VEGETATION PREPARATORY PROGRAMME
A comparative and synergistic study of ATSR-2 and
the VEGETATION systems’ responses to changes in
vegetation cover.
J. Wellens, M. Edwards and D. Llewellyn-Jones,
University of Leicester, UK
Phase 1 (Pre-Launch) Report
Principal Investigator
Prof. D. Llewellyn-Jones
Earth Observation Science
University of Leicester
University Road
Leicester LE1 7RH
UK
Tel: +44 116 252 5238
Fax: +44 116 252 5263
E-mail: dlj1@le.ac.uk
1. Introduction
The last decade has seen a dramatic increase in the use of remotely sensed data for
studies investigating changes in land cover at regional and global scales.
Such
information is an important input to studies which aim to increase our understanding
of the role that the land surface plays in the global climate system and biogeochemical
cycling. Most of these studies have utilised data from the AVHRR sensor carried on
board the NOAA series of satellites which provide daily coverage of the Earth’s
surface at a spatial resolution that is appropriate for regional and global scale studies
(e.g. Gutman, 1991; Townshend et al, 1991, Goward et al. 1991; and Justice et al.
1985). In particular, data from the red and near-infrared wavebands of the AVHRR
sensor have been utilised since these correspond with parts of the electromagnetic
spectrum where the reflectance properties of green photosynthetically active
vegetation are widely documented and well known (Belward 1991, Tucker and Sellers
1986). These two wavebands are frequently used to compute the Normalised
Difference Vegetation Index (NDVI); an index that is sensitive to changes in the
amount of photosynthetically active radiation (PAR) utilised by vegetation.
The AVHRR instrument was originally designed for meteorological applications and
despite its widespread and successful use in vegetation studies, the red and nearinfrared channels are not located optimally for such applications. Therefore, in recent
years the remote sensing community has shown considerable interest in the
development of new sensors with channels designed specifically for the detection of
vegetation canopies at regional and global scales (Townshend et al. 1991). Two such
sensors are the second Along Track Scanning Radiometer (ATSR-2), which was
launched on board ERS-2 in April 1995 (Mason and Delderfield 1990) and SPOT
VEGETATION that was launched in March 1998 (Saint 1994). Both these sensors
include a red and near-infrared channel designed for optimal detection of radiation
from healthy vegetation and both collect data at spatial scales identified by
Townshend and Justice (1988) as appropriate for regional and global scale studies
(ATSR-2: 1km; VEGETATION: 1.15km).
This project aims to evaluate the usefulness of these two sensors for detecting
vegetation in an arid environment where vegetation cover is inherently low.
Specifically, the project aims to:
2
(a) compare data from the ATSR-2 and VEGETATION systems for areas with large
variations in type and percentage vegetation cover and;
(b) investigate the synergistic use of data from these two sensors to improve the
extraction of information on land surface vegetation characteristics.
This document reports the results from Phase-1 of the project prior to the launch
phase of the VEGETATION instrument. This aspect of the project had the following
objectives:
(a) to simulate data from the VEGETATION instrument and evaluate its sensitivity to
changes in vegetation cover and;
(b) to compare the simulated response of the VEGETATION system to that of the
ATSR-2 and AVHRR sensors for areas of low vegetation cover.
The study area for this project was the Badia Research and Development Programme
area in the east of Jordan. This is an area in which the project members already have
ongoing research interests and for which considerable field and ancillary data had
been collected (Edwards 1999, Edwards et al. (submitted)). Vegetation cover in the
Badia is very sparse and typically less than ten percent. Despite this, the region is a
heavily utilised grazing area for the nomadic Bedouin and their animals and it forms
the hub of the trade routes between Saudi Arabia, Jordan, Syria and Iraq (figure 1).
Vegetation in the Badia is severely over-grazed and thus the area provides a
challenging environment in which to test the ability of these new sensors to detect low
levels of vegetation.
3
Figure 1: Location of the Jordan Badia Research and Development Programme
2. Remote Sensing of Vegetation
Satellite detection of vegetation generally utilises the visible and near-infrared
wavebands because the spectral properties of vegetation in these regions are well
established and understood. Recent studies have also demonstrated the potential of
microwave data such as ERS-1 Synthetic Aperture Radar (SAR) data (e.g. Smara et
al. 1998) and data from the middle infrared between 3-5m. (Boyd et al. 1998).
However, the use of these data for vegetation monitoring is not straightforward due to
problems associated with the lack of understanding of the relationship between
radiation and biophysical parameters in these wavebands.
