- Email: [email protected]
10. Airborne Line-Scanning in the 0.3-8 μm Region
246
10.
AIRBORNE LINE-SCANNING IN THE 0.3-8
pm REGION
Airborne scanning is treated separately from spaceborne or satellite scanning, since it differs from the latter in enabling observation at low altitudes and consequently it may show differences in spatial and spectral resolution. The spatial resolution of a scanning system is determined by its IFOV and the speed of detection in relation to the speed of the platform ( s e e par.
6.1).
The speed of platform and groundpass of spaceborne platforms is
normally taken higher than that of airborne platforms. The same can be stated for the speed of detection. Therefore, airborne scanning permits the use of relatively long observation times per pixel. Although narrow band data at low spatial resolution can be acquired from spaceborne platforms, the use of relatively narrow bands at high spatial resolution is applicable to airborne platforms only.
If these bands are chosen carefully with regard to their
spectral allocation, they may provide much specific information on terrain features. Of course, the general advantage of scanners, which is the provision of quantitative data on reflectance or emittance, is valid for arrborne as well as for satellite scanning. For the principles of airborne line-scanning, the reader is referred to par. 4 . 3 and to Lowe ( 1 9 7 5 ) .
Imagery from airborne line-scanners is briefly
discussed in par. 6 . 4 , the processing of digital data in par. 5.2-5.4.
10.1. Airborne line-scanners
The airborne line-scanners operating in the Visible and Infrared may be divided into (Higham e.a., a)
1973):
monospectral line scanners, which are generally modifications of military hardware; they usually operate in the Infrared region of the spectrum and many are uncalibrated systems suitable for qualitative sensing only; integral film recorders are usually present, which are not suitable for subsequent automatic data processing; uncalibrated systems are a.0.
EM1
Airscan, HSD Linescan, Texas Instruments RS 310, De Oude Delft Linescan and Reconofax; systems with black body reference are the Bendix TM/LN 3 and TI RS 18; b)
modified monospectral line scanners to obtain at least one additlonal channel; this is usually achieved by the insertion in some part of the
241
optical system of a dichroic mirror to split the beam into two spectral regions e.g.
Daedalus DS 1220/30, Texas Instruments RS 14,
SAT Super
Cyclops; c)
multispectral line scanners with limited spectral selection generally in the Visible through the use of an integrated array of silicon photodiodes e.g.
Daedalus with DS-1250
(analog) and DS-1260 (digital) scanners, and
Bendix M2S; this type of system may also be extended into the Infrared by the addition of a so-called dichroic mirror; d)
multispectral line scanners with full selection capability over the whole Visible and Infrared region of the spectrum; this is obtained in the Bendix
MSDS
by
using
dichroic
mirrors
and
two
separate
grating
spectrometers for the Visible and Infrared. A s an illustration of (c),
the channels of the DS-1260
spectrometer are given
below: 1. 2. 3.
0.38-0.42 0.42-0.45 0.45-0.50
pm um pm
4. 5. 6.
0.50-0.55
0.55-0.60
0.60-0.65
urn prn UIII
7. 8. 9. 10.
0.65-0.69 0.70-0.79 0.80-0.89 0.92-1.10
& .I!
~rm
pm
In the example, the channels have a bandwidth of 50 nm for the central wavelength range (channels 3 up to 6 ) . The Bendix MSDS (d) differs from the DS1260 in covering a wider spectral range, being part of the UV, the Visible as
well as the Infrared up to 1 3 um. The Bendix MSDS 24 channel allocation is detailed below: Channel Number 1 2 3 4 5 6 7 8 9 10 11 12
Bandwidth (micrometers) 0.34 - 0.4 0.4 - 0.44 0.46 - 0.5 0.53 - 0.57 0.57 - 0.63 0.64 - 0.68 0.71 - 0.75 0.76 - 0.80 0.82 - 0.87 0.97 - 1.05 1.18 - 1.30 1.52 - 1.73
Channel Number
Bandwidth (micrometers)
13 14 15 16 17 18 19 20 21 22 23 24
2.1 - 2.36 3.54 - 4.0 4.5 - 4.75 6.0 - 7.0 8.3 - 8.8 8.8 - 9.3 9.3 - 9.8 10.1 - 11.0 11.0 - 12.0 12.0 - 13.0 1.12 - 1.16 1.05 - 1.09
248
The Bendix MSDS has been developed for research and is not available on the market.
The most advanced system on today's market is the Daedalus eleven
channel multispectral scanner DS-1268,
also known as the Airborne Thematic
It was developed in 1981 as a modification of the DS-1260.
Mapper (ATM).
The
system covers the bands used by the Landsat 4 Thematic Mapper, the Landsat 3
MSS and the SPOT System: wavelength um
channel
channel wavelength vm
1.
0.42 -0.45
7.
0.76-0.90
2.
0.45
-0.52
8.
0.91-1.05
3.
0.52 -0.60
9.
1.55-1.75
4.
0.605-0.625
10.
2.08-2.35
5.
0.63 -0.69
11.
8.50- 1 3 .OO
6.
0.695-0.75
10.2.
