Comment on Chiesi et al. (2011): Use of BIOME-BGC to simulate Mediterranean forest carbon stocks

The mechanistic forest growth model BIOME-BGC utilizes a “spin-up” procedure to estimate site parameters for forests in a steady-state condition, as they may have been expected to be prior to anthropogenic influence. Forests in this condition have no net growth, as living biomass accumulation is balanced by mortality. To simulate current ecosystems it is necessary to reset the model to reflect a forest of the correct development stage. The alternative approach of simply post-adjusting the estimates of net primary production is fundamentally flawed, and should not be pursued

Relationships between the mean trees by basal area and by volume: reconciling form factors in the classic Bavarian yield and volume tables for Norway spruce

The Norway spruce forests of southern Germany and Austria have been at the forefront of forestry research for well over 100 years. The 1960s were a particularly productive period that saw the development of the yield tables of Ernst Assmann and Friedrich Franz, and the form and volume tables of Reinhard Kennel. Both of these works (or the equations that underpin them) are still in common use today. Even though both sets of tables were developed concurrently in the Institute for Growth and Yield at the Munich Forest Research Institute, a cursory examination suggests they are mutually incompatible, as they appear to use different values of form factor. The discrepancy can be explained by examining the differences between the mean tree by basal area (the tree with the quadratic mean diameter) and the mean tree by volume. This difference is shown to be predictable from data contained in the yield tables, and a conversion equation is developed and tested. The results show that the two sets of tables can be considered fully compatible if we accept that the volumetric mean tree is not identical to the mean tree described in the yield tables

Fire size/frequency modelling as a means of assessing wildfire database reliability

Many jurisdictions around the world have recently begun compiling databases of wildfire records, in an effort to determine patterns, quantify risks and detect possible changes in fire regimes. Such dataseis, if valid and comprehensive, could be used for fire hazard model validation, detection of trends and risk modelling under current and future climatic conditions. It may be however that data quality issues can hinder these efforts. In particular, older records may be less comprehensive, and smaller fires may have a greater chance of being unrecorded. A database of Austrian wildfires has been compiled, based on historic documentary records from a variety of sources that cover different time periods or geographical regions. The noncomprehensive and non-random nature of such dataseis (both spatially and temporally) makes the direct analysis of wildfire patterns impossible, ne-cessitating the use of models to identify trends and patterns. It is likely however that small fires are substantially underreported, particularly in early decades. We test this proposition by examining the fire size/ frequency distribution of all fires with recorded areas. The thesis behind the work is that we may compare the fire size/frequency relationships in the data across different time periods and that anomalies in the fire size/frequency distribution may indicate weak parts of the dataset. Our results lead us to suspect that data for smaller fires the current database is incomplete and imparts a bias to the size/frequency relationship in periods prior to the mid 1990s

Forest road networks: metrics for coverage, efficiency and convenience

The topological1 aspects of forest roads are most commonly quantified only by length and road density, which are poor indicators of many aspects of the network relevant to forest managers. This paper presents three new metrics - for coverage, efficiency and convenience - and uses a case study to demonstrate their utility in assisting road network decision-making. The procedures for determining the metrics are described, and suggestions made for their future application. The metric for road network coverage was found to be a useful guideline for road network planning

Assessing Forest Production Using Terrestrial Monitoring Data

Accurate assessments of forest biomass are becoming an increasingly important aspect of natural resource management. Besides their use in sustainable resource usage decisions, a growing focus on the carbon sequestration potential of forests means that assessment issues are becoming important beyond the forest sector. Broad scale inventories provide much-needed information, but interpretation of growth from successive measurements is not trivial. Even using the same data, various interpretation methods are available. The mission of this paper is to compare the results of fixed-plot inventory designs and angle-count inventories with different interpretation methods. The inventory estimators that we compare are in common use in National Forest Inventories. No method should be described as “right” or “wrong”, but users of large-scale inventory data should be aware of the possible errors and biases that may be either compensated for or magnified by their choice of interpretation method. Wherever possible, several interpretation methods should be applied to the same dataset to assess the possibility of error

Improved estimates of per-plot basal area from angle count inventories

Forest inventories were originally designed for the assessment of timber stocks over large areas. The large datasets gathered by these programs are becoming of increasing interest in other applications, particularly in ecosystem modeling. With inventory designs based on sampling proportional to size (angle-count plots) users should be cautious of using data pertaining to individual plots, as the plot-wise data is a statistical estimate rather than a true measurement. Estimates of per-plot basal area are mathematically unbiased, but the individual precision is extremely poor. Resampling of inventory datasets using multiple basal area factors can improve the precision of the estimates on single plots, thus providing better data for potential end users. Following two simulation studies to demonstrate our method we apply it to the sampling points of the Austrian National Forest Inventory, and show how the improved estimates of basal area give rise to more realistic estimates of basal area increment on indi - vidual points, reducing variance through the smoothing of extreme estimates. Our method will be useful in studies where angle count inventory data pertaining to individual plots is used to assess the precision of models or remote sensing methods

