国家森林资源调查体系改进探讨

通过对国内外国家森林资源连续清查体系的抽样设计、样地设计以及调查因子的介绍和对比,揭示了国内国家森林资源监测的不足和面临的挑战,指出国内国家森林资源监测需要进一步完善,以适应国内外不断扩张的信息需求.并从抽样设计、样地设计以及调查因子等方面给出了现有调查体系改进的可能性.     

森林资源连续清查中固定样地标志的改进

固定样地是森林资源清查体系最基本的抽样单元，通过对所有固定样地多期的调查监测，最终得出各地区森林资源的现状及动态变化情况，所以固定样地的准确复位便成为森林资源清查体系的核心工作。目前，我国森林资源连续清查主要依靠人工设置一些固定样地标志，以保持样地位置的固定，并保证样地在以后的调查监测工作中不再发生位置和形状的改变。     

一种衔接森林资源一类清查和二类调查的新方案

针对我国现行森林资源一类清查和二类调查数据不协调的问题,借鉴国外森林资源清查中的相片样地或景观样地设计,提出了衔接一类清查和二类调查的大样地设计新方案,分析了新方案的主要特点,并对一类清查和二类调查数据的协调性问题进行了讨论。     

森林资源连续清查体系改进建议

介绍了国外林业发达国家森林资源连续清查体系的抽样设计、样地设计以及调查因子;概述了我国森林资源连续清查的发展情况及存在问题;并从抽样设计、年度出数以及与林地年度变更结合使用方面提出了改进建议。     

湖南省森林资源连续清查遥感应用研究

森林资源连续清查是全国森林资源监测体系的重要组成部分,是掌握森林资源宏观现状、消长动态,制定和调整林业方针政策、规划、计划等的重要依据.随着现代遥感技术,特别是卫星遥感的发展,为准确、快速地获取森林资源相关信息提供了新途径.以2004年湖南省森林资源清查遥感样地的目视解译为例,探索了目视解译在森林资源连续清查中的应用.结果表明:采用遥感样地调查比人工调查节省时间,在某些调查因子方面可获得比人工调查更为准确的数据;目视解译结果精度较高,经实地验证,其加权精度达到85.31%.     

浅议森林资源清查样地复位

通过分析影响森林资源连续清查样地复位率的因子,寻找样地复位的途径,以提高森林资源连续清查样地复位率。     

森林抽样调查基础知识讲座(四) 连续森林资源清查——连续抽样

一、什么叫连续森林资源清查在总体内,以随机或系统抽取的与周围林分有相同经营措施的样地或样木组成样本,根据定期观测取得的成对数值配制的双重回归估计总体的方法,叫做连续森林资源清查。连续的含义是指同一总体中,在两个不同时期进行森林抽样的连续。第一时期的调查叫做初查,用符号A表示;第二时期的调查叫做复查,用符号B表示。以初查样本单元的观测值为辅助因子,用符号x表示;以复查样本单     

森林资源连续清查方形固定样地设置误差分析

采取固定样地是实现森林资源清查信息连续可比、动态评价的重要基础和前提。固定样地设置是影响森林资源调查准确度的重要因素之一。该文分析了方形固定样地产生误差的原因,同时提出了减少误差的措施,为提高森林资源连续清查的准确度提供了一种可行的途径。     

提高森林资源清查外业工作效率应注意事项及建议

在森林资源清查中,寻找样地、样地复位、样木复位、样地标志修复和完善是清查工作中的主要环节。该文从业务技术、人际关系、现地调查等方面,就提高森林资源清查外业工作效率应注意事项及有关建议进行了介绍。     

从野外调查工作量选择最佳样地设计

我国森林资源连续清查已进行了20多年，还没有一个省为单位记载外业调查的用工量。江西省森林资源连续清查第四次复查和联合国援华CPR／91／151项目试点外业调查对全省的2610套样地的每一道工序记载了用工量。本文通过统计每个工序的平均用工量指标，剖析各指标之间的关系。然后对几种样地设计用工量进行推算、比较、分析，以寻求一种最佳的样地设计。     

Statistical mapping of tree species over Europe

In order to map the spatial distribution of twenty tree species groups over Europe at 1 km × 1 km resolution, the ICP-Forest Level-I plot data were extended with the National Forest Inventory (NFI) plot data of eighteen countries. The NFI grids have a much smaller spacing than the ICP grid. In areas with NFI plot data, the proportions of the land area covered by the tree species were mapped by compositional kriging. Outside these areas, these proportions were mapped with a multinomial multiple logistic regression model. A soil map, a biogeographical map and bioindicators derived from temperature and precipitation data were used as predictors. Both methods ensure that the predicted proportions are in the interval [0,1] and sum to 1. The regression predictions were iteratively scaled to the National Forest Inventory statistics and the Forest map of Europe. The predicted proportions for the twenty tree species were validated by the Bhattacharryya distance between predicted and observed proportions at 230 plot data separated from the calibration data. Besides, the map with the predicted dominant species was validated by computing the error matrix. The median Bhattacharryya distance in the subarea with NFI plot data was 1.712, whereas in the subarea with ICP-Level-I data, this was 2.131. The scaling did not significantly decrease the Bhattacharryya distance. The estimated overall accuracy of this map was 43%. In areas with NFI plot data, overall accuracy was 57%, outside these areas 33%. This gain was mainly attributable to the much denser plot data, less to the prediction method.     