Detection in the Ultraviolet 10
About
percent
of
the
solar EMR
is incident on the earth's
that
atmosphere is in the Ultraviolet portion of the EMS. The atmosphere strongly attenuates the Ultraviolet at wavelengths shorter than 0 .2 8 urn, primarily due to Rayleigh scattering and absorption by ozone and molecular oxygen. Optical mechanical scanners using mirrors made of aluminum o r of silver metal films (Halter, 1973),
UV filters and UV sensitive detectors produce imagery of fair
quality. An example is the Daedalus DEI-238 UV-Visible detector. The low intensity of Ultraviolet radiation incident at the earth's surface and the
strong
atmospheric
influence
cause
the
information
content
of
the
Ultraviolet to be low when compared with that of the Visible and the Infrared. However, despite these limitations, some targets exhibit contrasts in the Ultraviolet that may be more useful than those obtained in other regions. In the near
Ultraviolet (read near
to Visible o r
wavelength region of the Visible spectrum (blue),
0.3-0.4
pm)
and
the short
the carbonates, phosphates
and evaporites are usually more reflective than other rock materials. Acidic rocks, such as granite and rhyolite, show little reflection in the Ultraviolet but considerable reflection in the Visible, while basic rocks such as basalt show little reflection in both regions (Cronin et al., 1973). Data are also available ahout the penetration of Ultraviolet radiation in soil materials. Coarse textured (dry)
Soil materials show deeper penetration than
249
fine textured (dry) soil materials (Cronin et al., 1973). 10.3. Detection in the Visible zone and near Infrared In chapter 3 the interaction of solar radiation with minerals, rocks, soils and plants is discussed. In summary, the following is stated about reflectance of soils and plants. Soils:
-
the general pattern reveals an increasing reflectance from 0.5 l m ~ to 2.5 um ; contrast between soils may be obtained in the 0.4-0.5
0.6-0.7
-
um and 1.7-2.5
um the
um regions;
increase of organic matter content and moisture content result in decreasing reflectance over broad spectral regions;
-
information about iron content may be obtained from a broad band at 1.1
urn, and weak bands at
0.87
!im
and in the Visible;
- H20 is indicated by bands at 1.4
~.lm and
1.9 um; OH-
by a band at
2.2 pm;
-
carbonate and gypsum are indicated by bands between 1.7 m and 2.5
um
(strong absorption due to the presence of Cog" at 2.35 m; gypsum shows a band at 1.75 pm). The
so-called
artificial
H20
bands
illumination
are
in
the
applicable
for moisture
laboratory.
Under
determination with
natural
conditions
the
radiation at 1.4 um and 1.9 pm is absorbed by atmospheric H20, which makes their application in remote sensing complicated. Plants:
-
reflection in 0.55 pm band and of near Infrared radiation; absorption in 0.44 um and 0.66 wn bands; damage
affecting
morphology
results
reflectance especially of near
in
a
decrease
of
overall
h f rared; a change in physiology
involves a shift of the green peak towards yellow wavelengths; a final change results in a shift towards red wavelengths. Besides by the reflectance characteristics of the materials, the choice of channels in remote sensing has to be directed by the wavelength regions as indicated by the major atmospheric windows (given by Lintz and Simonett, 1976). These are:
250 0.40-0.75
um
1.19-1.34
pm
0.77-0.91 1.00-1.12
um
1.55-1.75
pm
pm
2.05-2.40
pm
From the information given above, the following optimum channels for airborne scanning in detection of soils and plants are suggested (Mulders, 1986) :
information content
allocation hands in pm 0.5 3-0.58
green reflectance of plants
0.58-0.63
yellow reflectance of soils and plants
0.6 3-0.68
red absorptance by plants, contrast in
0.84-0.90
maximum NIR reflectance of plants, iron
soil reflectances content of soils 1.20- 1.30
reference value plants
1.60-1.68
reference value soils
1.72-1.78 2.10-2.25
gypsum layer silicates
2.32-2.38
carbonate.
Airborne scanning, using the suggested channels, will provide optimum contrast between soils and plants as well as between different soils and canopy types. However, there is no general agreement about the choice of channels. Tucker (1976)
found in his study on the reflectance of blue grama grass, the
spectral regions of 0.37-0.50, significant both early and
0.63-0.69
and 0.75-0.80
pm to be statistically
late in the growing season. Of these spectral
regions, only the second is given above in the selection on channels. Much agreement is found with the work of Bunnik (1978),
who considered the
influence of changing canopy morphology, and the effect of a dry o r moist bounding soil, for optimum selection of spectral bands to discriminate between different green plant canopies. He proposed four spectral bands with centre wavelength positions at 550,
670,
870 and 1650 nm. The optimum selection is
based on the determination of maximum between class separation in the feature space, defined by a minimum number of spectral bands selected within the available atmospheric windows.
251 Present scanning systems are not directed in their choice of channels as suggested above
OK
do show only part of these channels. Further testing of the
informative value of the suggested channels is necessary, especially in the near Infrared 1.72-178
>
urn and
1.60 pm, since only few data are available (e.g. 2.32-2.38
pm).
