Tron Eid

Improved allometric models to estimate the aboveground biomass of tropical trees

Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.

Models for individual tree mortality in Norway

Logistic models predicting probability of survival for individual trees were developed, respectively, for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), birch (Betula spp.), and for other broadleaved trees. The models were based on data from one remeasurement of a nation-wide grid of permanent sample plots recorded by the Norwegian National Forest Inventory. The data comprised 4506 sample plots with 46107 individual trees. Approximately 80% of the observations were used for model development and 20% for validation. The models were designed to be used in large scale forestry scenario models for even-aged and uneven-aged forests, as well as for forests with mixed and pure species composition. The explicatory variables in the models were the inverse of diameter at breast height, a competition index for individual trees, site index, and the proportion of basal area for the respective tree species. All parameter estimates were found highly significant (P<0.001) in predicting mortality except site index in the model for Norway spruce (P<0.05) and the inverse of diameter in the model for birch (P<0.01). Although the phenomenon of mortality is a stochastic, rare and irregular event, the model fit and validation tests were fairly good. Given the general uncertainty related to large scale forestry scenario analyses, and the uncertainty related to mortality as a phenomenon, the mortality models presented in this paper were considered to have an appropriate level of reliability.

Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses

Evaluations of inventory methods usually end when precision and bias are quantified. Additional information on the appropriateness of a method may be provided through cost-plus-loss analyses, where the total costs are calculated as the sum of net present value (NPV) losses, i.e. expected economic losses as a result of future incorrect decisions due to errors in measurements, and inventory costs. The aim of the study was to compare inventories of basal area, dominant height and number of trees per hectare based on photo-interpretation and laser scanning from two sites in Norway by means of cost-plus-loss analyses. In general, more precise estimates were found for laser scanning than for photo-interpretation, while the biases were about equally distributed between the two methods. On average for the two sites, the inventory costs, NPV losses and total costs for photo-interpretation were about 6, 49 and 54 euros ha−1, respectively, while they were 11, 13 and 25 euros ha−1 for laser scanning. The data used for the comparison were limited to two sites and 77 stands, and certain simplifying assumptions were made in the cost-plus-loss analyses. Still, there is reason to believe that the results of the study are of general validity with respect to the main conclusion when comparing the two methods.

Determination of stand volume in practical forest inventories based on field measurements and photo‐interpretation: The Norwegian experience

The accuracy of determination of stand volume (m3 ha−1) using two practical survey methods, i.e. relascope survey and photo‐interpretation, was tested on 333 forest stands of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and mixed stands of spruce, pine, and deciduous tree species. The material was collected at 14 different test sites in southern Norway and between 1 and 19 individual surveyors participated at each site. Reference values for stand volume of each stand were obtained from intensive field measurements. On average, the relascope surveyors underestimated the reference volume by 2% to 6%. The average standard deviation for the differences between practically determined volume and reference volume was in the range 15–31%. For stand volume determined by photo‐interpretation, an average underestimation of reference volume of 4% to 38% was found. The average standard deviation for the differences was in the range 13–33%. In the relascope surveys the practically determin...

AVVIRK-2000: A Large-scale Forestry Scenario Model for Long-term Investment, Income and Harvest Analyses

A computer model for long-term forest management analysis is described. It is a deterministic simulation model, and provides means for a range of possible analyses, different management strategies, i.e. harvest and silvicultural investment strategies, along with the corresponding cash flow, development of forest state and profitability. The description is accompanied by a case study, employing some features of the model, based on data covering Hedmark county in Norway, with the objective of mapping consequences for potential harvest level and net present value related to varying treatments for different border zone types.

Site index prediction from site and climate variables for Norway spruce and Scots pine in Norway

Abstract Site index prediction models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) were developed using Norwegian National Forest Inventory data. A number of multiple linear regression models with different combinations of site and climate variables were developed in order to facilitate their application to a range of situations where the accessibility of various explanatory data differs. The best models used year of stand origin, temperature sum, vegetation type groups, soil depth, aspect, slope and latitude to predict site index. These models explained a large part of the total variation ( = 0.86 and 0.72 for spruce and pine, respectively) and had little residual variation (RMSE = 2.04 and 1.95 m for spruce and pine, respectively). Alternative models using only year of stand origin, temperature sum and vegetation type groups, or soil depth in addition, had slightly lower but still useful predictive power. All the developed models exhibited a strong non-linear effect of the year of stand origin on site indices, which varied when temperature sum was included. The increase in site indices along with increasing year of stand origin was significantly faster after about 1940 for both species. Similar time trends were observed for mean temperature and precipitation sums for the periods of stand growth, but only exhibited a faster increase after about 1960. Even though increased temperature and precipitation after 1990 seem to contribute to increased site indices, increased nitrogen availability and atmospheric CO2 levels may also be important factors.