Figure 2 includes a typical spectral profile for a healthy green grassland vegetation. It
can be seen that in the visible region of the spectrum reflectance by vegetation is low.
Less than 10 percent of incoming solar radiation at these wavelengths is reflected with
most being absorbed by chloroplasts, particularly in the blue (0.4 – 0.5 m) and red
(0.6-0.7 m) regions. Conversely, at near-infrared wavelengths (0.7-1.1m) spectral
4
reflectance by healthy vegetation is at a maximum and reaches around 70 percent as a
result of internal scattering of radiation within the spongy mesophyll of the plants’
leaves. Beyond 1.2m, reflectance declines to less than 15 percent once again, since
the water contained within the vegetation canopy absorbs radiation strongly at these
wavelengths.
70
60
Reflectance (%)
50
Marab al Wassad
Feidat ed Dihikiya
Sumaysniyyat
Marab Salma
Marab Wutaydat
Marab Qitafi
Grass
40
30
20
10
9.79
10.09
9.49
9.19
8.89
8.59
8.29
7.99
7.69
7.39
7.09
6.79
6.49
6.19
5.89
5.59
5.29
4.99
4.69
4.39
4.09
um
3.79
0
Wavelength (um)
Figure 2: Spectral response curves recorded over temperate grassland and the
arid field site vegetation in Jordan.
It is these spectral characteristics of vegetation, and particularly the large difference in
the reflectance properties between the visible and the near-infrared wavebands, that
have been used to detect vegetation using remote sensing techniques since the launch
of the first Landsat sensor in 1972.
Above all, the application of Normalised
Difference Vegetation Index (NDVI) derived from the red and near-infrared channels
of the AVHRR sensor, has led to the widespread adoption of these data both within
the remote sensing community and beyond.
The Normalised Difference Vegetation Index (NDVI) is defined as:-
NDVI 
(nir  red )
(nir  red )
5
where nir refers to reflectance in the near infrared wavelengths and red refers to
reflectance in the red wavelengths. The NDVI ranges between -1 and 1 with vegetated
areas generally falling in the range 0 to 1. Studies have shown that in ecosystems that
contain a large amount of photosynthetically active vegetation, the NDVI correlates
well with vegetation parameters such as percentage cover, biomass and leaf area
index (Tucker et al. 1985, Wylie et al. 1991). In arid and less vegetated areas
however, the usefulness of the NDVI has been more variable. Choudbury and Tucker
(1987) found it was not sensitive below the 20 % green leaf ground cover of the Great
Victoria and Great Sandy deserts in western Australia and the Kalahari. Similarly
Kennedy (1993) studying the phenology of Tunisian grazing lands comments that
whilst the NDVI can be used as an indicator for monitoring inter-annual and intraannual variations in biomass and productivity, percentage soil contribution to total
recorded reflectance provided an important limiting factor beyond which the accuracy
of the index became less reliable.
These examples have all been based upon data from the AVHRR sensor and it must
be remembered that the AVHRR was not originally designed for vegetation
applications. Hence its waveband characteristics are not optimal for the detection of
vegetation. The characteristics of the red and near-infrared channels of the AVHRR
sensor are presented in table 1 and compared with those of the VEGETATION and
ATSR-2 sensors that were designed specifically for vegetation studies.
Table 1: Characteristics of the VEGETATION, ATSR-2 and AVHRR sensors
Red
Near-infrared
Near-infrared
Spatial
(m)
(m)
( m)
Resolution
VEGETATION
0.610-0.680
0.780-0.890
1.580-1.750
1.15 km
ATSR-2
0.649-0.669
0.855-0.875
1.580-1.640
1.00 km
AVHRR
0.580-0.680
0.720-1.100
NA
1.10 km
All three sensors contain a detector in the red region of the spectrum. However, the
bandwidth of the sensors at these wavelengths varies considerably. The red band on
6
ATSR-2 is the narrowest at only 0.02m, that of VEGETATION is 0.07m and that
on the AVHRR is 0.1m. The relative responses of the VEGETATION and ATSR-2
sensors are greatest around the region of maximum chlorophyll absorption by healthy
green vegetation,
(0.665m).
For the AVHRR sensor, the maximum relative
response occurs at 0.63m and its lower limit encroaches into the green part of the
spectrum where reflectance by healthy vegetation is at its highest in the visible
wavebands. Slater (1990) considers that the lower cut off point for the red band is not
important, however, he states that the upper one should not exceed 0.69m since this
encompasses the transition zone or so-called “red-edge”. All three sensors considered
here have an upper limit to their red band that lies below 0.69m.