Besides bands at 0.45-0.50
n .!n
the bands wn,
and 0.85-0.95
the 2.2 um band was pointed out to be valuable for the inventory of hydrothermally altered rocks (Rowan et al., 1977). Abrams et al.,
(1977)
used data of the Rendix scanner for the delineation of
altered rocks. The following ratios were used: expressed in channel numbers, or 1.6/2.2, in approximate centers of channels in
12/13,
1.6/0.48
12/3
and 0.6/1.0
and 5/10 when when expressed
um.
The dynamics of soil moisture have to be evaluated in determining the soil potential in rainfed agriculture. For this purpose, multitemporal reflectance data may be used effectively. Mc Culloch et al., (1975) considered the use of changes in reflectance of soil-vegetation units to detect changes in soil moisture more promising than thermal scanning, though the relationship would have to be derived for a large number of combinations. Both clay and sandy soils show a large decrease in reflectance over the 0.5-2.6
wn
region at
increasing moisture content (see Johannsen, 1969). Apart from application in the field of soil moisture mapping, airborne MSS has also been used for distinguishing freshly tilled soil from crusted surface soil. The surface roughness can be evaluated from reflectance data taken under different angles of illumination (e.g. different times of day). Furthermore, airborne Multispectral Scanning (MSS) has been used to examine soils with a moderate content of organic matter. For this purpose, Roth and Baumgardner
(1971)
studied a
soil test
area of
approximately 45
ha
in
Tippecanoe County (Indiana), lying in a transitional zone between Alfisols and Mollisols. They found a high correlation between multispectral response with the content of
organic matter
in the upper cm of soil. Since automated
processing is of great importance in the study of MSS data, the method used by Roth and Baumgardner is discussed as an example. In their study, the size of the training set for computer implemented analysis of multispectral data had an important effect on the correlation. A high rate of digitization gave much greater correlation coefficient values than does a low rate of digitization. Furthermore, the selection and number of channels had a profound influence. In stepwise regression analysis, the charnlel 0.66-0.72
252
was
the single best channel for predicting organic matter content in all
training set sizes, except for the single remote sensing unit ( R S U ) . channels
0.40-0.44
urn and
0.50-0.52
Dm
were
also
generally
high
in
The the
selection of the best two or three channels. Some details of the method are given below. Sampling:
the field was gridded at intervals of 46 m; at each grid-point a 1 kg surface soil sample was taken at a depth of up to 1 cm; the organic matter content was determined by a modified Walkley Black method. MSS data: May 6, 1 9 7 0 , altitude 1 0 0 0 m; 6 channels in the 0.40-1.00 wn range; RSU approximately 9 m2. Low digitization rate (LDR): every eighth scan line was digitized at the rate of 220 samples per scan line. High digitization rate (HDR): every third scan line was digitized at the rate of 440 samples per scan line. Size of training sets: 1 , 4 , 9, 2 5 , 6 4 , 100 and 144 RSU; the 25 RSU training set size produced maximum correlation with LDR data. The channels pointed out for predicting organic matter should be further tested for their value in other soil conditions. The correlation may be negative. That is, the absence of organic matter may be indicated by a high reflectance in a particular channel due to the absence of masking of soil material with specific properties. In that case, high content of organic matter would produce a low reflectance in that channel. Generally, reflectance data indicate the presence or absence of particular soil materials rather than the absolute contents of those materials. This will be due to the effect of the type of the materials e.g. fine textured accumulations. coatings
on
the
mineral
mineral grains or very
For example, iron or lime may be present as
grains
and
exert
a
stronger
influence on
the
reflectance values than would be expected from their real contents. On the contrary
from
uncoated
mineral
occurrences,
high
correlations
between
reflectance values and contents may be produced. Finally, attention is drawn to the research need for:
-
the study of the soil reflection model;
-
polarization techniques in discrimination between dry and moist soil
-
back and forward scattering zones (see par.
surfaces; 2.6
and par.
3.2),
for
example at the outer sides of large angle fields of view in airborne
253
scanning for discrimination of highly absorbent and highly reflectant features. 10.4.
Detection in the mid Infrared The mid Infrared shows two atmospheric windows which enable remote sensing
(Fig. 2 . 1 2 ) ,
these being: 3.4-4.1
and 4.5-5.2
um.
The information potential of these bands (Tables 2.3 and 2.4) is as follows: bands
information potential
(pm)
3.4-4.1
C-H, C - H 2 ,
C-H3 (organic matter)
4.5-5.2
oxides, S i - 0 bending
The value of these bands for remote sensing has to be tested in future (Mulders et al., 1 9 8 6 ) .
10.5.
