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.

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Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on the availability of reliable digital terrain models (DTMs). The main objective of this study was to evaluate application of 3D data derived from UAV imagery in biomass estimation and to compare impacts of DTMs generated based on different methods and parameter settings. Biomass was modeled using data acquired from 107 sample plots in a forest reserve in miombo woodlands of Malawi. The results indicated that there are no significant differences (p = 0.985) between tested DTMs except for that based on shuttle radar topography mission (SRTM). A model developed using unsupervised ground filtering based on a grid search approach, had the smallest root mean square error (RMSE) of 46.7% of a mean biomass value of 38.99 Mg·ha −1 . Amongst the independent variables, maximum canopy height (Hmax) was the most frequently selected. In addition, all models included spectral variables incorporating the three color bands red, green and blue. The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs.

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Eid, T. 2000. Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. Silva Fennica 34(2): 89-100. Uncertainty in long-term timber production analyses usually focus success of regeneration, growth/mortality of trees and future fluctuations of timber prices/harvest costs, while uncertainty related to inventory data is paid less attention. At the same time, evaluations of inventory methods usually stop when the error level is stated, while the uncertainty accompanied by using the data is seldom considered. The present work addresses uncertain inventory data in long-term timber production analyses. Final harvest decisions, i.e. possible outcome intervals with respect to timing and expected net present value-losses due to incorrect timing, were considered. A case study was presented where inventory data errors according to different error levels were generated randomly. The selected error levels were based on observations from practical forest inventories in Norway. The analysis tool was GAYA-JLP. The impact of errors on decisions was derived through repeated computations of management strategies maximising net present value without harvest path constraints. A real rate of discount of 3 % and an error level of 15 % resulted in expected net present value-losses of 1 NOK ha-1 for basal area, 63 NOK ha-1 for mean height, 210 NOK ha-1 for site quality, 240 NOK ha-1 for stand age, and 499 NOK ha-1 when random errors occurred simultaneously for all these variables. The expected net present valuelosses varied considerably. The largest losses appeared for stands with ages around optimal economical rotation ages. The losses were also relatively large for young stands, while they were relatively low for overmature stands. The experiences from the case study along with considerations related to other sources of uncertainty may help us to get a more realistic attitude to the reliability of long-term timber production analyses. The results of the study may also serve as a starting point in a decision oriented inventory planning concept, in which alternatives for inventory design and intensity are based on considerations with respect to inventory costs as well as net present value-losses. Keywords forest management, uncertain inventory data, final harvest decisions, expected net present value-losses, inventory planning