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Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study is the rational management of forest ecosystems for the sustainable conservation of plant biodiversity. Two types of analysis are carried out: the structural analysis and the analysis of the main species. In total, 3675 individuals are distributed in 5 families, separated between 7 species were identified. The dominant woody and grassy plant species are Pinus halepensis and Juniperusoxycedrus. Pinaceae and Cupressaceae are the most represented families. Mean values of species richness, Shannon diversity index are 3±1,2 et 0,71±0,30 species / plot respectively. The average density of woody trees in the Sidi R’Ghies forest is 735 ± 368 plants / ha with an average basal area of 23,01 ± 5,04m² / ha. The structure of the heights shows that the trees in the height classes between 2 and 4m have a very high density, proof of a natural regeneration. The total structure indicates that smaller trees are more abundant than larger ones (17,5

Une approche méthodologique s'appuyant sur la combinaison de données satellitaires et données de terrain est proposée pour l’estimation du volume-tige de peuplements forestiers hétérogènes ou peu homogènes. L'objectif est d'évaluer la disponibilité forestière, en inventoriant moins de 1% de la surface étudiée et avec une erreur max. de 15%. L'approche consiste en la réalisation de trois étapes principales : i) Analyse de la variance sur le volume-tige, ii) classification des données satellitaires et iii) Désignation et inventaire des pixels-échantillons. L'analyse et le calcul de la variance permet d'orienter les calculs du volume en fonction de sa variabilité dans les différentes strates de la forêt alors que la classification des données satellitaires vise à obtenir une stratification de la forêt. La troisième étape consiste en la sélection de pixels-échantillons sur l'image classifiée puis la géolocalisation, l'installation et le cubage des placettes-terrain correspondantes (même dimension spatiale que le pixel de l'image utilisée). Appliquée sur une futaie peu homogène de pin d'Alep (forêt de Tlemcen, Nord-Ouest algérien), l'approche a permis d'estimer un volume global sur pied du peuplement égal à 30 595 m3 m3±15.6% et ce en inventoriant 0.4% seulement de la surface totale. L'analyse de variance sur 12 placettes-échantillons a mis en évidence le caractère peu homogène de la forêt et la faible variabilité du volume-tige. Cependant, Elle fait apparaître aussi que la stratification apporte une légère amélioration à la précision (15.6%) contre 17.6% sans stratification. La classification supervisée d'une image Landsat (Mai 2002) par la méthode du maximum de vraisemblance (précision moyenne de 96%) a permis de stratifier la zone étudiée en six classes (forêt très dense, forêt dense, forêt claire, matorral, herbacées, autres). Pour chaque strate de forêt, le cubage complet de 4 placettes-échantillons comparables en dimension au pixel (30m—30m), a fourni le volume-tige moyen par pixel alors que la généralisation de celui-ci à l'ensemble des pixels a permis de déterminer le volume total de chaque strate. Vu les confusions générées par la classification supervisée au profit des objets pistes, routes et matorral, le volume global a été revu à la baisse (taux de réduction de 10%) et la valeur du volume total corrigé était de 27 535 m3±15.6%, une erreur, bien que non conforme à celle exigée par l'aménagement forestier (max ±10%), s'approche de celle généralement admise (une moyenne de ±15%) pour certains inventaires simplifiés.

Mapping Canada's forests is a significant challenge given their extent and the interprovincial differences in forest inventories. We created new sets of nationally consistent forest attribute maps for the years 2001 and 2011 by building upon previously published work with the objective to determine if sequential maps of forest attributes could be used to quantify changes over time. We first refined our previously published methodology of using the k nearest neighbors ( kNN) prediction method and MODIS spectral reflectance data as predictive variables. The maps were generated using an improved reference dataset and a new analytical kNN workflow. We then evaluated 2001 to 2011 changes in two key attributes, aboveground biomass and percent tree cover, on pixels identified from published sources as having undergone fire, harvest, or postdisturbance regrowth during that period. For all three change types, average changes in both aboveground biomass and percent tree cover between 2001 and 2011 matched expectations relative to the dynamics of Canadian forests. Our results support the use of sequential national maps of forest attributes for evaluating regionally aggregated disturbance-related changes in forest properties. The new forest attribute maps are available from ; doi: at . [ABSTRACT FROM AUTHOR]

