Articles containing the keyword 'forest inventories'

A calculation procedure is presented for calculating and analysing remeasured permanent sample plots. Data for eight different fixed and variable size plot types were simulated on the basis of two stands whose trees were mapped and measured in 1982 and 1986. The accuracy and efficiency of the plot types were assessed and compared.

The calculation procedure is based on tree-wise expansion factors and the division of tree sampled into state/measurement classes. Nine classes were required for variable size plots and six for fixed size plots. A relascope plot with basal-area factor 1 (m²/ha) proved to be most efficient for estimating basal-area at a given time and a fixed size circular plot with radius 10 m for estimating basal-area increment over a given time period.

The main problems were related to the estimation of non-measurable variables, e.g., the initial diameters of ingrowth trees, i.e., trees having passed the threshold size during the measurement period. Most problematic were cut trees belonging to the ingrowth or sample enlargement classes. It is nevertheless thought that the system is appropriate for monitoring forest changes and making sensitivity analyses with permanent sample plots.

• Poso, E-mail: sp@mm.unknown
• Waite, E-mail: mw@mm.unknown

Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.

• Kangas, E-mail: ak@mm.unknown
• Korhonen, E-mail: kk@mm.unknown

In this study, model-based and design-based inference methods are used for estimating mean volume and its standard error for systematic cluster sampling. Results obtained with models are compared to results obtained with classical methods. The data are from the Finnish National Forest Inventory. The variation of volume in ten forestry board districts in Southern Finland is studied. The variation is divided into two components: trend and correlated random errors. The effect of the trend and the covariance structure on the obtained mean volume and standard error estimates is discussed. The larger the coefficient of determination of the trend model, the smaller the model-based estimates of standard error, when compared to classical estimates. On the other hand, the wider the range and level of autocorrelation between the sample plots, the larger the model-based estimates of standard error.

• Kangas, E-mail: ak@mm.unknown

Regression models for estimating stem volume of Scots pine (Pinus sylvestris L.) were constructed using sample tree data measured in the 7th and 8th National Forest Inventory of Finland. Stem volume were regressed on diameter, basal area of growing stock, and geographic location. The results of the study show that using second order trend surface to describe the geographic variation of the residuals gives satisfactory results. Using mixed estimation for combining old and new sample tree data improves the efficiency of an inventory. The weight of the prior information must be low, because remarkable differences in stem form was found in the two inventories.

The PDF includes an abstract in Finnish.

• Korhonen, E-mail: kk@mm.unknown

Model-based information systems have proved valuable planning tools for analysing the production possibilities of forests as well as for understanding forest resources dynamics, stand management practices and forest economics. Computerized forest models implemented in the users’ information systems facilitate the transfer and application of research results in practical forestry.

Conclusions and visions concerning modelling are drawn from experiences in developing the MELA system and its application in solving timber production problems on both the national and forest holding level in Finland. The precondition for predicting forest resource dynamics and for planning the utilization of forests is to accept conditions, uncertainties and a restricted period of time.

The interactive process of forest resource, growth and drain monitoring, and forest management planning supported by forest research and modelling, are the means to enable an operational information base for a dynamic regulation and adaptation strategy for forest resource management under changing conditions and uncertainty.

The PDF includes an abstract in Finnish.

• Siitonen, E-mail: ms@mm.unknown

Models for estimating the upper diameter of trees were constructed using sample tree data measured in the 7th National Forest Inventory in Finland. Calibration of the models was tested with data from the 8th National Forest Inventory. The results showed that using mixed estimation for combining the two data sets improves the reliability of the models. Models and methods used in this study can be recommended for use in forest inventories.

The PDF includes an abstract in Finnish.

• Korhonen, E-mail: kk@mm.unknown

Old inventory data has widely been used as prior information in forest inventory using the method of sampling with partial replacement (SPR). In this method knowledge about forest growth has not been utilized. However, the accuracy of the inventory results can be improved if this knowledge is utilized. The usability of the inventory results can be improved if the prior information is updated by treewise growth models. In this paper a statistical basis is presented for a method in which such information can be used. The applicability of the method is also discussed. An example is given to demonstrate the method.

