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Articles containing the keyword 'nearest neighbour method'.

Category: Research article

article id 587, category Research article
Erkki Tomppo, Kari T. Korhonen, Juha Heikkinen, Hannu Yli-Kojola. (2001). Multi-source inventory of the forests of the Hebei Forestry Bureau, Heilongjiang, China. Silva Fennica vol. 35 no. 3 article id 587. https://doi.org/10.14214/sf.587
A multi-source forest inventory method is applied to the estimation of forest resources in the area of the Hebei Forest Bureau in Heilongjiang province in North-East China. A stratified systematic cluster sampling design was utilised in field measurements. The design was constructed on the basis of information from earlier stand-level inventories, aerial orthophotographs, experiences from other sampling inventories and the available budget. Sample tree volumes were estimated by means of existing models. New models were constructed and their parameters estimated for tallied tree volumes and volume increments. The estimates for the area of the Bureau were computed from field measurements, and for the areas of the forest farms estimated from field measurements and satellite images. A k-nearest neighbour method was utilised. This method employing satellite image data makes it possible to estimate all variables, particularly for smaller areas than that possible using field measurements only. The methods presented, or their modifications, could also be applied to the planning and realisation of forest inventories elsewhere in Temperate or Boreal zones. The inventory in question gave an estimate of 114 m3/ha (the multi-source inventory 119 m3/ha) instead of 72 m3/ha as previously estimated from available information. Totally nineteen tree species, genera of species or tree species groups were identified (Appendix 1). The forests were relatively young, 60% of them younger than 40 years and 85% younger than 60 years.
  • Tomppo, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland ORCID ID:E-mail: erkki.tomppo@metla.fi (email)
  • Korhonen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland ORCID ID:E-mail:
  • Heikkinen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland ORCID ID:E-mail:
  • Yli-Kojola, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland ORCID ID:E-mail:
article id 620, category Research article
Annika Kangas, Matti Maltamo. (2000). Performance of percentile based diameter distribution prediction and Weibull method in independent data sets. Silva Fennica vol. 34 no. 4 article id 620. https://doi.org/10.14214/sf.620
Diameter distribution is used in most forest management planning packages for predicting stand volume, timber volume and stand growth. The prediction of diameter distribution can be based on parametric distribution functions, distribution-free parametric prediction methods or purely non-parametric methods. In the first case, the distribution is obtained by predicting the parameters of some probability density function. In a distribution-free percentile method, the diameters at certain percentiles of the distribution are predicted with models. In non-parametric methods, the predicted distribution is a linear combination of similar measured stands. In this study, the percentile based diameter distribution is compared to the results obtained with the Weibull method in four independent data sets. In the case of Scots pine, the other methods are also compared to k-nearest neighbour method. The comparison was made with respect to the accuracy of predicted stand volume, saw timber volume and number of stems. The predicted percentile and Weibull distributions were calibrated using number of stems measured from the stand. The information of minimum and maximum diameters were also used, for re-scaling the percentile based distribution or for parameter recovery of Weibull parameters. The accuracy of the predicted stand characteristics were also compared for calibrated distributions. The most reliable results were obtained using the percentile method with the model set including number of stems as a predictor. Calibration improved the results in most cases. However, using the minimum and maximum diameters for parameter recovery proved to be inefficient.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland ORCID ID:E-mail:

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