Remote Sensing in Arid Regions: Challenges and Opportunities
Gregory S. Okin ([email protected])
Dar A. Roberts ([email protected])
Department of Geography
University of California
Santa Barbara, CA, 93106
THIS MANUSCRIPT TO BE PUBLISHED IN: THE MANUAL OF REMOTE SENSING,
Vol. 4. Susan Ustin, Ed.
INTRODUCTION
Deserts exist on every continent of the globe and cover more than 30% of the
Earth’s land surface. Although they typically do not have a large number of inhabitants,
they are often the loci of economic and cultural activity. For example, the oil-producing
nations of the Middle East are all found within a single arid region. The arid interior of
Australia is a vast area largely given to mining and livestock production.
At the same time, deserts tend to be fragile ecosystems, requiring little in the way
of perturbations in order to cause tremendous changes in the landscape (Schlesinger et
al., 1990; Okin et al., 2001a). The size, remoteness, and harsh nature of many of the
world’s deserts make it difficult and expensive to map or monitor these landscapes or to
determine the effect of land use on them. Remote sensing is potentially a time- and costeffective way to fulfill these goals. In this chapter, we will discuss the uses and
limitations of remote sensing in the world’s deserts. The discussion will center on using
remote sensing to detect and monitor landscape change and degradation in arid regions.
Because vegetation is often linked to both the causes and consequences of arid land
degradation, our discussion will further focus on the retrieval of vegetation parameters.
This chapter is organized into four parts. In the first section, examples of
successful applications of remote sensing to arid regions are given. In the second section,
limitations and important considerations of remote sensing in arid regions are discussed.
In the third section, atmospheric remote sensing as it relates to land degradation in arid
regions is discussed. In the fourth and final section, a case study is presented in which
various methods for estimation of vegetation cover are presented and compared.
APPLICATIONS OF REMOTE SENSING TO DRYLAND DEGRADATION
STUDIES
Remote monitoring has long been suggested as a time- and cost-efficient method
for monitoring change to desert environments. In this capacity, it can serve both to
enhance monitoring efforts as well as provide valuable information on dryland
degradation in specific areas. In this section, uses of remote sensing for deriving processrelevant environmental information from optical remote sensing data in deserts will be
highlighted with a focus on the use of remote sensing in land degradation studies.
High-Resolution Remote Sensing in Deserts
Schlesinger et al. (1996) have noted that a principal change that occurs in
1
degrading arid and semiarid lands is a change of the scale of heterogeneity. As semiarid
lands degrade from relatively homogenous grasslands to heterogeneous shrublands, the
scale of spatial variability increases. The use of spatial statistics on soil samples in the
Jornada LTER site allowed Schlesinger et al. (1996) to identify this change in scale. This
effect is clearly seen in remote sensing images. For a good review of the theory and uses
of spatial statistics see Bailey and Gatrell (1995). Chilès and Delfiner (1999) provide a
more technical review.
In a study aimed at biomass mapping in a degraded landscape at the Jornada
LTER site, Phinn et al. (1996) used spatial statistics to differentiate the spatial
characteristics of arid shrub versus semiarid grassland vegetation. They found that
significant information was lost when the spatial resolution of the images was larger than
mean shrub size, but little information on grasses was lost when pixels were up 16 m
across. The use of high spatial resolution multispectral digital video imagery allowed
these investigators to determine a suitable spatial resolution and ground sampling interval
to map these two spatially distinct communities.
Okin and Gillette (2001) extended the use of spatial statistics in desert remote
sensing to show their utility in determining the radial distribution of vegetation in the
same landscape as Phinn et al. (1996). They showed that high resolution panchromatic
aerial photographs (digital orthophoto quads from the USGS with ground resolution of 1
m) could be used to confirm the presence of and determine the orientation of “streets” in
mesquite (Prosopis glandulosa) dunelands. Streets are long corridors of unvegetated soil
in these dunelands which may account for the much higher rates of wind erosion and dust
emission observed in these landscapes.
The replacement of grasses by woody shrubs is a degradation process observed
throughout the world’s deserts (see, for example, Schlesinger et al., 1990) and operative
in the Jornada LTER site that was the focus of both the Phinn et al. (1996) and Okin and
Gillette (2001) studies. The change from grassland to shrubland is accompanied by a
change in the scale of variability seen in remote sensing images. In a third example of the
use of spatial statistics in studies of landscape processes in deserts, Hudak and Wessman
(1998) used both geostatistical and textural analysis of high-resolution aerial photographs
to characterize woody plant encroachment in a South African savanna. Their results
suggest that historical aerial photography combined with current sources of highresolution data (either aerial photography or high resolution space instruments such as
IKONOS) can be used to monitor changes in woody composition of desert landscapes.
In order for textural analysis or spatial statistics to work, the instantaneous field of
view of the instrument (IFOV- hereafter referred to as ‘pixel size’) must be smaller than
the scale of variability of at least one of the dominant landscape types—grasslands or
shrublands. If the pixel size is greater than the scale of variability then the differences
between landscape elements such as soil and plants will average out and sub-pixel spatial
information is lost. If the pixel size is significantly smaller than the scale of heterogeneity
(plant or inter-plant dimension), on the other hand, spatial statistics may be used to probe
the distribution of vegetation or soil in the landscape.
All current and near-future spaceborne optical remote sensors fall into one of
three categories: panchromatic sensors, multispectral sensors, or hyperspectral sensors. In
sensor design, significant tradeoffs exist between available light energy, signal to noise
ratio, temporal coverage, ground resolution, swath width, and downlink bandwidth. For
2
example, a hyperspectral sensor with a large number of bands has little light energy
available in each band which can result in low signal-to-noise or else be compensated for
by a coarse spatial resolution. Alternatively, panchromatic sensors that have only one
band in the visible spectrum may have very fine spatial resolution because sufficient
energy exists in this portion of the spectrum, especially for sensors with a single band.
Panchromatic sensors are therefore very useful in providing textural information because
their pixel size may be smaller than the scale of heterogeneity on the surface while
maintaining reasonable data rates. Very wide-band observation in the visible, and
possibly in the short-wavelength near infrared, will allow discrimination of vegetation
and soil or observation of shadows cast by ground elements. Either vegetation/soil
discrimination or shadow observation on a very fine scale allows textural analysis of
many types.
Large-scale spatial patterns may also be related to land degradation in arid
regions. For instance, grazing animals impact land close to point-sources of water relative
to the areas further removed (Lange, 1969). This grazing gradient or “piosphere” (from
the Greek pios – to drink) effect can create obvious features in many semiarid
rangelands. However, they are only important in terms of land degradation insofar as they
do not recover after a wet season (Pickup and Chewings, 1994; Jeltsch et al., 1997)
(Figure 1). Grazing gradients are best observed in aerial photographs and images with
fine spatial resolution.
Multispectral and Hyperspectral Remote Sensing in Deserts
Multispectral sensors collect data in a few broad spectral bands which cover
important regions of the reflected solar spectrum (about 350 to 2500 nm). Because these
sensors provide data in multiple bands, the ground resolution is degraded and total
number of pixels per line for these sensors is less than that for panchromatic sensors. This
is due to both the decreased light energy available in each band as well as downlink
bandwidth. Therefore, spatial resolution is usually poorer for spaceborne multispectral
sensors than for panchromatic sensors, although large swath widths are typically
Figure 1. Idealized view of
grazing gradients observed
near watering points in arid
and semiarid rangelands.
During grazing, biomass near
the water source is drawn
down. During wet seasons,
this gradient should recover.
If it does not, then it is
evidence of a semipermanent change in the
lands near the water source
and land degradation. (After
Pickup and Chewings, 1994)
3
considered desirable.
Hyperspectral sensors, also called imaging spectrometers, provide data in a large
number of narrow, contiguous bands that cover the entire reflected solar spectrum. These
sensors typically provide data in very narrow swaths (such as the New-Milennium Earth
Observer 1Hyperion instrument or the Airborne Visible/Infrared Imaging Spectrometer
(AVIRIS)) or with large pixel sizes (such as the Moderate Resolution Imaging
Spectrometer (MODIS) instrument). Hyperspectral data cover the visible/near-infrared
(VNIR) water absorption region and therefore atmospheric water vapor and apparent
surface reflectance can be retrieved on a per-pixel basis using purely radiative transfer
methods (Gao and Goetz, 1990; Green et al., 1993; Gao and Goetz, 1995; Roberts et al.,
1997b).
Unfortunately, a similar approach is not possible for band-limited data such as
Landsat Thematic Mapper (TM) or the Advanced Very High Resolution Radiometer
(AVHRR), where radiometric calibration may be uncertain (Markham and Barker, 1987;
Che and Price, 1992) and full characterization of atmospheric properties through radiative
transfer is not possible. Nonetheless, a large body of literature exists that describes
methods that can provide estimates of surface reflectance at sufficient accuracy for
analysis of historical satellite data. These techniques can be roughly divided into absolute
calibration (e.g. Moran et al., 1992; Gilabert et al., 1994) and relative reflectance retrieval
(e.g. Roberts and Green, 1985; Elvidge, 1990; Conel, 1999).
Each set of techniques has its own merits. For example, absolute calibration
techniques, as described by Kaufman (1989) and Gilabert et al. (1994) can be automated,
adjust for heterogeneous atmospheres and retrieve atmospheric properties in addition to
reflected surface radiance. However these techniques require highly accurate radiometric
calibration and atmospheric inputs that may not be available. Relative reflectance
techniques, in which field targets are used to develop linear equations relating encoded
radiance (DN) to apparent surface reflectance, can provide accurate estimates of apparent
reflectance with little knowledge of sensor or atmospheric characteristics. However,
these technique typically require large, well-characterize homogenous field calibration
targets and assume a uniform atmosphere, neither of which may be possible in areas of
high relief or areas dominated by closed canopy or heterogeneous vegetation. Some other
relevant references for calibration of multispectral images are: Pech et al. (1986a),
Caselles and López García (1989), Chavez (1989), Holm et al. (1989), Moran et al.