The PDF includes an abstract in Finnish.

• Kangas, E-mail: ak@mm.unknown

Correlation functions of the mean volume, land use class and soil class were estimated using the data of the Finnish National Forest Inventory. Estimated functions were used for approximating the standard error of e.g. the mean volume of a cluster of plots. Standard error estimates can be used for comparing different inventory designs.

The PDF includes an abstract in Finnish.

• Korhonen, E-mail: kk@mm.unknown
• Maltamo, E-mail: mm@mm.unknown

Three replacement strategies in continuous forest inventory of the Enso-Gutzeit Company have been presented and discussed. The first strategy adopts data from only the last two inventory occasions; the second strategy employs data from all four occasions, in which there are two groups of permanent plots measured on the first three occasions and independently on the last two occasions; the third strategy also utilizes data from all four occasions, but includes only one group permanent plots measured on all four occasions. Results indicate that the last strategy is best for efficiency. The difference between the first two strategies is small. 

The PDF includes an abstract in Finnish.

• Peng, E-mail: sp@mm.unknown

The paper presents a method based on two phase sampling and applicable to forest inventories. The first phase estimates are obtained from satellite imagery and, if required, from extra material such as maps. Second phase estimates are measured in the field. The method is flexible and also applicable to compartmentwise forest inventories. The experiments were based on six study areas with 439 relascope plots. The correlation coefficients between first and second stage estimates varied largely according to the study area.

The PDF includes a summary in Finnish.

• Poso, E-mail: sp@mm.unknown
• Häme, E-mail: th@mm.unknown
• Paananen, E-mail: rp@mm.unknown

Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed.

• Marcelli, University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems, Viterbo, Italy; Fondazione Edmund Mach, Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, Italy E-mail: agnese.marcelli@student.unisi.it
• Mattioli, University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems, Viterbo, Italy; CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: walter.mattioli@crea.gov.it
• Puletti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: nicola.puletti@crea.gov.it
• Chianucci, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: fchianucci@gmail.com
• Gianelle, Fondazione Edmund Mach, Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, Italy E-mail: damiano.gianelle@fmach.it
• Grotti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy; University of Roma La Sapienza, Department of Architecture and Design, Rome, Italy E-mail: mirkogrotti@gmail.com
• Chirici, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: gherardo.chirici@unifi.it
• D' Amico, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: giovanni.damico@unifi.it
• Francini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy; University of Molise, Department of Agricultural, Environmental and Food Sciences, Campobasso, Italy E-mail: saverio.francini@gmail.com
• Travaglini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: davide.travaglini@unifi.it
• Fattorini, University of Siena, Department of Economics and Statistics, Siena, Italy E-mail: lorenzo.fattorini@unisi.it
• Corona, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: piermaria.corona@crea.gov.it

• Laamanen, Metsähallitus, Vantaa, Finland E-mail: risto.laamanen@metsa.fi
• Kangas, Metsähallitus, Vantaa, Finland E-mail: ak@nn.fi

• Eskelson, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: bianca.eskelson@oregonstate.edu
• Barrett, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: tmb@nn.us
• Temesgen, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: ht@nn.us

• Vidal, Inventaire Forestier National, Château des Barres, Nogent-sur-Vernisson, France E-mail: claude.vidal@ifn.fr
• Lanz, WSL/FNP, Abteilung Landschaftsinventuren, Birmensdorf, Switzerland E-mail: al@nn.ch
• Tomppo, Finnish Forest Research Institute, Vantaa Research Unit, Vantaa, Finland E-mail: et@nn.fi
• Schadauer, Bundesamt und Forschungszentrum für Wald, Wien, Austria E-mail: ks@nn.at
• Gschwantner, Bundesamt und Forschungszentrum für Wald, Wien, Austria E-mail: tg@nn.at
• di Cosmo, ISAFA, Villazzano, Italy E-mail: ldc@nn.it
• Robert, Inventaire Forestier National, Ch‰teau des Barres, Nogent-sur-Vernisson, France E-mail: nr@nn.fr

• Lexerød, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway E-mail: nils.lexerod@umb.no
• Eid, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway. E-mail nils.lexerod@umb.no E-mail: te@nn.no