In their analysis of resampled and remeasured plot data from the USDA Forest Service Forest Inventory and Analysis (FIA) program, Barrett and Pattison (2017, Can. J. For. Res. 47(1): 97-105, doi:) suggest that there is neither evidence of a recent regional decrease in yellow-cedar ( Callitropsis nootkatensis (D. Don) Oerst. ex D.P. Little) live tree basal area nor a decrease in the species' extent in southeastern Alaska. Here, we identify substantial, broad-scale agreement between their estimated extent of concentrated yellow-cedar mortality and that resulting from a complementary, existing body of research into yellow-cedar decline spanning 35 years. However, we also discuss concerns that the FIA remeasurement data used did not match the spatial distribution of the decline (e.g., excluding areas of known active decline in wilderness areas) and that the temporal coverage of FIA data (1990s to 2000s) was inappropriately compared with a cumulative decline map that spans several decades, meshing recent mortality with mortality that occurred up to a century ago. We provide an alternative explanation of Barrett and Pattison's results in the context of ongoing yellow-cedar distribution and decline research in southeastern Alaska and support our interpretation by focusing on the temporal and spatial aspects of decline. [ABSTRACT FROM AUTHOR]

Copyright of Revue Francaise de Photogrammetrie et de Teledetection is the property of Societe Francaise de Photogrammetrie et de Teledetection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined ( i) the spatial correlation patterns of residuals from LiDAR linear models developed to predict volume, total and stem biomass per hectare, quadratic mean diameter (QMD), basal area, mean and dominant height, and stand density and ( ii) the impact of field plot size on the spatial correlation patterns in a standwise managed Mediterranean forest in central Spain. For all variables, the correlation range of model residuals consistently increased with plot radius and was always below 60 m except for stand density, where it reached 85 m. Except for QMD, correlation ranges of model residuals were between 1.06 and 8.16 times shorter than those observed for the raw variables. Based on the relatively short correlation ranges observed when the LiDAR metrics were used as predictors, the assumption of independent errors in many forest management inventories seems to be reasonable and appropriate in practice. [ABSTRACT FROM AUTHOR]

Copyright of Biotechnologie, Agronomie, Societe et Environnement is the property of Les Presses Agronomiques de Gembloux and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long-term forest inventories provide a unique opportunity to quantify changes in forest structure and evaluate how changes compare with current stand development models. An examination of a 70 year record at the Bartlett Experimental Forest, New Hampshire, indicated that although species abundances have primarily changed as expected under natural succession, some unexpected results were also detected. This included a significant decline in sugar maple ( Acer saccharum Marsh.) abundance driven by reduced regeneration and increases in red spruce ( Picea rubens Sarg.) at the expense of sympatric balsam fir ( Abies balsamea (L.) Mill.) and hardwoods at upper elevations. In contrast with accepted stand development models, biomass continues to accrue on these mid- to late-successional forests. Importantly, biomass accumulated at even greater rates in recent decades compared with historical norms. These results support evidence that the anthropogenic influences of a changing climate and the legacy of acid deposition may be altering stand dynamics in northeastern forests. [ABSTRACT FROM AUTHOR]

The planning of wood procurement requires reliable information about the species-specific timber assortments on which the economic value of a production forest depends. The timber assortments refer to the stem volumes of the sawlog and pulpwood fractions, specified in terms of both timber quality and allowable log dimensions, e.g., the stem diameter at breast height (dbh). We propose here an airborne laser scanning based calibration framework for generating species-specific dbh distributions that combines the area-based approach (ABA) and individual-tree detection (ITD), two established and independent approaches for retrieving forest attributes from airborne laser scanning data. Both ABA- and ITD-derived dbh distributions were generated nonparametrically for pine, spruce, coniferous, deciduous, and all species and assessed with respect to the plot-level species-specific total stem volume (m3·ha-1) and approximations of volume of timber assortments. Although after calibration, the total volume of all species and the volume approximations of coniferous sawlog and spruce pulpwood decreased in accuracy by 4%-7%, the calibration improved the accuracy of the other 12 species-specific estimates by 2%-17%, testifying to the general effectiveness of the proposed calibration framework. [ABSTRACT FROM AUTHOR]