(1995), Olsson (1995), Cahalan et al. (2001), Moran et al. (2001), and Teillet et al.
(2001).
Both multispectral and hyperspectral remote sensing have been used effectively in
studies of land degradation in arid and semiarid lands. In this section examples of the
application of both are given.
Multispectral Remote Sensing
Multispectral data have been used for a wide variety of landscape ecological
applications. Since rigorous reflectance retrieval is quite difficult with these data, a
frequent use of multispectral data is with vegetation indices. In arid and semiarid
environments, this approach is quite often not effective. Briefly, vegetation indices are
likely to underestimate live biomass in deserts, are insensitive to nonphotosynthetic
4
vegetation, and are sensitive to soil color (see Remote Sensing of Vegetation Parameters:
Challenges and Limitations section below for a more detailed discussion).
Spectral mixture analysis (SMA) has been widely used in studies of vegetation
cover in arid regions (Ustin et al., 1986; Drake et al., 1999; Elmore et al., 2000). Most
applications use a linear mixture technique that estimates the proportion of each ground
pixel’s area that belongs to different cover types (Gillespie et al., 1990; Smith et al.,
1990; Shimabukuro and Smith, 1991; Adams et al., 1993; Settle and Drake, 1993). SMA
is based on the assumption that the spectra of materials in an instrumental IFOV combine
linearly, with proportions given by their relative abundances. A combined spectrum thus
can be decomposed into a linear mixture of its “spectral endmembers”—spectra of
distinct materials within the IFOV. The weighting coefficients of each spectral
endmember, which must sum to one, are then interpreted as the relative area occupied by
each material in a pixel. The basic SMA equations are:
n
RP (λ ) = ∑ fi Ri (λ ) + ε (λ ) ,
(Eq. 1)
i =1
n
∑f
i
= 1,
(Eq. 2)
i =1
where fi are the best-fit coefficients that minimize RMS error (least-squares estimation),
ε(λ) is the difference between the actual and modeled reflectance, Rp(λ) is the apparent
surface reflectance of a pixel in an image, Ri(λ) are the reflectance spectra of spectral
endmembers in an n-endmember model, and fi are weighting coefficients, interpreted as
fractions of the pixel made up of endmembers i=1,2…n. RMS error is given by:
 m
2
∑ εj 
 j =1

RMS =
m
( )
0.5
,
(Eq. 3)
where εj are the error terms for each of the m spectral bands considered.
Smith et al. (1990) used SMA of Landsat TM data in the arid Owens Valley of
California to map vegetation cover and type. While their efforts at mapping vegetation
cover were successful, they were unable to distinguish between plant types for reasons
that will be discussed in the next section. Furthermore, they determined that all pixels
were a mixture of spectral components. Mixing models (sets of Ri(λ) above) must
therefore include all elements likely to be present in a pixel: soil, shade, live vegetation,
and senesced vegetation. Although it does not contribute to live biomass, inclusion of
senesced vegetation in SMA studies is necessary because of the high cover of nonphotosynthetic vegetation (NPV) often seen in desert areas.
In a study aimed at examining the relative merits of vegetation indices and SMA
for use in arid regions, Elmore and Mustard (2000) used multitemporal Landsat TM data
in combination with high-precision in situ data, also in Owens Valley. Their results
showed that SMA was able to determine percent live cover to within 4% and changes in
5
live cover to within 3.8%. The normalized difference vegetation index (NDVI) was
correlated with live cover, but the authors suggest that SMA results were superior
because NDVI is not a measure of total cover and was only marginally successful in
determining the correct sense of change (i.e. positive or negative) through time.
In a particularly interesting application of SMA, Franklin and Turner (1992) used
SPOT HRV XS multispectral data to determine the density and structural characteristics
of vegetation at several locations in the Jornada LTER site. Shrub density and structure
were estimated using the Li-Strahler geometric optical canopy reflectance model which
assumes that the pixel is much larger than the average plant canopy but small enough that
the number and size of plants varies among pixels. In this model, the reflected spectrum
is considered to be an area-weighted mixture of four components: sun-lit soil, sun-lit
canopy, shaded canopy, and shaded soil (Figure 2) (see also, Graetz and Gentle, 1982;
Pech et al., 1986b; Pech and Davis, 1987; Hall et al., 1995; Peddle et al., 1999; Gilabert
et al., 2000). Mathematical relations exist among plant size, plant shape, and the
proportions of these components for given sun and viewing geometries. The results of
Franklin and Turner (1992) showed that predictions of shrub sizes and density were
reasonably accurate when grouped by shrub class indicating that this approach, while not
perfect, has promise in providing parameters relevant to ecosystem function and
modeling.
Multispectral remote sensing may also be used to identify changes in land use in
desert areas. For instance, areas of active agriculture stand out in high contrast to the lowcover desert around them. This can make agricultural land use particularly easy to
identify in arid regions (Figure 3). Following this approach, Ray (1995) used Landsat TM
and multispectral scanner (MSS) images from 1973 to 1991 to document the history of
agriculture during this period a portion of the Mojave Desert of California (see also Okin
et al., 2001a). In another example of change detection to document land degradation,
Figure 2. Shrub shape and
illumination geometry for
a spheroid (After Franklin
and Turner, 1992).
Koch and El Baz (1998) used Landsat TM as well as other GIS layers to identify the
6
Figure 3. A KidSat image from Saudi Arabia taken in 1995. Reddish areas in the top
half of the image are aeolian sands overlying a lighter colored soil. The dark circles
are central pivot agricultural fields. The introduction of mechanized agriculture in
sandy desert areas such as this where fossil groundwater is used for irrigation
represents unsustainable and hazardous utilization of these drylands.
effects of the Gulf War on the deserts of Kuwait. The principal effects of the War on the
area were (1) postwar sand encroachment due to the destruction of vegetation cover and
protective soil crusts by military vehicles during and after the war, and (2) the formation
of tarcrete on the surface by oil pollution including the large oil fires lit by the retreating
Iraqi army. In all, they determined that more than 20% of the surface of Kuwait was
affected by the Gulf War. Robinove et al. (1981) and Graetz et al. (1988) have also
presented methods for monitoring landscape change in arid regions using Landsat.
Hyperspectral Remote Sensing
As discussed in the previous section, multispectral data have been widely used in
studies of land degradation in arid and semiarid regions. As hyperspectral data become
more widely available, these data contribute to a much greater extent to our
understanding of the dynamics of dryland environments and the degradation processes
that threaten them. At the same time, these data are faced with the same challenges that
confront all remote sensing in drylands.
Early use of AVIRIS data by Elvidge et al. (1993) to detect vegetation at
extremely low covers showed that the vegetation red edge could be detected for green
vegetation cover as low as ~5%. These investigators used cultivated Monterey pine
stands which likely had more distinct red edges than are commonly found in native
communities. In an attempt to develop a hyperspectral vegetation index that would be
insensitive to soil color and would work at low vegetation cover, Chen et al. (1998)
7
Figure 4. As with NDVI,
hyperspectral vegetation indices
are not robust with desert
vegetation. Here, the relationship between the 1DL_DGVI
derivative-based hyperspectral
vegetation index to simulated
vegetation cover for four vegetation types is shown. Three
different soils are represented in
the simulated spectra and
1DL_DGVI appears to be
insensitive to soil color. Scatter
in the y-direction is caused by
simulated noise. Lines are bestfits through the data.
proposed two derivative-based vegetation indices that can be used with hyperspectral
data. These indices calculate the integral of absolute value of the first or second
derivative of apparent surface reflectance from approximately 626 nm to 795 nm (the
range of the red edge) after the effect of local soil color has been removed.
In simulated spectra (noise added) with varying amounts of different vegetation
types overlying different soil (for more details see Okin et al., 2001b), the relationship
between the derivative-based vegetation indices do show high correlation with green
(lawn) vegetation (Figure 4). However, the slope of the correlations drop to near-zero
when simulated cover of live Atriplex polycarpa and Larrea tridentata, two common
Mojave Desert shrubs, are considered. In fact, the relationship between cover and the
derivative-based vegetation index (1DL_DGVI) for these two shrubs is nearly
indistinguishable from that of senesced grass (NPV). Clearly hyperspectral-based
vegetation indices cannot overcome the problems encountered with simple multispectral
vegetation indices.
As with multispectral data, SMA appears to be a more robust alternative to
vegetation indices when considering hyperspectral data. SMA is particularly amenable
for use with hyperspectral data where the number of useful bands is much higher than the
number of model endmembers. The unique capabilities of imaging spectrometers have
proven useful for SMA in a variety of different land-cover types with significant plant
cover. Roberts et al. (1993; 1997b; 1998) used linear mixture analysis of AVIRIS data to
map green vegetation, nonphotosynthetic vegetation, and soils at the Jasper Ridge
Biological Preserve and in the Santa Monica Mountains, CA.
In arid regions, García-Haro et al. (1996) have applied SMA to high spectral
resolution field spectroscopy in the detection of vegetation, finding it to be less sensitive
to soil background than the NDVI. McGwire et al. (2000)have determined that the use of
SMA with multiple soil endmembers is significantly better suited for quantifying sparse
vegetation cover in deserts than NDVI, the soil-adjusted vegetation index (SAVI), or the
8
modified-SAVI (MSAVI).
Unfortunately, the quantitative detection of sparse vegetation in remote sensing
imagery, and hence in many arid and semiarid areas worldwide, remains problematic
even using hyperspectral data with high signal-to-noise ratios. Okin et al. (2001b)
showed that even using hyperspectral data under best-case assumptions for noise and
intra-species variability, discrimination of different vegetation types using SMA and
other techniques was nearly impossible when cover was below ~30%. They showed that
soil surface type, on the other hand, can be reliably retrieved under many situations using
multiple-endmember SMA (MESMA).
