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Articles containing the keyword 'maps'

Category : Article

article id 7362, category Article
Olavi Linnamies. (1942). Metsätalouskartaston soveltaminen sotilastarkoituksiin. Acta Forestalia Fennica vol. 50 no. 9 article id 7362. https://doi.org/10.14214/aff.7362
English title: Use of forest maps in military purposes in Finland.
Original keywords: Metsähallitus; Suomi; kartastot; sotilaskartat; metsätalouskartat
English keywords: forestry; Finland; Forest Service; maps; cartographic material; military maps; forestry maps
Abstract | View details | Full text in PDF | Author Info

The Second World War revealed some weaknesses that affect also peacetime planning of military defence in Finland. One of the shortages were lack of maps applicable in military purposes in Northern Finland.

The state forests are mainly situated in the north. Consequently, cartographic material of Finnish Forest Service may be modified with little extra work for military purposes. Best suited for the purpose are forestry maps of different forest districts that have scales ranging from 1:20,000 to 1:100,000. In addition, general maps in the scale of 1:100,000 or 1:200,000 are available. The article discusses further the additions that can be made in the maps.

The PDF includes a summary in German.

  • Linnamies, E-mail: ol@mm.unknown (email)
article id 7505, category Article
Rauno Väisänen, Kari Heliövaara. (1994). Assessment of insect occurrence in boreal forests based on satellite imagery and field measurements. Acta Forestalia Fennica no. 243 article id 7505. https://doi.org/10.14214/aff.7505
Keywords: biodiversity; remote sensing; insect pests; geological maps; Scolytids; logistic regression models
Abstract | View details | Full text in PDF | Author Info

The presence/absence data of 27 forest insect taxa (Retinia resinella, Formica spp., Pissodes spp., several scolytids) and recorded environmental variation were used to investigate the applicability of modelling insect occurrence based on satellite imagery. The sampling was based on 1,800 sample plots (25 m by 25 m) placed along the sides of 30 equilateral triangles (side 1 km) in a fragmented forest area (approximately 100 km2) in Evo, Southern Finland. The triangles were overlaid on land use maps interpreted from satellite images (Landsat TM 30 m multispectral scanner imagery 1991) and digitized geological maps. Insect occurrence was explained using either environmental variables measured in the field or those interpreted from the land use and geological maps. The fit of logistic regression models carried between species, possibly because some species may be associated with characteristics of single trees while other species with stand characteristics. The occurrence of certain insect species at least, especially those associated with Scots pine, could be relatively accurately assessed indirectly on the basis of satellite imagery and geological maps. Models based on both remotely sensed and geological data better predicted the distribution of forest insects except in the case of Xylechinus pilosus, Dryocetes sp. and Trypodendron lineatum, where the differences were relatively small in favour of the models based on field measurements. The number of species was related to habitat compartment size and distance from the habitat edge calculated from the land use maps, but logistic regressions suggested that other environmental variables in general masked the effect of these variables in species occurrence at the present scale.

  • Väisänen, E-mail: rv@mm.unknown (email)
  • Heliövaara, E-mail: kh@mm.unknown

Category : Research article

article id 975, category Research article
Renats Trubins, Ola Sallnäs. (2014). Categorical mapping from estimates of continuous forest attributes – classification and accuracy. Silva Fennica vol. 48 no. 2 article id 975. https://doi.org/10.14214/sf.975
Keywords: Sweden; land cover maps; forest type maps; map accuracy assessment; class membership probability; Bayesian network; k-NN estimates
Highlights: The paper presents an approach to classification and accuracy assessment of ad-hoc categorical maps based on existing spatial datasets with estimates of continuous forest variables; Pixel level class membership probabilities are estimated using a Bayesian network model.
Abstract | Full text in HTML | Full text in PDF | Author Info
Spatially explicit data on forest attributes is demanded for various research with landscape perspective. Existing datasets with estimates of continuous forest variables are often used as the basis for producing categorical forest type maps. Normally, this type of maps are used without knowing their accuracy. This paper presents a Bayesian network model for estimating pixel level class membership probabilities of thus derived categorical maps. Class membership probabilities can be used as a post-classification measure of map accuracy and in the process of map classification affecting the assignments of class labels. The method is applied in mapping deciduous dominated forests on the basis of the k-NN Sweden 2005 dataset in a study area in southern Sweden. The results indicate rather low accuracy for deciduous class regardless of the map classification method: 0.48 versus 0.50 in the maps classified without and with the use of the class membership probabilities given equal deciduous area. When probability-based classification is applied, the level of accuracy varies with the assumed map class proportions. Thus, when deciduous class area corresponding to the National Forest Inventory estimate was used, the accuracy of only 0.35 was obtained for the deciduous map class.
  • Trubins, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 230 53 Alnarp, Sweden E-mail: renats.trubins@slu.se (email)
  • Sallnäs, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 230 53 Alnarp, Sweden E-mail: ola.sallnas@slu.se
article id 458, category Research article
Sakari Tuominen, Kalle Eerikäinen, Anett Schibalski, Markus Haakana, Aleksi Lehtonen. (2010). Mapping biomass variables with a multi-source forest inventory technique. Silva Fennica vol. 44 no. 1 article id 458. https://doi.org/10.14214/sf.458
Keywords: National Forest Inventory; remote sensing; biomass models; biomass maps
Abstract | View details | Full text in PDF | Author Info
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 multi-source 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.
  • Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi (email)
  • Eerikäinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ke@nn.fi
  • Schibalski, University of Potsdam, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam, Germany E-mail: as@nn.de
  • Haakana, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mh@nn.fi
  • Lehtonen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: al@nn.fi

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