Similarly, Slater (1990) considers that the lower cut off of the near-infrared band
should lie above 0.75m in order to again avoid the “red-edge”. As can be seen from
table 1, the near-infrared wavebands of the VEGETATION and ATSR-2 sensors
accord with this, but the lower limit of this channel of the AVHRR sensor is at
0.72m and thus incorporates this region. Furthermore, the relative response of the
AVHRR sensor is at a maximum at 0.765m, within the transition zone. The width
of the near-infrared waveband of the AVHRR sensor, at 0.29m, is considerably
broader that those of the VEGETATION and ATSR-2 (0.11m and 0.02m
respectively). Its upper range between 0.9-1.1 m incorporates a region where there
is an approximate 10 percent decline in the reflectance of radiation by healthy
vegetation as a result of absorption by water contained in the plants’ leaves.
For healthy green vegetation, such as the temperate grassland shown in figure 2, it is
clear that the VEGETATION and ATSR-2 sensors include wavebands that should be
more sensitive to the reflectance properties of vegetation than those of the AVHRR.
However, the ability of these new sensors to detect vegetation in arid environments,
where vegetation cover is sparse and the soil background is usually highly reflective,
has not been investigated. Furthermore, the typical plant species in arid environments
are often woody shrubs with grey-green foliage and xerorphytic habits (Graetz and
Gentle 1982), thus they do not conform with the characteristic spectral response of
healthy green vegetation. The contrast between the spectral characteristics of the
temperate grassland vegetation and the arid zone vegetation found in the field sites
7
used in this study, can be observed in figure 2. The problem of detecting and
monitoring vegetation in arid environments if further compounded due to the effects
of plant shadow (Graetz and Gentle 1982, Pech et al. 1986) and because the
vegetation is often composed of a mixture of live vegetation, senesced yellow
vegetation, weathered grey litter and background soil (Huete and Jackson 1987).
3. Methodology
In order to assess whether the narrow wavebands of the VEGETATION and ATSR-2
sensors show increased ability to detect arid land vegetation over AVHRR, the
spectral responses of all three sensors were simulated for six study sites in the Badia
region of Jordan.
Radiometric measurements of the dominant vegetation and
background soil reflectance had been made at these six sites during a field visit in
Spring 1996. The measurements were made using an ASD field spectroradiometer
that measures spectral radiance between 0.3m and 2.5m at 0.001m intervals. The
optical head of the radiometer was mounted on a tripod at a height of 1.5m and using
an 80 field of view (FOV) and nadir viewing position, reflectance was measured as the
ratio of radiance measured over the target to that measured over a barium sulphate
panel. In addition to the radiometric readings, field measurements were also made of
vegetation characteristics including height, width, density, percentage cover, and
species. Table 2 provides summary information about the six field sites for which
field data were collected.
Table 2: Characteristics of the field sites
Site
No.
1
2
3
4
5
6
Field Site
Name
Marab al Wassad
Feidat ed Dihikiya
Marab Qitafi
Sumaysniyyat
Marab Wutaydat
Marab Salma
% Cover
Spring 96
3.78
5.20
7.96
7.7
3.97
27.98
Dominant Species
Anabasis articulata, Zilla spinosa
Achillea fragrantissima, Tamarix spp.
Seidlitzia rosmarinus, Salsola vermiculata
Anabasis articulata
Anabasis syriaca
Artemisia herba-alba, Achillea fragrantissima
The field-measured reflectances were converted into simulated sensor waveband
responses multiplying them by the solar spectral radiance and the sensor spectral
response. This follows the method described by MacKay et al (1996) where:-
8

n
S () =
i 1
E i  S  i   i 

n
i 1
E( i )  i 
pS (I) = Spectral reflectance
E (I) = Solar spectral irradiance
 (I) = Sensor spectral response
Calculations were made for 0.005m wavelength intervals and used the appropriate
spectral distribution functions for the red and near-infrared waveband of each of the
three sensors. The reflectance values obtained for the red and near-infrared bands of
each sensor for each field site were then used to calculate the NDVI. The results from
these sensor simulations were also input to a simple geometric model. This assumed
the surface to be a series of solid spheres (vegetation) upon a contrasting background
(soil) (figure 3). The model uses the simulated sensor results and data on the viewing
geometry and percentage vegetation cover to calculate the proportion of illuminated
and shaded vegetation and soil and the resultant surface reflectance.
