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Articles containing the keyword 'measurement error'

Category : Research article

article id 23021, category Research article

Virpi Stenman, Annika Kangas, Markus Holopainen. (2023). Upper stem diameter and volume prediction strategies in the National Forest Inventory of Finland. Silva Fennica vol. 57 no. 3 article id 23021. https://doi.org/10.14214/sf.23021

Keywords: forest inventory; measurement errors; accuracy; Bland-Altman plot

Highlights: National Forest Inventory specific methods were applied with a number of measurement instruments, including a laser-based dendrometer, to collect tree stem diameter measurements; Bland-Altman plots and measurement error variances were used to determine measurement precision and accuracy; The laser-based dendrometer did not perform better than the other instruments in the study.

Abstract | Full text in HTML | Full text in PDF | Author Info

In forest inventories, field data are needed for the prediction of tree volumes. However, gathering field data requires resources, such as labour, equipment, and data management operations. This means that time and budget, as well as quality, must be carefully considered when National Forest Inventory (NFI) field measurement activities are planned. Therefore, the development of cost efficient, simple, safe and reliable measurement methods and tools are of great interest. To date, upper stem diameter (d6), which provides a more reliable estimation of tree stem volume, has typically been measured with a parabolic calliper. In this study, the performance of the Criterion laser-based dendrometer was examined for d6 measurements. A total of 326 sample trees were measured multiple times with three different measurement instruments. These instruments were used to measure diameter at breast height (dbh) as well as d6 measurements. Bland-Altman plots and measurement error variances were used to determine measurement instrument reliability. For all trees, the standard deviation for the laser based dendrometer was 18.73 mm at dbh and 15.36 mm for the d6 measurements. When the performance of Criterion was analysed with reference to the mean value of repeated measurements, the standard deviation in the dbh measurements was 12.21 mm, and 8.88 mm in the d6 measurements.

Stenman, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland https://orcid.org/0000-0003-1176-7840 E-mail: virpi.stenman@helsinki.fi
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi

article id 100, category Research article

Annika Kangas, Lauri Mehtätalo, Antti Mäkinen, Kalle Vanhatalo. (2011). Sensitivity of harvest decisions to errors in stand characteristics. Silva Fennica vol. 45 no. 4 article id 100. https://doi.org/10.14214/sf.100

Keywords: forest planning; inventory; measurement errors; decision making; logistic regression; regression tree

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In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.

Kangas, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
Mäkinen, Simosol Oy, Riihimäki, Finland E-mail: am@nn.fi
Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: kv@nn.fi

article id 133, category Research article

Fumiaki Kitahara, Nobuya Mizoue, Shigejiro Yoshida. (2010). Effects of training for inexperienced surveyors on data quality of tree diameter and height measurements. Silva Fennica vol. 44 no. 4 article id 133. https://doi.org/10.14214/sf.133

Keywords: diameter tape; measurement error; National Forest Inventory; training; Vertex III hypsometer

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Due to the large number of sample plots and variables to be measured, inexperienced surveyors are expected to take field measurements in National Forest Inventories (NFIs). However, very little information exists on the data quality that can be expected from inexperienced surveyors given different levels of training. We evaluated the quality of data produced by inexperienced undergraduate students when measuring the most fundamental variables: tree diameter using a diameter tape and height using an ultrasonic Vertex III hypsometer. We found that a single training session on how to use the instruments and how to reduce measurement errors was insufficient for inexperienced surveyors to achieve measurement quality objectives (MQOs). Providing a single feedback of control team measurements significantly improved data quality, except in the measurements of tree height of broad-leaved trees, but additional feedback did not contribute to further improvement. We propose that field training courses for inexperienced surveyors incorporate a one-day exercise with feedback instruction.

Kitahara, Graduate School of Bioresource and Bioenvironmental Science, Kyushu University, 6-10-1 Hakozaki, Higashiku, Fukuoka 812-8581, Japan E-mail: bunsho@ffpri.affrc.go.jp
Mizoue, Faculty of Agriculture, Kyushu University, Fukuoka, Japan E-mail: nm@nn.jp
Yoshida, Faculty of Agriculture, Kyushu University, Fukuoka, Japan E-mail: sy@nn.jp

article id 219, category Research article

Arto Haara, Pekka Leskinen. (2009). The assessment of the uncertainty of updated stand-level inventory data. Silva Fennica vol. 43 no. 1 article id 219. https://doi.org/10.14214/sf.219

