Map form information on forest biomass is required for estimating bioenergy potentials and monitoring carbon stocks. In Finland, the growing stock of forests is monitored using multisource forest inventory, where variables are estimated in the form of thematic maps and area statistics by combining information of field measurements, satellite images and other digital map data. In this study, we used the multi-source forest inventory methodology for estimating forest biomass characteristics. The biomass variables were estimated for national forest inventory field plots on the basis of measured tree variables. The plot-level biomass estimates were used as reference data for satellite image interpretation. The estimates produced by satellite image interpretation were tested by cross-validation. The results indicate that the method for producing biomass maps on the basis of biomass models and satellite image interpretation is operationally feasible. Furthermore, the accuracy of the estimates of biomass variables is similar or even higher than that of traditional growing stock volume estimates. The technique presented here can be applied, for example, in estimating biomass resources or in the inventory of greenhouse gases.

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Cultivated organic soils can be a major source of GHG emissions in countries with high coverage of peat soils. Targeting mitigation measures based on mapping of cultivated organic soils would reduce these emissions and increase sustainability of agriculture. Different georeferenced datasets were combined to study the area trend and describe current agricultural use of organic soils. The area was also mapped regionally into classes based on intensity of cultivation and organic layer depth, and an example allocation of potential mitigation measures was made at the country scale. The area and proportion of cultivated organic soils have increased in Finland since 1990 but the clearance rate has decreased in recent years. More than half of the area retains a peat layer deeper than 0.6 m indicating long-lasting mitigation potential with measures capable of slowing peat decomposition. Sixty-five percent of the cultivated organic soil area was not considered a priority area for radical management changes, for various reasons, but there are 85,000 ha of field with more realistic potential for GHG mitigation. The mapping method was found to be a practical tool for depicting the GHG mitigation potential of cultivated organic soils. Significant reductions in agricultural GHG emissions can be expected with implementation of the suggested mitigation measures.

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Satellite sensor data have traditionally been used in multi-source forest inventory for estimating forest characteristics. Their advantages generally are large geographic coverage and large spectral range. Another remote sensing data source for forest inventories offering a large geographic coverage is high altitude aerial photography. In high altitude aerial photographs the spectral range is very narrow but the spatial resolution is high. This allows the extraction of texture features for forest inventory purposes. In this study we utilized a Landsat 7 ETM satellite image, a photo mosaic composed of high altitude panchromatic aerial photographs, and a combination of the aforementioned in estimating forest attributes for an area covering approximately 281 000 ha in Forestry Centre Häme-Uusimaa in Southern Finland. Sample plots of 9th National Forest Inventory (NFI9) were used as field data. In the estimation, 6 Landsat 7 ETM image channels were used. For aerial photographs, 4 image channels were composed from the spectral averages and texture features. In combining both data sources, 6 Landsat channels and 3 aerial image texture channels were selected for the analysis. The accuracy of forest estimates based on the Landsat image was better than that of estimates based on high altitude aerial photographs. On the other hand, using the combination of Landsat ETM spectral features and textural features on high altitude aerial photographs improved the estimation accuracy of most forest attributes.