Incoming Solar Radiation
Shadowed
Ground
Reflectance
Illuminated
Ground
Reflectance
Shadowed CanopyIlluminated
Canopy
Reflectance
Reflectance
Figure 3: Surface reflectance characteristics simulated by the geometric-optical
model.
9
4. Results
Figure 4 presents the results obtained from simulating the red and near-infrared bands
of each sensor using the soil and vegetation spectra obtained for each field site. These
simulations are based on spectral measurements made when the field of view of the
radiometer was entirely filled by either bare soil or vegetation.
Thus they
approximate either a completely bare soil surface or 100 percent vegetation cover.
It can be seen that there is considerable variation in the reflectance characteristics of
the soil and vegetation at the different field sites. In general, however, it appears that
there is less variability between the field sites in terms of the vegetation reflectance
compared to the soil reflectance. In the red wavebands, vegetation reflectance ranges
from a minimum of 14%, for the field-site at Marab Salma, to a maximum of 26% at
Marab Qitafi. In the near infrared the minimum vegetation reflectance is 29% at
Feidat ed Dihikiya and at a maximum of 38% at Marab Qitafi. The corresponding
range of values for soil reflectance is much greater in both the red (minimum 27% at
Marab al Wassad, maximum 52% at Feidata ed Dihikiya) and the near infra-red
(minimum 36% at Marab al Wassad, maximum 58% at Feidat ed Dihikiya). Since
the simulations approximate either 100 percent bare soil or 100 percent vegetation
cover, the variability observed in the results for the different field sites illustrates the
variability in the soil and vegetation properties at these sites. The variability in the
vegetation response is likely to result from differences in plant species composition
and structure between the field sites, whilst the greater variability observed in the
simulated soil reflectances result from differences in soil properties, such as
mineralogy, texture and moisture, between field sites.
The simulations show that in the red wavebands, reflectance is considerably lower for
vegetation than for soil at all field sites and for all sensors. This result was expected
since chlorophyll in vegetation absorbs incoming solar radiation at these wavelengths.
Similar results are, however, observed for the near-infrared wavebands of all three
sensors, for all field sites, except Marab al Wassad. These results emphasise the
highly reflective nature of the arid soils of the Badia region since the soil reflectance
is greater than that of vegetation.
10
(a) Marab al Wassad
60
50
40
Reflectance (%) 30
20
10
0
SOIL
VEGN RED
VEGN NIR
ATSR-2 RED
VEG
ATSR-2 NIR
Surface
AVHRR RED
Sensor and Waveband
AVHRR NIR
(b) Feidat ed Dihikiya
60
50
40
Reflectance (%) 30
20
10
0
SOIL
VEGN RED
VEGN NIR
ATSR-2 RED
VEG
ATSR-2 NIR
Surface
AVHRR RED
Sensor and Waveband
AVHRR NIR
(c) Marab Qitafi
60
50
40
Reflectance (%) 30
20
10
0
SOIL
VEGN RED
VEGN NIR
ATSR-2 RED
VEG
ATSR-2 NIR
Sensor and Waveband
11
AVHRR RED
AVHRR NIR
Surface
60
(d) Sumaysniyyat
50
40
Reflectance (%) 30
20
10
0
SOIL
VEGN RED
VEGN NIR
ATSR-2 RED
VEG
ATSR-2 NIR
Sensor and Waveband
(e) Marab Wutaydat
Surface
AVHRR RED
AVHRR NIR
60
50
40
Reflectance (%) 30
20
10
0
SOIL
VEGN RED
VEGN NIR
ATSR-2 RED
VEG
ATSR-2 NIR
Surface
AVHRR RED
Sensor and Waveband
AVHRR NIR
60
(f) Marab Salma
50
40
Reflectance (%) 30
20
10
0
SOIL
VEGN RED
VEGN NIR
ATSR-2 RED
VEG
ATSR-2 NIR
Sensor and Waveband
Surface
AVHRR RED
AVHRR NIR
Figure 4: Simulated red and near-infrared soil and vegetation reflectance characteristics for
sensor and field site
12
A consequence of the highly reflective properties of the soils in both the red and nearinfrared wavebands is that there is less differential between the spectral characteristics
of the soil and vegetation in the Badia than would be observed in a more temperate
area.