MESMA is simply a SMA approach in which many mixture models are analyzed
in order to produce the best fit (Gardner, 1997; Roberts et al., 1997a; Roberts et al.,
1998). In the MESMA approach, a spectral library is developed containing the spectra of
all plausible ground components. As a result, MESMA requires an extensive library of
field, laboratory, and/or image spectra, where each plausible ground component is
represented at least once. A set of mixture models with n (n ≥ 2) endmembers from the
library is defined and each model is fit to every pixel in a remote sensing image. The
model that fits each pixel with the lowest RMS and physically reasonable fractions is
recorded along with the endmember fractions for that model.
One of the primary limitations on remote sensing in deserts is the fact that high
soil and vegetation variability can occur over short length scales. This can make choosing
endmembers for SMA that are representative over an entire scene a doomed prospect.
The problem of within-scene spectral variability affects most other remote sensing
techniques. MESMA circumvents this problem by providing a robust way to
accommodate spectral variability within a scene. Under MESMA, both soil and
vegetation endmembers are allowed to vary between pixels. As we shall see in the case
study presented at the end of this chapter, this fact makes MESMA a much better choice
than more traditional methods.
Summary
Despite the failure of both hyperspectral and multispectral vegetation indices to
be robust when considering desert vegetation, both may be used to retrieve vegetation
parameters. SMA and its close cousin, MESMA, appear to be the most promising
methods to determine information about soil surface type, vegetation cover, and even
vegetation canopy characteristics.
Important considerations in using SMA with either multispectral or hyperspectral
data are 1) the spatial coverage desired, and 2) the ability to convert an image into
meaningful units. Because often SMA is used with ground-based reflectance
measurements, quantitative use of multispectral data requires some way to convert sensor
DNs to reflectance. While this can be accomplished relatively easily with the high-quality
data from the Landsat ETM+ data, the published method (http://landsat.gsfc.nasa.gov/)
does not allow compensation for absorption or scattering in the atmosphere. Ancillary
data, including ground-based measurements taken at the time of image acquisition, are
required to improve these simplistic reflectance inversions. Multispectral data from
sensors with out-of-date or poor calibration require temporally invariant ground targets to
convert the raw DN to reflectance. Alternatively, image endmembers may be used for
SMA analysis. These endmembers will have the same sensor gains and offsets as the
9
complete image and will represent similar atmospheric conditions. Nonetheless, the use
of image endmembers requires having at least one pixel with only one component. The
high spatial variability in deserts, along with the sparse but ubiquitous vegetation will
tend to frustrate searches for pure image endmembers.
The spectral resolution in hyperspectral data is often sufficient to allow retrieval
of atmospheric characteristics during the reflectance retrieval. The resulting apparent
surface reflectance is therefore largely free from atmospheric effects and can be used for
SMA studies. These data often contain artifacts from the reflectance inversion making
rigorous analysis difficult. Temporally invariant ground targets are nonetheless often
required to remove the scene-wide artifacts that can result from even the most
sophisticated inversion techniques.
Multitemporal Remote Sensing in Deserts
When considering multitemporal remote sensing, it is useful to identify the
timescales of the principal phenomena to be imaged. In arid and semiarid regions, there
are three timescales on which landscape change occurs.
The landscape change with the fastest time constant is the nearly immediate
greening of the landscape after a rain event. Individual cloud bursts from convective
cloud cells can provide precipitation that is spatially discontinuous leading one area to
receive significantly more moisture than adjacent areas. While above-ground plant
biomass may not respond immediately to these localized precipitation events,
cryptobiotic crusts, communities of cyanobacteria, fungi, lichens, and mosses, can. These
communities are ubiquitous in the world’s deserts and have a major influence on the
spectral characteristics of deserts, particularly on fine-grained soils (Tsoar and Karnieli,
1996). These communities can respond nearly instantaneously to small amounts of
moisture, commencing photosynthesis and O2 production within minutes (Garcia-Pichel
and Belnap, 1996) and simultaneously changing the spectral response of the soil surface
by changing the species of free cyanobacteria present at the surface.
While some vegetation responds to individual precipitation events, arid and
semiarid landscapes as a whole are largely controlled by seasonal changes in temperature
and precipitation. Vegetation in the Chihuahuan Desert, for example, responds to two
rainfall regimes: winter/spring precipitation and summer monsoonal precipitation. Thus,
two distinct “greenings” may occur in this landscape, as C3 shrubs respond to
winter/spring precipitation and C4 grasses respond to the summer monsoon rains. Despite
the general problems with multispectral vegetation indices, Peters and Eve (1995)
demonstrated that coarse resolution multispectral data can aid in the analysis of
vegetation phenophases and the response of vegetation to moisture availability. By
looking at small areas over and over again, the spatial variations in soil color which can
confound single-data vegetation index analysis were obviated.
Finally, vegetation can vary interannually in response to changing climatic
conditions or on decadal timescales in response to human disturbance or recovery. For
example, over the past 150 years, the Jornada Basin has changed from a grass-dominated
landscape to a shrub-dominated landscape (Buffington and Herbel, 1965). Droughts
lasting a few years to decades can also have dramatic impacts on a vegetation community
(Muhs and Maat, 1993; Schultz and Ostler, 1993). In studies aimed at long-term
continental-scale monitoring of vegetation in arid regions, Tucker et al. (1991; 1994)
10
used daily AHVRR data from 1980-1992 to determine of the boundary between the
Sahara Desert and the Sahel Zone of Africa.
Changes in the soil typically occur on the annual to decadal timescale as well. For
example, the appearance and growth of the sand blow-outs observed in the Manix Basin
(in the Mojave Desert, California) can only be observed on annual timescales (Ray, 1995;
Okin et al., 2001a). While much of this erosion may happen in just a few large events
each year, the cumulative effects are not seen on seasonal timescales. Soil development
or the formation of desert pavement occurs on the timescale of centuries to millennia.
REMOTE SENSING OF VEGETATION PARAMETERS: CHALLENGES AND
LIMITATIONS
The hot deserts of the world are characterized by low precipitation and high
potential evapotranspiration. Vegetation is therefore largely limited by the lack of water
and deserts typically have low vegetation cover. This provides a near-optimal situation
for geological remote sensing in arid regions because there is little vegetation to obscure
the spectral signal of the geological substrate. This fact has lead to important advances in
geological remote sensing in arid regions and has allowed remote sensing to make
contributions to a wide variety of fields including geomorphology and economic geology
(for a review of geological remote sensing see, Clark, 1997)
Remote sensing is not limited, however, to identification of geological materials
on the earth’s surface. There are many applications of remote sensing where information
about vegetation is as important, or more important, than information about the substrate
on which it is growing. Land managers, for example, may wish to know the location of
sandy areas in their region, but will also likely want to know where different vegetation
communities are located and how they change with time or management strategy.
There are several real hurdles to accurate retrieval of vegetation parameters in
desert areas using remote sensing. The first and most obvious is the fact that because
vegetation cover is low, the contribution of vegetation to the area-averaged reflectance of
a pixel is small (Table I). Furthermore, because of their low organic matter content, soils
in desert areas tend to be bright and mineralogically heterogeneous. All of these factors
tend to swamp out the spectral contribution of vegetation in individual pixels (Huete et
al., 1985; Huete and Jackson, 1987; 1988; Smith et al., 1990; Escafadel and Huete, 1991;
Huete and Tucker, 1991). The dominance of the soil spectrum in desert regions also
Table I. Reflectance in ETM+ Band 2 and 4 for varying
amounts of desert vegetation on bright desert soil.
Percent Reflectance
Percent
Vegetation Cover ETM+ Band 2 ETM+ Band 4
0%
23.2
31.7
5%
23.5
32.2
10%
23.7
32.6
15%
24.0
33.0
20%
24.3
33.5
100%
28.9
11
40.4
requires remote sensing techniques that allow the underlying soil spectrum to vary across
an image. Using variable soil spectra has allowed Okin et al. (2001b) and McGwire et al.
(2000) to simultaneously map vegetation cover and soil surface type (see also Huete, this
volume).
Consider a sparse cover of vegetation on a bright desert soil (Table I). In ETM+
Band 2, the soil has a reflectance of 23.2% and the vegetation has a reflectance of 28.9%
while in ETM+ Band 4, the soil has a reflectance of 31.7% and the vegetation has a
reflectance of 40.4%. As the cover of the vegetation on the soil goes from 0% to 20%,
reflectance in Bands 2 and 4 change 1.1% and 1.7%. Absolute radiometric calibration of
the Landsat 7 ETM+ instrument is within 5% and the per-pixel relative noise is less than
1% (http://landsat7.usgs.gov/news/lpso_rpt.html; http://geo.arc.nasa.gov/sge/landsat/
l7.html). Thus, even with this relatively well calibrated and low-noise instrument, the
ability to distinguish between 0% and 20% vegetation cover is limited, although several
authors (Elmore et al., 2000; McGwire et al., 2000; Okin et al., 2001b) have shown that it
in principal, and sometimes in practice, is possible to measure extremely sparse desert
vegetation cover as well as cover change using remote sensing.
Many processes in deserts are controlled by thresholds or points which, if the
vegetation cover is lower than a certain amount some process is accelerated or
decelerated. For example, desert landscapes are susceptible to both wind and water
erosion when percent cover is below about 15% (see for example, Wiggs et al., 1995;
Lancaster and Baas, 1998). The difficulty in discerning between 10% cover and 20%
cover using remote sensing, therefore, can create problems determining whether an area
is susceptible to erosion.
The problem of vegetation cover retrieval in deserts is compounded by the fact
that senescent material can be a major component of the total surface cover. NPV,
whether in the form of dead shrubs, leafless drought deciduous plants, or senescent
annuals plays an important role in both the abiotic and biotic dynamics of desert regions.
For example, both live and dead material can help suppress of wind and water erosion by
contributing to the density of physical obstacles and total cover that protects the surface
from erosion; the ~15% cover threshold of Lancaster and Baas (1998) and Wiggs et al.