Keywords: uncertainty; measurement error; simulation; non-parametric methods; observed error; stand-level inventory

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Predictions of growth and yield are essential in forest management planning. Growth predictions are usually obtained by applying complex simulation systems, whose accuracy is difficult to assess. Moreover, the computerised updating of old inventory data is increasing in the management of forest planning systems. A common characteristic of prediction models is that the uncertainties involved are usually not considered in the decision-making process. In this paper, two methods for assessing the uncertainty of updated forest inventory data were studied. The considered methods were (i) the models of observed errors and (ii) the k-nearest neighbour method. The derived assessments of uncertainty were compared with the empirical estimates of uncertainty. The practical utilisation of both methods was considered as well. The uncertainty assessments of updated stand-level inventory data using both methods were found to be feasible. The main advantages of the two studied methods include that bias as well as accuracy can be assessed.

Haara, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: arto.haara@joensuu.fi
Leskinen, Finnish Environment Institute, Research Programme for Production and Consumption, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pl@nn.fi

article id 486, category Research article

Arto Haara. (2003). Comparing simulation methods for modelling the errors of stand inventory data. Silva Fennica vol. 37 no. 4 article id 486. https://doi.org/10.14214/sf.486

Keywords: measurement error; simulation; stand-level inventory; non-parametric estimation; Monte Carlo methods

Abstract | View details | Full text in PDF | Author Info

Forest management planning requires information about the uncertainty inherent in the available data. Inventory data, including simulated errors, are infrequently utilised in forest planning studies for analysing the effects of uncertainty on planning. Usually the errors in the source material are ignored or not taken into account properly. The aim of this study was to compare different methods for generating errors into the stand-level inventory data and to study the effect of erroneous data on the calculation of specieswise and standwise inventory results. The material of the study consisted of 1842 stands located in northern Finland and 41 stands located in eastern Finland. Stand-level ocular inventory and checking inventory were carried out in all study stands by professional surveyors. In simulation experiments the methods considered for error generation were the 1nn-method, the empirical errors method and the Monte Carlo method with log-normal and multivariate log-normal error distributions. The Monte Carlo method with multivariate error distributions was found to be the most flexible simulation method. This method produced the required variation and relations between the errors of the median basal area tree characteristics. However, if the reference data are extensive the 1nn-method, and in certain conditions also the empirical errors method, offer a useful tool for producing error structures which reflect reality.

Haara, Finnish Forest Research Institute, Joensuu Research Centre, P.O.Box 68, FIN-80101 Joensuu, Finland E-mail: arto.haara@metla.fi

Category : Research note

article id 1496, category Research note

Juha Lappi, Jaana Luoranen. (2016). Using a bivariate generalized linear mixed model to analyze the effect of feeding pressure on pine weevil damage. Silva Fennica vol. 50 no. 1 article id 1496. https://doi.org/10.14214/sf.1496

Keywords: measurement error; best linear predictor; correlated random effects; log-log link

Highlights: Probability of damage of treated seedlings can be predicted from the probability of damage of control seedlings (feeding pressure).

Abstract | Full text in HTML | Full text in PDF | Author Info

The objective of the study is to derive a method by which one can analyze how the probability of damage made by pine weevils on seedlings treated with insecticides depends on the probability of damage on untreated control seedlings, called feeding pressure. Because the probabilities vary from stand to stand and from block to block, the analysis is done using a generalized linear mixed model. The dependency of probability of damage on the feeding pressure cannot be properly analyzed using observed relative frequency of damage of control seedlings as a covariate, but it can be analyzed using a bivariate model. One equation describes damage of control seedlings and another equation damage of treated seedlings. The random stand and block effects of different equations are correlated. For a given probability of stand level control seedling damage, the random stand effect for control seedlings can be computed using a link function, then random stand effects for treated seedlings can be predicted using the best linear predictor from the random effect for control seedlings. Using an inverse link the prediction can again be presented in the probability scale which is of interest to the user. Using these three steps the probability of damage of treated seedlings can be predicted from the control damage probability. The probability of damage of treated seedlings can also be predicted from the observed relative frequency of damaged control seedlings using simulation. The complementary log-log link was used for control seedlings and the log-log link for treated seedlings.

Lappi, Natural Resources Institute Finland (Luke), Economics and society, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: juha.lappi@luke.fi
Luoranen, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: jaana.luoranen@luke.fi

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