Current issue: 57(2)
Under compilation: 57(3)
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.
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.