ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Simultaneous optimization of harvest schedule and data quality (2015)
    Canadian Science Publishing
    Details
    Publication Date: 2015-08-01
    Description: In many recent studies, the value of forest inventory information in harvest scheduling has been examined. In a previous paper, we demonstrated that making measurement decisions for stands for which the harvest decision is uncertain simultaneously with the harvest decisions may be highly profitable. In that study, the quality of additional measurements was not a decision variable, and the only options were between making no measurements or measuring perfect information. In this study, we introduce data quality into the decision problem, i.e., the decisionmaker can select between making imperfect or perfect measurements. The imperfect information is obtained with a specific scenario tree formulation. Our decision problem includes three types of decisions: harvest decisions, measurement decisions, and decisions about measurement quality. In addition, the timing of the harvests and measurements must be decided. These decisions are evaluated based on two objectives: discounted aggregate income for the planning periods and the end value of the forest at the end of the planning horizon. Solving the bi-objective optimization problem formed using the ε-constraint method showed that imperfect information was mostly sufficient for the harvest timing decisions during the planning horizon but perfect information was required to meet the end-value constraint. The relative importance of the two objectives affects the measurements indirectly by increasing or decreasing the number of certain decisions (i.e., situations in which the optimal decision is identical in all scenarios).
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    The effects of sample plot selection strategy and the number of sample plots on inoptimality losses in forest management planning based on airborne laser scanning data (2019)
    Canadian Science Publishing
    Details
    Publication Date: 2019-09-01
    Description: In forest management planning, errors in predicted stand attributes might lead to suboptimal decisions that result in decreased net present value (NPV). Forest inventory data will have higher value if the amount of suboptimal decisions can be decreased. Therefore, the value of information can be measured through the decrease in inoptimality losses, which are the NPV differences between the optimal and suboptimal decisions. In this study, four alternative sample plot selection strategies with different numbers of sample plots were compared in terms of expected mean inoptimality losses. Stand-level mean inoptimality losses varied between €41.1·ha–1 and €80.7·ha−1, depending on the sample plot selection strategy and the number of sample plots used as training data in the k-nearest neighbors imputation method. Mean inoptimality losses decreased substantially when the number of sample plots increased from 25 to 100, and the decreasing trend continued until 500 sample plots. Total inoptimality losses can decrease by approximately €1 million in an inventory area of 100 000 ha when the number of sample plots is increased from 100 to 500. The measurement of more sample plots can be justified as long as the field measurement costs do not exceed the decrease in inoptimality losses.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    Model-assisted forest inventory with parametric, semiparametric, and nonparametric models (2016)
    Canadian Science Publishing
    Details
    Publication Date: 2016-06-01
    Description: Survey sampling with model-assisted estimation has been gaining popularity in forest inventory recently, as the availability of cheap, good-quality remotely sensed data that can be used as auxiliary information has improved. Most of the studies have been carried out using parametric (linear or nonlinear) models. However, nonparametric and semiparametric models such as k nearest neighbor, kernel, and generalized additive are widely used in forest inventory. The results are usually calculated using the difference estimator (i.e., assuming an external model), even though the models used are based on the sample (i.e., an internal model). In that case, variances will likely be underestimated. In this study, we analyze how well the difference estimator works for different types of models, both internal and external. The study is based on simulated populations produced using a C-vine copula model with empirical marginals. The external model is based on real data, and the internal models are estimated from samples from the simulated population. The results show that the analytical variance estimates for a difference estimator based on an overfitted kernel model can seriously underestimate the true variance.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    The efficiency of poststratification compared with model-assisted estimation (2017)
    Canadian Science Publishing
    Details
    Publication Date: 2017-04-01
    Description: Survey sampling with model-assisted estimation has recently gained popularity in forest inventory. Another option for utilizing the auxiliary information is to use poststratification, which is a special case of model-assisted estimation with class variables as explanatory variables. In this study, we compared the efficiency of poststratification with an increasing number of strata with model-assisted estimation. We carried out a study based on a simulated population. We considered four different types of poststratifications, namely (i) stratification based on predictions of a linear model, (ii) stratification based on a regression tree model, (iii) stratification based on the first principal component of the explanatory variables, and (iv) stratification based on the regression tree model with the first principal component as the only explanatory variable. Furthermore, we examined both the traditional poststratification mean and variance estimators and the difference estimator and its variance estimator for poststratification. Within the recommended range of number of strata, the model-assisted approach was more efficient than poststratification. With a large number of strata, poststratification produced smaller standard error of estimates, but problems such as empty strata were encountered with small sample sizes. Using the first principal component directly for stratification or as an explanatory variable was the most efficient approach.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 5
    facet.materialart.
    Unknown
    Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume (2015)
    Canadian Science Publishing
    Details
    Publication Date: 2015-11-01
    Description: In this study, we investigate the use of model-based inference in forest surveys in which auxiliary data are available as a probability sample. We evaluate the effects of model form and sample size on estimators of growing stock volume, based on different types of remotely sensed auxiliary data. The study was performed through Monte Carlo sampling simulation using a two-phase sampling design within a simulated study area resembling the conditions in mid-western Finland. We show that the choice of model has a minor to moderate effect on the precision of model-based estimators. Similarly, the choice of estimator of the variance–covariance matrix of model parameter estimates, which is at the core of uncertainty assessment in model-based inference, was also found to have a minor to moderate effect on the precision of model-based estimators. Regarding sample sizes, the model error contribution to the total variance remains the same regardless of the sample size of the first phase (i.e., the size of the sample of auxiliary data); to reduce the model-error contribution, there is a need to increase the sample size of the second phase (i.e., the size of the sample of field plots for developing regression models). As a baseline for comparisons, model-assisted estimators were applied and found to be about equally precise as the model-based estimators, in accordance with the theory for the case when models are estimated from the sample data.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 6
    facet.materialart.