This reduces the usefulness of vegetation indices such as the Normalised
Differential Vegetation Index (NDVI) which rely on these features in order to detect
or monitor vegetation. This is illustrated by examining the NDVI values calculated
from the simulated red and near-infrared responses for each field site.
The
reflectances assume 100 percent vegetation cover for each field site and the values
obtained using the simulated VEGETATION data range from 0.19 for Fedat ed
Dihikiya to 0.37 for Marab al Wassad. The corresponding range for ATSR-2 is 0.21
– 0.43, and for AVHRR is 0.20-0.39. For each sensor, NDVI values were also
simulated using the spectral characteristics of a typical temperate grassland. An
NDVI value of 0.76 was obtained for VEGETATION, 0.89 for ATSR-2 and 0.82 for
AVHRR. If these values are compared with the ranges obtained for the Badia field
sites, it can be seen that the weaker reflective characteristics of arid zone vegetation
and the reduced differential reflectance between the red and near-infrared wavebands,
compromise the use of vegetation indices even in hypothetical situations where
vegetation cover is assumed to be 100 percent.
The patterns discussed so far are broadly similar for all three sensors under
consideration. When the simulated vegetation reflectances obtained for each sensor
are examined (table 3), it can be seen that for all the field sites, the VEGETATION
sensor produces the highest reflectance in the red waveband, and the ATSR-2 and
AVHRR have similar but lower responses. These are approximately 3-8% less than
those found for VEGETATION but the differences vary according to field site. For
the simulated near-infrared responses, the ATSR-2 sensor measures the greatest
reflectance and this is approximately 3% greater than those for the VEGETATION
sensor and 8% greater than the simulated AVHRR near-infrared reflectances. The
AVHRR data also produces the lowest reflectance if the data simulations are
performed using the temperate grassland spectral response curves but the simulated
NIR reflectance are considerably greater, averaging 68%, compared to the 35% found
for the Badia vegetation.
The simulated ATSR-2 and VEGETATION reflectances
for the temperate grassland are identical.
13
Table 3: Simulated soil and vegetation reflectances for each sensor and field site.
Field Site
Red Wavebands
VEG'N
ATSR-2
Near-infrared Wavebands
AVHRR
VEG'N
ATSR-2
AVHRR
Simulated Vegetation Reflectance (%)
Marab Al Wassad
18.0
16.6
16.7
40.0
41.6
38.6
Feidat ed Dihikiya
18.6
17.9
17.5
28.5
29.0
27.4
Marab Qitafi
26.3
25.4
24.9
38.4
39.1
37.2
Sumaysniyyat
18.9
18.2
17.9
32.4
33.0
30.9
Marab Wutaydat
21.3
20.6
19.8
33.9
34.5
32.8
Marab Salma
14.5
13.6
13.6
29.9
30.5
28.5
Simulated Soil Reflectance (%)
Marab Al Wassad
30.6
30.1
27.6
36.7
36.7
36.2
Feidat ed Dihikiya
51.6
51.0
48.5
58.2
58.1
57.6
Marab Qitafi
45.0
44.4
42.0
50.8
50.8
50.3
Sumaysniyyat
34.5
33.8
30.7
42.6
42.8
42.0
Marab Wutaydat
33.9
33.3
30.1
41.5
41.7
41.0
Marab Salma
14.9
13.6
13.6
29.9
30.5
28.5
Table 3 also details the simulated soil reflectances obtained for each sensor. As was
the case with the simulated vegetation reflectances, the VEGETATION sensor
produces the highest response in the red waveband for all field sites. However, these
values are less than 1% different than those for the ATSR-2 sensor.
The
VEGETATION response is between 6 and 12% greater than that obtained using the
AVHRR spectral characteristics and varies according to field site. Near-infrared soil
reflectances display less variability between sensors than those found in the red
wavebands, and there is less than 1% difference in the results obtained.
The simulations using the VEGETATION sensor spectral characteristics produce the
highest reflectances in the red wavebands for both the vegetation and soil. The
differential between the simulated red and near infrared soil and vegetation
reflectances for the VEGETATION sensor are consequently less than for the ATSR-2
and AVHRR.
This means that when vegetation indices such as the normalised
difference vegetation index (NDVI) are applied to the simulated vegetation and soil
14
data (table 4), the lowest values are found for VEGETATION. However, in terms of
vegetation monitoring and mapping, it is the difference in the NDVI values for
vegetation and soil that is critical in distinguishing between these two surfaces. Once
again, these data simulate a situation where the entire field of view of the sensor is
filled with either vegetation or soil, but comparison of the different sensors reveals
that the ATSR-2 data show the largest difference in vegetation and soil NDVI values
followed by VEGETATION and then AVHRR.
