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  • Canadian Science Publishing  (3)
  • 2015-2019  (3)
  • 1
    Publication Date: 2016-10-01
    Description: Mixed-species stands are on the advance in Europe. They fulfil many functions better than monocultures. Recent papers show that mixed stands can have higher yields, but it remains open whether mixed stands simply grow faster along the same self-thinning lines as pure stands or have higher maximum stand densities. We analyzed the effect of species mixing on maximum density based on triplets of pure and mixed stands at approximately maximum density. Most considered mixtures include Norway spruce (Picea abies (L.) H. Karst.). We show that (i) in mixed stands, maximum density is, on average, 16.5% higher than in neighbouring pure stands, and (ii) species mixtures with Norway spruce exceed densities of pure stands by 8.8%, on average. For individual species mixtures, we find a significant density effect of +29.1% for Norway spruce mixed with European larch (Larix decidua Mill.) and +35.9% for Scots pine (Pinus sylvestris L.) in association with European beech (Fagus sylvatica L.). No significant links with stand variables such as age and mean tree size and site fertility were found. The results indicate that species mixing substantially increases stand density, indicating a higher carrying capacity caused by a higher supply and use efficiency of resources. The implications for inventory, silviculture, and forest modelling are discussed.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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  • 2
    Publication Date: 2015-01-01
    Description: Dense image-based point clouds have great potential to accurately assess forest attributes such as growing stock. The objective of this study was to combine height and spectral information obtained from UltraCamXp stereo images to model the growing stock in a highly structured broadleaf-dominated forest (77.5 km2) in southern Germany. We used semi-global matching (SGM) to generate a dense point cloud and subtracted elevation values obtained from airborne laser scanner (ALS) data to compute canopy height. Sixty-seven explanatory variables were derived from the point cloud and an orthoimage for use in the model. Two different approaches — the linear regression model (lm) and the random forests model (rf) — were tested. We investigated the impact that varying amounts of training data had on model performance. Plot data from a previously acquired set of 1875 inventory plots was systematically eliminated to form three progressively less dense subsets of 937, 461, and 226 inventory plots. Model evaluation at the plot level (size: 500 m2) yielded relative root mean squared errors (RMSEs) ranging from 31.27% to 35.61% for lm and from 30.92% to 36.02% for rf. At the stand level (mean stand size: 32 ha), RMSEs from 14.76% to 15.73% for lm and from 13.87% to 14.99% for rf were achieved. Therefore, similar results were obtained from both modeling approaches. The reduction in the number of inventory plots did not considerably affect the precision. Our findings underline the potential for aerial stereo imagery in combination with ALS-based terrain heights to support forest inventory and management.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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  • 3
    Publication Date: 2016-01-01
    Description: A common method for estimating forest biomass is to measure forest height and apply allometric equations. However, changing forest density or structure heterogeneity increases the variability of the known allometric relationship. Here, we investigated the potential of allometric relationships based on vertical forest structure for biomass inversions with a global potential. First, vertical biomass profiles, which were calculated from ground forest inventory data, were used to model forest vertical structure. Then, a vertical structure ratio based on Legendre polynomials was proposed as a structural descriptor and its sensitivity to biomass was evaluated. Finally, we developed a structure-to-biomass inversion expression that could be extrapolated for aboveground biomass estimations. This is a case study based on inventory data from the Traunstein and Ebersberg test sites, two temperate forests located in southeastern Germany with different forest structural conditions. Results from the structure-to-biomass inversion algorithm show a clear improvement with respect to traditional height-to-biomass expressions, with increasing correlation factor (r2) from 0.52 to 0.73 for Traunstein and from 0.51 to 0.76 for Ebersberg and reducing the root mean square errors from 75.32 to 47.56 Mg·ha−1 and from 73.25 to 48.31 Mg·ha−1, respectively.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Location Call Number Expected Availability
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