Conclusions Line
scanners
may
be
distinguished
roughly
into
monospectral
and
multispectral line scanners. The information content of the Ultraviolet window is low when compared with ,the Visible and Infrared. However, some targets such as carbonates, phosphates and evaporites exhibit higher reflectances in the Ultraviolet than in the Visible. A number of channels can be selected on the basis of spectral properties of soils and plants as well as on the allocation of the atmospheric windows. Application of airborne MSS in soil survey is found in acquisition of spectral signatures of soil surfaces and in discrimination of moist and dry soil surfaces. It is especially in arid and semi-arid regions where the soil is barely covered that airborne MSS is expected to be of great value for soil survey when applied in combination with
airphoto-interpretation. In other
regions, MSS may give much information about soil conditions at the time that there are large areas of bare soil (e.g.
extensive cotton or grain fields out
of the growing season). Much research is necessary to explore the high potential information content of airborne MSS. The results may be used for inventory and monitoring of the environment at a large scale, as well as for the improvement of satellite MSS techniques.
254
10.6.
References
Abrams, M.J., Ashley, R.P., Rowan, L.C., Goetz, A.F.H. and Kahle, A.B., 1977. Use of Imaging in the 0.46-2.36 um Spectral Region for Alteration Mapping in the Cuprite Mining District, Nevada. U.S. Geol. Survey. Open-file Report 77-585. Bunnik, N.J.J., 1978. The Multispectral Reflectance of shortwave Radiation by Agricultural Crops in Relation with their Morphological and Optical Properties. Thesis Agricultural University, Wageningen, The Netherlands: 176 pp. Cronin, J.F., Rooney, T.P. e.a., 1973. Ultraviolet Radiation and the Terrestrial Surface. In the Surveillant Science. Remote Sensing of the Environment (ed. by R.K. Holz), Houghton Mifflin Cy, Boston: pp. 67-77. Higham, A.C., Wilkinson, B. and Kahn, D., 1973. Multispectral Scanning System and their potential Application to Earth Resources Surveys. Basic Physics & Technology, ESRO CR-231: 186 pp. Holter, M.R., 1973. Ultraviolet Imaging. In the Surveillant Science. Remote Sensing of the Environment (ed. by R.K. Holz), Houghton Mifflin Cy, Boston: pp. 78-82. Johannsen, C.J., 1969. The detection of available s o i l moisture by remote sensing techniques. Ph.D. Thesis, Purdue University: 266 pp. Lintz, J.Jr and Simonett, D.S., 1976. Remote Sensing of Environment. AddisonWesley Publ. Cy, Reading, Massachusetts: 694 pp. Lowe, D.S., 1975. Imaging and Nonimaging Sensors. Chapter 8 i n Manual of Remote Sensing. her. SOC. of Photogrammetry, Falls Church, Virginia: pp. 367397. Mc Culloch, J.S.G., Painter, R.B., 1975. Application of Multispectral Scanning Systems to Hydrology. In ESRO CR-234, Plessey, United Kingdom: pp. 127149. Mulders, M.A., 1986. Band Selection in Multispectral Scanning for S o i l Survey of Arid Zones. Proc. ISSS hth intern. Symposium Remote Sensing for soil Survey. March 1985 (Wageningen, Enschede). ITC Journal, Enschede, The Netherlands. Mulders, M.A., Schurer, K., Jong, A.N. de, Hoop, D. de, 1986. Selection of Bands for a newly developed Multispectral Airborne Reference-aided Calibrated Scanner (MARCS). Proc. ISPRS Congress August 1986, Enschede, The Netherlands; pp. 301-303. Roth, C.B. and Baumgardner, M.F., 1971. Correlation Studies with Ground Truth and Multispectral Data: Effect of Size of Training Field. 7th Symposium Remote Sensing Michigan: 12 pp. Rowan, L.C., Goetz, A.F.H. and Ashley, R.P., 1977. Discrimination of Hydrothermally Altered and Unaltered Rocks i n Visible and Near Infrared Multispectral Images. Geophysics, Vol. 42, No 3: pp. 522-535. Tucker, C.J., 1976. Sensor Design for Monitoring Vegetation Canopies. Photogrammetric Engineering and Remote Sensing, Vol 42, No. 11: pp. 13991410. 10.7. Additional reading Heide, G. van der and Koolen, A.J., 1980. Soil Surface Albedo and Multispectral Reflectance of short-wave Radiation at a Function of Degree of Soil Slaking. Neth. J. Agric. Sci 28: pp. 252-258. LARS, 1968. Remote Multispectral Sensing i n Agriculture. Annual Report Vol. no. 3. Laboratory for Agricultural Remote Sensing (LARS). Purdue Univ.,
255
Indiana: 175 pp. LARS, 1970. Item Annual Report, Vol. no. 4: 112 pp. Lillesand, T.M. and Kiefer, R.W., 1979. Remote Sensing and Image Interpretation. John Wiley & Sons, New York: 612 pp. Polcyn, F.C., Spansail, N.A. and Mulida, W.A., 1973. How Multispectral Sensing can help the Ecologist. In The Surveillant Science. Remote Sensing of the Environment (ed. by R.K. Holz). Houghton Mifflin Cy, Boston: pp. 349-359. Sabins, F.F. Jr, 1978. Remote Sensing. Principles and Interpretation. W.H. Freeman and Cy, San Francisco: 426 pp. Savigear, R.A.G., 1975. An Approach to the Evaluation of Multispectral Scanning Systems. In ESRO CR-234, Plessey, United Kingdom: pp. 7-50.
