Michael Henke, Stephan Huckemann, Winfried Kurth, Branislav Sloboda. (2014). Reconstructing leaf growth based on non-destructive digitizing and low-parametric shape evolution for plant modelling over a growth cycle. Silva Fennica vol. 48 no. 2 article id 1019. https://doi.org/10.14214/sf.1019

Keywords: growth modelling; non-destructive data acquisition; automated data extraction; image processing tool; leaf shape modelling; reusable modules; Populus x canadensis

Highlights: A complete pipeline for plant organ modelling (at the example of poplar leaves) is presented, from non-destructive data acquisition, over automated data extraction, to growth and shape modelling; Leaf contour models are compared; Resulting “organ” modules are ready for use in FSPMs.

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A simple and efficient photometric methodology is presented, covering all steps from field data acquisition to binarization and allowing for leaf contour modelling. This method comprises the modelling of area and size (correlated and modelled with a Chapman-Richards growth function, using final length as one parameter), and four shape descriptors, from which the entire contour can be reconstructed rather well using a specific spline methodology. As an improvement of this contour modelling method, a set of parameterized polynomials was used. To model the temporal kinetics of the shape, geodesics in shape spaces were employed. Finally it is shown how this methodology is integrated into the 3D modelling platform GroIMP.

Henke, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: mhenke@uni-goettingen.de
Huckemann, Institute of Mathematical Stochastics, University of Göttingen, 37077 Göttingen, Germany E-mail: huckeman@math.uni-goettingen.de
Kurth, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: wk@informatik.uni-goettingen.de
Sloboda, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: bslobod@web.de

article id 496, category Research article

Hampus Holmström, Hans Kallur, Göran Ståhl. (2003). Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fennica vol. 37 no. 3 article id 496. https://doi.org/10.14214/sf.496

Keywords: uncertainty; data acquisition; imputation; forestry planning

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The usefulness of kNN (k Nearest Neighbour)-assigned reference sample plot data as a basis for forest management planning was studied. Cost-plus-loss analysis was applied, whereby the inventory cost for a specific method is added to the expected loss due to non-optimal forestry activities caused by erroneous descriptions of the forest state. Four different strategies for data acquisition were evaluated: 1) kNN imputation of sample plots based on traditional stand record information, 2) imputation based on plot-wise aerial photograph interpretation in combination with stand record information, 3) sample plot inventory in the field with 5 plots per stand, and 4) sample plot inventory with 10 plots per stand. Expected losses were derived as mean values of differences between the maximum net present value and the corresponding value obtained when the treatment schedule believed to be optimal (based on data simulated according to method 1–4) was selected. The optimal choice of method was found to depend on factors such as stand maturity, stand area, and time to next treatment (thinning or clearcutting). In general, the field sample plot methods were competitive in large mature stands, especially when the time to the next (optimal) treatment was short. By in each stand (within an estate) employing the method with the lowest cost-plus-loss rather than choosing the method that performed best on average for the entire estate, the total cost for inventory at the estate level could be decreased by 15–50%. However, it was found difficult to identify what method should optimally be employed in a stand based on general stand descriptions.

Holmström, Regional Board of Forestry of Västra Götaland, P.O. Box 20008, SE-50420 Borås, Sweden E-mail: hampus.holmstrom@svsvg.svo.se
Kallur, ÖKA Skogsplan, Kopparvägen 45 O, SE-90750 Umeå, Sweden E-mail: hk@nn.se
Ståhl, Swedish Univ. of Agricultural Sciences, Dept. of Forest Resource Management and Geomatics, SE-90183 Umeå, Sweden E-mail: gs@nn.se

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