(1995) for wind erosion exists irrespective of whether the vegetation is living, green
vegetation or NPV. Many common methods for estimating vegetation cover, such as
most vegetation indices, are insensitive to the presence of NPV. Because of this,
vegetation indices may not be used as a proxy for total cover in situations where NPV is a
significant component of surface cover, particularly in cases where NPV does not co-vary
in space or time with green vegetation. Furthermore, Huete and Jackson (1987) have
shown that senescent vegetation and weathered litter can dramatically impact the
reflectance of mixtures. Because even live vegetation in desert regions can be comprised
of both photosynthetic and senescent components, the effect of NPV on desert remote
sensing exists down to, at least, the canopy scale.
Further characteristics of deserts can contribute to difficulties in remote sensing in
these regions. The values in Table I were created by letting soil and vegetation spectra
mix linearly. In deserts, as in other landscapes, this may often not be the case. There is
the potential of nonlinear mixing in arid and semi-arid regions due to multiple scattering
of light rays (Huete, 1988; Roberts et al., 1993; Ray and Murray, 1996). Simple single
scattering is represented as a product of the reflectance of an object times the intensity of
12
the incoming radiation:
Ir(λ)=ρ(λ)Ii(λ),
(Eq. 4)
where Ir(λ) is the intensity of the reflected light, Ii(λ) is the intensity of the incident light
and ρ(λ) is the reflectance spectrum for the object. Multiple scattering results when a ray
of light from the sun intercepts more than one object on the surface before it is reflected
back up for observation by a remote sensing instrument:
Ir(λ)=ρ1(λ)ρ2(λ)Ii(λ),
(Eq. 5)
where ρ1(λ) is the reflectance spectrum for the first object encountered and ρ2(λ) is the
reflectance spectrum for the second object. The product, ρ 1(λ)ρ2(λ), is why multiple
scattering is called “non-linear mixing”.
The greater the value of either ρ 1 (λ ) or ρ 2 (λ), the greater the non-linear
contribution to the reflected radiation. In deserts where bright soils often underlie
vegetation with open canopies the potential for contamination of the reflected light by
this type of multiple scattering is high. Furthermore, the open canopies of desert shrubs
can contribute to poor correlations of reflected near-infrared radiation with leaf area
index (LAI) (Hurcom and Harrison, 1998). Roberts et al. (1990) have also suggested that
canopy structure can affect plant reflectance, particularly in the near-infrared where leaf
to leaf scattering can nonlinearly accentuate the vegetation spectrum. Nonlinear mixing in
this case is likely to lead to an overestimation of green vegetation cover and an
underestimation of shade (Roberts et al., 1993).
To further complicate the story, evolutionary adaptations to the harsh desert
environment make desert plants spectrally dissimilar from their humid counterparts
(Billings and Morris, 1951; Gates et al., 1965; Ehleringer and Björkman, 1976; Mooney
et al., 1977; Ehleringer and Björkman, 1978; Ehleringer and Mooney, 1978; Ehleringer,
1981; Ray, 1995). This difference is not merely in the overall brightness of desert
vegetation or the ratio of green vegetation (GV) to NPV within the canopy, but actual
perturbations to the shape of the spectrum at specific wavelengths (Figure 5).
13
Figure 5. Comparison of spectrum of green vegetation (GV), non-photosynthetic
vegetation (NPV) and creosote canopy spectrum. The Best Fit line is the optimal linear
least-squares mixture of GV and NPV to match the creosote spectrum. The residual of
this mixture (Residual=Creosote Canopy – Best Fit) is given in the bottom panel.
Vegetation in more humid environments have largely adapted to maximize their
interception and absorption of photosynthetically active radiation (PAR). In deserts PAR
is an over-abundant resource which leads to high temperatures and high evaporative
losses. Therefore, desert vegetation has evolved methods to avoid overheating and loss of
moisture from evapotranspiration. One way that they accomplish this is to minimize their
surface areas by reducing leaf size or avoiding leaves altogether and moving
photosynthesis to the stalks and stems. Furthermore, the high densities of reflective
spines on many plants serve not only to protect the soft flesh from predation, but also to
partially shade the photosynthetic surface of the plant, to reflect a large proportion of
incoming radiation, and to create a still-air layer around individual segments which can
reduce losses to evapotranspiration.
14
Figure 6. In this striking example of intraspecies variability, two adjacent Atriplex
polycarpa bushes exhibit different
phenologies (the bush on the left has
produced fruit and gone into dormancy, the
bush on the right has yet to produce
flowers) and therefore different reflectance
spectra. (G. Okin, Manix Basin, September
13, 1997)
Spines and their gentler cousins—leaf hairs—are common features in desert vegetation
that increase the reflectance of vegetation especially in the visible (Ehleringer and
Mooney, 1978). In addition, because PAR is an over-abundant resource, many desert
plants have low concentrations of chlorophyll in their leaves and stems, as exhibited by
their typically pale green color. The absorption of a photon by chlorophyll generates heat.
Having low concentrations of chlorophyll thus helps vegetation further avoid
overheating. Further adaptations include a waxy leaf cuticle that helps reduce losses due
to evapotranspiration. These waxy films often give vegetation anomalous absorptions
around 1720 nm (Figure 5 and Ray, 1995).
Leaf hairs, spines, and reduced chlorophyll concentration in desert plants all act to
reduce leaf absorption in the visible which decreases the red edge in many of these plants.
The adaptations of desert plants to their surroundings are largely responses to the
radiation environment (intense sun and heat) present in deserts, and thus influence
directly the spectral characteristics of vegetation. Different plant species have evolved
different strategies for coping with the harsh desert environment. The resulting interspecies spectral variability is a consideration in quantitative vegetation remote sensing in
arid and semiarid environments because the full range of vegetation spectra must be
taken into account. Unfortunately, most types of desert shrubs are not different enough
from one another to allow discernment of vegetation type. Okin et al. (2001b) have
shown that even under simplified but realistic best-case conditions, vegetation type
cannot be discerned at low covers. Intra-species variability exacerbates this problem
because spectral variability within a species can be greater than variability between
species (Figure 6 and Duncan et al., 1993; Franklin et al., 1993).
Intra-species variability is largely a function of climatic variability in deserts.
Precipitation in the world’s arid and semiarid regions is often highly variable in both
space and time. Desert plants have adapted to this by coordinating their phenological
stages with the availability of soil moisture. When water becomes available after the dry
season or a period of drought, perennial vegetation will emerge from dormancy, begin
photosynthesis, and if time permits, produce flowers and fruits. When water again
becomes scarce, vegetation will resume dormancy. The total cycle often takes place
during a relatively short (2-3 month) growing season. Under extended periods of dryness,
some vegetation will drop their leaves in “drought deciduous” behavior. Annuals in
desert regions also respond opportunistically and rapidly to available soil moisture,
15
sometimes going from germination to fruiting to senescence in a few weeks. The
temporal variability of vegetation in arid regions contributes to spectral variability within
a single region and on smaller scales (Figure 6). No single reflectance spectrum can
represent the full “spectral phenology” of desert plants and spectra representing different
phenological stages must be incorporated for quantitative information about vegetation
change in both space and time.
Summary
The retrieval of vegetation parameters from remote sensing presents some
significant challenges:
1) Low vegetation cover over bright soils means the vegetation signal can be
swamped out of the pixel-averaged signal,
2) Exposed, variable soil surfaces can contribute significantly to within-scene
variability,
3) Many remote sensing techniques are insensitive to NPV which can be a
major and important component of total cover in deserts,
4) Open canopies and bright soils in desert areas can contribute to significant
multiple scattering and nonlinear mixing in deserts,
5) Desert vegetation is spectrally dissimilar to its humid counterparts lacking,
most notably, a strong red edge, and
6 ) Rapid phenological changes are accompanied by spectral changes in
desert vegetation which can lead to significant temporal and spatial intraspecies spectral variability.
Despite these challenges, it is possible to retrieve quantitative information about
vegetation remotely as was discussed in the previous section. Nonetheless, in the
application of remote sensing to problems in desert regions, the limitations and
considerations discussed here must be anticipated and worked around. Before we turn to
a case study of remote sensing in an arid North American shrubland, we will discuss an
important use of remote sensing techniques in desert regions: detection of atmospheric
dust.
ATMOSPHERIC REMOTE SENSING IN ARID REGIONS: CLEAR SKY AND
DUSTY DAYS
Atmospherically, arid environments are one of the most hospitable for remote
sensing. Many arid regions throughout the globe experience cloudless conditions much of
the year. This raises the probability that any single pass of an instrument overhead will
get a clear view of the surface.
Due in large part to their dryness, however, arid regions are also the world’s
major source of atmospheric mineral aerosols—dust. Mineral aerosols may impact global
climate through their ability to scatter and absorb light (Sokolik and Toon, 1996) and to
affect cloud properties (Wurzler et al., 2000). Dust is thought to play a major role in
ocean fertilization and CO2 uptake (Duce and Tindale, 1991; Piketh et al., 2000).
Deposition of dust can be important for soil formation and nutrient cycling (Swap et al.,
1992; Wells et al., 1995; Reynolds et al., 2001) and serious public health concerns arise
in regions affected by high concentrations of atmospheric dust (Griffin et al., 2001).
Dust emission is also a fundamental part of the functioning of arid landscapes.
16
The extent of dust emission has been observed to be a function of many landscape and
climate parameters (for a good review see, Gillette, 1999). Humans impact conditions in
the landscape through land use by removing vegetation and breaking up soil crusts (Okin
et al., 2001a). Due to this, some authors have suggested that land use accounts for
observed significant increases in wind erosion in North Africa, the world’s single largest
source of atmospheric dust (Tegen and Fung, 1995; Prospero et al., In Press). Other
investigators have suggested that variations in topography and climate can account for the
spatial and temporal patterns seen in atmospheric dust using remote sensing (Prospero et
al., In Press). Whether natural processes are alone responsible for increases in
atmospheric dust concentrations or whether land use also has an impact will remain a
question for some time to come. In combination with other datasets, remote sensing data
will help address this question.
