Forest Resources Study in Mongolia
using Remote Sensing and GIS
Forest Change Analyses
At the beginning the taxonomical maps and AFA photographs of the study area were converted to digital form by the use f CCD came and scanjet Plus scanner, respectively. Then, screen digitizing of the maps was done using the DIGSCRN’ – module. To detect the major forest changes, an air photograph of 1963 and a taxonomical map of 1963 were compared with the air photograph of 1975 and taxonomical map of 1988.
Figure 2 Comparison of multitemporal data
The analysis indicated, that the fire, ripe age deciduous forest was restored and instead, the young age forest has been formed. To investigate the further restoration process we have used Landsat – TM, SPOT-XS and SPOT-PAN data taken in different seasons.
By analysis of multitemporal air photos and satellite information anthropogenic influence as forest fires, felling, cattle herding etc the modern condition of the forest ecosystems have been detected.
SPOT-XS and Landsat-TM data were classified into coniferous, deciduous & mixed forest area and in further the coniferous was classified into subclassed by area. For the quality improvement of Landsat-TM and SPOT-XS a high spatial resolution of SPOT-PAN has been used. To do this, at first both multispectral data were normalized and then multiplied by panchromatic data.
The further analyses require a creation of a DEM because it was necessary to see the forest changes and burned area from various sides by different angles for this purpose, contours were digitized from the topographic map scale 1:50,000 and then interpolated. The created DEM integrated with SPOT-XS image which indicates the burned areas, is shown in figure 3.
Figure 3 DEM and detection of the burned area
For the identification of the forest areas the following techniques have been applied to the RS images:
1. Normalized Difference Vegetation Index (NDV) maps were estimated for the SPOT-XS as
NDVI = (b3 – b2)/(b3 + b20
NDVI = (b2*b3/b1**b2
and for the Landsat TM as
NDVI = (b4 – b3) / (b3 + b4)
NDVI = b2 **2/b1*b3
NDVI = b3 * b4/b2**2, etc
More details about application of this technique are described in (3). Data cloud of SPOT-XS data was almost in the origin and was rotated 24 degree. Fro the intensity of TM data 6 bands, excluding the thermal band have been used and all the transformations applied. For the final affine transformation of the images the following matrices were used:
3. Principle Component Analysis (PCA) for TM data and the Following PCs were
PCI=89.21%; PC2=6.8%; PC3=2.21%; PC4=1.11%; PC5 =0.72%; PC6= 0.04%
4. Rational and supervised classification using maximum likelihood decision rule. Almost same procedures used in (3) were used here.
Colour differences in the images indicate such forms of the forest spreads, like missed forest, deciduous forest, coniferous forest, reservoir and pasture. After applying saturation enhancement in TM data different hues of the coniferous forest have been formed.
Analysis of the hues indicated that there is the correlation between the colour tones age classes. Thus, in the image 4 groups of age classes : your, ripe old and pasture/deciduous have been distinguished.
As seen from the analyses, multitemporal air and satellite data together with other cartographic and ground information can be effectively used for the study of the condition of the forest ecosystem.
In result of the digital image processing, GIS analyses and visual interpretation of the available RS data it is concluded that primary satellite data (Scale 1:100, 000 and less) can be used to detect the structure of the forest formation and macro-geocomplexes, where as RS data with enlarged scale (Scale 1:50,000 n larger) can be used to interpret the structure of classes and groups of forest associations as well as meso and microgeocomplexes (2).
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