While the use of field plots and airborne LiDAR data for the estimation of forest parameters has been intensively investigated in the past ten years, the issue of the evaluation of their accuracy at the compartment level (surface of a few hectares) remains poorly documented. Based on a full-calliper inventory of 35 compartments representing 380 ha, the present study compares different strategies for the mapping of LiDAR predictions and their aggregation by compartment. Results show that the prediction error decreases between estimations at the plot and those at the compartment levels: from 15 to 6.4% for basal area, 26 to 7.7% for stem density and 6.5 to 3.4% for mean diameter. At the compartment level, a LiDAR-based inventory thus displays an accuracy similar to a full-calliper inventory, for basal area. For the mapping step, it is crucial to respect the size of field plots used for calibration, whereas for the aggregation step the handling of compartment borders remains tricky for all forest parameters. [ABSTRACT FROM AUTHOR]

The design bias in the sample mean obtained from sampling the trees nearest to points randomly and uniformly distributed over a forested area can be exactly quantified in terms of the Voronoi polygons (V polygons) surrounding each tree in the forest of interest. For this sampling method, the V polygon for a prospective sample tree is its inclusion zone. The sides of such polygons are perpendicular to a line joining adjacent trees and equidistant from these trees. For any individual tree attribute Y, the design bias in such a sample mean for estimating the population mean of Y will be equal to the covariance between Y and V-polygon area V divided by the mean V-polygon area. The bias as a percent of the population mean of Y is the product of the correlation coefficient between Y and V and the coefficients of variation for Y and V multiplied by 100. This implies that attempts to estimate the means of commonly measured individual tree variables such as DBH, basal area, and crown diameter or the area from sampling trees nearest to randomly located points will likely be positively biased, and the magnitude of that bias will depend on the strength of the linear relationship to the V-polygon area, as well as the variability among the V-polygon areas and the variable of interest. It is not obvious whether increment core data will be positively or negatively biased, because this depends on the characteristics of the forest of interest. The main conclusion of the study is that the bias formula derived for unweighted estimation from sampling the tree nearest to a point indicates that bias in the range of 5%-10% or greater can occur in many forest populations. [ABSTRACT FROM AUTHOR]

A simple design-based approach to estimating net change of a forest attribute such as timber volume is to observe the change directly on the plot level and then make that the response variable of interest using established estimation techniques such as multiphase regression estimators. This direct approach is only possible for inventories with permanent plots and is constrained to estimating net change over time periods matching the duration of the remeasurement cycle. Indirect estimation involves applying one of the aforementioned techniques to estimate the state at two time points and taking their difference. Indirect methods, although less common, are not necessarily constrained to permanent plots and can estimate net change over any desired time span for annual designs. This article compares design-based direct and indirect regression estimators under the Monte Carlo approach and illustrates their performances with data from the Swiss National Forest Inventory. The major finding is that direct estimation should be preferred whenever change is observable directly on the plot level but that multiphase indirect estimation can still improve precision when direct estimation is not possible such as for inventories employing only temporary plots. [ABSTRACT FROM AUTHOR]

