Ecoinformatics and Soil-Plant Formations - New Ecoinformatics Tools in Environmental Science: Applications and Decision-making - page 649

As shown in Figs. 1.9 and 1.11 , the GIMS comprises 30 or 12 models for the
SPF. The list of SPFs given in Table 1.11 was adopted to spatial resolution of
4

cation. The results
obtained by Krapivin et al. (1982) were used to synthesize these models. All of
these are based on Eq. ( 8.1 ) for the balance of the biomass Qi(φ, i (

°× 5 ° according to Bazilivich and Rodin (1967) classi

φ , ˈ ,t). The actual

plant productivity is approximated as follows:

exp b i = ;

i
c

i
e ; d

i
Z ; d

i
W ; d

i
B

i

R i ¼ d 1 þ a

T D

T = 100 ð Q i Þ min d

i
T

where a and ʲ i are indices of dependence of production on the temperature var-

i
f

iation ʔ T and biomass Qi, i , respectively; d is the index of limitation of production

by the factor : e = illumination, Z = pollution, W = soil moisture, B = nutrient salts

of the soil and c = atmosphere CO 2 concentration. The

ʶ

i
f

functions actually used in
the framework of real situations are calculated based on existing or preliminary
receiving data. Thus, the role of the atmospheric CO 2 concentration C A in photo-
synthesis is described by the equation

d

, where C i 0 : 5

i
c ¼ b i C A =

C A þ C i 0 : 5 d is the

i

CO 2 concentration for which d

c ¼ b i =

2. The in fl uence of the solar radiation

intensity e( ˆ , ˈ , t) on photosynthesis is parameterized by the relation

i
e ¼ d

i exp ð 1 d i Þ , where i ¼ e

e i and e i is the optimal illumination for ith type
of plant. A more detailed description of the correlations between the biocenotic
processes is given by Kondratyev et al. (2002).

d d =

8.3.3 Classi cation of Soil-Plant Formations

and Their Archive

Figure 1.9 gives an example of the ecological subdivision of the SPFs taking into
account many abiotic factors. From the standpoint of microwave monitoring, the set
of principal vegetation features play a signi

cant role in the solution of the elec-
tromagnetic wave attenuation problem. It is known that there are three priority
factors for each type of vegetation cover: wet biomass Q, productivity R and water
content ˁ P . These factors are functions of the climatic zones. Therefore, the

assessment of the impact of climate change on the potential productivity of the
vegetation requires a model development to describe the vegetation dynamics.
There exist many approaches toward solving this task. Some classi

cations are

based on the re ection of the principle variables describing the structure of the

ground ecosystem: land cover and soil types, phenology, biomass, soil moisture and
temperature pro

fl

cations take into account the vertical
structure of the ecosystems (layered ecosystems), their age structure, canopy
chemistry, disturbance, decomposition, degree of anaerobiosis of the ecosystem,
rate of succession, ecosystem stratification, ratio of C 3 - C 4 plants, etc. It is clear that
the classi

les. High precision classi

cation of the vegetation cover is a complex problem of biocenology. The