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National greenhouse gas inventories often use variations of the gain–loss approach whereby emissions are estimated as the products of estimates of areas of land-use change characterized as activity data and estimates of emissions per unit area characterized as emission factors. Although the term emissions is often intuitively understood to mean release of greenhouse gases from terrestrial sources to the atmosphere, in fact, emission factors can also be negative, meaning removal of the gases from the atmosphere to terrestrial sinks. For remote and inaccessible forests for which ground sampling is difficult if not impossible, emission factors may be based on map-based estimates of biomass or biomass change obtained from regional maps. For the special case of complete deforestation, the emission factor for the aboveground biomass pool is simply mean aboveground, live-tree, biomass per unit area prior to the deforestation. If biomass maps are used for these purposes, estimates must still comply with the first IPCC good practice guideline regarding accuracy relative to the true value and the second guideline regarding uncertainty. Accuracy assessment for a map-based estimate entails comparison of the estimate to a second estimate obtained using independent reference data. Assuming ground sampling is not feasible, a map of greater quality than the regional map may be considered as a source of reference data where greater quality connotes attributes such as finer resolution and/or greater accuracy. For a local, sub-regional study area in Minnesota in the USA, the accuracy of an estimate of mean aboveground, live-tree biomass per unit area (AGB, Mg/ha) obtained from a coarser resolution, regional, MODIS-based biomass map was assessed using reference data sampled from a finer resolution, local, airborne laser scanning (ALS)-based biomass map. The rationale for a local assessment of a regional map is that, although assessment of a regional map would be difficult for the entire extent of the map, it can likely be assessed for multiple local sub-regions in which case expected local regional accuracy for the entire map can perhaps be inferred. For this study, the local assessment was in the form of a test of the hypothesis that the local sub-regional estimate from the regional map did not deviate from the local true value. A hybrid approach to inference was used whereby design-based inferential techniques were used to estimate uncertainty due to sampling from the finer resolution map, and model-based inferential techniques were used to estimate uncertainty resulting from using the finer resolution map unit values which were subject to prediction error as reference data. The test revealed no statistically significant difference between the MODIS-based and ALS-based map estimates, thereby indicating that for the local sub-region, the regional, MODIS-based estimate complied with the first IPCC good practice guideline for accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because sufficient numbers of observations of forest/non-forest change were not available for direct classification. Further, the approach uses a model-assisted estimator with information from a traditional error matrix for the forest/non-forest classifications to compensate for bias as the result of classification errors and to estimate variances. Classifications were obtained using a logistic regression model, forest inventory data, and two dates of Landsat imagery, although the approach to inference can be used with multiple classification approaches. For the study area in northeastern Minnesota, USA, overall pixel-level accuracies for the year 2002 and 2007 forest/non-forest classifications were 0.85–0.88, and estimates of proportion net deforestation for the 2002–2007 interval were less in absolute value than 0.015. However, standard errors for the remote sensing-based estimates of net deforestation were on the order of 0.02–0.04, meaning that the estimates were not statistically significantly different from zero. Particular attention is directed to the potentially severe sample size and classification accuracy requirements necessary for estimates of net deforestation to be detected as statistically significantly different from zero. [Copyright &y& Elsevier]