The remote sensing techniques that can be used to observe atmospheric aerosols
can be divided into 1) those which work over oceans but not land, and 2) those which
work over both oceans and land. Multispectral sensors that function in the visible are, for
the most part, only valuable for mineral aerosol remote sensing over the oceans. Two
VNIR sensors, AVHRR and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), are
commonly used to provide information about aerosol optical depth over oceans. These
instruments can be used to map atmospheric dust distributions because dust, when
illuminated by the sun, scatters a fraction of the solar radiation back to space (Husar et
al., 1997). Over the continents the methods developed for these instruments do not work
because the radiation scattered by dust is swamped out by that reflected from the surface.
Nonetheless, the combination of radiative transfer models and hyperspectral visible/nearinfrared data does allow retrieval of aerosol information over the continents for the
hyperspectral MODIS instrument (Kaufman et al., 1997; Tanre et al., 1997). Over the
oceans, the problem of surface reflectance is minimized and multispectral instruments
can be used to retrieve aerosol information. The algorithm used to retrieve dust
distributions from AVHRR data uses the backscattered radiance in the 0.63 µm band of
the instrument and a radiative transfer model that includes idealized mineral aerosols
(Husar et al., 1997). Work by Husar et al. (1997) has characterized tropospheric dust over
the oceans, and has revealed many spatially coherent plumes that can be interpreted in
terms of reasonable sources, with the dust plumes coming off of North Africa and
northwestern China being prominent features.
Two methods have been developed to measure atmospheric aerosols using nonVNIR methods. The Total Ozone Mapping Spectrometer (TOMS) instrument has a UV
spectrometer designed to provide accurate global estimates of total column ozone. Recent
developments have shown that it is also capable of estimating both absorbing and nonabsorbing aerosols (Figure 7). Herman et al. (1997) have developed at Aerosol Index
(AI) derived from TOMS which is defined as:
AI = −100{log 10 [( I 340 / I 380 ) meas ] − log 10 [( I 340 / I 380 ) calc ]}, (Eq. 6)
where I340 is the radiance 340 nm, I380 is the radiance 380 nm, meas denotes the measured
radiance using the TOMS instrument and calc denotes the radiance calculated using a
radiative transfer model that is constructed to give nearly zero AI in the presence of
clouds.
17
Prospero et al. (In Press) have used the TOMS AI index to identify areas that are
sources of atmospheric dust on a global scale. By looking at the number of days where
the AI value was above a pre-determined threshold, Prospero et al. were able to improve
our understanding of where, within large desert areas, dust tends to be generated. They
suggest that dust emission is a spatially varying process that tends to be concentrated in
large basins where there are ample fine-grained sediments to be eroded. The Infrared
Difference Dust Index (IDDI) is derived from images obtained from the Meteosat 10.5
µm- to 12.5-µm thermal infrared channel (Chomette et al., 1999). The IDDI is sensitive
to the decrease of the thermal infrared radiance due to the present of dust in the
atmosphere during daytime. To compute the IDDI, a time series of geometrically and
radiometrically calibrated images are used to create a reference image representing
approximately clear and dust-free conditions. Clouds and dust are separated from the
surface information by subtracting the calibrated images from the reference image and
cloudy pixels are masked out. The resulting images provide a time-series of IDDI values
related to atmospheric optical depth.
In an application of the IDDI to understanding the distribution of dust emission
and potentially land degradation in North Africa, Chomette et al. (1999) combined IDDI
values for North Africa and wind speed at 10-m height to determine threshold wind
speed—the wind speed above which wind erosion can occur. This parameter is very
sensitive to both surface texture and vegetation cover (Marticorena et al., 1997). Gillette
et al. (1980) have also shown that threshold wind speed is very sensitive to disturbance
through land use.
The results of the Chomette et al. (1999) and Prospero et al. (In Press) papers
Figure 7. A dust storm coming off the west coast of North Africa on March 6, 1998.
The image on the left is a SeaWIFS color composite (www.gsfc.nasa.gov/
SEAWIFS/IMAGES/SEAWIFS_GALLERY.html). The image on the left is a
TOMS AI image of the same storm (courtesy N. Mahowald). Red are high AI values,
blue are low AI values. TOMS AI is defined as: -100{log10[(I340/ I340)meas- log10[(I340/
I340)calc]},where I340 is the radiance 340 nm, I380 is the radiance 380 nm, meas denotes
the measured radiance using the TOMS instrument and calc denotes the radiance
calculated using a radiative transfer model that is constructed to give nearly zero AI
in the presence of clouds.
18
provides a important basis for work aimed at identifying the spatial locations of desert
dust emission as well as the conditions that make these areas particularly susceptible to
wind erosion. With this information, scientists will be able to evaluate the effect that
humans play in affecting the global dust cycle through the degradation of arid lands.
CASE STUDY: THE MANIX BASIN, SAN BERNARDINO COUNTY,
CALIFORNIA
Several techniques that may be used for analysis of remote sensing data in arid
regions were discussed in the previous section. In this section, three of those
techniques—NDVI, SMA, and MESMA—plus a fourth technique—matched
filtering—will be compared for a single site.
Figure 8. Location of the
Manix Basin, California.
Maps
from
www.nationalatlas.gov.
Figure 9. Landsat TM false
color image of the Manix
Basin (RGB=542). Interstate
15 runs through the center of
the image and Interstate 40
runs through the bottom of
the image. The Mojave River
runs from left to right across
the image below I-15. The
playa in the north of the
Basin is Coyote Dry Lake.
The bright green circles are
active
central
pivot
agriculture.
Several
abandoned fields can also be
seen throughout the Basin,
particularly in the North.
TM image from the Mojave
Desert Ecosystem Project.
19
The Manix Basin is in the Mojave Desert, about 25 miles ENE of Barstow in
southeastern California (centered around 34°56.5’ N 116°41.5’ W at an elevation of
about 540 m) (Figure 8). The basin has an area of 40,700 ha and was the site of ancient
Lake Manix which existed during the peak pluvial episode of the last glaciation and
drained through Afton Canyon to the east (Smith and Street-Perrott, 1983; Meek, 1989).
Much of the basin is filled with lacustrine, fluvial, and deltaic sediments capped by weak
armoring (Meek, 1990). There is clear evidence of pre-modern wind erosion, indicating
that wind erosion, transport, and deposition has long been a dominant geological process
in the area (Evans, 1962).
The modern climate of the Manix Basin is arid with the average annual
precipitation of 10 cm falling mostly in the winter, although there can be significant
summer precipitation in some years (Okin et al., 2001a). The average annual temperature
is 19.6°C, the average winter temperature is 9.1°C, and the average summer temperature
is 31.4°C (Meek, 1990). The vegetation in undisturbed areas of the basin is dominated by
an association of Larrea tridentata and Ambrosia dumosa with minor occurrence of
Atriplex polycarpa, Atriplex hymenelytra, Atriplex canescens, Ephedra californica, and
Opuntia spp. Prosopis glandulosa occurs in some areas of the basin. Vegetation cover in
undisturbed areas rarely exceeds (10%) (Okin et al., 2001a). Areas that have been
disturbed directly by human activity are dominated by At. polycarpa with total cover
often greater than that in undisturbed desert (~30%). Schismus, an exotic annual grass, is
ubiquitous, but grass cover varies significantly with yearly precipitation.
Human activity in the Manix Basin (Figure 9) has been extensive, with several
phases of agricultural activity utilizing groundwater recharged by the Mojave River,
which carries runoff from the San Bernardino Mountains to the south-southwest (Tugel
and Woodruff, 1978). The basin was used for dryland farming in the 1800s (Tugel and
Woodruff, 1978). Limited irrigated farming started in the basin in 1902 with the acreage
of irrigated land increasing sharply after World War II (Tugel and Woodruff, 1978).
Today alfalfa hay is the major agricultural product. In the Coyote Dry Lake sub-basin,
square flood-irrigated fields and abandoned flood irrigation equipment are seen in early
Landsat images. After the mid-1970s, central-pivot agriculture became the dominant
form of land use in the area, but many fields have now been abandoned throughout the
northern part of the basin due to increasing costs of pumping groundwater to the surface
for agriculture (Ray, 1995).
AVIRIS data were acquired over the Manix Basin on April 30, 1998. The
northern lobe of the Manix Basin is the focus of this study and is covered by flight
980430 run 10 scene 3 (Figure 10). The data were radiometrically calibrated at the
AVIRIS data facility (Jet Propulsion Laboratory, Pasadena, California). Apparent surface
reflectance was retrieved using a technique developed by Green et al. (Green et al., 1993;
Green et al., 1996; Roberts et al., 1997b). The reflectance spectrum from a gravel parking
lot in the scene was used to correct the apparent surface reflectance spectra of the entire
scene (Clark et al., 1995). The products derived from the AVIRIS image were rectified
using a nearest-neighbor triangulation method that employed 107 ground-control points
chosen in the image and in a series of 1-m resolution USGS digital orthophotos.
20
Figure 10. Landsat TM
image (RGB=542) of the
northern part of the Manix
Basin. Letters denote features
in the basin discussed in the
text. The heavy black
rectangle in the image
denotes the extent of the
AVIRIS image discussed in
the text.
Landsat TM data from the Mojave Desert Ecosystem Project (MDEP) were used
to create NDVI images for the area colocated with the April 30, 1998 AVIRIS image
(Figure 11). The NDVI values for the image on the left in Figure 11, which has not been
stretched to enhance contrast within the area of interest, shows the low response of NDVI
to the vegetation in the Basin. This is largely due to the fact that NDVI is insensitive to
NPV which dominates vegetation cover in the area. The generally low NDVI values in
the Basin are thus due to the low cover generally as well as the relatively low proportion
of GV to NPV. In the contrast-enhanced image on the left, it is evident that NDVI is
relatively high for two areas that have high cover of At. polycarpa: B and C. Area A,
however, also has relatively high NDVI values, but these two abandoned fields lack
vegetation cover completely. Here, the high NDVI value relative to the surrounding
undisturbed desert, is likely due to a change in soil color associated with tillage of these
fields. In addition, the northernmost small playa on the right edge of the image displays
high NDVI values. This playa has no vegetation cover.