10.
AIRBORNE LINE-SCANNING IN THE 0.3-8
pm REGION
Airborne scanning is treated separately from spaceborne or satellite scanning, since it differs from the latter in enabling observation at low altitudes and consequently it may show differences in spatial and spectral resolution. The spatial resolution of a scanning system is determined by its IFOV and the speed of detection in relation to the speed of the platform ( s e e par.
6.1).
The speed of platform and groundpass of spaceborne platforms is
normally taken higher than that of airborne platforms. The same can be stated for the speed of detection. Therefore, airborne scanning permits the use of relatively long observation times per pixel. Although narrow band data at low spatial resolution can be acquired from spaceborne platforms, the use of relatively narrow bands at high spatial resolution is applicable to airborne platforms only.
If these bands are chosen carefully with regard to their
spectral allocation, they may provide much specific information on terrain features. Of course, the general advantage of scanners, which is the provision of quantitative data on reflectance or emittance, is valid for arrborne as well as for satellite scanning. For the principles of airborne line-scanning, the reader is referred to par. 4 . 3 and to Lowe ( 1 9 7 5 ) .
Imagery from airborne line-scanners is briefly
discussed in par. 6 . 4 , the processing of digital data in par. 5.2-5.4.
10.1. Airborne line-scanners
The airborne line-scanners operating in the Visible and Infrared may be divided into (Higham e.a., a)
1973):
monospectral line scanners, which are generally modifications of military hardware; they usually operate in the Infrared region of the spectrum and many are uncalibrated systems suitable for qualitative sensing only; integral film recorders are usually present, which are not suitable for subsequent automatic data processing; uncalibrated systems are a.0.
EM1
Airscan, HSD Linescan, Texas Instruments RS 310, De Oude Delft Linescan and Reconofax; systems with black body reference are the Bendix TM/LN 3 and TI RS 18; b)
modified monospectral line scanners to obtain at least one additlonal channel; this is usually achieved by the insertion in some part of the
241
optical system of a dichroic mirror to split the beam into two spectral regions e.g.
Daedalus DS 1220/30, Texas Instruments RS 14,
SAT Super
Cyclops; c)
multispectral line scanners with limited spectral selection generally in the Visible through the use of an integrated array of silicon photodiodes e.g.
Daedalus with DS-1250
(analog) and DS-1260 (digital) scanners, and
Bendix M2S; this type of system may also be extended into the Infrared by the addition of a so-called dichroic mirror; d)
multispectral line scanners with full selection capability over the whole Visible and Infrared region of the spectrum; this is obtained in the Bendix
MSDS
by
using
dichroic
mirrors
and
two
separate
grating
spectrometers for the Visible and Infrared. A s an illustration of (c),
the channels of the DS-1260
spectrometer are given
below: 1. 2. 3.
0.38-0.42 0.42-0.45 0.45-0.50
pm um pm
4. 5. 6.
0.50-0.55
0.55-0.60
0.60-0.65
urn prn UIII
7. 8. 9. 10.
0.65-0.69 0.70-0.79 0.80-0.89 0.92-1.10
& .I!
~rm
pm
In the example, the channels have a bandwidth of 50 nm for the central wavelength range (channels 3 up to 6 ) . The Bendix MSDS (d) differs from the DS1260 in covering a wider spectral range, being part of the UV, the Visible as
well as the Infrared up to 1 3 um. The Bendix MSDS 24 channel allocation is detailed below: Channel Number 1 2 3 4 5 6 7 8 9 10 11 12
Bandwidth (micrometers) 0.34 - 0.4 0.4 - 0.44 0.46 - 0.5 0.53 - 0.57 0.57 - 0.63 0.64 - 0.68 0.71 - 0.75 0.76 - 0.80 0.82 - 0.87 0.97 - 1.05 1.18 - 1.30 1.52 - 1.73
Channel Number
Bandwidth (micrometers)
13 14 15 16 17 18 19 20 21 22 23 24
2.1 - 2.36 3.54 - 4.0 4.5 - 4.75 6.0 - 7.0 8.3 - 8.8 8.8 - 9.3 9.3 - 9.8 10.1 - 11.0 11.0 - 12.0 12.0 - 13.0 1.12 - 1.16 1.05 - 1.09
248
The Bendix MSDS has been developed for research and is not available on the market.
The most advanced system on today's market is the Daedalus eleven
channel multispectral scanner DS-1268,
also known as the Airborne Thematic
It was developed in 1981 as a modification of the DS-1260.
Mapper (ATM).
The
system covers the bands used by the Landsat 4 Thematic Mapper, the Landsat 3
MSS and the SPOT System: wavelength um
channel
channel wavelength vm
1.
0.42 -0.45
7.
0.76-0.90
2.
0.45
-0.52
8.
0.91-1.05
3.
0.52 -0.60
9.
1.55-1.75
4.
0.605-0.625
10.
2.08-2.35
5.
0.63 -0.69
11.
8.50- 1 3 .OO
6.
0.695-0.75
10.2.
