Ecoinformatics and Soil-Plant Formations - New Ecoinformatics Tools in Environmental Science: Applications and Decision-making - page 712

8.9 Land Cover Classi cations

Remote sensing resources, image interpretation and classi cation tools provide land

cover classi cation using remotely sensed data. There exist different options for

conducting land cover classi

cation including types of imagery, methods and

algorithms, and classi cation process depends on

the monitoring data types and quality. It is important to have the possibility to
distinguish each class of vegetation on the map and to clearly define and distinct it
from other classes. The most important problem is the forest ecosystem classifi-

cation schemes. The image classi

-
cation. The solution of the tasks arising here depends on the methods and algo-
rithms for remote sensing data. Table 8.16 gives the list of basic indicative
characteristics of forest ecosystems evaluated on the base of remote sensing of the
forest. Table 8.17 enumerates the most known vegetative indexes used in the
remote sensing monitoring. A vegetation index is a number that is generated by a
combination of remote sensing bands and may have some relationship to the
amount of vegetation in a given image pixel.
As far as the climate change and sustainable development problems are con-
cerned, forest ecology plays more important role than other branches of ecology.
Forests are studied at a number of organizational levels, from individual organisms

Table 8.16 List of basic indicative characteristics of the forest ecosystems evaluated on the
remote sensing data
Characteristic Sensors Spatial

resolution

Periodicity

Soil-plant formation type, area and age Optical,

microwave

1 km 5 years

Tree diameter, tree height, canopy
biomass, parameters of the forest
ecosystem models

Optical,
microwave

0.5 - 30 m 5 years

Soil type and moisture Optical,

microwave

1 - 50 rm Monthly

Forest canopy state: defoliation and color
change

Optical 1 30 m Monthly

-

Characteristics of the disturbing impacts
on the forest: type of the in

Optical,
microwave

1 20 m Depending on the

impact type

-

uence, area
and level of the damage, time of the
event, velocity of the vegetation recovery

fl

Biophysical characteristics: LAI, NDVI,
FPAR, chlorophyll concentration

Optical 0.25 Daily or monthly

depending on the
solved task

-
1km

Phenological characteristics: snow cover
timeline, depth and water content of the
snow cover, vegetative period long time

Optical,
microwave

1 - 50 km Monthly

Surface characteristics: albedo and
temperature

Optical,
microwave

1 50 km Daily

-