Browsing by discipline "Skogsresursvetenskap och -teknologi (skogsplanering)"

The aim of the thesis was to evaluate the effect of the data size and estimation method on the yield estimates of Pinus patula in Sao Hill forest plantation in Tanzania. The data consisted of even aged and nonthinned forest stands. The yield was estimated with a simultaneous system of equations (Eerikäinen 2002) that was originally developed for Pinus kesiya plantations in Zambia and Zimbabwe. The system of equations included non linear models for the stand dominant height, the number of stems per hectare, the basal area median diameter, the stand basal area and the total stand volume. Using the previously developed models could be useful in the case there is lack of time and the inventory/modelling budget is low. The system of equations was estimated from different sizes of samples of the original data in order to research the effect of the data size on the yield estimates. The models were estimated with a Two Stage Least Squares method (2SLS). In addition, two different kinds of model estimation methods were tested. Firstly, all the model parameters were estimated from the samples and, secondly, only the levels of the models were estimated from the samples and the parameter values for the model forms were derived from Eerikäinen´s (2002) research. This method was expected to be particularly efficient in the case that the amount of the estimation data would be small. The system of equations fitted quite well to the data of Sao Hill plantation as all the assumption related to the estimation were met quite well. On the other hand, the RMSE values were quite high compared to ones in Eerikäinen´s (2002) research. This could refer to quite a considerable variation in the data that the models could not explain very well. The yield estimates were more precise and accurate as the amount of data was increased in estimation. In general the 95 % confidence intervals, the ranges, the medians and means of the RMSE values and biases decreased as the amount of data increased. The estimation method where all the parameters were estimated from the data seemed in general better than the method where only the levels of the models were estimated from the data and the form parameters were given the values from Eerikäinen´s (2002) research. The differences between the estimation methods were, however, quite small in small sample sizes.

In this study, a quality assessment method based on sampling of primary laser inventory units (microsegments) was analysed. The accuracy of a laser inventory carried out in Kuhmo was analysed as a case study. Field sample plots were measured on the sampled microsegments in the Kuhmo inventory area. Two main questions were considered. Did the ALS based inventory meet the accuracy requirements set for the provider and how should a reliable, cost-efficient and independent quality assessment be undertaken. The agreement between control measurement and ALS based inventory was analysed in four ways: 1) The root mean squared errors (RMSEs) and bias were calculated. 2) Scatter plots with 95% confidence intervals were plotted and the placing of identity lines was checked. 3) Bland-Altman plots were drawn so that the mean difference of attributes between the control method and ALS-method was calculated and plotted against average value of attributes. 4) The tolerance limits were defined and combined with Bland-Altman plots. The RMSE values were compared to a reference study from which the accuracy requirements had been set to the service provider. The accuracy requirements in Kuhmo were achieved, however comparison of RMSE values proved to be difficult. Field control measurements are costly and time-consuming, but they are considered to be robust. However, control measurements might include errors, which are difficult to take into account. Using the Bland-Altman plots none of the compared methods are considered to be completely exact, so this offers a fair way to interpret results of assessment. The tolerance limits to be set on order combined with Bland-Altman plots were suggested to be taken in practise. In addition, bias should be calculated for total area. Some other approaches for quality control were briefly examined. No method was found to fulfil all the required demands of statistical reliability, cost-efficiency, time efficiency, simplicity and speed of implementation. Some benefits and shortcomings of the studied methods were discussed.

This study investigates the effect of the data input on the forest management plan. The objective was to determine the differences between a forest plan where simulation units were either traditional stand compartments, or alternatively subcompartments delineated around measured sample plots. The simulations were compared with respect to the growth of the compartments as well as timing, income and yield from the first harvet operation suggested. The data was collected from a forest area of 72 hectares in Juuka, Eastern Finland. It consisted of 682 sample plots placed in a 30 m x 30 m grid. Independently of the sample plots, the area was divided into compartments as in normal compartmentwise planning, with the exception that no stand data was collected. Instead, the compartmentwise data was calculated from the systematic sample plot inventory. Three simulations were carried out with a planning package SIMO for a period of ten years and using one-year time step. Sim(I) presented the traditional compartmentwise planning, where the variables on compartment level were aggregated from sample plot data in the beginning of the simulation, and then used as simulation units. The other two simulations used a mosaic of sample-plot-based subcompartments as the simulation unit, and aggregate compartmentwise values were only used to determine the harvest decisions and for the comparison of the simulations. Of these mosaic setups, sim(II) was used to evaluate differences in growth rate and harvest yield to sim(I). In this simulation, the operations were adopted from sim(I) and applied simultaneously for each subcompartment of the respective compartment. The third simulation, mosaic setup sim(III) used the same simulation data as sim(II), but harvested the compartments according to the subcompartmentwise values, although using the compartments as harvest units to enable direct comparison in operation timing. Only compartments where harvests were expected during the simulation period were studied further, resulting in 14 compartments in the study. The simulations resulted in a greater growth rate estimate for sim(I). The difference between sim(I) and sim(II) varied among the compartments from 0.1 m3ha-1a-1 up to 2.0 m3ha-1a-1. The timing differences of harvest operations were 0-3 years. Income estimates were 5-10 % greater in the mosaic simulations, as well as especially the yield estimate of logwood. The differences in pulpwood estimations were more moderate, except on final cuts where mosaic simulations expected a better yield in minor species pulpwood than sim(I) which neglected these almost totally. The most effective single factor behind the differences in the simulations seemed to be the variation of site class within the compartment. The amount of compartments in the study was, however, too small and the variation between the compartment results too large to allow the application of the results elsewhere. Based on this study, the choice of data unit has an effect on the forest plan. Especially the effects of variation in site class are likely to be taken better into account if the spatial information of stand characteristics is maintained in the planning calculations. Still, small units are not necessarily better in describing the forest development, as they may result in biased estimations in the growth models.