In this study, we explored the consequences of using leaf-on and leaf-off airborne laser scanning (ALS) data on area-based model outcomes in a lodgepole pine ( Pinus contorta var. latifolia Engelm.) dominated forest in the foothills of the Rocky Mountains in Alberta, Canada. We considered eight forest attributes: top height, mean height, Lorey's mean height, basal area, quadratic mean diameter, merchantable volume, total volume, and total aboveground biomass. We used 787 ground plots for model development, stratified by ALS acquisition conditions (leaf-on or leaf-off) and dominant forest type (coniferous or deciduous). We also generated pooled models that combined leaf-on and leaf-off ALS data and generic models that combined plot data for all forest types. We evaluated differences in ALS metrics and leaf-on and leaf-off model outcomes, as well as the impacts of pooling leaf-on and leaf-off ALS data, creating generic models, and of applying leaf-on models to leaf-off data (and vice versa). In general, leaf-off and leaf-on ALS metrics were not significantly different ( p < 0.05), except for the 5th percentile of height (coniferous) and canopy density metrics (deciduous). Overall, coniferous leaf-on and leaf-off models were comparable, with differences in relative root mean square error (RMSE) and bias of <2% for all attributes except volume, which differed by <4%. RMSE and bias for deciduous leaf-on and leaf-off models for height attributes and quadratic mean diameter differed by <2%, whereas models for volume and biomass differed by <7%. These results affirm that leaf-off data can be used in an area-based approach to estimate forest attributes for both coniferous and deciduous forest types. Relative RMSE and bias for pooled models (combining leaf-on and leaf-off ALS data) differed by <2% relative to leaf-on and leaf-off models, suggesting that in the forests studied herein, combining leaf-on and leaf-off data in an area-based approach does not adversely impact model outcomes. Generic models that did not account for forest type had large errors for volume and biomass (e.g., the relative RMSE for merchantable volume was twice as large as forest type specific models). Likewise, the mixing of leaf-on models with leaf-off data and vice versa resulted in large RMSE and bias for both forest types, and therefore mixing of models and data types should be avoided. [ABSTRACT FROM AUTHOR]

In many recent studies, the value of forest inventory information in harvest scheduling has been examined. In a previous paper, we demonstrated that making measurement decisions for stands for which the harvest decision is uncertain simultaneously with the harvest decisions may be highly profitable. In that study, the quality of additional measurements was not a decision variable, and the only options were between making no measurements or measuring perfect information. In this study, we introduce data quality into the decision problem, i.e., the decisionmaker can select between making imperfect or perfect measurements. The imperfect information is obtained with a specific scenario tree formulation. Our decision problem includes three types of decisions: harvest decisions, measurement decisions, and decisions about measurement quality. In addition, the timing of the harvests and measurements must be decided. These decisions are evaluated based on two objectives: discounted aggregate income for the planning periods and the end value of the forest at the end of the planning horizon. Solving the bi-objective optimization problem formed using the ε-constraint method showed that imperfect information was mostly sufficient for the harvest timing decisions during the planning horizon but perfect information was required to meet the end-value constraint. The relative importance of the two objectives affects the measurements indirectly by increasing or decreasing the number of certain decisions (i.e., situations in which the optimal decision is identical in all scenarios). [ABSTRACT FROM AUTHOR]

Individual tree heights are needed in many situations, including estimation of tree volume, dominant height, and simulation of tree growth. However, height measurements are tedious compared to tree diameter measurements, and therefore height-diameter (H-D) models are commonly used for prediction of tree height. Previous studies have fitted H-D models using approaches that include plot-specific predictors in the models and those that do not include them. In both these approaches, aggregation of the observations to sample plots has usually been taken into account through random effects, but this has not always been done. In this paper, we discuss four alternative model formulations and report an extensive comparison of 16 nonlinear functions in this context using a total of 28 datasets. The datasets represent a wide range of tree species, regions, and ecological zones, consisting of about 126 000 measured trees from 3717 sample plots. Specific R-functions for model fitting and prediction were developed to enable such an extensive model fitting and comparison. Suggestions on model selection, model fitting procedures, and prediction are given and interpretation of the predictions from different models are discussed. No uniformly best function, model formulation, or model fitting procedure was found. However, a 2-parameter Näslund and Curtis function provided satisfactory fit in most datasets for the plot-specific H-D relationship. Model fitting and height imputation procedures developed for this study are provided in an R-package for later use. [ABSTRACT FROM AUTHOR]

