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When conducting a forest inventory, sometimes portions of plots cannot be measured due to inaccessibility. Two primary methods have been presented to account for partial nonresponse in the estimation phase: (i) use a ratio-to-size estimator or (ii) apply an adjustment factor to all plot observations in proportion to the missing area. Both approaches provide identical estimates of the population mean, but the estimates of variance differ when partial nonresponse is present. The performance of variance estimators was examined for a range of population forest area and partial nonresponse proportions in the sample. The ratio-to-size variance estimator performed unbiasedly with respect to simulation results, but the adjustment factor variance estimates were biased, with magnitude and direction dependent upon the forest area proportion and amount of partial nonresponse. The bias is relatively small when the partial nonresponse is small, which is often the case; however, the ratio-to-size method is preferred to ensure accurate variance estimation for a wide range of circumstances. Key words: nonforest, ratio estimate, bias, correlation. Lors de la realisation d'un inventaire forestier, il arrive parfois que des portions de placettes ne puissent pas etre mesurees parce qu'elles sont inaccessibles. Deux methodes principales ont ete presentees pour tenir compte de l'absence partielle de resultat pendant la phase d'estimation : (i) utiliser un estimateur quotient selon la taille, ou (ii) appliquer un facteur d'ajustement proportionnel a la zone manquante a toutes les observations de la placette. Les deux approches produisent une estimation identique de la moyenne de la population, mais l'estimation de la variance est differente lorsqu'il y a une absence partielle de resultat. La performance de l'estimateur de la variance a ete examinee en fonction d'une gamme de superficies forestieres et de proportions d'absence partielle de resultat dans l'echantillon. L'estimateur quotient selon la taille de la variance produisait des resultats de simulation non biaises, mais les estimations de la variance provenant du facteur d'ajustement etaient biaisees en fonction de l'amplitude et de la direction des resultats qui variaient selon la proportion de la superficie forestiere et de la quantite d'absence partielle de resultat. Le biais est relativement petit lorsque l'absence partielle de resultat est de petite taille, ce qui est souvent le cas. Malgre tout, la methode de l'estimateur quotient selon la taille est preferee pour s'assurer d'une estimation precise de la variance pour une large gamme de circonstances. [Traduit par la Redaction] Mots-cles: absence de foret, estimation quotient, biais, correlation., Introduction Various factors affect the specification of field plot measurement protocols in forest inventories. For large-area inventories in which many different land characteristics are likely to be encountered, a mapped-plot [...]

Tradeoffs occur when deciding between improving forest inventory precision by increasing sample size or by augmenting cluster plot design factors like size or subplot separation distance. The nature of these tradeoffs changes with variation in type and scale of the spatial pattern of the attribute of interest. To understand the impacts of relationships between type and scale of spatial heterogeneity and cluster plot design efficiency, we constructed a factorial simulation experiment and analysed relationships among forest inventory cost, cluster plot design factors, and different spatial heterogeneity scenarios constructed via simulation. To calculate cost, we constructed a cost model that accounted for both on- and between-plot costs. We found that type and scale of heterogeneity have important implications for plot design choices. Homogeneous stands and landscapes are the least costly to inventory. Subplot area and count have stronger impacts than subplot separation on cost efficiency, particularly in landscapes with aggregated forest patterns and in stands with homogeneous tree patterns. We discuss results in the context of the physical interaction between cluster plot geometry and spatial patterns at different scales, provide computer code for simulations, and suggest principles that forest inventory cluster plot design specialists should consider when designing inventories. Key words: cluster plot design, forest inventory design optimization, forest inventory cost model, forest pattern simulation, forest sampling simulation. Pour ameliorer la precision d'un inventaire forestier, des compromis sont possibles entre l'augmentation de la taille de l'echantillon ou l'augmentation des facteurs de deploiement des placettes en grappes comme la taille des sous-placettes ou la distance qui les separent. La nature de ces compromis change selon la variation du type et de l'echelle de l'arrangement spatial de l'attribut d'interet. Afin de comprendre l'impact des relations entre l'efficacite du deploiement des placettes en grappes et le type et l'echelle de l'heterogeneite spatiale, nous avons realise une experience de simulation factorielle et avons analyse les relations entre le cout de l'inventaire forestier, les facteurs de deploiement des placettes en grappes et differents scenarios d'heterogeneite spatiale conqus