退耕还林固土量及固土防泥沙保肥效益初步研究

通过对退耕还林4年的林地上径流量、径流系数、泥沙流量的监测,测定出黄土区退耕还林4年的林地上乔木型、乔草型、经济林型、农田(对照)及荒造封禁区、未造林荒山区在年618 mm降水量下12°～15°坡面上的土壤侵蚀模数分别为22.24、27.24、21.34、122.20、57.42、820.00 t(/km2.a),从而测算出天水市秦州区退耕还林工程年固土量为223 757 t,年固土防泥沙和保肥效益分别为90.45万元、471.38万元。     

不同选择条件下预期遗传进度的可靠性分析

从遗传力与回归系数的关系入手．通过对亲子回归中子代平均数理论估计值Ｙ的误差进行分析，结果表明：遗传力是一个群体概念，不适合估算个体的遗传进度．特别是在遗传力较低或选择差较小时，个体的遗传进度估计值的可靠性更差；用遗传力估算由多个个体组成的小群体的遗传进度时，其遗传进度估计值的可靠性将大大提高．只要入选样本容量足够大．其遗传进度估计值的误差估计可以降低到允许的范围．     

退耕还林后续产业已成为助农增收的支柱产业

陕西省宝鸡市陈仓区自1999年启动实施退耕还林工程以来，全区上下齐心协力，狠抓落实，强势推进，10年累计完成人工造林22．54万亩，其中退耕地造林9．26万亩，荒山造林13．28万亩，完成封山育林0．5万亩，累计兑现资金8528．9万元。通过实施退耕还林工程等一批林业重点工程，使全区绿化覆盖率由37．3％提高到40．2％。农民群众依托退耕还林工程，每年人均增加收入400多元。退耕还林后续产业已经成为全区助农增收和新农村建设的一大支柱产业。     

退耕还林成果巩固及后续产业发展

定西市安定区退耕还林工程自2000年实施以来,已累计完成退耕还林工程面积100．23万亩,其中,退耕地造林48．73万亩,荒山造林49．2万亩。封山育林2．3万亩。项目覆盖全区19个乡镇289个村5．65万户24．82万人,占全区农业人口的67％,     

应用回归估计方法调查淡竹林分生长量

本文应用回归估计的方法,测定了18株溪滩淡竹,组成9组样本数据,利用胸径与竹高,用材长、枝下高、用材重、表面积、材积、平均厚度之间的关系,建立回归方程,通过F检验和误差限计算,其线性回归关系极为显著,可靠性为99％,估计精度较高,可用于估测林分生长指标。     

退耕还林生物量监测及固碳释氧效益初步研究

通过对退耕还林林地上生物量的监测,退耕还林6年的乔木型、乔草型、经济林型、荒山造林封禁区典型林地上生物量分别为26043、6575、18790、2980kg／hm^2。在生物量研究的基础上,根据光合作用制造有机物与固碳释氧之间的关系,初步研究了退耕还林林地上固碳释氧总效益为22916．4万元,从而科学客观地评估了退耕还林工程在固碳释氧方面的效益,为退耕还林效益监测、工程总体效益评估、生物固碳的研究提供基础数据和方法依据。     

广西退耕还林试点完成造林六万亩

今年2月，广西在接到国家计委、西部开发办、财政部、林业局、粮食局安排我区2001年退耕还林试点县2个、退耕还林18万亩的任务后，坚持“全面规划、分步实施、突出重点、先易后难、先行试点、稳步推进”的原则，采取先造林后补作业设计的办法，积极调备种苗，组织开展退耕地造林和宜林荒山荒地造林，努力抓好全国退耕还林试点工作，取得了初步成效。目前，乐业、东兰2个试点县共完成退耕还林造林6．36万亩，占计划的35．1％。具体措施有：　　一是立即开展摸底调查。组织试点县根据计划任务，在原资源调查的基础上，开展退耕地和宜林荒山荒…     

抓住时机 全民动手 切实做好退耕还林工作—汝州市二○○二年退耕还林工作纪实

汝州市2002年的退耕还林工作按照林业厅的部署,以退耕地造林和荒山造林为重点,抓住有利时机,全党动员,全民动手,迅速掀起退耕还林高潮。截至8月底,全市共完成退耕还林4.32万亩,其中退耕地造林2.3万亩,占任务1.9万亩的121％。荒山荒地造林2.03万亩,占任务2万亩的102％。他们的主要做法:     