Detection in the Ultraviolet 10
About
percent
of
the
solar EMR
is incident on the earth's
that
atmosphere is in the Ultraviolet portion of the EMS. The atmosphere strongly attenuates the Ultraviolet at wavelengths shorter than 0 .2 8 urn, primarily due to Rayleigh scattering and absorption by ozone and molecular oxygen. Optical mechanical scanners using mirrors made of aluminum o r of silver metal films (Halter, 1973),
UV filters and UV sensitive detectors produce imagery of fair
quality. An example is the Daedalus DEI-238 UV-Visible detector. The low intensity of Ultraviolet radiation incident at the earth's surface and the
strong
atmospheric
influence
cause
the
information
content
of
the
Ultraviolet to be low when compared with that of the Visible and the Infrared. However, despite these limitations, some targets exhibit contrasts in the Ultraviolet that may be more useful than those obtained in other regions. In the near
Ultraviolet (read near
to Visible o r
wavelength region of the Visible spectrum (blue),
0.3-0.4
pm)
and
the short
the carbonates, phosphates
and evaporites are usually more reflective than other rock materials. Acidic rocks, such as granite and rhyolite, show little reflection in the Ultraviolet but considerable reflection in the Visible, while basic rocks such as basalt show little reflection in both regions (Cronin et al., 1973). Data are also available ahout the penetration of Ultraviolet radiation in soil materials. Coarse textured (dry)
Soil materials show deeper penetration than
249
fine textured (dry) soil materials (Cronin et al., 1973). 10.3. Detection in the Visible zone and near Infrared In chapter 3 the interaction of solar radiation with minerals, rocks, soils and plants is discussed. In summary, the following is stated about reflectance of soils and plants. Soils:
-
the general pattern reveals an increasing reflectance from 0.5 l m ~ to 2.5 um ; contrast between soils may be obtained in the 0.4-0.5
0.6-0.7
-
um and 1.7-2.5
um the
um regions;
increase of organic matter content and moisture content result in decreasing reflectance over broad spectral regions;
-
information about iron content may be obtained from a broad band at 1.1
urn, and weak bands at
0.87
!im
and in the Visible;
- H20 is indicated by bands at 1.4
~.lm and
1.9 um; OH-
by a band at
2.2 pm;
-
carbonate and gypsum are indicated by bands between 1.7 m and 2.5
um
(strong absorption due to the presence of Cog" at 2.35 m; gypsum shows a band at 1.75 pm). The
so-called
artificial
H20
bands
illumination
are
in
the
applicable
for moisture
laboratory.
Under
determination with
natural
conditions
the
radiation at 1.4 um and 1.9 pm is absorbed by atmospheric H20, which makes their application in remote sensing complicated. Plants:
-
reflection in 0.55 pm band and of near Infrared radiation; absorption in 0.44 um and 0.66 wn bands; damage
affecting
morphology
results
reflectance especially of near
in
a
decrease
of
overall
h f rared; a change in physiology
involves a shift of the green peak towards yellow wavelengths; a final change results in a shift towards red wavelengths. Besides by the reflectance characteristics of the materials, the choice of channels in remote sensing has to be directed by the wavelength regions as indicated by the major atmospheric windows (given by Lintz and Simonett, 1976). These are:
250 0.40-0.75
um
1.19-1.34
pm
0.77-0.91 1.00-1.12
um
1.55-1.75
pm
pm
2.05-2.40
pm
From the information given above, the following optimum channels for airborne scanning in detection of soils and plants are suggested (Mulders, 1986) :
information content
allocation hands in pm 0.5 3-0.58
green reflectance of plants
0.58-0.63
yellow reflectance of soils and plants
0.6 3-0.68
red absorptance by plants, contrast in
0.84-0.90
maximum NIR reflectance of plants, iron
soil reflectances content of soils 1.20- 1.30
reference value plants
1.60-1.68
reference value soils
1.72-1.78 2.10-2.25
gypsum layer silicates
2.32-2.38
carbonate.
Airborne scanning, using the suggested channels, will provide optimum contrast between soils and plants as well as between different soils and canopy types. However, there is no general agreement about the choice of channels. Tucker (1976)
found in his study on the reflectance of blue grama grass, the
spectral regions of 0.37-0.50, significant both early and
0.63-0.69
and 0.75-0.80
pm to be statistically
late in the growing season. Of these spectral
regions, only the second is given above in the selection on channels. Much agreement is found with the work of Bunnik (1978),
who considered the
influence of changing canopy morphology, and the effect of a dry o r moist bounding soil, for optimum selection of spectral bands to discriminate between different green plant canopies. He proposed four spectral bands with centre wavelength positions at 550,
670,
870 and 1650 nm. The optimum selection is
based on the determination of maximum between class separation in the feature space, defined by a minimum number of spectral bands selected within the available atmospheric windows.
251 Present scanning systems are not directed in their choice of channels as suggested above
OK
do show only part of these channels. Further testing of the
informative value of the suggested channels is necessary, especially in the near Infrared 1.72-178
>
urn and
1.60 pm, since only few data are available (e.g. 2.32-2.38
pm).
