the orbit calculation was started and the track information was given to the raw data. The images were ortho-rectified with geocoded ASTER images and digital elevation （DEM） data. Results showed that with 16 ground control points （GCPs）, the correction accuracy decreased with view zenith angle, and the RMSE value increased to be over one pixel at 36 degree off-nadir. When the GCPs were approximately chosen as in Toutin＇s model, a RFM with three coefficients produced the same accuracy trend versus view zenith angle while the RMSEs for all angles were improved and within about one pixel.     

Predicting stand-thinning maturity from airborne laser scanning data

Abstract

Airborne laser scanning (ALS) has been used in recent years to acquire accurate remote-sensing material for carrying out practical forest inventories. Still, much of the information needed in forest management planning must be collected in the field. For example, forest management proposals are often determined in the field by an expert. In the present study, statistical features extracted from ALS data were used in logistic regression models and in nonparametric k-MSN estimation to predict the thinning maturity of stands. The research material consisted of 381 treewise measured circular plots in young and advanced thinning stands from the vicinity of Evo, in southern Finland. Timing of thinning was determined in the field by an expert and coded as a binary variable. Models were developed (1) to locate stands that will reach thinning maturity within the next 10-year period and (2) for stands in which thinning should be done immediately. For comparison purposes, logistic regression models were formulated from accurately field-measured stand characteristics. Logistic regression models based on ALS features predicted the thinning maturity with a classification accuracy of 79% (1) and 83% (2). The respective percentages were 66% and 83% with models based on field-measured stand characteristics and 70% and 86% with k-MSN. The study showed that ALS data can be used to predict stand-thinning maturity in a practical way.     

Object-based forest gaps classification using airborne LiDAR data

Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.