Performance analysis of a process‐based stand growth model using Monte Carlo techniques

Mäkelä, Annikki

doi:10.1080/02827588809382520

Cited by 26 publications

(12 citation statements)

References 26 publications

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“…The effects of the errors in the ITD were analyzed with Monte Carlo (MC) simulations (e.g., [30][31][32][33][34]). With the MC method the calculation procedure is run dozens or hundreds of times, the results of which are used to determine the final predicted value error statistics.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations

Vastaranta

Holopainen

et al. 2011

Remote Sensing

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Abstract:The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees (e td ), error in tree height prediction (e h ) and error in tree diameter prediction (e d ). The effects of errors were analyzed with Monte Carlo simulations. e td was modeled empirically based on a tree's relative size. A total of five different tree detection scenarios were tested. Effect of e h was investigated using 5% and 0% and effect of e d using 20%, 15%, 10%, 5%, 0% error levels, respectively. The research material comprised 15 forest stands located in Southern Finland. Measurements of 5,300 trees and their timber assortments were utilized as a starting point for the Monte Carlo simulated ITD inventories. ITD carried out for the same study area provided a starting point (Scenario 1) for e td . In Scenario 1, 60.2% from stem number and 75.9% from total volume (V total ) were detected. When the only error source was e td (tree detection varying from 75.9% to 100% of V total ), root mean square errors (RMSEs) in stand characteristics ranged between the scenarios from 32.4% to 0.6%, 29.0% to 0.5%, 7.8% to OPEN ACCESSRemote Sens. 2011, 3 1615 0.2% and 5.4% to 0.1% in stand basal area (BA), V total , mean height (Hg) and mean diameter (Dg), respectively. Saw wood volume RMSE varied from 25.1% to 0.2%, as pulp wood volume respective varied from 37.8% to 1.0% when errors stemmed only from e td . The effect of e d was most significant for V total and BA and the decrease in RMSE was from 12.0% to 0.6% (BA) and from 10.9% to 0.5% (V total ) in the most accurate tree detection scenario when e d varied from 20% to 0%. The effect of increased accuracy in tree height prediction was minor for all the stand characteristics. The results show that the most important error source in ITD is tree detection. At stand level, unbiased predictions for tree height and diameter are enough, given the present tree detection accuracy.

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Section: Monte Carlo Simulationsmentioning

confidence: 99%

Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations

Vastaranta

Holopainen

et al. 2011

Remote Sensing

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“…The relative importance of the three sources of uncertainty in forest NPV computations was determined by simulating each stand within each forest property repeatedly with the MC method (e.g., [25,26,29,39]). In MC methodology, confidence estimates are obtained by generating an error term from the model's error distribution for each output estimate.…”

Section: Simulation Of the Sources Of Uncertaintymentioning

confidence: 99%

Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant OPEN ACCESSForests 2010, 1 178 in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

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“…Consequently, the model predictions have been sensitive to the parameter for sapwood senescence (Mäkelä 1988). The actual mechanism at cellular level that turns sapwood into heartwood is not completely understood (Saranpää 1992).…”

Section: Introductionmentioning

confidence: 99%

“…This is probably in part due of its ease of use: simple linear equations (between foliage quantity and wood cross-sectional areas) can be used in the model which then conveniently translate into relationships defining the partitioning of the growth increments (e.g. Mäkelä 1990). Cross-sectional areas of water conducting sapwood is often used instead total area since part of the pipes become disused (Shinozaki et al 1964).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of importance of sapwood senescence on tree growth using the model Lignum.

Sievänen¹,

Nikinmaa²,

Perttunen³

1997

Silva Fenn.

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Perttunen, J. 1997. Evaluation of importance of sapwood senescence on tree growth using the model lignum. Silva Fennica 31(3): 329-340.The effects of two alternative formulations of sapwood senescence on the behavior of model LIGNUM (with parameter values adjusted for Scots pine growing southern Finland) were studied. The two alternatives were: autonomous sapwood senescence assuming a maximum age for the tree ring and sapwood senescence that is controlled by the mortality of foliage. For the latter alternative two hypothetical further mechanisms were stipulated. All the formulations were implemented in LIGNUM. Simulations were made with all model variants for fertile and poor soil conditions using high, normal and low rates of foliage mortality. The simulation results were compared against of a data set consisting of 11 open grown Scots pine trees from southern Finland. Observations of heartwood proportion were used in this study. They show that heartwood starts to increase in trees from age of approximately 20 years onwards. The simulation results showed no differences between fertile and poor soil conditions as regards heartwood formation. Of the variants of foliage controlled sapwood senescence the one where death of sapwood in a tree segment induces sapwood senescence in the tree parts below only slightly was the best. This and the autonomous sapwood senescence corresponded equally well to the observations. In order to make more refined conclusions additional data and simulations are necessary.

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Performance analysis of a process‐based stand growth model using Monte Carlo techniques

Cited by 26 publications

References 26 publications

Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations

Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations

Uncertainty in Forest Net Present Value Estimations

Evaluation of importance of sapwood senescence on tree growth using the model Lignum.

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