par simulation. Pour calculer les couts, nous avons conqu un modele de couts qui tient compte a la fois des couts a l'interieur et a l'exterieur des placettes. Nous avons constate que le type et l'echelle de rheterogeneite ont des implications importantes pour le choix du deploiement des placettes. Les peuplements et les paysages homogenes sont les moins couteux a inventorier. La superficie et le nombre de sous-placettes ont de plus grands impacts sur la rentabilite que la distance entre les sous-placettes, en particulier dans les paysages avec des arrangements spatiaux de peuplements regroupes et dans les peuplements avec des arrangements spatiaux d'arbres homogenes. Nous discutons des resultats dans le contexte de l'interaction physique entre la geometrie des placettes en grappes et les arrangements spatiaux a differentes echelles. De plus, nous fournissons un programme informatique pour les simulations et suggerons des principes dont les specialistes du deploiement des placettes d'inventaire forestier en grappes devraient tenir compte lors de la conception des inventaires. [Traduit par la Redaction] Mots-cles : deploiement des placettes en grappes, optimisation de la conception de l'inventaire forestier, modele de couts d'inventaire forestier, simulation de l'arrangement spatial des forets, simulation de l'echantillonnage forestier., Introduction Forest inventories are becoming increasing important as countries seek to generate information for policy and management decisions, as well as participate in incentives programs aimed at reducing carbon emissions [...]

Models of relationships among forest inventory sampling efficiency and cluster plot configuration variables inform decisions by inventory planners. However, relationships vary under different spatial heterogeneity scenarios. To improve understanding of how spatial patterns of forests affect these relationships, we implemented a factorial experiment by simulating forest pattern at both the landscape and stand scales. We sampled these simulated forests with a variety of cluster plot configurations, calculated coefficient of variation (CV) of trees per hectare for each replicate, and tested the relationships among CV and the heterogeneity and cluster plot configuration factors within a linear mixed model framework. Both landscape- and stand-scale pattern aggregation had a significant relationship with CV. Changing cluster plot configuration factors did little to change the overall CV when using larger subplots but had some important effects when using smaller subplots. These impacts were stronger in the more uniform landscapes. Results were opposite for stand-scale heterogeneity; changing plot configuration in areas with aggregated patterns had a stronger impact than it did in areas with more uniform patterns. Results of this study reveal the importance of accounting for spatial pattern at multiple scales when making cluster configuration choices if the goal is statistical efficiency. Key words: cluster plot design, forest inventory design optimization, forest inventory efficiency, forest pattern simulation, forest sampling simulation. Les modeles qui relient l'efficacite d'echantillonnage de l'inventaire forestier et les variables de configuration des grappes de parcelles eclairent les decisions des planificateurs d'inventaire. Cependant, ces relations varient en fonction des differents scenarios d'heterogeneite spatiale. Afin d'ameliorer la comprehension de la facon dont les patrons spatiaux des forets influencent ces relations, nous avons mis en oeuvre une experience factorielle en simulant les patrons forestiers aux echelles du paysage et du peuplement. Nous avons echantillonne ces forets simulees avec une variete de configurations de grappes de parcelles, calcule le coefficient de variation (CV) du nombre d'arbres a l'hectare pour chaque repetition, et teste les relations entre le CV et les facteurs d'heterogeneite spatiale et de configuration des grappes de parcelles, dans un cadre de modele lineaire mixte. Les patrons d'agregation spatiale avaient une relation significative avec le CV, tant a l'echelle du paysage que du peuplement. La modification des facteurs de configuration des grappes de parcelles n'a pas change le CV global lors de l'utilisation de sous-parcelles plus grandes, mais a eu des effets importants lors de l'utilisation de sous-parcelles plus petites. Ces effets etaient plus importants dans les paysages plus uniformes. Les resultats etaient a l'oppose pour l'heterogeneite a l'echelle du peuplement; la modification de la configuration des parcelles dans les zones avec des patrons agreges a eu un impact plus marque que dans les zones avec des patrons plus uniformes. Les resultats de cette etude revelent l'importance de tenir compte du patron spatial a plusieurs echelles lors des choix de configuration des grappes si l'objectif est l'efficacite statistique. [Traduit par la Redaction] Mots-cles: configuration des grappes de parcelles, optimisation de la conception de l'inventaire forestier, efficacite de l'inventaire forestier, simulation de patrons forestiers, simulation d'echantillonnage forestier., Introduction The impacts of the spatial distribution of forest resources on the efficiency of forest monitoring systems vary with the scale of analysis and with the attribute of interest. Spatial [...]