Table 4: Simulated vegetation and soil NDVI values for each field site.
Field Site
NDVI Vegetation
NDVI Soil
VEG'N
ATSR-2
AVHRR
VEG'N
ATSR-2
AVHRR
Marab Al Wassad
0.38
0.43
0.40
0.09
0.10
0.13
Feidat ed Dihikiya
0.21
0.24
0.22
0.06
0.07
0.09
Marab Qitafi
0.19
0.21
0.20
0.06
0.07
0.09
Sumaysniyyat
0.26
0.29
0.27
0.11
0.12
0.16
Marab Wutaydat
0.23
0.25
0.25
0.10
0.11
0.15
Marab Salma
0.35
0.38
0.35
0.07
0.08
0.10
The results until now have been based on simulated sensor data for which the field of
view of each sensor has been assumed to be filled entirely by either vegetation or bare
soil. The second part of this investigation involved utilising these data to simulate
situations where the field of view contained different proportions of vegetation and
bare soil. A simple geometric-optical model was utilised to calculate the proportion of
illuminated and shadowed vegetation and illuminated and shadowed soil that would
be viewed as percentage vegetation cover increased. The model was applied to four
different percentage cover levels for each field site: the actual percentage cover
recorded during field work (see table 2) and 10%, 20% and 30% vegetation cover.
The simulated red and near-infrared reflectances obtained for each sensor were then
applied to the model and these were used to calculate an NDVI value for each
combination of soil and vegetation for each field site. The results of this simulation
are provided in figure 5.
15
0.18
(a) Marab al Wassad
0.16
0.14
0.12
0.1
NDVI
0.08
0.06
0.04
AVHRR
0.02
ATSR-2
0
Sensor
4
10
VEGN
20
Percentage Vegetation Cover
30
0.18
(b) Feidat ed Dihikiya
0.16
0.14
0.12
0.1
NDVI
0.08
0.06
0.04
AVHRR
0.02
ATSR-2
0
Sensor
5
10
VEGN
20
Percentage Vegetation Cover
30
0.18
(c) Marab Qitafi
0.16
0.14
0.12
0.1
NDVI
0.08
0.06
0.04
AVHRR
0.02
ATSR-2
0
8
10
VEGN
20
Percentage Vegetation Cover
30
16
Sensor
0.18
(d) Sumaysniyyat
0.16
0.14
0.12
0.1
NDVI
0.08
0.06
0.04
AVHRR
0.02
ATSR-2
0
Sensor
3
10
VEGN
20
Percentage Vegetation Cover
30
0.18
(e) Marab Wutaydat
0.16
0.14
0.12
0.1
NDVI
0.08
0.06
0.04
AVHRR
0.02
ATSR-2
0
Sensor
3
10
VEGN
20
Percentage Vegetation Cover
30
0.18
(f) Marab Salma
0.16
0.14
0.12
0.1
NDVI
0.08
0.06
0.04
AVHRR
0.02
ATSR-2
0
Sensor
10
20
VEGN
28
Percentage Vegetation Cover
30
Figure 5: Simulated sensor NDVI responses17for increasing levels of percentage vegetation
cover at each fieldsite
For all field sites and all percentage vegetation covers, the VEGETATION sensor
displays the lowest NDVI values and the AVHRR the highest. AVHRR-NDVI values
are up to 34% greater than for the VEGETATION sensor but the difference between
the AVHRR and VEGETATION-NDVI values declines as the modelled percentage
vegetation cover increases (table 5). For example, for the Marab al Wassad field site
AVHRR-NDVI is 32% greater than VEGETATION-NDVI when vegetation cover is
4% but when vegetation cover is increased to 30%, the difference between the two
NDVI values falls to 24%.
This pattern of a declining difference between the
AVHRR and VEGETATION NDVI values with increasing percentage vegetation
cover is observed for all field sites, and with the exception of the Marab Salma site,
there appears to be some consistency in the range of values observed.
