Estimating above-ground biomass of trees: comparing Bayesian calibration with regression technique

The commitment to report greenhouse gas emissions requires an estimation of biomass stocks and their changes in forests. When this was first done, representative biomass functions for most common tree species were very often not available. In Germany, an estimation method based on solid volume was developed (expansion procedure). It is easy to apply because the required information is available for nearly all relevant tree species. However, the distributions of neither parameters nor prediction intervals are available. In this study, two different methods to estimate above-ground biomass for Norway spruce (Picea abies), European beech (Fagus sylvatica), and Scots pine (Pinus sylvestris) are compared. First, an approach based on information from the literature was used to predict above-ground biomass. It is basically the same method used in greenhouse gas reporting in Germany and was applied with prior and posterior parameters. Second, equations for direct estimation of biomass with standard regression techniques were developed. A sample of above-ground biomass of trees was measured in campaigns conducted previously to the third National Forest Inventory in Germany (2012). The data permitted the application of Bayesian calibration (BC) to estimate posterior distribution of the parameters for the expansion procedure. Moreover, BC enables the calculation of prediction intervals which are necessary for error estimations required for reporting. The two methods are compared with regard to predictive accuracy via cross-validation, under varying sample sizes. Our findings show that BC of the expansion procedure performs better, especially when sample size is small. We therefore encourage the use of existing knowledge together with small samples of observed biomass (e.g., for rare tree species) to gain predictive accuracy in biomass estimation

Complementarity effects on tree growth are contingent on tree size and climatic conditions across Europe

Although sustainable development was defined in the Brundtland Report almost 30 years ago, the current usage of the concepts of sustainability and sustainable development remain highly equivocal. In the context of rural communities, multiple interpretations and weak definitions lead to confusion in understanding what comprises a sustainable rural community. Building on existing definitions (e.g. Baker's, 2006, ‘Ladder of Sustainable Development'), models (principally, The Egan Review's, 2004, ‘Components of Sustainable Communities') and findings of this study, a sustainable community is defined and a holistic model of a sustainable place-based rural community is presented. This model, the sustainable community design (SCD) is used as the basis for analysing community sustainability, which is measured using mixed methods and scorecard assessment. Sensitivity of the method is demonstrated with inter- and intra-community variations in sustainability across three diverse Scottish rural communities. Intra-community variations illustrate heterogeneity in community sustainability, explain ambiguity in characterisations of an individual community's sustainability, and highlight the importance of an interdisciplinary and holistic approach to community development. The SCD framework is presented as a useful tool for meso-level sustainability assessment and to facilitate the sustainable development of rural communities

Generating Tree-Level Harvest Predictions from Forest Inventories with Random Forests

Wood supply predictions from forest inventories involve two steps. First, it is predicted whether harvests occur on a plot in a given time period. Second, for plots on which harvests are predicted to occur, the harvested volume is predicted. This research addresses this second step. For forests with more than one species and/or forests with trees of varying dimensions, overall harvested volume predictions are not satisfactory and more detailed predictions are required. The study focuses on southwest Germany where diverse forest types are found. Predictions are conducted for plots on which harvests occurred in the 2002–2012 period. For each plot, harvest probabilities of sample trees are predicted and used to derive the harvested volume (m³ over bark in 10 years) per hectare. Random forests (RFs) have become popular prediction models as they define the interactions and relationships of variables in an automatized way. However, their suitability for predicting harvest probabilities for inventory sample trees is questionable and has not yet been examined. Generalized linear mixed models (GLMMs) are suitable in this context as they can account for the nested structure of tree-level data sets (trees nested in plots). It is unclear if RFs can cope with this data structure. This research aims to clarify this question by comparing two RFs—an RF based on conditional inference trees (CTree-RF), and an RF based on classification and regression trees (CART-RF)—with a GLMM. For this purpose, the models were fitted on training data and evaluated on an independent test set. Both RFs achieved better prediction results than the GLMM. Regarding plot-level harvested volumes per ha, they achieved higher variances explained (VEs) and significantly (p < 0.05) lower mean absolute residuals when compared to the GLMM. VEs were 0.38 (CTree-RF), 0.37 (CART-RF), and 0.31 (GLMM). Root means squared errors were 138.3, 139.9 and 145.5, respectively. The research demonstrates the suitability and advantages of RFs for predicting harvest decisions on the level of inventory sample trees. RFs can become important components within the generation of business-as-usual wood supply scenarios worldwide as they are able to learn and predict harvest decisions from NFIs in an automatized and self-adapting way. The applied approach is not restricted to specific forests or harvest regimes and delivers detailed species and dimension information for the harvested volumes

Ash Dieback on Sample Points of the National Forest Inventory in South-Western Germany

The alien invasive pathogen Hymenoscyphus fraxineus causes large-scale decline of European ash (Fraxinus excelsior). We assessed ash dieback in Germany and identified factors that were associated with this disease. Our assessment was based on a 2015 sampling of national forest inventory plots that represent a supra-regional area. In the time from 2012 to 2015, the number of regrown ash trees corresponded to only 42% of the number of trees that had been harvested or died. Severe defoliation was recorded for almost 40% of the living trees in 2015, and more than half of the crowns mainly consisted of epicormic shoots. Necroses were present in 24% of root collars. A total of 14% of the trees were in sound condition, which sum up to only 7% of the timber volume. On average, trees of a higher social status or with a larger diameter at breast height were healthier. Collar necroses were less prevalent at sites with a higher inclination of terrain, but there was no evidence for an influence of climatic variables on collar necroses. The disease was less severe at sites with smaller proportions of the basal area of ash compared to the total basal area of all trees and in the north-eastern part of the area of investigation. The regeneration of ash decreased drastically

