×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Title
Mapping without the sun
Author
Zhang, Jixian

177
NOAA AVHRR data is relatively low, to obtain higher
categorized area precision, and it needs to use the pixel
unmixing method to make the precision of pixel classifications
reach the level of the sub-pixel.
Fig.4 Structure of BP Neural Network
Up to now, the methods of the remote sensing image
classifications include the traditional supervising classification
(such as the parallel algorithm, the minimum distance
algorithm and the maximum likelihood method),
ISODATA( iterative self-org-anizing data analysis technique)
not supervising classification, the latest fuzzy classification,
expert system classification and neural network
classification(Sun Hong-yu et al,2003). Furthermore, the pixel
unmixing method includes message extracting methods for sub
pixel imaging based on linear models, geometrical optics model,
geometirc model at random, probability model, fuzzy model,
etc.
BP neural network is a neural network model adopting the back
propagation learning algorithm. Nowadays it is applied on
many fields. The application of images classification and pixel
unmixing method also make heavy effect(Wang Xi-peng et
al, 1998). A typical BP neural network is made up of three
neuron layers, which are the input layer, the output layer and
the implicating layer (as shown in Fig. 4).
This text on the basis of NDVI data and the third band AVF1RR
data with the elevation data of the Yellow River source and the
temperature precipitation data as the input of BP neural
network, and the auto-correlation characteristics of
geographical space and geography experts knowledge and field
measuring data as the training reference of BP nerve network,
presents some exercises on the image classification of the
neural networks and the pixel unmixing function, and
demonstrates the classifying process of the land cover type of
the Yellow River source. Among them, the selection of the land
cover type consults the description of the land cover type of
Qinghai-Tibet Plateau made by Zhengyi Wu. Finally, we obtain
the categorized result of the land cover type of the Yellow
River source in 1990- 2000 years, as shown in Table 2.
The value region of NDVI reflects the whole trend of area
changes of land coverage. Also in the table 2 the changes of
area of land coverage are very distinct. Among which the area
of all kinds of meadow types show the distinctly decreasing
trend while the desert type shows increasing tend. However, the
changes of area of grassland and desert grassland in different
altitude are different. The area of alpine grassland and the
representative grassland decreased while the ones of alpine
desert and alpine grassland, alpine desert, subalpine grassland
and subalpine desert grassland show the increasing trend. The
area of desert grassland in lower altitude did not change too
much.
Tablet 3 is the correlation modulus between the changes of land
coverage and the changes of climate factors. The tablet shows
that there is strong negative relation between the land coverage
of alpine meadow, alpine grassland, subalpine meadow,
meadow and grassland and annual average temperature and
annual highest temperature. While between the area of alpine
grassland and desert grassland, alpine desert, subalpine desert
grassland, subalpine desert and desert and the annual average
temperature and annual highest temperature, there exists a
strong positive correlation. It suggests that the increasement of
temperature is a very important inducement of meadow
degeneration. However, the strong positive correlation between
annual average temperature and annual highest temperature and
the area of subalpine grassland indicates the increase of
temperature accelerates the growth of the vegetation in
subalpine area.
The relations between precipitation and the different land
coverageage type are not accordant. The coverage area of
meadow shows great negative correlation with precipitation
which indicates the more precipitation could lessen the value of
NDVI. While between alpine grassland, subalpine grassland,
steepe and precipitation, there is a positive correlation which
suggests the increase of precipitation could improve the
environment of vegetation growth in normal coverage land.
Meanwhile, all types of desert area have a weak negative
correlation with precipitation which indicates that including
temperature the decrease of precipitation also promote land
desertification.