We report estimates of the amount, distribution, and uncertainty of aboveground biomass (AGB) of the different ecoregions and forest land cover classes within the North American boreal forest, analyze the factors driving the error estimates, and compare our estimates with other reported values. A three-phase sampling strategy was used ( i) to tie ground plot AGB to airborne profiling lidar metrics and ( ii) to link the airborne estimates of AGB to ICESat-GLAS lidar measurements such that ( iii) GLAS could be used as a regional sampling tool. We estimated the AGB of the North American boreal forest at 21.8 Pg, with relative error of 1.9% based on 256 GLAS orbits (229 086 pulses). The distribution of AGB was 46.6% for western Canada, 43.7% for eastern Canada, and 9.7% for Alaska. With a single exception, relative errors were under 4% for the three regions and for the major cover types and under 10% at the ecoregion level. The uncertainties of the estimates were calculated using a variance estimator that accounted for only sampling error, i.e., the variability among GLAS orbital estimates, and airborne to spaceborne regression error, i.e., the uncertainty of the model coefficients. Work is ongoing to develop robust statistical techniques for integrating other sources of error such as ground to air regression error and allometric error. Small ecoregions with limited east-west extents tended to have fewer GLAS orbits and a greater percent sampling error. AGB densities derived from GLAS agreed closely with the estimates derived from both forest inventories (<17%) and a MODIS-based interpolation technique (<26%) for more southern, well-inventoried ecoregions, whereas differences were much greater for unmanaged northern and (or) mountainous ecoregions. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation) - across both space and time - four forest inventory attributes: aboveground carbon (AGC), basal area (BA), stand density index (SDI), and total stem volume (Vol). Models were independently developed from 2003 and 2009 LiDAR datasets to spatially predict response variables at both times. Annual rates of change were calculated by comparing response variables between the two collections. Additionally, a pooled model was built by combining reference observations from both years to test if imputation can be performed across measurement dates. The R2 values for the pooled model were 0.87, 0.90, 0.89, and 0.87 for AGC, BA, SDI, and Vol, respectively. Mapping response variables at the landscape level demonstrates that the relationship between field data and LiDAR metrics holds true even though the data were collected in different years. Pooling data across time increases the number of reference observations available to resource managers and may ultimately improve inventory predictions. [ABSTRACT FROM AUTHOR]

Undetected trees and inaccuracies in the predicted allometric relationships of tree stem attributes seriously constrain single-tree remote sensing of seminatural forests. A new approach to compensate for these error sources was developed by applying a histogram matching technique to map the transformation between the cumulative distribution functions of crown radii extracted from airborne laser scanning (ALS) data and field-measured stem diameters (dbh, outside bark measured at 1.3 m aboveground). The ALS-based crown data were corrected for the censoring effect caused by overlapping tree crowns, assuming that the forest is an outcome of a homogeneous, marked Poisson process with independent marks of the crown radii. The transformation between the cumulative distribution functions was described by a polynomial regression model. The approach was tested for the prediction of plot-level stem number ( N), quadratic mean diameter (DQM), and basal area ( G) in a managed boreal forest. Of the 40 plots studied, a total of 18 plots met the assumptions of the Poisson process and independent marks. In these plots, the predicted N, DQM, and G had best-case root mean squared errors of 299 stems·ha−1 (27.6%), 2.1 cm (13.1%), and 2.9 m2·ha−1 (13.0%), respectively, and the null hypothesis that the mean difference between the measured and predicted values was 0 was not rejected ( p > 0.05). Considerably less accurate results were obtained for the plots that did not meet the assumptions. However, the goodness-of-fit of the predicted diameter distribution was especially improved compared with the single-tree remote sensing prediction, and observations realistically obtainable with ALS data showed potential to further localize the predictions. Remarkably, predictions of N showing no evidence against zero bias were derived solely based on the ALS data for the plots meeting the assumptions made, and limited training data are proposed to be adequate for predicting the stem diameter distribution, DQM, and G. Although this study was based on ALS data, we discuss the possibility of using other remotely sensed data as well. Taken together with the low requirements for field reference data, the presented approach provides interesting practical possibilities that are not typically proposed in the forest remote sensing literature. [ABSTRACT FROM AUTHOR]