Combining ALS and NFI training data for forest management planning: a case study in Kuortane,Western Finland

Forest inventories based on airborne laser scanning (ALS) have already become common practice in the Nordic countries. One possibility for improving their cost effectiveness is to use existing field data sets as training data. One alternative in Finland would be the use of National Forest Inventory (NFI) sample plots, which are truncated angle count (relascope) plots. This possibility is tested here by using a training data set based on measurements similar to the Finnish NFI. Tree species-specific stand attributes were predicted by the non-parametric k most similar neighbour (k-MSN) approach, utilising both ALS and aerial photograph data. The stand attributes considered were volume, basal area, stem number, mean age of the tree stock, diameter and height of the basal area median tree, determined separately for Scots pine, Norway spruce and deciduous trees. The results obtained were compared with those obtained when using training data based on observations from fixed area plots with the same centre point location as the NFI plots. The results indicated that the accuracy of the estimates of stand attributes derived by using NFI training data was close to that of the fixed area plot training data but that the NFI sampling scheme and the georeferencing of the plots can cause problems in practical applications.     

Plot location errors of National Forest Inventory: related factors and adverse effects on continuity of plot data

Plot location errors (distance between recorded and true coordinates) of National Forest Inventory (NFI) cause a mismatch between plot data and remotely sensed data and might prevent re-measurement because plots with large location errors are hard to find. However, few studies have examined the detailed distribution of errors and related factors, and no studies have verified whether the large errors prevent re-measurement. This study analyzed data related to 101 plots in central Japan that had been established by the First NFI (1999–2003) on a 4-km grid. Plot location errors of these plots were measured by revisiting. Selective Availability (SA) of the Global Positioning System, which degraded location accuracy until May 2000, was the most important factor in increasing plot location errors. The mean errors were 58.6 and 15.0 m, with and without SA, respectively. In 12 plots with large plot location errors (the mean error was 84.6 m), re-measurement in the Second NFI (2004–2008) was not conducted because plot locations could not be found. In these situations, alternative new plots were established; however, their species compositions were significantly different from the initial NFI plots. Plot location errors of NFI adversely affect the continuity of plot data as well as the analysis with remotely sensed data.     

瑞典国家森林资源清查的经验与借鉴

瑞典国家森林资源清查体系历经 80年的建设历程 ,积累了丰富经验。收集近期瑞典有关国家森林资源清查的资料 ,按基本发展情况、抽样方案设计、调查因子设计、数据统计分析、调查工作组织等方面编译整理 ,以便我们在新的一次全国森林资源清查工作中学习和借鉴     

Forest edge length estimation – a case study using the Swedish National Forest Inventory (NFI)

Forest edges may affect the organisms in a forest fragment by changing biotic and abiotic conditions, and they have important effects on biodiversity. This characteristic of forest landscape is usually assessed using remotely sensed data. When doing so, forest edge length is estimated using either polygon delineation (manually or automatically) or sampling methods. This study attempts to estimate the total forest edge length (at regional level) using three estimation techniques: mapped plot, line intersects sampling, and buffer zone. Another aim is to compare the precision of the results using these techniques with field data from the Swedish National Forest Inventory (NFI). The Swedish NFI data allows reasonable precision (less than 10% SE) estimating total forest edge length. In most cases, line intersects sampling and mapped plot (at the subplot level) produce more precise estimates than buffer zone (at cluster level). All three techniques can be applied in monitoring programs aimed at measuring important biodiversity metrics from forest landscape patterns. It was also found that edge density was highest in the south of Sweden. Another future application worth investigating is the ability to determine how forest edge length metrics change over time.     

Evaluation of tree and stand-level growth models using national forest inventory data

Two models, Carbware (CW) and Growfor (GF), of different resolution and based on different frameworks were evaluated in relation to stand-level forecasts of volume and basal area using Ireland’s National Forest Inventory (NFI) data. CW is a distance-independent single-tree model that is based on diameter increment. GF is a stand-level dynamic empirical model that uses the von Bertalanffy–Richards growth equation in a state-space framework. NFI data were used as input to the models, and each model’s projections were compared to NFI data at the next measurement cycle. The NFI is a permanent sampling system with the objective to assess the composition and extent of the forest estate. A subset of the NFI was used in the study, single-species even-aged plots comprising Sitka spruce and lodgepole pine. The accuracy and performance of the CW and GF models were analysed using residual analysis and standard statistical techniques. Results show that both models require improvement, though the study has raised concerns regarding the suitability of the NFI data for this type of investigation.     

浅析森林抚育成效监测方法与内容

森林抚育监测采用样地调查法,通过定期观测样地上森林因子的变化取得数据,来分析林分生长、林分健康、林下植被、森林土壤状况等,综合评价森林抚育成效。     

Estimating Stem Volume and Basal Area in Forest Compartments by Combining Satellite Image Data with Field Data

The weighted k - nearest neighbour (kNN) method was used for estimating stem volume (m3 ha?¹) and basal area (m2 ha?¹) on a compartment level (average 19 ha) by combining satellite image data with measurements from Swedish National Forest (NFI) inventory plots. In the kNN method each estimation location (target plot) is assigned a value that is an average, which is weighted, of the attribute data from the k closest reference plots (NFI plots). The distance between target and reference plot was measured on different scales, which were transforms of spectral values and/or ancillary data. The standard error (assuming bias with no trend) of stem volume estimates in the compartments was 36% using only spectral data. This estimation accuracy improved to 17% if site index, age of the forest and mean tree height (ancillary data) were known for the compartments. Low volumes were overestimated and high volumes underestimated. This bias was reduced if ancillary data were added but was also dependent on the transform of the original scales.