21
Figure 11. NDVI image of the study area in the Manix Basin.. The image on the
left has been scaled so that black is essentially no vegetation and white is the value
of riparian vegetation with nearly 100% cover (in an area not colocated with the
AVIRIS image). The image on the right has been stretched to enhance the contrast
within the bounds of the AVIRIS image.
Figure 12. MESMA-derived map
of soil and vegetation cover in the
AVIRIS image. This figure is the
product of 1,885 four-endmember
models utilizing ground spectra of
shrubs, grasses (senesced), and
soils from the Manix Basin.
White areas are places where no
model was fit within the
constraints. For more information
see, Okin et al. (2001b).
Despite these discrepancies between NDVI values and actual cover in the Basin,
there is relatively good agreement between the NDVI image and the MESMA-derived
vegetation (GV + NPV) cover map (Figure 12). Both MESMA and NDVI vegetation
generally decrease from south to north and west to east in the image, with nearly zero
cover close to Coyote Dry Lake (H), on the relic beach terrace in the northern part of the
basin (I), on the wash on the left corner of the image, on Agate Hill (G) and on alluvial
22
fan #2. MESMA and NDVI disagree on the relative amounts of vegetation cover on
abandoned fields A, E, and F, on the grounds of St. Francis Monastery (D), and on
alluvial fan #1. In general, the vegetation cover map based on MESMA represents most
closely the degree of cover in this part of the basin (G.S. Okin, personal observations, and
Okin et al., 2001a).
Many discrepancies exist in comparing the MESMA-derived vegetation cover
map with that from simple unmixing (SMA) (Figure 13). As with NDVI, SMA
overestimates the degree of vegetation on the bare abandoned fields A, E, and F, and on
the grounds of the Monastery (G). On abandoned fields B and C, SMA does identify the
presence of significant vegetation, but the rightmost field in B is modeled as having a
large amount of GV. This is also seen in the northern part of the image (H and I) which
according to MESMA, NDVI, and field observations has low cover. Under SMA, this
area is modeled as nearly 100% GV cover. Indeed, SMA fails to display the same pattern
of vegetation cover in undisturbed areas seen in both MESMA and NDVI (cover
decreasing from left to right and bottom to top across the image). Furthermore, SMA
fails to identify the plume of vegetation cover downwind of the abandoned fields, B. This
plume is located on bright wind-blown soils derived from the abandoned fields and can
Figure 13. Simple unmixing
(SMA)-derived map of soil and
vegetation cover in the AVIRIS
image calculated using GV
(Poplar leaf), NPV (dry grass),
and a field spectrum of soil from
within the basin (see Figure 5 for
vegetation spectra). Areas
appearing white in the image are
modeled by SMA as having
anomalously high amounts of GV
(approaching 100%).
be covered quite heavily by Schismus grass during wet years such as 1998.
Many of the problems with SMA in the current case study can be traced to the
variability of soils in the Basin. For instance, the soils in the north (H) are sandy soils that
are spectrally distinct from the armored soils of the rest of the Basin floor and used in the
SMA model. This leads to dramatic overestimation of green vegetation in these areas.
The disturbed soils of A, E, F, and G also display overestimations in total cover in these
areas probably due to the deviation of the soil spectra here from that used in the SMA
model.
In general, due to inflexibility in the soil endmember in this highly variable
landscape, SMA yields the poorest results of the three methods in identifying patterns of
23
vegetation cover in the Manix Basin although this can depend on the endmember used.
NDVI, while capturing some of the basic patterns in the image, yields many inaccuracies,
particularly in areas where the soils have been disturbed by land use. These discrepancies
can be largely attributed to variations in soil color and the insensitivity of NDVI to NPV
which dominates the vegetation cover in the image. Only MESMA, which can
accommodate variability in both the soil and vegetation endmembers yields believable
vegetation cover results.
Okin et al. (2001b) have also shown that MESMA is able to correctly identify
surface soil type (sandy soils from armored soils) in the Basin. Their analysis, however,
indicates that MESMA is not able to correctly discriminate between vegetation types
below at least 30% cover. They conclude that many other techniques that use subtle
spectral differences to differentiate between like materials (such as spectral matching)
will also be unable to discriminate between vegetation types under these conditions.
Despite the difficulty of using many standard remote sensing techniques in arid
regions, current research is exploring new avenues for vegetation detection. One method
which is currently being considered is matched filtering, a technique that was developed
to detect extremely weak signals that are essentially in the noise. Unlike SMA, MESMA,
and spectral matching, matched filtering is optimized to detect extremely weak signals
(for a good review see Funk et al., 2001) and therefore may be able to discriminate
between vegetation types in arid regions. Matched filtering works by separating
hyperspectral reflectance into “signal” and “noise” components. The signal is the desired
spectrum scaled to represent its radiance in a pixel. Everything else is assumed to be
noise. Matched filtering returns σ-values which denote the number of standard
deviations a given pixel’s matched filter score is from zero. Large positive sigma values
indicate high likelihood that a signal is present in a pixel.
Using matched filtering to try to identify the distributions of various vegetation
types within the Manix Basin yields promising results (Figure 14) when compared to
observations of vegetation distribution in the area (G.S. Okin, personal observations, and
Okin et al., 2001a). The dominance of At. polycarpa and senesced grass on the right side
Figure 14. Matched filter results from
the Manix AVIRIS scene. The red
channel σ -values for senesced
Schismus grass. The green channel is
σ-values for L. tridentata. The blue
channel is σ-values for At. polycarpa.
24
of B, for instance, agrees with the dominance of these cover types there. The dominance
of L. tridentata and senesced grass in the undisturbed portions of the Basin is consistent
with observations. The dominance of senesced grass on D, E, in the vicinity of I, and
downwind of A is also consistent with observations. But the results are far from perfect.
Some areas of no cover in the basin (A and the bare playas) falsely indicate the presence
of L. tridentata and the two white areas strongly show the presence of all three vegetation
types, which is unrealistic in this situation.
The use of advanced spectral techniques to discriminate vegetation type in arid
areas remains the subject of ongoing research. The matched filter example here provides
insight into the cutting edge to solving this significant technical hurdle.
SUMMARY
Remote sensing is a cost- and time-efficient way to determine the spatial
characteristics of desert-derived mineral aerosols, desert soils and vegetation, and land
use and land degradation in arid regions. Several robust techniques exist to estimate the
concentration of mineral aerosols over both the oceans and continents. Further use of
these data along with ground data will help to constrain the global sources and impacts of
desert dust.
The use of remote sensing to understand land use and land degradation is a
common goal in modern research. Nonetheless, special complications are presented
when using remote sensing to look at soils and vegetation in deserts. These arise from the
sparse vegetation cover, spectrally unique vegetation, and bright soils common to arid
regions and cannot be taken for granted. As the example from the Manix Basin shows,
the use vegetation indices, while easy to calculate and conceptually simple, can be
dangerous: they provide deceiving results which may be correct in one part of an image
and seriously flawed nearby. Other spectral techniques such as SMA show promise in
mapping vegetation cover, and the SMA-superset, MESMA, is by far the most robust
spectral analysis technique for use in arid regions. Given both the challenges and
promises of remote sensing in deserts, research continues in the use of remote sensing to
detect process-relevant surface parameters.
BIBLIOGRAPHY
Adams J. B., Smith M. O., and Gillespie A. R. (1993), Remote Geochemical Analysis: Elemental and
Mineralogical Composition (C. M. Pieters and P. A. J. EnglertEds.), Cambridge University Press,
Cambridge, pp. Ch. 7.
Bailey T. C., and Gatrell A. C. (1995), Interactice Spatial Data Analysis. Addison Wesley Longman
Limited, Essex, England.
Billings W. D., and Morris R. J. (1951), Reflection of visible and infrared radiation for leaves of different
ecological groups. American Journal of Botany. 39: 327-331.
Buffington L. C., and Herbel C. H. (1965), Vegetational changes on a semidesert grassland range from
1858 to 1963. Ecological Monographs. 35: 139-164.
Cahalan R. F., Oreopoulos L., Wen G., Marshak A., Tsay S. C., and DeFelice T. (2001), Cloud
characterization and clear-sky correction from Landsat-7. Remote Sensing of Environment. 78: 83-98.
Caselles V., and Garcia M. J. L. (1989), An alternative simple approach to estimate atmospheric correction
in multitemporal studies. International Journal of Remote Sensing. 10: 1127-1134.
Chavez P. R., Jr. (1989), Radiometric calibration of Landsat Thematic Mapper multispectral images.
Photogrammetric Engineering and Remote Sensing. 55: 1285-1294.
Che N., and Price J. C. (1992), Survey of radiometric calibration results and methods for visible and nearinfrared channels of NOAA-7, NOAA-9, and NOAA- 11 AVHRRs. Remote Sensing of Environment.
25
41: 19-27.
Chen Z., Elvidge C. D., and Groeneveld D. P. (1998), Monitoring seasonal dynamics of arid land
vegetation using AVIRIS data. Remote Sensing of Environment. 65: 255-266.
Chilès J.-P., and Delfiner P. (1999), Geostatistics. John Wiley and Sons, Inc., New York.
Chomette O., Legrand M., and Marticorena B. (1999), Determination of the wind speed threshold for the
emission of desert dust using satellite remote sensing in the thermal infrared. Journal of Geophysical
Research. 104: 31207-31215.
Clark R. N. (1997), Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In Manual of
Remote Sensing (A. Rencz. Ed.), John Wiley and Sons, Inc., pp. Chapter 1.
Clark R. N., Swayze G. A., and Heidebrecht K. (1995), Calibration to surface reflectance of terrestrial
imaging spectrometry data: Comparison of methods. Summaries of the Fifth Annual JPL Airborne
Earth Science Workshop, Jet Propulsion Laboratory, Pasadena, CA, 41-42.
Conel J. E. (1999), Determination of surface reflectance and estimates of atmospheric optical depth and
single scattering albedo from Landsat Thematic Mapper. International Journal of Remote Sensing. 11:
783-828.
Drake N. A., Mackin S., and Settle J. J. (1999), Mapping vegetation, soils, and geology in semiarid
shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery. Remote Sensing
of Environment. 68: 12-25.
