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Articles containing the keyword 'National forest inventories'.

Category: Research article

article id 10247, category Research article
Agnese Marcelli, Walter Mattioli, Nicola Puletti, Francesco Chianucci, Damiano Gianelle, Mirko Grotti, Gherardo Chirici, Giovanni D' Amico, Saverio Francini, Davide Travaglini, Lorenzo Fattorini, Piermaria Corona. (2020). Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information. Silva Fennica vol. 54 no. 2 article id 10247. https://doi.org/10.14214/sf.10247
Highlights: A two-phase sampling for large-scale assessment of fast-growing forest crops is developed; Vegetation indices from Sentinel-2 are exploited in a linear regression estimator; The linear regression estimator turns out to be better than the estimator based on the sole sample information; The approach represents a reference for supporting outside-forest resource monitoring and assessment.

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 ORCID ID:E-mail: agnese.marcelli@student.unisi.it (email)
  • 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 ORCID ID:E-mail: walter.mattioli@crea.gov.it
  • Puletti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy ORCID ID:E-mail: nicola.puletti@crea.gov.it
  • Chianucci, CREA, Research Centre for Forestry and Wood, Arezzo, Italy ORCID ID: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 ORCID ID: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 ORCID ID:E-mail: mirkogrotti@gmail.com
  • Chirici, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy ORCID ID:E-mail: gherardo.chirici@unifi.it
  • D' Amico, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy ORCID ID: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 ORCID ID:E-mail: saverio.francini@gmail.com
  • Travaglini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy ORCID ID:E-mail: davide.travaglini@unifi.it
  • Fattorini, University of Siena, Department of Economics and Statistics, Siena, Italy ORCID ID:E-mail: lorenzo.fattorini@unisi.it
  • Corona, CREA, Research Centre for Forestry and Wood, Arezzo, Italy ORCID ID:E-mail: piermaria.corona@crea.gov.it
article id 185, category Research article
Bianca N. I. Eskelson, Tara M. Barrett, Hailemariam Temesgen. (2009). Imputing mean annual change to estimate current forest attributes. Silva Fennica vol. 43 no. 4 article id 185. https://doi.org/10.14214/sf.185
When a temporal trend in forest conditions is present, standard estimates from paneled forest inventories can be biased. Thus methods that use more recent remote sensing data to improve estimates are desired. Paneled inventory data from national forests in Oregon and Washington, U.S.A., were used to explore three nearest neighbor imputation methods to estimate mean annual change of four forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha). The randomForest imputation method outperformed the other imputation approaches in terms of root mean square error. The imputed mean annual change was used to project all panels to a common point in time by multiplying the mean annual change with the length of the growth period between measurements and adding the change estimate to the previously observed measurements of the four forest attributes. The resulting estimates of the mean of the forest attributes at the current point in time outperformed the estimates obtained from the national standard estimator.
  • Eskelson, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA ORCID ID:E-mail: bianca.eskelson@oregonstate.edu (email)
  • Barrett, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA ORCID ID:E-mail:
  • Temesgen, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA ORCID ID:E-mail:
article id 255, category Research article
Claude Vidal, Adrian Lanz, Erkki Tomppo, Klemens Schadauer, Thomas Gschwantner, Lucio di Cosmo, Nicolas Robert. (2008). Establishing forest inventory reference definitions for forest and growing stock: a study towards common reporting. Silva Fennica vol. 42 no. 2 article id 255. https://doi.org/10.14214/sf.255
International agreements such as the Kyoto protocol and Convention on Biological Diversity (1992), as well as, criteria and indicator processes require reports on the status of nations’ forests. Any comparison of the current status and trends of forest resources among nations presumes that the nations’ applied definitions and concepts produce comparable estimates of the status of forests. In spite of this, the FAO has already collected global information for 60 years and made noticeable efforts in creating common definitions, but forest related data are still collected using diverse definitions, even regarding basic concepts such as forest and forest area. A simple consequence is that the cross-countries estimates are not comparable. The reasons behind the differences in the definitions are diverse histories, and sometimes different use of forests. In an ideal case, national forest inventories should fulfil both national and international needs. In addition to the FAO’s Forest Resources Assessment process, other efforts are made to assess the status of forests in European countries, e.g. European Forest Information and Communication System (EFICS). EFICS produced reports about forest inventories but does not suggest any common definition or method to convert estimates from one definition to another one. This article presents principles and methods to create commonly acceptable and adoptable definitions for forest inventories. The principles and methods are demonstrated using two examples: the reference definitions of forest and growing stock. The article is based on the work of COST Action E43 (http://www.metla.fi/eu/cost/e43/).
  • Vidal, Inventaire Forestier National, Château des Barres, Nogent-sur-Vernisson, France ORCID ID:E-mail: claude.vidal@ifn.fr (email)
  • Lanz, WSL/FNP, Abteilung Landschaftsinventuren, Birmensdorf, Switzerland ORCID ID:E-mail:
  • Tomppo, Finnish Forest Research Institute, Vantaa Research Unit, Vantaa, Finland ORCID ID:E-mail:
  • Schadauer, Bundesamt und Forschungszentrum für Wald, Wien, Austria ORCID ID:E-mail:
  • Gschwantner, Bundesamt und Forschungszentrum für Wald, Wien, Austria ORCID ID:E-mail:
  • di Cosmo, ISAFA, Villazzano, Italy ORCID ID:E-mail:
  • Robert, Inventaire Forestier National, Ch‰teau des Barres, Nogent-sur-Vernisson, France ORCID ID:E-mail:
article id 376, category Research article
Nils Lexerød, Trond Eid. (2005). Recruitment models for Norway spruce, Scots pine, birch and other broadleaves in young growth forests in Norway. Silva Fennica vol. 39 no. 3 article id 376. https://doi.org/10.14214/sf.376
The objective of the present study was to develop recruitment models for Norway spruce, Scots pine, birch and other broadleaves in young growth forests in Norway. The models were developed from permanent sample plots established by the National Forest Inventory, and they will be included in a growth simulator that is part of a large-scale forestry scenario model. The modelling was therefore restricted to independent variables directly or indirectly available from inventories for practical forest management planning. A two-stage modelling approach that suited the stochastic nature of recruitment in boreal forests was used. Models predicting the probability of recruitment were estimated in a first stage, and conditional models for the number of recruits were developed in a second. The probability models as well as the conditional models were biologically realistic and logical. The goodness of fit tests revealed that the probability models fitted the data well, while the coefficients of determination for the conditional models were relatively low. No independent test data were available, but comparisons of predicted and observed number of recruits in different sub-groups of the data revealed few large deviations. The high level of large random errors was probably due to the great variability observed in number of recruits rather than inappropriate specifications of the models. Provided the generally high level of uncertainty connected to analysis performed with large-scale forestry scenario models and the stochastic nature of recruitment, the presented models seem to give satisfactory levels of accuracy.
  • Lexerød, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail: nils.lexerod@umb.no (email)
  • 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 ORCID ID:E-mail:

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