Besides bands at 0.45-0.50
n .!n
the bands wn,
and 0.85-0.95
the 2.2 um band was pointed out to be valuable for the inventory of hydrothermally altered rocks (Rowan et al., 1977). Abrams et al.,
(1977)
used data of the Rendix scanner for the delineation of
altered rocks. The following ratios were used: expressed in channel numbers, or 1.6/2.2, in approximate centers of channels in
12/13,
1.6/0.48
12/3
and 0.6/1.0
and 5/10 when when expressed
um.
The dynamics of soil moisture have to be evaluated in determining the soil potential in rainfed agriculture. For this purpose, multitemporal reflectance data may be used effectively. Mc Culloch et al., (1975) considered the use of changes in reflectance of soil-vegetation units to detect changes in soil moisture more promising than thermal scanning, though the relationship would have to be derived for a large number of combinations. Both clay and sandy soils show a large decrease in reflectance over the 0.5-2.6
wn
region at
increasing moisture content (see Johannsen, 1969). Apart from application in the field of soil moisture mapping, airborne MSS has also been used for distinguishing freshly tilled soil from crusted surface soil. The surface roughness can be evaluated from reflectance data taken under different angles of illumination (e.g. different times of day). Furthermore, airborne Multispectral Scanning (MSS) has been used to examine soils with a moderate content of organic matter. For this purpose, Roth and Baumgardner
(1971)
studied a
soil test
area of
approximately 45
ha
in
Tippecanoe County (Indiana), lying in a transitional zone between Alfisols and Mollisols. They found a high correlation between multispectral response with the content of
organic matter
in the upper cm of soil. Since automated
processing is of great importance in the study of MSS data, the method used by Roth and Baumgardner is discussed as an example. In their study, the size of the training set for computer implemented analysis of multispectral data had an important effect on the correlation. A high rate of digitization gave much greater correlation coefficient values than does a low rate of digitization. Furthermore, the selection and number of channels had a profound influence. In stepwise regression analysis, the charnlel 0.66-0.72
252
was
the single best channel for predicting organic matter content in all
training set sizes, except for the single remote sensing unit ( R S U ) . channels
0.40-0.44
urn and
0.50-0.52
Dm
were
also
generally
high
in
The the
selection of the best two or three channels. Some details of the method are given below. Sampling:
the field was gridded at intervals of 46 m; at each grid-point a 1 kg surface soil sample was taken at a depth of up to 1 cm; the organic matter content was determined by a modified Walkley Black method. MSS data: May 6, 1 9 7 0 , altitude 1 0 0 0 m; 6 channels in the 0.40-1.00 wn range; RSU approximately 9 m2. Low digitization rate (LDR): every eighth scan line was digitized at the rate of 220 samples per scan line. High digitization rate (HDR): every third scan line was digitized at the rate of 440 samples per scan line. Size of training sets: 1 , 4 , 9, 2 5 , 6 4 , 100 and 144 RSU; the 25 RSU training set size produced maximum correlation with LDR data. The channels pointed out for predicting organic matter should be further tested for their value in other soil conditions. The correlation may be negative. That is, the absence of organic matter may be indicated by a high reflectance in a particular channel due to the absence of masking of soil material with specific properties. In that case, high content of organic matter would produce a low reflectance in that channel. Generally, reflectance data indicate the presence or absence of particular soil materials rather than the absolute contents of those materials. This will be due to the effect of the type of the materials e.g. fine textured accumulations. coatings
on
the
mineral
mineral grains or very
For example, iron or lime may be present as
grains
and
exert
a
stronger
influence on
the
reflectance values than would be expected from their real contents. On the contrary
from
uncoated
mineral
occurrences,
high
correlations
between
reflectance values and contents may be produced. Finally, attention is drawn to the research need for:
-
the study of the soil reflection model;
-
polarization techniques in discrimination between dry and moist soil
-
back and forward scattering zones (see par.
surfaces; 2.6
and par.
3.2),
for
example at the outer sides of large angle fields of view in airborne
253
scanning for discrimination of highly absorbent and highly reflectant features. 10.4.
Detection in the mid Infrared The mid Infrared shows two atmospheric windows which enable remote sensing
(Fig. 2 . 1 2 ) ,
these being: 3.4-4.1
and 4.5-5.2
um.
The information potential of these bands (Tables 2.3 and 2.4) is as follows: bands
information potential
(pm)
3.4-4.1
C-H, C - H 2 ,
C-H3 (organic matter)
4.5-5.2
oxides, S i - 0 bending
The value of these bands for remote sensing has to be tested in future (Mulders et al., 1 9 8 6 ) .
10.5.
