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The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field. ARTICLE HISTORY

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This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small sub-populations such as municipalities or protected areas. Frequently, too few sampled observations are available for such small areas to allow estimates with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates. The study aimed at estimating the mean above-ground forest biomass for small areas with high precision and accuracy, using SAE techniques. For this purpose, the simple random sampling (SRS) estimator, the generalized regression (GREG) estimator, and the unit-level empirical best linear unbiased prediction (EBLUP) estimator were compared. Mean canopy height obtained from a photogrammetric canopy height model (CHM) was the auxiliary variable available for every population element. The small areas were 14 municipalities within a 2,184 km 2 study area for which an estimate of the mean forest biomass was sought. The municipalities were between 31 and 527 km 2 and contained 1-35 NNFI sample plots located within forest. The mean canopy height obtained from the CHM was found to have a strong linear correlation with forest biomass. Both the SRS estimator and the GREG estimator result in unstable estimates if they are based on too few observations. Although this is not the case for the EBLUP estimator, the estimators were only compared for municipalities with more than five sample plots. The SRS resulted in the highest standard errors in all municipalities. Whereas the GREG and EBLUP standard errors were similar for small areas with many sample plots, the EBLUP standard error was usually smaller than the GREG standard error. The difference between the EBLUP and GREG standard error increased with a decreasing number of sample plots within the small area. The EBLUP estimates of mean forest biomass within the municipalities ranged between 95.01 and 153.76 Mg ha -1 , with standard errors between 8.20 and 12.84 Mg ha -1 .

We investigated the effects of site properties, forest structure, and time on snow breakage, insect outbreaks, windthrow, and total damage for predominantly planted forests. A time series of forest damage in southwestern Germany spanning 77 years, from 1925 to 2001, was available along with a database on site properties and forest structure. The statistical modeling procedure successively addressed (i) probability of damage occurrence, (ii) timber loss in damaging events, and (iii) interaction among damage agents over time. Logistic and linear regressions were combined with multivariate autoregressive techniques. Natural disturbances were responsible for a total timber loss of 3.0 m 3 Á ha -1 Á year -1 . The distribution of the timber loss values over the years and over sites and stands with different properties was modeled with a standard error of 6.7 m 3 Á ha -1 Á year -1 . Disturbances are more likely to occur in previously damaged stands. Storm events typically provoke subsequent insect outbreaks between 2 and 6 years later. Large windthrow and snow breakage events tend to occur periodically, once every 10th, 11th, or 15th year. Analysis of disturbances as a time series significantly enhances understanding of forest risk processes.Résumé : Nous avons étudié les effets des propriétés de la station, de la structure de la forêt et du temps sur les bris causés par la neige, les épidémies d'insectes et les chablis ainsi que sur les dommages totaux dans des forêts issues principalement de plantations. Une série temporelle des dommages causés aux forêts couvrant une période de 77 ans, de 1925 à 2001, dans le sud-ouest de l'Allemagne était disponible ainsi qu'une base de données sur les propriétés de la station et la structure de la forêt. La procédure de modélisation statistique a successivement abordé : (i) la probabilité que surviennent des dommages, (ii) la perte de matière ligneuse lors d'événements entraînant des dommages et (iii) l'interaction dans le temps entre les agents responsables des dommages. Des régressions logistiques et linéaires ont été combinées à des techniques autorégressives multidimensionnelles. Les perturbations naturelles étaient responsables d'une perte totale de 3,0 m 3 Áha -1 Áan -1 de matière ligneuse. La distribution de ces pertes dans le temps et parmi les stations et les peuplements avec différentes propriétés a été modélisée avec un écart type de 6,7 m 3 Áha -1 Áan -1 . Des perturbations ont plus de chances de survenir dans les peuplements déjà endommagés. Typiquement, les tempêtes sont suivies d'épidémies d'insectes deux à six ans plus tard. Les chablis importants et les épisodes de bris causés par la neige ont tendance à survenir périodiquement à tous les 10, 11 ou 15 ans. L'analyse des perturbations sous forme de série temporelle améliore de façon significative la compréhension des processus de risques en forêt.[Traduit par la Rédaction]

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Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m 2 .TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen's Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models. OPEN ACCESSRemote Sens. 2013, 5 1913