Post-stratification is often used to increase the precision of estimates arising from large-area forest inventories with plots established at permanent locations. Remotely sensed data and associated spatial products are often used for developing the post-stratification, which offers a mechanism to increase precision for less cost than increasing the sample size. While important variance reductions have been shown from post- stratification, it remains unknown where observed gains lie along the continuum of possible gains. This information is needed to determine whether efforts to further improve post-stratification outcomes are warranted. In this study, two types of optimal post-stratification were compared with typical production-based post-stratifications to estimate the magnitude of remaining gains possible. Although the optimal post-stratifications were derived using methods inappropriate for operational usage, the results indicated that substantial further increases in precision for estimates of both forest area and total tree biomass could be obtained with better post-stratifications. The potential gains differed by the attribute being estimated, the population being studied, and the number of strata. Practitioners seeking to optimize post-stratification face challenges, such as evaluation of numerous auxiliary data sources, temporal misalignment between plot observations and remotely sensed data acquisition, and spatial misalignment between plot locations and remotely sensed data due to positional errors in both data types. Key words: relative efficiency, sampling error, remote sensing, optimal stratification. La stratification a posteriori est souvent utilisee pour augmenter la precision des estimations qui decoulent des resultats d'inventaires forestiers a grande echelle realises a partir de places echantillons permanentes. Des donnees de teledetection et des produits spatiaux associes sont souvent utilises pour elaborer la stratification a posteriori, laquelle offre un mecanisme pour augmenter la precision a un cout moindre que l'augmentation de la taille de l'echantillon. Bien que d'importantes reductions de la variance dues a la stratification a posteriori aient ete demontrees, on ne sait toujours pas ou se situent les gains observes le long du continuum des gains potentiels. Cette information est necessaire pour determiner si des efforts pour ameliorer davantage les resultats de la stratification a posteriori sont justifies. Dans cette etude, deux types de stratification a posteriori optimale ont ete compares a des stratifications a posteriori typiques fondees sur la production pour estimer l'ampleur des gains potentiels restants. Bien que les stratifications a posteriori optimales aient ete derivees a l'aide de methodes inappropriees pour un usage operationnel, les resultats indiquent que d'importants gains supplementaires de precision dans l'estimation de la superficie forestiere et de la biomasse totale des arbres peuvent etre obtenus avec de meilleures stratifications a posteriori. Les gains potentiels different selon l'attribut estime, la population etudiee et le nombre de strates. Les praticiens qui cherchent a optimiser la stratification a posteriori font face a des defis tels que l'evaluation de nombreuses sources de donnees auxiliaires, le defaut d'alignement temporel entre les observations des places echantillons et l'acquisition des donnees de teledetection, ainsi que le mauvais alignement spatial entre la localisation des places echantillons et les donnees de teledetection a cause d'erreurs positionnelles dans les deux types de donnees. [Traduit par la Redaction] Mots-cles: efficacite relative, erreur d'echantillonnage, teledetection, stratification optimale., Introduction Most forest inventory programs, including both national forest inventories (NFI) and greenhouse gas inventories, seek explicit means of increasing the precision of estimates they report. Specifically, because the combination [...]

Due to the relatively high cost of measuring sample plots in forest inventories, considerable attention is given to sampling and plot designs during the forest inventory planning phase. A two-stage design can be efficient from a field work perspective as spatially proximate plots are grouped into work zones. A comparison between subsampling with units of unequal size (SUUS) and a simple random sample (SRS) design in a panelized framework assessed the statistical and economic implications of using the SUUS design for a case study in the Northeastern USA. The sampling errors for estimates of forest land area and biomass were approximately 1.5–2.2 times larger with SUUS prior to completion of the inventory cycle. Considerable sampling error reductions were realized by using the zones within a post-stratified sampling paradigm; however, poststratification of plots in the SRS design always provided smaller sampling errors in comparison. Cost differences between the two designs indicated the SUUS design could reduce the field work expense by 2–7 %. The results also suggest the SUUS design may provide substantial economic advantage for tropical forest inventories, where remote areas, poor access, and lower wages are typically encountered. [ABSTRACT FROM AUTHOR]