Considerations towards a Novel Approach for Integrating Angle-Count Sampling Data in Remote Sensing Based Forest Inventories

Integration of remote sensing (RS) data in forest inventories for enhancing plot-based forest variable prediction is a widely researched topic. Geometric consistency between forest inventory plots and areas for extraction of RS-based predictive metrics is considered a crucial factor for accurate modelling of forest variables. Achieving geometric consistency is particularly difficult with regard to angle-count sampling (ACS) plots, which have neither distinct shape nor distinct extent. This initial study considers a new approach for integrating ACS and RS data, where the concept of ACS is transferred to RS-based metrics extraction. By using the relationship between tree height and diameter at breast height (DBH), pixels of a RS-based canopy height model are extracted if their value suggests a DBH that would lead to inclusion in an angle-count sample at the given distance to the plot centre. Different variations of this approach are tested by modelling timber volume in national forest inventory plots in Germany. The results are compared to those achieved using fixed-radius plots. A root mean square error of approximately 42% is achieved by both the new and fixed-radius approaches. Therefore, the new approach is not yet considered sufficient for overcoming all difficulties concerning the integration of ACS plot and RS data. However, possibilities for improvement are discussed and will be the subject of further research

Forest Inventory-based Projection Systems for Wood and Biomass Availability

Well-managed forests and woodlands are a renewable resource, producing essentialraw material with minimum waste and energy use. Rich in habitat and species diversity, forests may contribute to increased ecosystem stability. They can absorb the effects of unwanted deposition and other disturbances and protect neighbouring ecosystems by maintaining stable nutrient and energy cycles and by preventing soil degradation and erosion. They provide much-needed recreation and their continued existence contributes to stabilizing rural communities.Forests are managed for timber production and species, habitat and process conservation. A subtle shift from multiple-use management to ecosystems management is being observed and the new ecological perspective of multi-functional forest management is based on the principles of ecosystem diversity, stability and elasticity, and the dynamic equilibrium of primary and secondary production. Making full use of new technology is one of the challenges facing forest management today. Resource information must be obtained with a limited budget. This requires better timing of resource assessment activities and improved use of multiple data sources. Sound ecosystems management, like any other management activity, relies on effective forecasting and operational control.The aim of the book series Managing Forest Ecosystems is to present state-ofthe-art research results relating to the practice of forest management. Contributions are solicited from prominent authors. Each reference book, monograph or proceedings volume will be focused to deal with a specific context. Typical issues of the series are: resource assessment techniques, evaluating sustainability for evenaged and uneven-aged forests, multi-objective management, predicting forest development, optimizing forest management, biodiversity management and monitoring, risk assessment and economic analysis

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Abstract: 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. Remote Sens. 2013, 5 191

The Influence of DEM Quality on Mapping Accuracy of Coniferous- and Deciduous-Dominated Forest Using TerraSAR‑X Images

Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a correct interpretation of SAR images requires an adequate preprocessing of the data consisting of orthorectification and radiometric calibration. The resolution and quality of the digital elevation model (DEM) used as reference is crucial for this purpose. Therefore, the primary aim of this study was to analyze the influence of the DEM quality used in the preprocessing of the SAR data on the mapping accuracy of forest types. In order to examine TerraSAR-X images to map forest dominated by deciduous and coniferous trees, High Resolution SpotLight images were acquired for two study sites in southern Germany. The SAR images were preprocessed with a Shuttle Radar Topography Mission (SRTM) DEM (resolution approximately 90 m), an airborne laser scanning (ALS) digital terrain model (DTM) (5 m resolution), and an ALS digital surface model (DSM) (5 m resolution). The orthorectification of the SAR images using high resolution ALS DEMs was found to be important for the reduction of errors in pixel location and to increase the classification accuracy of forest types. SAR images preprocessed with ALS DTMs resulted in the highest classification accuracies, with kappa coefficients of 0.49 and 0.41, respectively. SAR images preprocessed with ALS DTMs resulted in greater accuracy than those preprocessed with ALS DSMs in most cases. The classification accuracy of forest types using SAR images preprocessed with the SRTM DEM was fair, with kappa coefficients of 0.23 and 0.32, respectively.Analysis of the radar backscatter indicated that sample plots dominated by coniferous trees tended to have lower scattering coefficients than plots dominated by deciduous trees. Leaf-off images were only slightly better suited for the classification than leaf-on images. The combination of leaf-off and leaf-on improved the classification accuracy considerably since the backscatter changed between seasons, especially in deciduous-dominated forest

Designing Wood Supply Scenarios from Forest Inventories with Stratified Predictions

Forest growth and wood supply projections are increasingly used to estimate the future availability of woody biomass and the correlated effects on forests and climate. This research parameterizes an inventory-based business-as-usual wood supply scenario, with a focus on southwest Germany and the period 2002–2012 with a stratified prediction. First, the Classification and Regression Trees algorithm groups the inventory plots into strata with corresponding harvest probabilities. Second, Random Forest algorithms generate individual harvest probabilities for the plots of each stratum. Third, the plots with the highest individual probabilities are selected as harvested until the harvest probability of the stratum is fulfilled. Fourth, the harvested volume of these plots is predicted with a linear regression model trained on harvested plots only. To illustrate the pros and cons of this method, it is compared to a direct harvested volume prediction with linear regression, and a combination of logistic regression and linear regression. Direct harvested volume regression predicts comparable volume figures, but generates these volumes in a way that differs from business-as-usual. The logistic model achieves higher overall classification accuracies, but results in underestimations or overestimations of harvest shares for several subsets of the data. The stratified prediction method balances this shortcoming, and can be of general use for forest growth and timber supply projections from large-scale forest inventories