    Unknown
    Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory (2020)
    Canadian Science Publishing
    Details
    Publication Date: 2020-04-01
    Description: National forest inventories (NFIs) are designed to provide accurate information on forest resources at the national and regional levels, but there is also a demand for such information at smaller spatial scales. Auxiliary data such as satellite imagery have been used to facilitate small-area estimation. The commonly used method, k-nearest neighbour (k-NN), provides a model-based estimator for small areas, but a design-unbiased estimator for the mean square error is not available. Post-stratification (PS) is an alternative approach to using auxiliary information that allows for design-based variance estimation. In a case study using real inventory data of the Finnish NFI, we applied this method to the municipality level to explore the lower limit to the area for which the key forest parameters, forest area and growing stock volumes, can be estimated with sufficient precision. For PS, we employed exogenous forest resources maps based on the previous NFI round. In the municipalities of the two study provinces, the relative standard errors of total volume estimates ranged from 2.3% to 26.9%. They were smaller than 10% for municipalities with an area of 390 km2 or larger, corresponding to approximately 100 or more sample plots on forestland. We also demonstrated the usefulness of design-unbiased variance estimation in showing discrepancies between design-based PS and model-based k-NN estimates.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 7
    facet.materialart.
    Unknown
    Integrating risk management tools for regional forest planning: an interactive multiobjective value-at-risk approach (2018)
    Canadian Science Publishing
    Details
    Publication Date: 2018-07-01
    Description: In this paper, we present an approach employing multiobjective optimization to support decision making in forest management planning under risk. The primary objectives are biodiversity and timber cash flow, evaluated from two perspectives: the expected value and the value-at-risk (VaR). In addition, the risk level for both the timber cash flow and biodiversity values are included as objectives. With our approach, we highlight the trade-off between the expected value and the VaR, as well as between the VaRs of the two objectives of interest. We employ an interactive method in which a decision maker iteratively provides preference information to find the most preferred management plan and learns about the interdependencies of the objectives at the same time. The method is illustrated with a case study in which biodiversity is assessed through an index calculated from the characteristics of the forest. Uncertainty is included both through modifying the input data according to the accuracy of current inventory methods and through growth model errors. This uncertainty is described using a set of 25 scenarios. Involving multiple components of risk is a highly relevant approach in multiobjective forestry; however, estimation of the uncertainty of biodiversity needs further attention.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 8
    facet.materialart.
    Unknown
    Small-area estimates using model-based methods (1996)
    Canadian Science Publishing
    Details
    Publication Date: 1996-05-01
    Description: In small areas, the number of sample plots is usually small, and the classical estimators have a large variance. Information from nearby areas can be utilized to improve the subarea estimates using either nonparametric or parametric models. In this study, a number of model-based estimators for small-area estimation are presented. To illustrate the presented methods a numerical example in a real inventory situation is given. The auxiliary information used in this study is pure coordinate information, but the methods are applicable also for other kinds of auxiliary information. The object of this study is to compare the features of the presented small-area estimation methods and to discuss the applicability of these methods in different situations.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 9
    facet.materialart.
    Unknown
    Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution (1998)
    Canadian Science Publishing
    Details
    Publication Date: 1998-08-01
    Description: In the Finnish compartmentwise inventory systems, growing stock is described with means and sums of tree characteristics, such as mean height and basal area, by tree species. In the calculations, growing stock is described in a treewise manner using a diameter distribution predicted from stand variables. The treewise description is needed for several reasons, e.g., for predicting log volumes or stand growth and for analyzing the forest structure. In this study, methods for predicting the basal area diameter distribution based on the k-nearest neighbor (k-nn) regression are compared with methods based on parametric distributions. In the k-nn method, the predicted values for interesting variables are obtained as weighted averages of the values of neighboring observations. Using k-nn based methods, the basal area diameter distribution of a stand is predicted with a weighted average of the distributions of k-nearest neighbors. The methods tested in this study include weighted averages of (i)Weibull distributions of k-nearest neighbors, (ii)distributions of k-nearest neighbors smoothed with the kernel method, and (iii)empirical distributions of the k-nearest neighbors. These methods are compared for the accuracy of stand volume estimation, stand structure description, and stand growth prediction. Methods based on the k-nn regression proved to give a more accurate description of the stand than the parametric methods.
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 10
    facet.materialart.
    Unknown
    Effects of imaging conditions on crown diameter measurements from high-resolution aerial images (2006)
    Canadian Science Publishing
    Details
    Publication Date: 2006-05-01
    Description: Imaging geometry, the structure of the forest, and certain tree properties can cause inaccuracy in image measurements of the crown dimensions of individual trees. Measurement error of the crown diameter was studied in relation to various factors to explain this error. A secondary aim was to generate calibration models for improving the accuracy of crown diameter image measurements. The crown diameters of a total of 715 sample trees in southern Finland were measured in the field and from aerial photographs at scales 1:6000, 1 : 12 000, and 1 : 16 000. The photo grammetric image measurement seemed to systematically underestimate the true crown diameter, and the major factor affecting the bias was tree species. The mean underestimation varied from 0.30 to 0.80 m, with root mean square errors of 0.951.10 m depending on the tree species. Linear regression analysis was employed to define the factors that had an effect on the image measurements, and calibration models in the form of linear regression models were generated. The calibration models worked reasonably well, and the root mean square error for the calibrated observations decreased by 22% for Scots pine (Pinus sylvestris L.), 53% for Norway spruce (Picea abies (L.) Karst), and 47% for silver birch (Betula pendula Roth).
    Print ISSN: 0045-5067
    Electronic ISSN: 1208-6037
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Canadian Science Publishing
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...