Although kind descriptions are the most valuable source of information for analysis, the second level of classification — variant — is important too. There are some differences between the large geographic regions of Russia, which are not reflected inkind descriptions.
For example, there are three main species ofLarix, which form the Siberian larch forests. In the descriptions all of them are addressed as “larch”, but comparisons of thevariant maps (Figure 2) with areas of those species show that they could be almost clearly distinguished on a variant basis.
Therefore avariant-based approach is important for linking of the landscape informa-tion with other sources of informainforma-tion, as described in Secinforma-tion 5.3.
Point
Zoogenic Pathogenic
Harvesting Pollution Recreation Agriculture
dispersion
Without species change
speciesWith change
Degressive Denudational
Alluvial Volcanogenic ....
Forest successions
Climatic-geomorphologic Cenogenic Pyrogenic
Anthropogenic Biogenic
Figure 3: Classification of forest successions.
3 Forest Succession Database Applied to Landscapes
Availability of highly detailed information on natural conditions, which could be obtained from a landscape map, allows to develop spatial databases of forest types and possible trend of forest development, suitable for simulation models of forest growth and carbon balance. This information is also an important tool for biodiversity analyses.
Currently, information about the forest resources in Russia is available from the State Forest Account (SFA). This account presents data per enterprise. However, the forest enterprises can be large and occupy heterogeneous territory, especially in the less populated regions of Siberia.
Therefore it is essential to link statistical data obtained from this account to lesser and more homogeneous territorial units, i.e., landscape kinds.
Another source of forest specific information is the Forest Succession Database, de-veloped by IIASA’s Boreal Forest Resources Project. This latter database has almost no spatial references, but it is based on a forest succession classification, which is very close to the landscape classification in some aspects.
The upper level of this classification on successions is based on the cause of forest change, which can be either natural (e.g., climate change and geomorphological processes) or anthropogenic (e.g., forest harvesting and atmospheric pollution). This level of classi-fication is shown in Figure 3. Some information for these levels, especially for “natural”
(cenogenic and climate-geomorphologic) successions, can be easily obtained from the land-scape kind description.
The next levels of succession classification present the type of development and tree species, which change during succession. There are three types of development: dynamical stability, progress, and degradation (Figure 4).
Species 1 (Pine) Species 2 (Birch)
6 7 8
Age
1 2 3 4 5
Climax (Stable)
Progressive Degressive
Development types and succession phases
Development types
Phases
Figure 4: Dynamic of two tree species ratio during phases of different types of development.
Succession is described as a set of development phases. A phase is defined as a specific period of a succession process which has a definite morphological structure (e.g., secondary birch forest after fire in spruce-fir forest). A phase could be divided into age stages (young, middle-aged, immature, and mature). A complete succession may have a duration of up to a thousand years, including all phases.
Information, which could be obtained from the landscape descriptions, i.e., geomor-phological process, types of forests, distribution of different species, allow us to select few succession types, which could be applicable to this kind of landscape from a long list of succession types, possible in a given ecoregion.
4 Analysis of Non-formalized Text
4.1 Problem of free text
The landscape classification was designed for human reading, not for computerized pro-cessing. Therefore most information is contained in descriptions, which are free-form text and have no explicit structure. This makes database and GIS-processing very difficult, if possible at all. The amount of information contained in the landscape kind description is enormous. The information includes dominant and subdominant landcover type, main vegetation associations, anthropogenic disturbances, relief type, parent material genesis and granulometry and many more attributes.
Therefore it is essential to convert the plain text description into some formalized tabular structure, which can be used for conventional database processing.
This conversion inevitably causes a loss of information, because information in human readable text is contained not only in words themselves, but also in the order of words.
The amount of alternatives (for example, alternative tree species) is also important in order to estimate the significance of these alternatives.
But there are also some aspects that will simplify the problem of text formalization, and therefore make this work possible at all.
1. The various properties of the landscapes are listed in all descriptions in the same order (relief type first, then other geology and geomorphology information, followed by parent material genesis, then landcover and vegetation types from dominant to least dominant).
2. The set of words used in the descriptions is highly standardized. There are only 2,767 distinct words, including the grammatic forms.
3. The grammatic form of the words normally have a well-established meaning. This can be counted as benefit of the Russian language due to the fact that in Russian information can often be derived from word suffixes.
Therefore, the following steps of analysis are used:
1. Separation of the description into parts concerning geological and biological compo-nents of landscape.
2. Classification of words in each part and replacing words of written language in all their various forms with a fixed set of terms.
3. Creation of a list of terms, which occurs in each description with their relative weight.
Sets of such lists conform some constraints which are usually applied to databases and thus can be used in GIS processing, and other computer-based applications.
The achieved result differs from the original goal of converting textual descriptions into a relational database, but have some advantages.
All the information of original description is retained, because all operations above are applied to the original legend text.
Not only structured information, but also technology and a set of software tools are provided, so if research with totally different goals is carried out, this approach could be applied again.