A statistical thinning model for initialising large-scale ecosystem models

Large-scale ecosystem models are important tools for carbon assessment at national scales. Many of these models are not initialised with known field data from any particular time, but simulate the growth of each stand from its estimated germination year up to the present or future. The models will overestimate current-day standing volume or biomass unless historic stand management (biomass removal due to thinning) is taken into account. The full management history of each stand is rarely known, and must be somehow estimated. One possibility is to build statistical thinning models based on data in a National Forest Inventory, which could then be integrated into the ecosystem models. If the harvesting model is constructed using only variables that are also used within the ecosystem model, then the management impacts can be included in the ecosystem model for the entire simulated life of the stand. In the case of most flux dynamics models, this precludes the use of the tree-level data that harvesting models have traditionally relied on. In this article, we develop a novel means to interrogate a subset of the Austrian National Forest Inventory based on deriving probability density functions for particular combinations of stand and site variables. We determine the parameters of a probabilistic model to estimate historic patterns of timber removals and validate it against inventory estimates. Our procedure can establish supportable estimates of historic management regimes suitable as input data for subsequent modelling of national-scale forest carbon stocks, sources and sinks

Biases in volume increment estimates derived from successive angle count sampling

Several large-scale forest inventories are now being conducted using angle count sampling, and the method is commonly used for timber cruising and corporate forest assessment. The calculation of basal area or volume increment from angle count sample data is not trivial, and three alternative methods are currently in common use: the difference method, the starting value method, and the end value method. This article develops the hypothesis that in various circumstances these methods are susceptible to bias as a result of measurement error and mis-sampling of trees. After reviewing prior work in angle count mathematics and developing the theoretical basis of our hypothesis, we present a supporting example based on a large permanent sampling plot at Hirschlacke in northern Austria. Our results suggest that the errors resulting from using calculation methodologies susceptible to bias from measurement error may in practical circumstances be more than 10% of volume increment, which could have ramifications for sustainable forest management or carbon sequestration budgetin

Incorporating management history into forest growth modelling

Mechanistic modelling is an important tool for understanding the impacts of climate change and pollutants on forest growth. One of the common practical limitations of these models is a lack of specific information regarding management activities such as thinning or harvesting, which can have a very strong influence on the accuracy of results. The use of inventory data for model parameterization and calibration is also problematic, as inventories are designed to have large volumes of data amalgamated to give accurate mean results across large areas. The precision of single point estimates is often quite low.This study uses BIOME-BGC to model forest growth on 1133 sites of the Austrian National Forest Inventory, and develops a method to estimate timber removal patterns prior to the commencement of record keeping on the sites. Recognizing the poor precision of individual point estimates in the data, we do not seek to precisely calibrate the model to the data on each point. Rather, we assume that the point-wise inventory estimates will be normally distributed around the true values. We then model each site assuming no management interventions, and compare this with inventory results. Plotting the error between model results and NFI data shows a strong right-skew, reflecting the modelled lack of timber removals. A Box-Cox transformation of the error plot, centred on zero, would represent an unbiased model estimate of the data, thus we can determine the historic timber removals as the difference between the original error curve and its Box-Cox transformation. Calibrating the model with this information allow us to represent forest volume with greater accuracy than would otherwise be possibl

Forest road and fuelbreak siting with respect to reference fire intensities

Forest roads and permanent fuelbreaks are an important part of fire suppression infrastructure, but due to maintenance and environmental costs many forest agencies seek to reduce the extent of these networks. The question of which roads should be retained or where fuelbreaks should be established is contentious, and few quantified methods exist to aid management decisions. This study uses GIS procedures and develops a metric for road network vulnerability, which may be used to determine the relative effectiveness of a road network or a particular fuelbreak as a fire control line. The method constructs \u27reference fire\u27 intensities, and compares the fire intensity at roadsides or fuelbreaks with the overall forest average. In the case study area in Victoria\u27s Central Highlands (southeast Australia), average fire intensities on the forest road network are found to closely match the forest average, indicating that roads in their current locations are not skewed towards more dangerous parts of the forest. The fuelbreak network however is likely to face fire intensities substantially greater than those in the average forest area