Models for Prediction of Mortality in Even-aged Forest

Models for predicting mortality in even‐aged stands were developed. The models rely on data from the Norwegian National Forest Inventory, and were designed for use in large‐scale forestry scenario models. A two‐step modelling strategy was applied: (1) logistic regression models predicting the probability of complete survival occurring,” and (2) multiplicative regression models for stem number reduction and diameter calibration. A joint model for all species predicting the probability of survival occurring on a plot was developed. Separate models for forests dominated by spruce, pine and broadleaved trees were developed for stem number reduction, while no appropriate models for diameter calibration were found. The phenomenon mortality is a stochastic, rare and irregular event, and this was reflected as low R 2 in the models. However, the model performance appeared logical and the results of validations based on independent data were reasonably good, i.e. the presented models may be applied to large‐scale forestry scenario analyses. With new rotations of permanent sample plot measurements, the models should be evaluated and, if necessary, revised.

Relationships between diameter and height of trees in natural tropical forest in Tanzania

The relationship between tree height (h) and tree diameter at breast height (dbh) is an important element describing forest stands. In addition, h often is a required variable in volume and biomass models. Measurements of h are, however, more time consuming compared to those of dbh, and visual obstructions, rounded crown forms, leaning trees and terrain slopes represent additional error sources for h measurements. The aim of this study was therefore to develop h–dbh relationship models for natural tropical forest in Tanzania. Both general forest type specific models and models for tree species groups were developed. A comprehensive data set with 2 623 trees from 410 different tree species collected from a total of 1 191 plots and 38 sites covering the four main forest types of miombo woodland, acacia savanna, montane forest and lowland forests was applied. Tree species groups were constructed by using a k-means clustering procedure based on the h–dbh allometry, and a number of different non-linear model forms were tested. When considering the complexity of natural tropical forests in general and in particular variations of h–dbh relationships due to high species diversity in such forests, the model fit and performance were considered to be appropriate. Results also indicate that tree species group models perform better than forest type models. Despite the fact that the residual errors level associated with the models were relatively high, the models are still considered to be applicable for large parts of Tanzanian forests with an appropriate level of reliability.

Impact of decentralised forest management on forest resource conditions in Tanzania

Impacts of decentralised forest management on forest resource changes were assessed. Six contrasting forest reserves regarding management regimes, that is, Joint Forest Management (JFM; in National Forest Reserves, owned by the State), Community Based Forest Management (CBFM; in village lands or general lands), and ordinary centralized state management, were selected. The forest resources were assessed by means of systematic sample plot inventories. Number of stems, basal area, volume, biomass, and carbon ha− 1 were compared with results from previous studies in the same reserves. Harvesting activities were also assessed as part of the sample plot inventories. In general, the results were somewhat ambiguous regarding the impacts of different management regimes. There was, however, some empirical evidence indicating that JFM and CBFM performed better than the ordinary state management, although uncontrolled exploitation of the forest has continued under decentralised forest management in the studied forests. The two regimes are promising forest decentralisation models for Tanzania, but more research is needed to understand the functions of different governance structures and how they may facilitate sustainability in both forest use and livelihoods.

Applying simulated annealing using different methods for the neighborhood search in forest planning problems

Adjacency constraints along with even flow harvest constraints are important in long term forest planning. Simulated annealing (SA) is previously successfully applied when addressing such constraints. The objective of this paper was to assess the performance of SA under three new methods of introducing biased probabilities in the management unit (MU) selection and compare them to the conventional method that assumes uniform probabilities. The new methods were implemented as a search vector approach based on the number of treatment schedules describing sequences of silvicultural treatments over time and standard deviation of net present value within MUs (Methods 2 and 3, respectively), and by combining the two approaches (Method 4). We constructed three hundred hypothetical forests (datasets) for three different landscapes characterized by different initial age class distributions (young, normal and old). Each dataset encompassed 1600 management units. The evaluation of the methods was done by means of objective function values, first feasible iteration and time consumption. Introducing a bias in the MU selection improves solutions compared to the conventional method (Method 1). However, an increase of computational time is in general needed for the new methods. Method 4 is the best alternative because, for large parts of the datasets, produced the best average and maximum objective function values and had lower time consumption than Methods 2 and 3. Although Method 4 performed very well, Methods 2 and 3 should not be neglected because for a considerable number of datasets the maximum objective function values were obtained by these methods.