封山禁牧是保护退耕还林成果有效措施

地处晋西吕梁山西麓的石楼县，是山西实施退耕还林工程以来退还面积最大的县份。2000年该县被列为山西省16个退耕还林试点示范县以来，到2003年底，4年各种林业工程总计造林4．06万hm^2，其中退耕还林工程3．50万hm^2。全县林地面积累计增加到8．96万hm^2，森林覆盖率达到16．40％，林木覆被率达到31．50％，水土流失得到了明显控制，年减少注入黄河的泥沙量216．70万t。特别是退耕还林工程的实施，全县退耕还林村委已达到130个，占农业户数的43．10％，占农业人口的44．10％。     

汉中市飞播造林成苗分析

汉中市2004—2006三年连续完成飞播造林53．4万亩。汉中市飞播站分别在播后第二年10月组织进行了成苗调查,成苗面积183328亩,成苗面积率34．3％,取得了良好的效果。一、飞播造林成苗调查方法调查依据国家GB／T15162—2005（飞播造林技术规程）,以播区为总体,参照出苗观察情况,估测播区有苗面积成数（精度80％、可靠性95％）计算出样方总数和间距,     

基于森林资源二类调查数据的森林生物量抽样设计对比研究

探究和对比不同抽样方法对森林生物量抽样调查中样地数量、样地布设及估算精度等方面的影响,优选适宜的抽样方案,在保证精度的前提下节省外业调查的工作量和成本。基于云南省大理市2016年森林资源二类调查小班数据,以森林起源、龄组、优势树种作为分层变量,在可靠性为95%、抽样精度为90%和85%的条件下,计算分层抽样所需的样本单元数,并与系统抽样进行比较分析;以分层抽样方法进行大理市森林地上生物量的抽样设计,将抽样结果与系统抽样方法进行比较分析。结果表明,抽样精度为90%时,分层抽样所需的样本单元数分别比系统抽样少21.91%、12.92%和45.51%;抽样精度为85%时,分层抽样所需的样本单元数分别比系统抽样少21.51%、12.66%和45.57%;在相同的抽样精度条件下,抽样单元数呈现起源>优势树种>龄组的变化趋势。按抽样精度90%,系统抽样估测的大理市森林地上总生物量为1 914.83万t,按起源、优势树种和龄组,其总生物量估测值分别为1 608.22万t、1 679.03万t、1 536.08万t。在可靠性和抽样精度相同时,实际抽样精度排序为起源>优势树种>龄...     

运用机载LiDAR数据对橡胶林地上生物量估测的采样尺度效应分析

气候变化下,森林生物量遥感监测是当前研究的热点,机载LiDAR作为重要的遥感信息源,其采样大小对生物量估测精度有着一定的影响。以机载LiDAR数据为信息源,以44块30m×30m的方形橡胶林实测样地数据为基础,对机载激光雷达数据进行不同尺寸采样(共21个采样尺寸,边长从10m至30m,间隔为1m),提取不同采样尺寸下的激光雷达参数,并与橡胶林地上生物量建立PLSR模型,就机载激光雷达采样大小对橡胶林地上生物量估测精度的影响进行研究。研究表明:当采样尺寸小于18m时,估测精度随着采样尺寸的增大而增大;而当采样尺寸大于18m时,估测精度随着采样尺寸的增大而减小,进而趋于平缓。结果虽然呈现出一定的规律性,但是差异并不是很明显。当采样尺寸为18m时估测效果最佳,模型决定系数(R^2)为0.718,均方根误差(RMSE)为17.830 t/hm^2;交叉验证精度P和RMSEcv分别为82.741%和18.874t/hm^2。相较于实际样地(30m)尺寸下的估测结果,18m采样尺寸下的R^2提高了1.989%,RMSEcv降低了2.611%。因此,生物量的估测精度受机载激光雷达数据采样尺寸大小的影响,在生物量估测过程中需结合研究对象和研究区的实际情况对采样尺寸进行选择,从而提高生物量估测精度。     

谈谈抽样控制总体法的差值衡量标准问题

对抽样控制总体法中的差值(累偏)衡量的尺度进行了分析，指出用允许误差作尺度是恰当的，若用标准误作尺度势必造成小班调查的大量返工。     

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。     

Field sampling of cork value before extraction in Portuguese ‘montados’