Conclusions Line
scanners
may
be
distinguished
roughly
into
monospectral
and
multispectral line scanners. The information content of the Ultraviolet window is low when compared with ,the Visible and Infrared. However, some targets such as carbonates, phosphates and evaporites exhibit higher reflectances in the Ultraviolet than in the Visible. A number of channels can be selected on the basis of spectral properties of soils and plants as well as on the allocation of the atmospheric windows. Application of airborne MSS in soil survey is found in acquisition of spectral signatures of soil surfaces and in discrimination of moist and dry soil surfaces. It is especially in arid and semi-arid regions where the soil is barely covered that airborne MSS is expected to be of great value for soil survey when applied in combination with
airphoto-interpretation. In other
regions, MSS may give much information about soil conditions at the time that there are large areas of bare soil (e.g.
extensive cotton or grain fields out
of the growing season). Much research is necessary to explore the high potential information content of airborne MSS. The results may be used for inventory and monitoring of the environment at a large scale, as well as for the improvement of satellite MSS techniques.
254
10.6.
References
Abrams, M.J., Ashley, R.P., Rowan, L.C., Goetz, A.F.H. and Kahle, A.B., 1977. Use of Imaging in the 0.46-2.36 um Spectral Region for Alteration Mapping in the Cuprite Mining District, Nevada. U.S. Geol. Survey. Open-file Report 77-585. Bunnik, N.J.J., 1978. The Multispectral Reflectance of shortwave Radiation by Agricultural Crops in Relation with their Morphological and Optical Properties. Thesis Agricultural University, Wageningen, The Netherlands: 176 pp. Cronin, J.F., Rooney, T.P. e.a., 1973. Ultraviolet Radiation and the Terrestrial Surface. In the Surveillant Science. Remote Sensing of the Environment (ed. by R.K. Holz), Houghton Mifflin Cy, Boston: pp. 67-77. Higham, A.C., Wilkinson, B. and Kahn, D., 1973. Multispectral Scanning System and their potential Application to Earth Resources Surveys. Basic Physics & Technology, ESRO CR-231: 186 pp. Holter, M.R., 1973. Ultraviolet Imaging. In the Surveillant Science. Remote Sensing of the Environment (ed. by R.K. Holz), Houghton Mifflin Cy, Boston: pp. 78-82. Johannsen, C.J., 1969. The detection of available s o i l moisture by remote sensing techniques. Ph.D. Thesis, Purdue University: 266 pp. Lintz, J.Jr and Simonett, D.S., 1976. Remote Sensing of Environment. AddisonWesley Publ. Cy, Reading, Massachusetts: 694 pp. Lowe, D.S., 1975. Imaging and Nonimaging Sensors. Chapter 8 i n Manual of Remote Sensing. her. SOC. of Photogrammetry, Falls Church, Virginia: pp. 367397. Mc Culloch, J.S.G., Painter, R.B., 1975. Application of Multispectral Scanning Systems to Hydrology. In ESRO CR-234, Plessey, United Kingdom: pp. 127149. Mulders, M.A., 1986. Band Selection in Multispectral Scanning for S o i l Survey of Arid Zones. Proc. ISSS hth intern. Symposium Remote Sensing for soil Survey. March 1985 (Wageningen, Enschede). ITC Journal, Enschede, The Netherlands. Mulders, M.A., Schurer, K., Jong, A.N. de, Hoop, D. de, 1986. Selection of Bands for a newly developed Multispectral Airborne Reference-aided Calibrated Scanner (MARCS). Proc. ISPRS Congress August 1986, Enschede, The Netherlands; pp. 301-303. Roth, C.B. and Baumgardner, M.F., 1971. Correlation Studies with Ground Truth and Multispectral Data: Effect of Size of Training Field. 7th Symposium Remote Sensing Michigan: 12 pp. Rowan, L.C., Goetz, A.F.H. and Ashley, R.P., 1977. Discrimination of Hydrothermally Altered and Unaltered Rocks i n Visible and Near Infrared Multispectral Images. Geophysics, Vol. 42, No 3: pp. 522-535. Tucker, C.J., 1976. Sensor Design for Monitoring Vegetation Canopies. Photogrammetric Engineering and Remote Sensing, Vol 42, No. 11: pp. 13991410. 10.7. Additional reading Heide, G. van der and Koolen, A.J., 1980. Soil Surface Albedo and Multispectral Reflectance of short-wave Radiation at a Function of Degree of Soil Slaking. Neth. J. Agric. Sci 28: pp. 252-258. LARS, 1968. Remote Multispectral Sensing i n Agriculture. Annual Report Vol. no. 3. Laboratory for Agricultural Remote Sensing (LARS). Purdue Univ.,
255
Indiana: 175 pp. LARS, 1970. Item Annual Report, Vol. no. 4: 112 pp. Lillesand, T.M. and Kiefer, R.W., 1979. Remote Sensing and Image Interpretation. John Wiley & Sons, New York: 612 pp. Polcyn, F.C., Spansail, N.A. and Mulida, W.A., 1973. How Multispectral Sensing can help the Ecologist. In The Surveillant Science. Remote Sensing of the Environment (ed. by R.K. Holz). Houghton Mifflin Cy, Boston: pp. 349-359. Sabins, F.F. Jr, 1978. Remote Sensing. Principles and Interpretation. W.H. Freeman and Cy, San Francisco: 426 pp. Savigear, R.A.G., 1975. An Approach to the Evaluation of Multispectral Scanning Systems. In ESRO CR-234, Plessey, United Kingdom: pp. 7-50.