5.3 Radar Remote Sensing of Forests
Valery Pershikov, Boris HajrulinSummary
This work deals with classification methods of remote sensing and tasks arising in choosing either of these methods for practical purposes. A great deal of experimental work on radar of forests has been carried out. In these experiments depolarization effect of electromagnetic waves is used as informative factor for making classification.
5.3.1 Introduction
Lately considerable progress has been made in remote sensing of the earth surface [1 ). The main problems in radar remote sensing of forests:
l ) It is necessary to carry out theoretical researches to define the model of the scattering process of electromagnetic waves on forest-cover;
2) Standard radar sensors need not be used any longer, instead,there is a strong demand to concentrate on designing more perspective sensors to increase informative capacity of a final result;
3) It is necessary to keep on searching the ways to define more strict statistical ties between model parameters of forest-cover and radar reflection character;
4 ) It is necessary to solve the problem that concerns "lighting" of radar image correction;
5 ) Quantitative measurement of vegetation cover biometrical parameters according to radar image requires developing of common calibration system to quarantee diverse data comparison.
The present paper makes an attempt to solve these problems.
Methods of remote sensing of earth surface condition and vegetation cover that are used nowadays are very diverse. We believe the methods remote sensing can be classified as it is shown in Fig. 1.
Here four levels are singled out: the 1st - according their activity; the 2nd - on their spectral band; the 3d - on the way of information receiving; the 4th - on the type of sensors. Choosing either of these methods is determined by the character of tasks to be solved.
It should be noted that active methods have some advantages over passive ones.
1 ) It is possible to control the parameters of radiation (it is important) and obtain more information in active methods.
2) Dynamic band of signals at an inlet of antenna is rather large and is about 80 dB.
3) Radar remote sensing permits to carry on observations in spite of metereological situation and lighting up. It provides the possibility to enlarge the cycle of observations for earth surface condition.
Methods of Remote Sensing
Active Passive
I
I
P hoto-I
U ltra- red VH F- M u l t i -
Radar Laser Srund graphy Radiometry spectral
I
Radar Image Other Types Single Zone Multy Zones
I I I I
Usual Synthesis Dispersion Diffraction M i r r o r Optical
Ape rture Aperture P ri s m Screen System F i l t e r
Fig. 1. Classification of the methods of remote sensing.
5.3.2 Radar System Description
The system used in this investigation is the 10 GHz (3.2 cm rn wavelength) aircraft radar; a simplified block diagram of which is shown in Fig. 2.
It is known that if "scene" is lighted up by the electromagnetic wave of fixed polarization then the scattered field will be represented as a random vector process E(x), where x is the generalized co- ordinate. This process includes 2 ortogonal-polarized constituents E1 and Ei
E(x) = E1(x)e1 + E2(x)e2 (1)
where e2 is a complex art polarization, ortogonal to
e
1.
Therefore when having 2 radar image in a certain polarized basis it is possible to synthesise a new image according to algorithm
(2)
-
Antenna-
Chanal 1(_
Tra nsmitter
...
Switch...
E14 •
� Synchronous
Chanal 2
\._-
E2 .L E 1 Mixer 1.... -
Heterodyne- ..
Mixer•
2, ,
AIF 1 AIF 2
,, ,,
Videoampl. 1 Videoampl. 2
....
Ratio E2 / E1....
�,
Fig. 2. Simplified block diagram of the experimental system.
It is known [2] that polarization transformations of scattered electromagnetic wave by forest cover depends upon geometric form of scatters. Geometric form of scatters defines the formula of species composition and that is the key factor for desiphering of radar images (according their polarization properties).
5.3.3 Discussion of Results
We are of the opinion the desipher technology of radar images must include the following stages.
1 ) Picking out the fragments of radar images for analysis, their geometrical correction, matching of images and scaling are resolved at this stage.
2) This stage deals with improving images by filtering. Figure 3 shown the original radar images in linear ortogonal basis after filtering (Fig. 3a vertical polarized constituent, Fig. 3b cross
polarized constituent).
3 ) Synthesis of radar image in accordance with algorithm is carried at this stage 2. Fig. 4 shows the synthesised image of original images shown in Fig. 3. This image shows a detailed inner structure of polarization change and hence, the change of species constituents of forest vegetation.
4) Now, it is possible to make a classifier find out of composition species determination according to algorithm
p = i = l
I
WiPl (3)where Wj is probability of known uniform species composition growing, Pl polarized relation 1- woody species.
According to (3) it is possible to get a set of training models that are typical specimen of a definite class of cover.
Table 1 . List of the operating parameters.
Transmitter
Receivers
Antennas
Radiated frequency Modulating waveform Pulsewidth
Pulse repeat frequency Polarization
Transmitted peak power
Dynamical range Sensitivity
Type
Separation on polarization Recording film
Number of chanels Width of recorded strip Average film speed
Radar Parameters
9.4 GHz Pulse 2.5 microsec 400 Hz Horiz.transmit
Horiz.receive(hh)
Vert.transmitHoriz.reccive(vh) 9 kW
60 dB 100 dB
Running wave About 18 dB
2 35 mm 0.1 mm/sec
a)
Fig. 3. Radar images (a-vertical polarization, b-cross-polarization).
Fig. 4. Synthesised radar image.
5.3.4 References
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HAJRULIN, B.; PERSHIKOV, V., 1994: Radar Response to Forests: Theory and Application (in this issue).
SWAIN, P.H.; DAVIS, S.M. (eds.) 1 978: Remote Sensing: The Quantitative Approach. McGraw-Hill lnt.Book Company, 415 pp.
b)