The assessment of cork quality and the estimation of cork value are very important to forest landowners, for management purposes and for cork commercialisation. The Forest Producers Associations have been using a sampling scheme with the objective of estimating cork value (price per unit of weight, usually kg) before extraction, based on the sampling of individual trees along a zigzag transect that covers the entire stand. The sampling error is usually too high, but, from a practical standpoint, it is difficult to increase the sampling intensity if it would imply an increase in sampling costs. The aim of this work was to propose, from data collected in six stands representative of the cork oak stands in Portugal, an alternative sampling methodology with a more efficient precision/cost ratio. Precision and costs of alternative sampling designs based on clusters of different sizes, complemented with analysis of the intracluster correlation coefficient, were studied in order to propose the most adequate sampling strategy. Single-stage cluster sampling with clusters of 5–7 trees guarantees a reasonable sampling error (10–15%) and can be conducted without a large increase in cost.     

异方差对生物量模型构建的影响

生物量建模中的误差项往往存在异方差性,影响模型的可信度.通过介绍异方差检验和消除的方法,结合兴安落叶松的实测数据,借助SPSS统计软件实现了建模过程中对异方差的检验和消除,并达到了很好的估计效果.结合实例分析,采用模型转换和加权函数的方法对模型的预估效果起到了很好的作用.建立树干的相容性生物量模型,模型决定系数R2为0.947,估计值的标准差SEE为17.368,平均估计误差MPE为2.637,平均相对偏差ME为3.957,平均相对偏差绝对值MAE为0.408,以及预估精度P为92.39％,各项检验指标均符合建模要求.     

A study of alley-cropping data from Northern Brazil. II. A comparison of methods to estimate sample size

Data from a Brazilian Inga/maize/bean alleycrop experiment are used to illustrate patterns of variation between and within crop rows and their possible effects in the determination of a sampling strategy. Three methods for the determination of sample sizes in agroforestry experiments are compared: the combined plant technique, the formula of Snedecor and Cochran and variance component estimation. Results show that variability in the edge rows was generally greater than variability in the inner rows and suggest three sampling strata – one for each of the two edge rows and the third for the inner rows. For all three sampling methods a sample size greater than ten plants per stratum per block gave little extra precision. Increasing the number of blocks improves precision and may permit a smaller sample size per row to achieve the same precision. The comparison of different methods of sample size determination shows that variance component estimation is the most flexible and efficient approach. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

Integration of remote sensing-based bioenergy inventory data and optimal bucking for stand-level decision making

This paper tests the reliability of a biomass prediction procedure which combines aerial data collection, biometric models and optimisation for forest management planning. Tree stock information is obtained by predicting species-specific diameter and height distributions by a combination of field sampling, ALS data and aerial photographs. The subsequent steps in the chain are (1) assignment of the plots to forestry operation classes by means of remote sensing-based tree stock estimates, (2) estimation of the biomass components removed by simulating forestry operations, and (3) estimation of forest owners’ income flow from optimised bucking of the species-specific diameter distributions. The error effects caused by these steps are analysed, and the applicability of remote sensing–based data collection for biomass inventories and planning is assessed. The approach used for assigning the plots to operation classes resulted in moderate accuracies (75%). The reliability estimates indicated quite poor performance when predicting the biomass components removed in forest treatments, with RMSEs of 33.0–69.4% in the case of final cutting and 76.9–228.0% in the case of thinning. The relative RMSEs of the above-ground biomass estimates of the standing stock were about 19%. The relative bias for the biomasses removed was 10.0–88.6% and that for the standing stock biomasses 0.0%. When optimising bucking, the bucked assortments were larger and the incomes enhanced with this estimation method relative to the reference. This explains why the estimation of forest owner’s incomes in the energy wood thinning simulations led to suboptimal decisions and income losses.     

Analysis of Autocorrelation Associated with Spatial Sampling Estimation on Urban Greenland Coverage

By taking the urban greenland as testing objective,the GeoEye-1 fusion image as background, and using Moran’s index,the correlativity of greenland units has been measured.Then,the presampling was conducted based on the measured correlativity,the systematic sampling design has been carried out at the precision 95%and 85%.By using of Kriging interpolation method,the weight of different type of greenland has been divided into the stratifications.Through the stratified sampling,the estimation of greenland coverage has been obtained. The results show that the urban greenland coverage area is 14.81 km~2,accounting for 27.73%of all urban land areas.Compared with the common sampling method,the precision of that increased about 3%. This method can also be utilized in monitoring dynamic variation of urban greenland with the real time images.