Une soixantaine d'espèces ligneuses ont été inventoriées dans les savanes du Nord-Cameroun comme entrant dans l'alimentation des ruminants domestiques. La phénologie de certaines d'entre elles a été suivie tant sur parcours naturels qu'en plantation pendant 4 ans. De cette étude, il ressort que les arbres et arbustes les plus exploités sont par ordre de préférence : Ficus sycomorus, Daniellia oliveri, Afzelia africana pour ce qui est du feuillage, Acacia albida, Dichrostachys cinerea, Prosopis africana pour ce qui est des fruits. Les fleurs de Daniellia oliveri, Pterocarpus erinaceus, Bombax costatum sont également très appréciées. Ficus sycomorus, Pericopsis laxiflora, Daniellia oliveri et Detarium microcarpum présentent en outre une phénologie en milieu naturel compatible avec une exploitation optimale du feuillage pendant la période de soudure alimentaire.

In 2009, the Swiss National Forest Inventory (NFI) turned from a periodic into an annual measurement design in which only one-ninth of the overall sample of permanent plots is measured every year. The reduction in sample size due to the implementation of the annual design results in an unacceptably large increase in variance when using the standard simple random sampling estimator. Thus, a flexible estimation procedure using two- and three-phase regression estimators is presented with a special focus on utilizing updating techniques to account for disturbances and growth and is applied to the second and third Swiss NFIs. The first phase consists of a dense sample of systematically distributed plots on a 500m x 500mgrid for which auxiliary variables are obtained through the interpretation of aerial photographs. The second phase is an eightfold looser subgrid with terrestrial plot data collected from the past inventory, and the third and final phase consists of the three most recent annual subgrids with the current state of the target variable (stem volume). The proposed three-phase estimators reduce the increase in variance from 294% to 145% compared with the estimator based on the full periodic sample while remaining unbiased. [ABSTRACT FROM AUTHOR]

Methods for constructing a sampling design for large area forest inventories are presented. The methods, data sets used, and the procedures are demonstrated in a real setting: constructing a sampling design for the first national forest inventory for Tanzania. The approach of the paper constructs a spatial model of forests, landscape, and land use. Sampling errors of the key parameters as well as the field measurement costs of the inventory were estimated using sampling simulation on data. Forests and land use often vary within a country or an area of interest, implying that stratified sampling is an efficient inventory design. Double sampling for stratification was taken for the statistical framework. The work was motivated by the approach used by The Food and Agriculture Organization of the United Nations (FAO) in supporting nations to establish forest inventories. The approach taken deviates significantly from the traditional FAO approaches, making it possible to calculate forest resource estimates at the subnational level without increasing the costs. [ABSTRACT FROM AUTHOR]

A nearest neighbors method for generating simulated tree lists has been developed. The method employs an implicit two-scale hierarchy to incorporate information from a coarse scale representing the distribution of stand attributes across a region and a fine scale representing the distribution of tree attributes within a stand. The tree list generation method was implemented and tested using data from untreated, naturally regenerated and planted forests in western Oregon, western Washington, and southern British Columbia west of the Cascade Mountains. Simulated tree lists were generated from stand scale attributes for each of the actual tree lists in the data. Distributions of stand scale and tree scale attributes were estimated and used to compare the simulated and actual tree lists. At the stand scale, distributions of quadratic mean diameter and average height for the simulated and actual stands were in very good agreement, having approximately 98% of their probability mass in common for each attribute. At the tree scale, comparisons of the distributions of diameter at breast height, height, and species composition between the simulated and actual stands were more variable, with approximately 84% of the simulated stands identified as statistically similar to their respective actual stands. [ABSTRACT FROM AUTHOR]