Current issue: 57(2)

Under compilation: 57(3)

Scopus CiteScore 2021: 2.8
Scopus ranking of open access forestry journals: 8th
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Silva Fennica 1926-1997
Acta Forestalia Fennica

Articles containing the keyword 'forest area'

Category: Article

article id 7437, category Article
Kustaa Kallio. (1954). Asutustoiminnassa muodostettujen viljelystilojen metsämaa-alojen suuruussuhteista. Acta Forestalia Fennica vol. 61 no. 25 article id 7437.
English title: Area of wood lots of the farms established in connection with settlement in Finland.
Original keywords: metsätalous; yksityismetsät; metsämaa; asutus; kotitarvepuu; asutustilat; viljelytilat; tilat; metsäpinta-ala
English keywords: forestry; forest area; private forests; state-owned lands; settlement; farms; household timber; forest holdings
Abstract | View details | Full text in PDF | Author Info

In Finland a large land reform has been accomplished which has increased the number of small farms and forest holdings by over 100,000. It is estimated that 4-5 million ha of forest land has been transferred to these smallholdings. The aim of this investigation was to study the areas of the wood lots of the farms established in connection to settlement activities during the time Finland has been independent.

The study shows that the farms established on the state-owned lands have been given forest areas big enough to enable them timber sales, provided that the forests were in a moderately good silvicultural condition. Relatively largest forest areas have been given to farms established from tenant farms. The farms established on private lands have got in average forest areas that are smaller than would be required for growing of household timber. In Southern Finland the area has been adequate, but in Northern Finland too small in part of the farms. Also, variation in the size of the farms has been larger. The farms established under the Land Acquisition Act have been given in average more than the principle of according to which half of the forests should be suitable for cultivation of household timber and half for timber sales.

The Acta Forestalia Fennica issue 61 was published in honour of professor Eino Saari’s 60th birthday.

The PDF includes a summary in German.

  • Kallio, E-mail: kk@mm.unknown (email)

Category: Research article

article id 1405, category Research article
Lauri Korhonen, Daniela Ali-Sisto, Timo Tokola. (2015). Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data. Silva Fennica vol. 49 no. 5 article id 1405.
Keywords: logistic regression; beta regression; forest area; international forest definition; ALOS AVNIR-2; vegetation index
Highlights: The fusion of airborne lidar data and satellite images enables accurate canopy cover mapping; The zero-and-one inflated beta regression is demonstrated in large area estimation; Forest/non-forest classification should be done directly, for example by using logistic regression.
Abstract | Full text in HTML | Full text in PDF | Author Info

The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R2 = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa.

  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; (current) University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID E-mail: (email)
  • Ali-Sisto, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Tokola, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland. E-mail:

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