Forest structural diversity was estimated for an ecological province in the north-central region of the United States of America using data for nearly 350 000 trees observed on >12 000 forest inventory plots. Each plot was 672 m2 in area, and the sampling intensity was approximately 1 plot/2400 ha. Two indices were used for each of two commonly and accurately measured inventory variables: species count and the Shannon index for tree species and standard deviation and the Shannon index for tree diameter. The primary results of the study were fourfold: (i) ranges of spatial correlation for diversity indices were small, on the order of 5–10 km, (ii) high proportions of provincewide diversity were realized for circular areas with radii as small as 15 km, (iii) diversity for both species and diameter exhibited strong northwest to southeast spatial patterns, and (iv) plot-level α diameter diversity was highly correlated with mean plot-level tree diameter. La diversité structurale de la forêt a été estimée dans une province écologique du centre-nord des tats-Unis d’Amérique en utilisant les données de près de 350 000 arbres étudiés dans plus de 12 000 placettes d’inventaire forestier. Chaque placette couvrait une superficie de 672 m2 et l’intensité d’échantillonnage était approximativement d’une placette par 2 400 ha. Deux indices ont été utilisés pour chacune des deux variables d’inventaire communément et précisément mesurées : le nombre d’espèces et l’indice de Shannon pour l’espèce d’arbre, et l’écart-type et l’indice de Shannon pour le diamètre des arbres. Quatre résultats importants peuvent être tirés de cette étude : (i) l’étendue de la corrélation spatiale des indices de diversité était faible, de l’ordre de 5 à 10 km, (ii) une proportion élevée de la diversité à l’échelle de la province a été calculée dans des surfaces circulaires dont le rayon est aussi petit que 15 km, (iii) la diversité en espèces et en diamètres avait une structure spatiale nettement définie du nord-ouest vers le sud-est et (iv) la diversité en diamètres α à l’échelle de la placette était fortement corrélée avec le diamètre moyen des arbres à l’échelle de la placette. [ABSTRACT FROM AUTHOR]

Forest sustainability has emerged as a crucial component of all current issues related to forest management. The seven Montreal Process Criteria are well accepted as categories of processes for evaluating forest management with respect to sustainability, and data collected by the Forest and Inventory Analysis (FIA) program of the United States Forest Service are well suited for such evaluations. The FIA program focuses on the collection, analysis, and distribution of data for a core set of variables obtained using a plot configuration, a sampling design, and measurement protocols that all feature national consistency. Plot, subplot, and tree-level observations include traditional mensurational measurements such as forest area, tree species, diameter, and survival and a suite of non-tree measurements related to the health of the forest. FIA data are recognized for their completeness, geographic coverage, and accessibility to users via a user-friendly interface to a national database. Three examples for three different regions of the United States illustrate the relevance and utility of FIA data for environmental and ecological assessments in the context of the Montreal Process. Several conclusions may be drawn from the examples: (1) for the Southern region, the forest land base is stable, and growing stock volume is increasing; (2) for the Mid-Atlantic region, contributions to carbon accumulation are slightly greater than for storage, and storage increases from north to south; and (3) for the Central Hardwoods region, tree species richness increases from north to south and from west to east and is stable or slightly increasing. [ABSTRACT FROM AUTHOR]

The Enhanced Forest Inventory and Analysis program of the U.S. Forest Service has established a nationwide array of permanent field plots, each representing approximately 2400 ha. Each plot has been assigned to one of five interpenetrating, nonoverlapping panels, with one panel selected for measurement on a rotating basis each year. As with most large surveys, observations for some plots are not obtained. Several techniques for dealing with missing observations were compared with respect to the bias and precision of stratified estimates of mean volume per unit area using data from the State of Indiana in the north-central region of the United States for illustrative purposes. For the proportion of missing plots ranging from 1% to 10%, acceptable results were obtained for techniques that both ignored and replaced missing plot observations, but only when the techniques accounted for differences between plots in private and public ownerships. [ABSTRACT FROM AUTHOR]

Accurate spatial estimation of forest carbon stocks and their spatial uncertainties at local, regional, national, and global scales is a critical step in global carbon cycle modeling and management. This study aimed at enhancing the methods that are currently used in this area by combining plot data from the forest inventory and analysis program of the U.S. Forest Service and free landsat thematic mapper image data. Three mapping methods including linear regression, sequential Gaussian co-simulation, and block co-simulation algorithm were compared with respect to the accuracy of forest carbon stock estimates obtained for a study area in Southern Illinois, USA. The results indicated that although the linear regression resulted in smaller prediction errors than the sequential Gaussian co-simulation and the block co-simulation approaches, it also produced both negative and unreasonably large estimates, which is a serious drawback. Moreover, the sequential Gaussian co-simulation and the block co-simulation produced not only accurate carbon predictions, but also uncertainties for the local estimates. In addition, the block co-simulation approach scaled up both forest carbon stocks and the input uncertainties from finer to coarser spatial resolutions as is required for mapping forest carbon at national and global scales. Thus, the co-simulation and block co-simulation algorithms resolved an important current methodological challenge. [ABSTRACT FROM AUTHOR]

Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance (V Total) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance (V ̂ Total) and the variance component attributable to interpreters (V ̂ 1) were obtained for the case of two reference class interpretations per repeated sample unit. To evaluate the effect of interpreter variability on variance estimation, we used land cover reference data interpreted by seven analysts who each interpreted the same 300 sample pixels from a region of the Pacific Northwest of the United States. From these data, we estimated the contribution of interpreter variance to the total variance (i.e., V ̂ 1 / V ̂ Total) and the relative bias of the standard simple random sampling variance estimator (V ̂ stand) as an estimator of V Total , defined as 100%*(V ̂ stand − V ̂ Total)/ V ̂ Total. For each of five land cover classes, we computed V ̂ 1 , V ̂ Total , and V ̂ stand using the sample data from each of the 21 possible pairwise combinations of the seven interpreters, and then calculated the mean of V ̂ 1 / V ̂ Total and the mean of the estimated relative bias of V ̂ stand over these 21 pairs. Based on the mean of V ̂ 1 / V ̂ Total per class, interpreter variance contributed from 25% (cropland) to 76% (grass/shrub) of the total variance, indicating that interpreter variance was a non-negligible component of the total variance. Typically, the standard variance estimator, V ̂ stand , underestimated the total variance with the mean estimated relative bias ranging from −3% (cropland) to −33% (grass/shrub). Classes with greater inconsistency between pairs of interpreters had larger contributions of interpreter variance to the total variance (V ̂ 1 / V ̂ Total) and larger negative estimated relative bias of V ̂ stand. Given that interpreter variance can contribute substantially to the total variance, the repeated measurements approach offers a practical way to incorporate this variability into an estimator of the total variance. • Interpreter variability in reference data arises when interpreters disagree. • A simple measurement model is used to obtain an estimator of total variance. • Interpreter variability may contribute substantially to the total variance. • The standard variance estimator underestimates total variance. • Protocols to ensure consistency among interpreters are critical. [ABSTRACT FROM AUTHOR]

The relationship between number of species and area observed has been described using numerous approaches and has been discussed for more than a century. The general objectives of our study were fourfold: (1) to evaluate the behaviour of species-area curves across geographic scales, (2) to determine sample sizes necessary to produce acceptably precise estimates of tree species diversity, (3) to evaluate relationships among estimates of tree species diversity for local to large geographic scales, and (4) to determine whether the proportion of native tree species may be precisely estimated using sample sizes smaller than those necessary to estimate total tree species diversity. Such investigations are necessary to improve biodiversity monitoring at large scales. The analytical approaches included Monte Carlo bootstrap simulations and two geospatial methods and relied on a database populated with data for more than 12,000 national forest inventory plots (NFI) from 16 regional units, 13 European countries and three ecoprovinces of the United States of America (USA). Four primary results were obtained. First, tree species diversity may be precisely estimated using observations for a random subsample of 2,000-4,000 NFI plots. Second, large sample sizes are necessary to estimate tree species diversity for regional units or forest categories, except possibly for boreal forests for which the number of tree species is small. Third, estimates of the proportion of native tree species may be precisely estimated using tree species information for a random sample of approximately 30 NFI plots. Finally, our estimated species-area curves show that the curve shapes and relationships among estimates of tree species diversity at large scales clearly depend on the relative geographic location of the anchor regional unit (a European country or ecoprovince of the USA) relative to the other regional units. None of the well-known models for species-area curves adequately describes our results. The conclusion was that the uncertainties of the estimates reflect the unfavourable state of global biodiversity monitoring of species groups. The total numbers of tree species in our regional units, which cannot be adequately estimated, are small relative to other tree species-rich regions. Consequently, monitoring of tree species diversity is currently a highly uncertain enterprise at large scales. [ABSTRACT FROM AUTHOR]