Comparison of percentile based prediction methods and the Weibull distribution in describing the diameter distribution of heterogeneous Scots pine stands

By Matti Maltamo, Annika S. Kangas, J. Uuttera, T. Torniainen, J. Saramäki. 2000

138 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/fbc6a7435a3ad9b3221478a776cda383c84abe64

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Citations in the corpus

Comparison of beta, Johnson’s SB, Weibull and truncated Weibull functions for modeling the diameter distribution of forest stands in Catalonia (north-east of Spain) (2007, methodology): “Various probability density functions, such as normal, gamma, Johnson’s SB, Gram-Charlier, beta and Weibull, have been used in describing diameter distributions of forest stands (e.g., Cajanus 1914; Bailey and Dell 1973; Hafley and Schreuder 1977; Maltamo et al. 1995, 2000; Palahı́ et al. 2006).”
Modelling the diameter distribution of Pinus sylvestris, Pinus nigra and Pinus halepensis forest stands in Catalonia using the truncated Weibull function (2006, methodology): “Various distribution functions, such as normal, gamma, Johnson’s SB, Gram-Charlier, beta and Weibull, have been used in describing diameter distributions of forest stands (e.g. Cajanus, 1914 ; Bailey and Dell, 1973 ; Hafl ey and Schreuder, 1977 ; Maltamo et al. , 1995 ; Maltamo et al. , 2000 ).”
Weibull and percentile models for lidar-based estimation of basal area distribution (2005, methodology): “…used in parameter recovery have usually been derived from intensively field-measured variables such as mean diameter by basal area, basal area weighted median diameter, total stand basal area, total number of trees and age (e.g. Maltamo, 1997; Kangas & Maltamo, 2000; Maltamo et al., 2000).”
Modeling Tree Diameter Distributions in Natural Forests: An Evaluation of 10 Statistical Models (2015, background): “This may be the case for early-successional species (Wright et al. 2003), species or stands presenting peaks of growth and/or mortality (Leak 2002), even-aged stands (Schmelz and Lindsey 1965), and forests under thinning (Maltamo et al. 2000) or harvesting focused on specific tree sizes.”
Characterisation of diameter distribution using the Weibull function: method of moments (2006, methodology): “The method of moments is used infrequently and often only when the intention is to compare the applicability of selected analytical methods for the estimation of Weibull-parameters (Kangas and Maltamo 2000; Maltamo et al. 2000; Shiver 1988; Nanang 1998; Ueno and Ôsawa 1987; Zarnoch and Dell 1985; Sh”
Predicting the growth of stands of trees of mixed species and size: A matrix model for Norway (2008, background): “This species size class definition is simply understood, and the measurements can be readily obtained from field measurements or remote sensing (Maltamo et al., 2000, 2005; Bollandsås & Næsset, 2007), or with models that predict diameter distributions based on stand data (Vestjordet, 1972; Mønnes,…”
Performance of percentile based diameter distribution prediction and Weibull method in independent data sets (2000, methodology): “Maltamo et al. (2000) used the percentile based approach to predict irregular stem frequency diameter distributions of stands in a natural state.”
Fitting irregular diameter distributions of forest stands by Weibull, modified Weibull, and mixture Weibull models (2006, background): “However, the use of unimodal statistical distributions can lead to an oversimplified description for irregular stand structures (e.g., Murphy and Farrar 1981; Maltamo et al. 2000).”
Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland (2013, methodology): “The few existing dbh-frequency distributions used in Finland were presented at the beginning of 21st century (Maltamo et al. 2000; Sarkkola et al. 2003; Sarkkola et al. 2005).”
Percentile based basal area diameter distribution models for Scots pine, Norway spruce and birch species. (2000, methodology): “The percentile based method presented by Borders et al. (1987) has been proved to be a good alternative for describing diameter distribution in Finnish conditions (see also Maltamo et al. 2000).”
Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data (2007, methodology): “When modelling diameter distributions using the percentile method, previous authors have most frequently used seemingly unrelated regression (SUR) (Zellner, 1962) to estimate model parameters (Kangas & Maltamo, 2000a , b ; Maltamo et al., 2000; Gobakken & Næsset, 2005).”
Estimation of stem volume using laser scanning-based canopy height metrics (2006, methodology): “…model does not smooth out the distributions as much as the classical probability density functions which is an advantageous property for the diameter distribution model applied for the description of structurally heterogeneous stands with multimodal size distributions (e.g. Maltamo et al. , 2000 ).”