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Articles containing the keyword 'predictions'

Category : Article

article id 5592, category Article
Ari Talkkari. (1996). Regional predictions concerning the effects of climate change on forests in southern Finland. Silva Fennica vol. 30 no. 2–3 article id 5592. https://doi.org/10.14214/sf.a9237
Keywords: climate change; Finland; simulation; growing stock; wood production; Gap model; regional predictions; cutting yield
Abstract | View details | Full text in PDF | Author Info

A gap-model was used with forest inventory data in taking ground-true site, soil and tree characteristics into account in predicting the effects of climate change on forests. A total of 910 permanent sample plots established in the course of national forest inventory (NFI) in Finland and located on mineral soil sites in southern Finland were selected as the input data. The climatological input used in the simulations consisted of interpolated means of and deviations from long-term local temperature and precipitation records. The policy-oriented climate scenarios of SILMU (Finnish Research Programme on Climate Change) were used to describe the climate change. The temperature changes in the climate scenarios were increases of ca. +1.1 °C (low), +4.4 °C (medium) and +6.6 °C (high) compared to the current climate in 110 years. The simulation period was 110 years covering the time years 1990–2100.

Southern Finland, divided into fifteen forestry board districts, was used as the study region. Regional development of stand volume, cutting yield, and total wood production of forests under different climate scenarios were examined. The annual average growth in simulations under current climate was close to that observed in NFL Forests benefited from a modest temperature increase (Scenario 2), but under Scenario 1 the growing stock remained at a lower level than under the current climate in all parts of the study region. In wood production and cutting yield there were regional differences. In the southern part of the study regional wood production under Scenario 1 was ca. 10% lower than under the current climate, but in the eastern and western parts wood production was 5–15% higher under Scenario 1 than under the current climate. The relative values of total wood production and cutting yield indicated that the response of forests to climate change varied by geographical location and the magnitude of climate change. This may be a consequence of not just varying climatic (e.g. temperature and precipitation) and site conditions, but of varying responses by different kind of forests (e.g. forests differing in tree species composition and age).

  • Talkkari, E-mail: at@mm.unknown (email)

Category : Research article

article id 10573, category Research article
Jari Miina, Inka Bohlin, Torgny Lind, Jonas Dahlgren, Kari Härkönen, Tuula Packalen, Anne Tolvanen. (2021). Lessons learned from assessing the cover and yield of bilberry and lingonberry using the national forest inventories in Finland and Sweden. Silva Fennica vol. 55 no. 5 article id 10573. https://doi.org/10.14214/sf.10573
Keywords: forest management planning; berry models; field measurements; predictions
Highlights: Model-based predictions of the berry yields of an average crop year are produced using the Finnish National Forest Inventory (NFI); Inventory-based estimates of seasonal berry yields are produced using the Swedish NFI observations; The inventory-based method provides seasonal estimates, whereas models can be utilised to integrate vegetation cover and berry yields in numerical multi-objective forest planning.
Abstract | Full text in HTML | Full text in PDF | Author Info

Bilberry (Vaccinium myrtillus L.) and lingonberry (V. vitis-idaea L.) can be a part of healthy diet and are important for many animals. Two approaches are described to assessing their vegetation cover and berry yield via national forest inventory (NFI) observations. The aim was to provide estimates and predictions of the abundance and yield of the species at regional and national levels in Finland and Sweden. In Finland, the model-based predictions are used in evaluating the impacts of cutting intensity on forest berries needed in forest-related decision making. In Sweden, seasonal inventory-based estimates are used to evaluate the annual national and regional berry yields, and in a forecasting system aimed at large public and berry enterprises. Based on the NFI sample plots measured between 2014 and 2018, the total annual yields are estimated to be 208 Mkg of bilberry and 246 Mkg of lingonberry on productive forest land (increment at least 1 m3 ha–1 year–1) in Finland, and 336 and 382 Mkg respectively in Sweden (average of NFI inventories in 2015–2019). The predicted development of berry yields is related to the intensity of cuttings in alternative forest management scenarios: lower removals favoured bilberry, and higher removals lingonberry. The model-based method describes the effects of stand development and management on berry yields, whereas the inventory-based method can calibrate seasonal estimates through field observations. In providing spatially and timely more accurate information concerning seasonal berry yields, an assessment of berry yields should involve the elements of both inventory-based and model-based approaches described in this study.