Duce R. A., and Tindale N. W. (1991), Atmospheric transport of iron and its deposition in the ocean.
Limnology and Oceanography. 36: 1715-1726.
Duncan J., Stow D., Franklin J., and Hope A. (1993), Assessing the relationship between spectral
vegetation indices and shrub cover in the Jornada Basin, New Mexico. International Journal of Remote
Sensing. 14: 3395-3416.
Ehleringer J. (1981), Leaf absorptances of Mohave and Sonoran Desert plants. Oecologia. 49: 366-370.
Ehleringer J., and Björkman O. (1976), Leaf pubescence: effects on absorptance and photosynthesis in a
desert shrub. Science. 192: 376-377.
Ehleringer J. R., and Björkman O. (1978), Pubescence and leaf spectral characteristics in a desert shrub,
Encilia farinosa. Oecologia. 36: 151-162.
Ehleringer J. R., and Mooney H. A. (1978), Leaf hairs: effects on physiological activity and adaptive value
to a desert shrub. Oecologia. 37: 183-200.
Elmore A. J., Mustard J. F., Manning S. J., and Lobell D. B. (2000), Quantifying vegetation change in
semiarid environments: Precision and accuracy of spectral mixture analysis and the Normalized
Difference Vegetation Index. Remote Sensing of Environment. 73: 87-102.
Elvidge C. D. (1990), Visible and near-infrared reflectance characteristics of dry plant materials.
International Journal of Remote Sensing. 11: 1775-1795.
Elvidge C. D., Chen Z., and Groeneveld D. P. (1993), Detection of trace quantities of green vegetation in
1990 AVIRIS data. Remote Sensing of Environment. 44: 271-279.
Escafadel R., and Huete A. R. (1991), Improvement in remote sensing of low vegetation cover in arid
regions by correcting vegetation indices for soil "noise". C. R. Academie des Sciences Paris. 312:
1385-1391.
Evans J. R. (1962), Falling and climbing sand dunes in the Cronese ("Cat") Mountain Area, San Bernardino
County, California. Journal of Geology. 70: 107-113.
Franklin J., Duncan J., and Turner D. L. (1993), Reflectance of vegetation and soil in Chihuahuan desert
plant communities from ground radiometry using SPOT wavebands. Remote Sensing of Environment.
46: 291-304.
Franklin J., and Turner D. L. (1992), The application of a geometric optical canopy reflectance model to
semiarid shrub vegetation. IEEE Transactions on Geoscience and Remote Sensing. 30: 293-301.
Funk C. C., Theiler J., Roberts D. A., and Borel C. C. (2001), Clustering to improve matched filter
detection of weak gas plumes in hyperspectral thermal imagery. IEEE Transactions on Geoscience and
Remote Sensing. 39: 1410-1420.
Gao B.-C., and Goetz A. F. H. (1990), Column atmospheric water vapor and vegetation liquid water
retrievals from airborne imaging spectrometer data. Journal of Geophysical Research. 95: 3549-3564.
Gao B. C., and Goetz A. F. H. (1995), Retrieval of equivalent water thickness and information related to
biochemical-components of vegetation canopies from AVIRIS data. Remote Sensing of Environment.
52: 155-162.
García-Haro F. J., Gilabert M. A., and Meliám J. (1996), Linear spectral mixture modelling to estimate
26
vegetation amount from optical spectral data. International Journal of Remote Sensing. 17: 3373-3400.
Garcia-Pichel F., and Belnap J. (1996), Microenvironments and microscale productivity of cyanobacterial
desert crusts. Journal of Phycology. 32: 774-782.
Gardner M. (1997), Mapping Chaparral with AVIRIS Using Advanced Remote Sensing Techniques. M.A.
Thesis, University of California.
Gates D. M., Keegan H. J., Schleter J. C., and Weidner V. R. (1965), Spectral properties of plants. Applied
Optics. 4: 11-20.
Gilabert M. A., Conese C., and Maselli F. (1994), An atmospheric correction method for the automatic
retrieval of surface reflectances from TM images. International Journal of Remote Sensing. 15: 20652086.
Gilabert M. A., García-Haro F. J., and Melia J. (2000), A mixture modeling approach to estimate
vegetation parameters for heterogeneous canopies in remote sensing. Remote Sensing of Environment.
72: 328-345.
Gillespie A. R., Smith M. O., Adams J. B., Willis S. C., Fischer A. F., III, and Sabol D. E., Jr. (1990),
Interpretation of residual images: Spectral mixture analysis of AVIRIS images, Owens Valley,
California. Second AVIRIS Workshop, Jet Propulsion Laboratory, Pasadena, California, 243-270.
Gillette D. A. (1999), A qualitative geophysical explanation for "hot spot" dust emission source regions.
Contributions to Atmospheric Physics. 72: 67-77.
Gillette D. A., Adams J., Endo A., Smith D., and Kihl R. (1980), Threshold velocities for input of soil
particles into the air by desert soils. Journal of Geographical Research. 85: 5621-5630.
Graetz R. D., and Gentle M. R. (1982), The relationships between reflectance in the Landsat wavebands
and the composition of an Australian semi-arid shrub rangeland. Photogrammetric Engineering and
Remote Sensing. 48: 1721-1730.
Graetz R. D., Pech R. P., and Davis A. W. (1988), The assessment and monitoring of sparsely vegetated
rangelands using calibrated Landsat data. International Journal of Remote Sensing. 9: 1201-1222.
Green R. O., Conel J. E., and Roberts D. A. (1993), Estimation of aerosol optical depth and additional
atmospheric parameters for the calculation of apparent surface reflectance from radiance measured by
the Airborne Visible/Infrared Imaging Spectrometer. Fourth Annual JPL Airborne Geoscience
Workshop, Pasadena, CA, 73-76.
Green R. O., Roberts D. A., and Conel J. E. (1996), Characterization and compensation of the atmosphere
for the inversion of AVIRIS calibrated radiance to apparent surface reflectance. Sixth Annual JPL
Airborne Earth Science Workshop, Pasadena, CA, 135-146.
Griffin D. W., Garrison V. H., Herman J. R., and Shinn E. A. (2001), African desert dust in the Caribbean
atmosphere: Microbiology and public health. Aerobiologia. 17: 203-213.
Hall F. G., Shimabukuro Y. E., and Huemmrich K. F. (1995), Remote sensing of forest biophysical
structure using mixture decomposition and geometric reflectance models. Ecological Applications. 5:
993-1013.
Herman J. R., Bhartia P. K., Torres O., Hsu C., Seftor C., and Celarier E. (1997), Global distribution of
UV-absorbing aerosols from Nimbus 7/TOMS data. Journal of Geophysical Research. 102: 1691116922.
Holm R. G., Jackson R. D., Yuan B., Moran M. S., Slater P. N., and Biggar S. F. (1989), Surface
reflectance factor retrieval from Thematic Mapper data. Remote Sensing of Environment. 27: 47-57.
Hudak A. T., and Wessman C. A. (1998), Textural analysis of historical aerial photography to characterize
woody plant encroachment in South African savanna. Remote Sensing of Environment. 66: 317-330.
Huete A. R. (1988), A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 25: 295309.
Huete A. R., and Jackson R. D. (1987), Suitability of spectral indices for evaluating vegetation
characteristics on arid rangelands. Remote Sensing of Environment. 23: 213-232.
Huete A. R., and Jackson R. D. (1988), Soil and atmosphere influences on the spectra of partial canopies.
Remote Sensing of Environment. 25: 89-105.
Huete A. R., Jackson R. D., and Post D. F. (1985), Spectral response of a plant canopy with different soil
backgrounds. Remote Sensing of Environment. 17: 37-53.
Huete A. R., and Tucker C. J. (1991), Investigation of soil influences in ABHRR red and near-infrared
vegetation index imagery. International Journal of Remote Sensing. 12: 1223-1242.
Hurcom S. J., and Harrison A. R. (1998), The NDVI and spectral decomposition for semi-arid vegetation
abundance estimation. International Journal of Remote Sensing. 19: 3109-3125.
27
Husar R. B., Prospero J. M., and Stowe L. L. (1997), Characterization of tropospheric aerosols over the
oceans with the NOAA advanced very high resolution radiometer optical thickness operational
product. Journal of Geophysical Research. 102: 16889-16909.
Jeltsch F., Milton S. J., Dean W. R. J., and van Rooyen N. (1997), Simulated pattern formation around
artificial waterholes in the semi-arid Kalahari. Journal of Applied Ecology. 8: 177-188.
Kaufman Y. J. (1989), Theory and Applications of Optical Remote Sensing (G. Asrar, Ed.), John Wiley and
Sons, New York, pp. Chapter 9.
Kaufman Y. J., Tanre D., Remer L. A., Vermote E. F., Chu A., and Holben B. N. (1997), Operational
remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging
spectroradiometer. Journal of Geophysical Research-Atmospheres. 102: 17051-17067.
Koch M., and El Baz F. (1998), Identifying the effects of the Gulf War on the geomorphic features of
Kuwait by remote sensing and GIS. Photogrammetric Engineering and Remote Sensing. 64: 739-747.
Lancaster N., and Baas A. (1998), Influence of vegetation cover on sand transportation by wind: Field
studies at Owens Lake, California. Earth Surface Processes and Landforms. 23: 69-82.
Lange R. T. (1969), The piosphere: sheep track and dung patterns. Journal of Range Management. 22: 396400.
Markham B. L., and Barker J. L. (1987), Radiometric Properties of U.S. processed Landsat MSS data.
Remote Sensing of Environment. 22: 39-71.
Marticorena B., Bergametti G., Gillette D. A., and Belnap J. (1997), Factors controlling threshold friction
velocity in semiarid and arid areas of the United States. Journal of Geophysical Research. 102: 2327723287.
McGwire K., Minor T., and Fenstermaker L. (2000), Hyperspectral mixture modeling for quantifying
sparse vegetation cover in arid environments. Remote Sensing of Environment. 72: 360-374.