Similar
patterns can be observed in the differences between the AVHRR-NDVI and ATSR-2NDVI values but the differences between the NDVI values is not so great as those
observed for AVHRR and VEGETATION. Nevertheless, the difference in NDVI
values between the sensors declines with increasing vegetation cover. By contrast,
the relationship between VEGETATION-NDVI and ATSR-2 NDVI values appears to
be more consistent and stable. As table 5 indicates, the difference between the NDVI
values from these two sensors is generally less than 10% and does not vary
significantly with changes in vegetation cover. In fact, for all field sites (except
Marab Salma) the largest difference between the VEGETATION and ATSR-2 NDVI
values occurs when vegetation cover is at the maximum value (30%) used in the
model. However, overall the differences between these two sensors’ performances are
small and not significant and for the Marab Salma field site, the difference in the
NDVI values recorded by the two sensors is constant at 9.7% no matter what the
percentage vegetation cover.
18
Table 5: Range in differences between sensor NDVI values with changes in
percentage vegetation cover.
Field Site
Range of differences (%) in sensor NDVI values
With increasing vegetation cover
AVHRR-VEG’N
AVHRR-ATSR-2
ATSR-2-VEG’N
Marab Al Wassad
24.2-31.9
15.3-25.0
9.3-10.5
Feidat ed Dihikiya
26.3-29.7
18.7-23.2
8.4-9.3
Marab Qitafi
27.1-31.1
18.5-24.9
8.3-10.6
Sumaysniyyat
27.4-31.8
19.0-23.9
10.4-10.5
Marab Wutaydat
29.9-33.5
23.0-26.5
9.1-9.6
Marab Salma
14.6-26.4
5.4-18.5
9.7
The differences observed in the simulated NDVI values from the three sensors
demonstrate that it is not possible to directly compare vegetation indices derived from
different sensors without developing a technique for inter-sensor calibration. This is
necessary to ensure that any changes in vegetation cover measured from satellite
images are real and not artefacts resulting from the use of different sensors. The
simulated AVHRR data produces the highest NDVI values of the three sensors but
this does not mean that the results are better than those from the VEGETATION and
ATSR-2 sensors. What is more important is the rate of change in the NDVI value
with increasing percentage vegetation cover. This is effectively a measure of the
sensitivity of the each sensor to changes in vegetation cover and thus is of more value
in a monitoring context than the absolute NDVI value.
The percentage change in NDVI values that occurs for a corresponding one percent
increase in vegetation cover is summarised for each sensor in table 6 below. The rate
of change in the NDVI is found to be similar for the VEGETATION and ATSR-2
sensors although there is considerable variability between individual field sites. The
VEGETATION and ATSR-2 rates of change are significantly greater than those
found for the AVHRR sensor indicating that both the VEGETATION and ATSR-2
sensors are more sensitive to changes in percentage vegetation cover than the
AVHRR. The rates of change detailed in table 6 were calculated using the same
19
range of percentage vegetation cover as utilised in the geometric-optical model. This
for example means that for the site at Marab al Wassad, the rate of change in NDVI
was calculated using the input range 4-30% vegetation cover. However, investigation
of the rates of change in NDVI over smaller input ranges (e.g. 4-10% or 10-20%)
suggest that the rates of change in all three sensors are relatively constant across the
input range.
Table 6: Rate of change in NDVI values for a one percent increase in vegetation
cover
Field Site
Rate of change in NDVI (%)
VEG’N
ATSR-2
AVHRR
Marab Al Wassad
1.38
1.42
0.85
Feidat ed Dihikiya
0.52
0.56
0.32
Marab Qitafi
0.64
0.77
0.36
Sumaysniyyat
0.55
0.55
0.22
Marab Wutaydat
0.41
0.37
0.24
Marab Salma
3.25
3.25
2.10
The final analysis undertaken in this phase of the project was to investigate whether
any relationship existed between the simulated reflectance characteristics for each
sensor and the percentage vegetation cover. This analysis was undertaken for each
field site and assumed the same four levels of percentage vegetation cover as before
(i.e. the actual level measured during field work, 10%, 20% and 30% cover). The
sample size over which the reflectance and vegetation percentage cover characteristics
can be compared is, therefore, extremely small, for each sensor and field site. Least
squares linear regression was used to ascertain the strength of the relationship
between the simulated NDVI values for each sensor and the percentage cover for the
six field sites. Multiple regression analysis was also used to determine whether
combining information from two sensors improved their overall ability to predict the
percentage vegetation cover. The results of the analysis are presented in table 7
which details the correlation coefficients obtained. Due to the small sample sizes
utilised these correlation coefficients are all generally low (r2<0.4). Comparison of
the observed and critical F values however, indicates that for both the VEGETATION
20
and ATSR-2 sensors the relationships are significant and unlikely to be due to chance
at three of the field-sites (Marab Al Wassad, Marab Qitafi and Marab Salma). In
general, the correlation coefficients found when the AVHRR-NDVI values and
percentage vegetation cover are compared are approximately half those found for the
other two sensors.