  • Miina, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland ORCID https://orcid.org/0000-0002-8639-4383 E-mail: jari.miina@luke.fi (email)
  • Bohlin, Swedish University of Agricultural sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, S-901 83 Umeå, Sweden E-mail: inka.bohlin@slu.se
  • Lind, Swedish University of Agricultural sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, S-901 83 Umeå, Sweden E-mail: torgny.lind@slu.se
  • Dahlgren, Swedish University of Agricultural sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, S-901 83 Umeå, Sweden E-mail: jonas.dahlgren@slu.se
  • Härkönen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: kari.harkonen@luke.fi
  • Packalen, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland; Ministry of Agriculture and Forestry of Finland, P.O. Box 30, FI-00023 Government, Finland E-mail: tuula.packalen@mmm.fi
  • Tolvanen, Natural Resources Institute Finland (Luke), Paavo Havaksentie 3, FI-90014 University of Oulu, Finland E-mail: anne.tolvanen@luke.fi
article id 595, category Research article
Kenneth Nyström, Göran Ståhl. (2001). Forecasting probability distributions of forest yield allowing for a Bayesian approach to management planning. Silva Fennica vol. 35 no. 2 article id 595. https://doi.org/10.14214/sf.595
Keywords: basal area growth model; mixed-model; uncertainty of predictions; Monte Carlo simulation
Abstract | View details | Full text in PDF | Author Info
Probability distributions of stand basal area were predicted and evaluated in young mixed stands of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and birch (Betula pendula Roth and Betula pubescens Ehrh.) in Sweden. Based on an extensive survey of young stands, individual tree basal area growth models were estimated using a mixed model approach to account for dependencies in data and derive the variance/covariance components needed. While most of the stands were reinventoried only once, a subset of the stands was revisited a second time. This subset was used to evaluate the accuracy of the predicted stand basal area distributions. Predicting distributions of forest yield, rather than point estimates, allows for a Bayesian approach to planning and decisions can be made with due regard to the quality of the information.
  • Nyström, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: kenneth.nystrom@resgeom.slu.se (email)
  • Ståhl, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: gs@nn.se
article id 618, category Research article
Steen Magnussen, Paul Boudewyn, Mike Wulder, David Seemann. (2000). Predictions of forest inventory cover type proportions using Landsat TM. Silva Fennica vol. 34 no. 4 article id 618. https://doi.org/10.14214/sf.618
Keywords: neural net; maximum likelihood classification; agreement of predictions
Abstract | View details | Full text in PDF | Author Info
The feasibility of generating via Landsat TM data current estimates of cover type proportions for areas lacking this information in the national forest inventory was explored by a case study in New Brunswick. A recent forest management inventory covering 4196 km2 in south-eastern New Brunswick (the test area) and a coregistered Landsat TM scene was used to develop predictive models of 12 cover type proportions in an adjacent 4525 km2 region (the validation area). Four prediction models were considered, one using a maximum likelihood classifier (MLC), and three using the proportions of 30 TM clusters as predictors. The MLC was superior for non-vegetated cover types while a neural net or a prorating of cluster proportions was chosen for predicting vegetated cover types. Most predictions generated for national inventory photo-plots of 2 x 2 km were closer to the most recent inventory results than estimates extrapolated from the test area. Agreement between predictions and current inventory results varied considerably among cover types with model-based predictions outperforming, on average, the simple spatial extensions by about 14%. In this region, an 11-year-old forest inventory for the validation area provided estimates that in half the cases were closer to current inventory estimates than predictions using the optimal Landsat TM model. A strong temporal correlation of photo-plot-level cover type proportions made old-values more consistent than predictions using the optimal Landsat TM model in all but three cases. Prorating of cluster proportions holds promise for large-scale multi-sensor predictions of forest inventory cover types.
  • Magnussen, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: smagnussen@pfc.forestry.ca (email)
  • Boudewyn, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: pb@nn.ca
  • Wulder, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: mw@nn.ca
  • Seemann, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: ds@nn.ca

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