Tree diameter distribution modelling: introducing the logitlogistic distribution

Wang, Mingliang; Rennolls, K.

doi:10.1139/x05-057

Cited by 66 publications

(36 citation statements)

References 18 publications

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“…The most suitable distribution shape was the logit-logistic distribution, which is a flexible distribution with minimum and maximum values ψ and λ, as well as shape and scale parameters φ and σ (Tadikamalla & Johnson 1982, Wang & Rennolls 2005.…”

Section: Simulation Of Forest Inventory Errors (Ii III Iv)mentioning

confidence: 99%

Uncertainty in forest simulators and forest planning systems

Mäkinen¹

2010

Diss. For.

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The forest simulator is a computerized model for predicting forest growth and future development as well as effects of forest harvests and treatments. The forest planning system is a decision support tool, usually including a forest simulator and an optimisation model, for finding the optimal forest management actions.The information produced by forest simulators and forest planning systems is used for various analytical purposes and in support of decision making. However, the quality and reliability of this information can often be questioned. Natural variation in forest growth and estimation errors in forest inventory, among other things, cause uncertainty in predictions of forest growth and development. This uncertainty stemming from different sources has various undesirable effects. In many cases outcomes of decisions based on uncertain information are something else than desired.The objective of this thesis was to study various sources of uncertainty and their effects in forest simulators and forest planning systems. The study focused on three notable sources of uncertainty: errors in forest growth predictions, errors in forest inventory data, and stochastic fluctuation of timber assortment prices. Effects of uncertainty were studied using two types of forest growth models, individual tree-level models and stand-level models, and with various error simulation methods. New method for simulating more realistic forest inventory errors was introduced and tested. Also, three notable sources of uncertainty were combined and their joint effects on stand-level net present value estimates were simulated.According to the results, the various sources of uncertainty can have distinct effects in different forest growth simulators. The new forest inventory error simulation method proved to produce more realistic errors. The analysis on the joint effects of various sources of uncertainty provided interesting knowledge about uncertainty in forest simulators. Metsäsimulaattori on tietokoneistettu malli, jolla ennustetaan metsän kasvua ja tulevaa kehitystä, sekä hakkuiden ja käsittelyiden vaikutuksia metsiin. Metsäsuunnittelujärjestelmä on päätöstukijärjestelmä, joka yleisesti koostuu metsäsimulaattorista ja optimointimallista ja jonka avulla metsien käsittelyitä voidaan optimoida.Metsäsimulaattoreilla ja metsäsuunnittelujärjestelmillä tuotettua tietoa käytetään monenlaisiin analyyseihin sekä metsien käyttöä koskevan päätöksenteon perustaksi. Tiedon laatu ja luotettavuus on kuitenkin usein kyseenalaista. Esimerkiksi metsien kasvun luontainen vaihtelu sekä virheet metsien nykytilaa koskevissa tiedoissa aiheuttavat metsien tulevan kehityksen ennusteissa epävarmuutta. Epävarmuudella, tai tiedon puutteella on monia epäedullisia seurauksia. Epävarmalle tiedolle perustuvat päätökset johtavat usein ei-toivottuun lopputulokseen.Tämän väitöskirjan tarkoituksen oli tarkastella erilaisia epävarmuuden lähteitä sekä niiden vaikutuksia metsäsimulaattoreissa ja metsäsuunnittelujärjestelmissä. Tutkimuksessa tarkasteltiin pääasiassa kolmea m...

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Section: Simulation Of Forest Inventory Errors (Ii III Iv)mentioning

confidence: 99%

Uncertainty in forest simulators and forest planning systems

Mäkinen¹

2010

Diss. For.

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“…These authors estimated two of the four parameters of each marginal GDB-2 (β1, β2, β3 and β4) by substituting β1 = xmin and β2 = xmax -β1, where xmin and xmax are the minimum and the maximum values of the marginal distributions of heights and diameters. Wang & Rennolls (2007) presented a bivariate distribution (LL-2) based on the univariate Logit-Logistic (Wang & Rennolls 2005), which is obtained by the parametrization of the Johnson's SB distribution. The location parameter is the same in both distributions.…”

Section: Discussionmentioning

confidence: 99%

Modeling extreme values for height distributions in Pinus pinaster, Pinus radiata and Eucalyptus globulus stands in northwestern Spain

Gorgoso-Varela¹,

García-Villabrille²,

Alboreca³

2016

iForest

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Methods of estimating extreme height values can be used in forest modeling to improve fits to the marginal distribution of heights in the following bivariate diameter-height models: the SBB Johnson's distribution, the bivariate beta (GDB-2) distribution, the bivariate Logit-Logistic (LL-2) distribution and the power-normal (PN) distribution. Some applications to LiDAR derived data are also possible, e.g., for error calibration. Practical applications in forest management may also be considered, e.g., for pruning. In probability theory and statistics, the generalized extreme value (GEV) distribution, also known as the Fisher-Tippett distribution, is a family of continuous probability distributions that combine the Gumbel, Fréchet and Weibull distributions. This study compared the three distributions for fitting extreme values of tree heights (maximum and minimum heights), which were measured in 185 permanent research plots in Pinus pinaster Ait.

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“…Therefore, logistic regression does not need to be normally distributed like linear regression. The logit transformation [17,18] converts a probability measurement between 0 and 1 into values in the interval (−∞, ∞). The logit transformation is defined as…”

Section: Logistic Regression Analysismentioning

confidence: 99%

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

Kim

Chi

Kim³

2018

Sustainability

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Abstract:In recent years, the increasing gap between available funding and preservation needs has influenced district pavement engineers to select and prioritize projects to effectively use funding. However, currently, projects are often selected after an informal assessment, based on local conditions and local district engineers' experience, in the absence of a statewide systematic process. The primary objective of this study is to determine network-level project sections for effective sustainable pavement management using logistic regression analysis. A large volume of inventory data, documented using pavement-management information systems (PMIS), was used to develop the logistic regression (LR) model for selecting candidate sections. The LR model was subsequently validated using a single 50/50 split sample method. The findings of this study will assist the Austin, Texas, USA district to select and evaluate candidate projects. Furthermore, the study will eventually contribute to improved efficiency in project selection and prioritization by reducing not only the amount of time necessary to review the district PMIS data to identify project candidates, but also the potential for human error.

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Tree diameter distribution modelling: introducing the logitlogistic distribution

Cited by 66 publications

References 18 publications

Uncertainty in forest simulators and forest planning systems

Uncertainty in forest simulators and forest planning systems

Modeling extreme values for height distributions in Pinus pinaster, Pinus radiata and Eucalyptus globulus stands in northwestern Spain

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

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Tree diameter distribution modelling: introducing the logitlogistic distribution

Cited by 66 publications

References 18 publications

Uncertainty in forest simulators and forest planning systems

Uncertainty in forest simulators and forest planning systems

Modeling extreme values for height distributions in Pinus pinaster, Pinus radiata and Eucalyptus globulus stands in northwestern Spain

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

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Tree diameter distribution modelling: introducing the logitlogistic distribution