Meek N. (1989), Geomorphic and hydrologic implications of the rapid incision of Afton Canyon, Mojave
Desert, California. Geology. 17: 7-10.
Meek N. (1990), Late Quaternary Geochronology and Geomorphology of the Manix Basin, San Bernardino
County, California. Ph.D., University of California at Los Angeles.
Mooney H. A., Ehleringer J., and Björkman O. (1977), The energy balance of leaves of the evergreen
desert shrub Atriplex hymenelytra. Oecologia. 29: 301-310.
Moran M. S., Bryant R., Thome K., Ni W., Nouvellon Y., Gonzalez-Dugo M. P., and Qi J. (2001), A
refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7
ETM+. Remote Sensing of Environment. 78: 71-82.
Moran M. S., Jackson R. D., Clarke T. R., Qi J., Cabot F., Thome K. J., and Markham B. L. (1995),
Reflectance factor retrieval from Landsat TM and Spot HRV data for bright and dark targets. Remote
Sensing of Environment. 52: 218-230.
Moran M. S., Jackson R. D., Slater P. N., and Teillet P. M. (1992), Evaluation of simplified procedures for
retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of
Environment. 41: 169-184.
Muhs D. R., and Maat P. B. (1993), The potential response of eolian sands to greenhouse warming and
precipitation reduction on the Great Plains of the USA. Journal of Arid Environments. 25: 351-361.
Okin G. S., and Gillette D. A. (2001), Distribution of vegetation in wind-dominated landscapes:
Implications for wind erosion modeling and landscape processes. Journal of Geophysical Research.
106: 9673-9683.
Okin G. S., Murray B., and Schlesinger W. H. (2001a), Degradation of sandy arid shrubland environments:
observations, process modelling, and management implications. Journal of Arid Environments. 47:
123-144.
Okin G. S., Okin W. J., Murray B., and Roberts D. A. (2001b), Practical limits on hyperspectral vegetation
discrimination in arid and semiarid environments. Remote Sensing of Environment. 77: 212-225.
Olsson H. (1995), Reflectance Calibration of Thematic Mapper Data for Forest Change Detection.
International Journal of Remote Sensing. 16: 81-96.
Pech R. P., and Davis A. W. (1987), Reflectance modeling of semiarid woodlands. Remote Sensing of
Environment. 23: 365-377.
Pech R. P., Davis A. W., Lamacraft R. R., and Graetz R. D. (1986a), Calibration of Landsat data for
sparsely vegetated semiarid rangelands. International Journal of Remote Sensing. 7: 1729-1750.
Pech R. P., Graetz R. D., and Davis A. W. (1986b), Reflectance modeling and the derivation of vegetation
indexes for an Australian semiarid shrubland. International Journal of Remote Sensing. 7: 389-403.
28
Peddle D. R., Hall F. G., and LeDrew E. F. (1999), Spectral mixture analysis and geometric-optical
reflectance modeling of boreal forest biophysical structure. Remote Sensing of Environment. 67: 288297.
Peters A. J., and Eve M. D. (1995), Satellite monitoring of desert plant community response to moisture
availability. Environmental Monitoring and Assessment. 37: 273-287.
Phinn S., Franklin J., Hope A., Stow D., and Huenneke L. (1996), Biomass distribution mapping using
airborne digital video imagery and spatial statistics in a semi-arid environment. Journal of
Environmental Management. 47: 139-164.
Pickup G., and Chewings V. H. (1994), A grazing-gradient approach to land degradation assessment in arid
areas from remotely-sensed data. International Journal of Remote Sensing. 15: 597-617.
Piketh S. J., Tyson P. D., and Steffen W. (2000), Aeolian transport from southern Africa and iron
fertilization of marine biota in the South Indian Ocean. South African Journal of Geology. 96: 244-246.
Prospero J. M., Ginoux P., Torres O., and Nicholson S. E. (In Press), Environmental characterization of
global sources of atmospheric soil dust derived from the NIMBUS-7 absorbing aerosol product.
Reviews of Geophysics.
Ray T. W. (1995), Remote Monitoring of Land Degradation in Arid/Semiarid Regions. Ph.D., California
Institute of Technology.
Ray T. W., and Murray B. C. (1996), Nonlinear spectral mixing in desert vegetation. Remote Sensing of
Environment. 55: 59-64.
Reynolds R., Belnap J., Reheis M., Lamothe P., and Luiszer F. (2001), Aeolian dust in Colorado Plateau
soils: Nutrient inputs and recent change in source. Proceedings of the National Academy of Sciences of
the United States of America. 98: 7123-7127.
Roberts D. A., Adams J. B., and Smith M. O. (1990), Predicted distribution of visible and near-infrared
radiant-flux above and below a transmittant leaf. Remote Sensing of Environment. 34: 1-17.
Roberts D. A., Gardner M., Church R., Ustin S., Scheer G., and Green R. O. (1998), Mapping chaparral in
the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sensing of
Environment. 65: 267-279.
Roberts D. A., Gardner M., Church R., Ustin S. L., and Green R. O. (1997a), Optimum strategies for
mapping vegetation using multiple endmember spectral mixture models. SPIE Conference Imaging
Spectrometry III, San Diego, CA, 108-119.
Roberts D. A., and Green R. O. (1985), Detection of hydrocarbon microseeps and related
geobotanical anomalies using multi-date image subtraction, Railroad Valley, Nevada. Proceedings of the
19th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan.
Roberts D. A., Green R. O., and Adams J. B. (1997b), Temporal and spatial patterns in vegetation and
atmospheric properties from AVIRIS. Remote Sensing of Environment. 62: 223-240.
Roberts D. A., Smith M. O., and Adams J. B. (1993), Green vegetation, nonphotosynthetic vegetation, and
soils in AVIRIS data. Remote Sensing of Environment. 44: 255-269.
Robinove C. J., Chavez P. S., Jr., Gehring D., and Holmgren R. (1981), Arid land monitoring using Landsat
albedo difference images. Remote Sensing of Environment. 11: 133-156.
Schlesinger W. H., Raikes J. A., Hartley A. E., and Cross A. F. (1996), On the spatial pattern of soil
nutrients in desert ecosystems. Ecology. 77: 364-374.
Schlesinger W. H., Reynolds J. F., Cunningham G. L., Huenneke L. F., Jarrell W. M., Virginia R. A., and
Whitford W. G. (1990), Biological feedbacks in global desertification. Science. 247: 1043-1048.
Schultz B. W., and Ostler W. K. (1993), Effects of prolonged drought on vegetation association in the
Northern Mojave Desert. Wildland shrubs and arid land restoration symposium, Las Vegas, Nevada,
228-235.
Settle J. J., and Drake N. A. (1993), Linear mixing and the estimation of ground cover proportions.
International Journal of Remote Sensing. 14: 1159-1177.
Shimabukuro Y. E., and Smith J. A. (1991), The least-squares mixing models to generate fraction images
derived from remote sensing multispectral data. IEEE Transactions on Geoscience and Remote
Sensing. N1: 16-20.
Smith G. I., and Street-Perrott F. A. (1983), Pluvial Lakes of the Western United States. In Late
Quaternary Environments of the United States, Vol. 1 (H. E. Wright, Jr._Ed., University of Minnesota
Press, Minneapolis, Minnesota, pp. 190-212.
Smith M. O., Ustin S. L., Adams J. B., and Gillespie A. R. (1990), Vegetation in deserts: I. a regional
measure of abundance from multispectral images. Remote Sensing of Environment. 31: 1-26.
29
Sokolik I. N., and Toon O. B. (1996), Direct radiative forcing by anthropogenic airborne mineral aerosols.
Nature. 381: 681-683.
Swap R., Garstang M., Greco S., Talbot R., and Kallberg P. (1992), Saharan dust in the Amazon Basin.
Tellus Series B-Chemical and Physical Meteorology. 44: 133-149.
Tanre D., Kaufman Y. J., Herman M., and Mattoo S. (1997), Remote sensing of aerosol properties over
oceans using the MODIS/EOS spectral radiances. Journal of Geophysical Research-Atmospheres. 102:
16971-16988.
Tegen I., and Fung I. (1995), Contribution to the atmospheric mineral aerosol load from land surface
modification. Journal of Geophysical Research. 100: 18707-18726.
Teillet P. M., Barker J. L., Markham B. L., Irish R. R., Fedosejevs G., and Storey J. C. (2001), Radiometric
cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets.
Remote Sensing of Environment. 78: 39-54.
Tsoar H., and Karnieli A. (1996), What determines the spectral reflectance of the Negev-Sinai sand dunes.
International Journal of Remote Sensing. 17: 513-525.
Tucker C. J., Dregne H. E., and Newcomb W. W. (1991), Expansion and contraction of the Sahara Desert
from 1980 to 1990. Science. 253: 299-301.
Tucker C. J., Newcomb W. W., and Dregne H. E. (1994), AVHRR data sets for determination of desert
spatial extent. International Journal of Remote Sensing. 15: 3547-3565.
Tugel A. J., and Woodruff G. A. (1978), Soil Survey of San Bernardino County, California: Mojave River
Area. Soil Conservation Service, United States Department of Agriculture.
Ustin S. L., Adams J. B., Elvidge C. D., Rejmanek M., Rock B. N., Smith M. O., Thomas R. W., and
Woodward R. A. (1986), Thematic Mapper studies of semiarid shrub communities. BioScience. 36:
446-452.
Wells S. G., McFadden L. D., Poths J., and Olinger C. T. (1995), Cosmogenic 3He surface-exposure dating
of stone pavements: implications for landscape evolution in deserts. Geology. 23: 613-616.
Wiggs G. F. S., Thomas D. S. G., and Bullard J. E. (1995), Dune mobility and vegetation cover in the
southwest Kalahari Desert. Earth Surface Processes and Landforms. 20: 515-529.
Wurzler S., Reisin T. G., and Levin Z. (2000), Modification of mineral dust particles by cloud processing
and subsequent effects on drop size distributions. Journal of Geophysical Research. 105: 4501-4512.
30