Consequently, the relationship between AVHRR–NDVI and
percentage vegetation cover is only found to be significant at two sites (Marab al
Wassad and Marab Salma).
Multiple regression analysis was used to determine whether the relationship between
NDVI and percentage cover improved when the VEGETATION and ATSR-2 were
considered together. The results in table 7 indicate that there is a dramatic increase in
the correlation coefficient for all field sites. Furthermore, the comparison of observed
and critical F values reveals that for all field sites, these results are significant and
unlikely to be due to chance.
Table 7: Correlation coefficient (r2) for the relationship between simulated sensor
NDVI values and percentage vegetation cover for each field site.
Field Site
VEG
ATSR-2
AVHRR
VEG + ATSR-2
Marab Al Wassad
0.411
0.427
0.281
0.999
Feidat ed Dihikiya
0.194
0.208
0.122
0.999
Marab Qitafi
0.229
0.261
0.136
0.916
Sumaysniyyat
0.170
0.169
0.082
0.998
Marab Wutaydat
0.147
0.134
0.072
0.689
Marab Salma
0.875
0.875
0.725
0.996
Values in bold indicate that the observed F value is greater than the critical F value and hence the relationships are unlikely to be
due to chance.
21
5. Conclusions
This work aimed to simulate the spectral characteristics of soil and vegetation data
from the VEGETATION instrument for the arid Badia region of Jordan; and to
compare these with similar data for the ATSR-2 and AVHRR sensors. In particular,
the study aimed to investigate if the narrower wavebands of the VEGETATION and
ATSR-2 offer any advantage for mapping and monitoring arid zone vegetation
compared to data from the AVHRR.
A further objective of the work was to
investigate the synergistic use of VEGETATION and ATSR-2 data to determine
whether this improves the ability to extract biophysical parameters from remotely
sensed data.
Analysis of the results presented in this work have revealed the
following:
1. The VEGETATION and ATSR-2 sensors both demonstrate increased ability to
distinguish between arid zone soil and vegetation spectra over the AVHRR sensor
due to their narrower and more optimally placed red and near-infrared wavebands.
2. NDVI values derived from the AVHRR are up to 34% greater than those derived
from the VEGETATION data, and up to 27% greater than those from the ATSR-2
data. As the percentage cover increases the differential between the AVHRRNDVI and the NDVI values from the other two sensors declines, however the rate
of decline varies between field sites. This implies that it would be difficult to
compare AVHRR data with that of either VEGETATION or ATSR-2 data due to
calibration difficulties.
3. NDVI values derived from VEGETATION are approximately 9% less than those
from the ATSR-2 sensor.
This differential appears relatively constant with
changes in percentage vegetation cover. Providing a calibration coefficient is
applied, it should therefore be possible to compare NDVI data from these two
sensors.
4. NDVI values derived from both the VEGETATION and ATSR-2 sensors show a
similar rate of change with increasing vegetation cover. This rate of change is
60% greater than that found for the AVHRR-NDVI values. This implies that the
22
VEGETATION and ATSR-2 sensors are considerably more sensitive to changes
in percentage vegetation cover than the AVHRR sensor.
5. Significant relationships were found between NDVI values derived from the
VEGETATION and ATSR-2 sensors and percentage vegetation cover for three of
the six field sites examined. The correlation coefficients found between the
AVHRR-NDVI and percentage vegetation cover were approximately 50% less
than those for the other two sensors and were only significant for two field sites.
6. Multiple regression analysis revealed that if data from both the VEGETATION
and ATSR-2 sensor were simultaneously compared to the percentage vegetation
cover data, the correlation coefficients for all six field sites increased and the
relationship was found to be significant and unlikely to be due to chance in all
cases.
These results suggest that in comparison with AVHRR, the detection of arid zone
vegetation is improved if data from either the VEGETATION or the ATSR-2 sensor
are utilised.
Moreover, the comparison of NDVI and percentage vegetation cover
data suggests that the most significant results are found when the VEGETATION and
ATSR-2 data are utilised together. The results have also demonstrated it is essential
that calibration coefficients are derived before the two data sets are utilised
synergistically. In phase 2 of the project, these aspects of the work will be explored
further using real VEGETATION and ATSR-2 data for the Badia region.
23
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