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Detecting changes in biomass productivity in a differentland management regimes in drylands using satellite-derivedvegetation index
D. HELMAN1, A. MUSSERY
1, I. M. LENSKY1 & S. LEU
2,3
1Department of Geography and Environment, Bar Ilan University, Ramat-Gan 5290002, Israel, 2Jacob Blaustein Institute of Desert
Research, Sde Boker Campus 8499000, Israel, and 3Judea Research and Development Center, Moshav Carmel 90404, Israel
Abstract
We investigated the use of a satellite-derived vegetation index to detect changes in biomass
productivity in different land management regimes in drylands of the Northern Negev. Two well-
documented management regimes, conservation and afforestation using a contour trenching technique
were monitored. Biomass data on annual vegetation were collected from field survey and compared to
a time series of the Normalized Difference Vegetation Index (NDVI). A significant relationship
between NDVI and biomass (r = 0.83, P < 0.01) confirmed the applicability of satellite information to
monitoring biomass production in this low productivity area. However, a strong positive relationship
between NDVI and precipitation (r = 0.96 � 0.01, P < 0.001) prevented the conventional use of trend
analysis to detect changes in biomass productivity. Trends in the NDVI and precipitation use
efficiency were similar in both sites due to a rainfall effect. Use of a reference site revealed the
magnitude and direction of change in biomass productivity in the different land management regimes.
Measures of soil organic matter confirmed these differences between the two managed sites and the
reference site. We suggest that the use of abandoned lands for a reference may enhance the ability to
detect changes in biomass productivity in drylands.
Keywords: Land degradation, MODIS, NDVI, Negev, SOM, trend analysis
Introduction
A continuous decrease in biomass production may lead to
desertification in environments with low productivity (Lal,
1997), accelerated by uncontrolled grazing, tillage and
mechanical soil movement (Kassas, 1995; Holland, 2004).
Desertification processes have serious impacts on food
production, future food security and economic development
(Hussein, 2008). They also have severe economic
implications for farming. Moreover, desertification processes
increase carbon dioxide emissions estimated at 450–500 Gt
of carbon from decomposition of soil organic matter (SOM)
and plant biomass during the last millennium (Ruddiman,
2003; Lal, 2004).
Monitoring changes in biomass productivity as caused by
land management are essential to guide appropriate
management. The conventional approach for detecting
biomass change is by field sampling (Lal, 1997), a costly and
time-consuming task. Moreover, field sampling suffers from
several drawbacks in low productivity environments of
drylands (Huenneke et al., 2001). It causes damage to the
low vegetative cover, and the measurements are usually not
representative of a heterogeneous landscape. Remote sensing
is the only viable way to assess changes in biomass
production over large heterogeneous area without damaging
the vegetation cover.
Data derived from satellite remote sensing have been widely
used for monitoring the vegetation status of drylands (Blanco
et al., 2008) and for detecting land degradation or recovery
processes (Bai et al., 2008; Bai & Dent, 2009). In particular,
the satellite-derived Normalized Difference Vegetation Index
(NDVI) has been shown to be a good estimator of the fraction
of photosynthetic active radiation by plants (Sellers et al.,
1992). In ecosystems dominated by annual vegetation,
photosynthetic active radiation and biomass production are
directly related (Tucker & Sellers, 1986; Prince, 1991). Thus,
time series data of satellite-derived NDVI can be used to
estimate biomass production in different geographical areas
and ecosystems (Paruelo et al., 1997).Correspondence: I. M. Lensky. E-mail: [email protected]
Received March 2013; accepted after revision December 2013
© 2014 British Society of Soil Science 1
Soil Use and Management doi: 10.1111/sum.12099
SoilUseandManagement
However, the use of time series satellite-derived NDVI data
is not always adequate for detecting real changes in biomass
productivity due to human activity. Separating the effect of
land management on biomass productivity from climatic
influences is not straightforward (Wessels et al., 2007). The
use of precipitation use efficiency (PUE = biomass/
precipitation) has been suggested to offset the effect of
precipitation, thus enabling the detection of human-induced
land degradation (Wessels et al., 2004; Bai et al., 2008). In
some dryland regions where the relationship between
precipitation and the NDVI is strong, PUE reflects the effect
of precipitation (Wessels et al., 2007). The RESTREND
technique was proposed for such cases (Evans & Geerken,
2004). This technique consists of removing any rainfall effect
by differentiating between the expected NDVI (calculated
from linear regression of precipitation vs. NDVI) from the
measured NDVI. Then, the residual is examined for positive
(land rehabilitation) or negative (land degradation) trends that
are not caused by climate.
The reliability of NDVI-based PUE and RESTREND
trends to detect changes in biomass productivity is
challenged by Wessels et al. (2012). Using a simulation
approach, they show that when there is a positive trend in
precipitation, human-induced degradation is difficult to
detect using the PUE and RESTREND trends. However,
trend analysis is still the most affordable existing method to
detect land degradation as long as it is further validated by
field sampling.
Here, we use time series of NDVI data derived from
satellites to detect and quantify changes in biomass
productivity in different land managements in a dryland
system. Two management regimes in the Northern Negev
drylands, (i) conservation and (ii) afforestation that began in
1992, were compared to lands managed by traditional
agricultural practices. First, we validate the applicability of
NDVI as a surrogate for biomass of annual vegetation in
this low productivity environment using field sampling.
Then, we examine changes in NDVI, PUE (calculated from
NDVI) and SOM from field sampling in the two land-
managed sites with respect to the traditionally managed
lands, hereafter termed the Control field. Finally, we discuss
the feasibility of using NDVI time series to detect the impact
of land management on biomass productivity.
Materials and methods
Study area
The study area is located within the semi-arid Northern
Negev, northeast of the city of Beer-Sheva, Israel (31°19′25″N, 34� 59′05″E, Figure 1a). This is a 90-ha area about 460 m
above sea level. For 2001–2010, the average annual
precipitation in the area was 232 mm/yr (similar to the
long-term 40-yr average). Rainfall mostly occurs during
autumn, winter and early spring (October–April). Annual
precipitation and average precipitation were calculated by
averaging data from the two nearest meteorological stations
of the Israeli Meteorological Service (www.ims.gov.il),
Beer-Sheva station (18 km south west, elevation 300 m) and
Yatir Forest station (8 km north-east, elevation 680 m). This
NORTH
A
B
C
D
E
G
F
Conservation
Contour-trenched
Control field250 m
(a) (b)
(c)
(d)
Figure 1 (a) Image of the study area
showing the two different managed sites
since 1992 (Conservation and Contour
trenched) and the traditional agricultural
lands (Control) in the Northern Negev
drylands (image taken from Google Earth�:
DigitalGlobe). Locations of plots for
biomass and SOM sampling are denoted
with letters. Black arrows show the size of a
MODIS pixel (250 m) on the map. Insert:
general location of the study area (star).
View of the (b) Conservation, (c) Control and
(d) Contour-trenched sites. Trenches in
Contour-trenched site are used to attract
runoff water along contour lines for
afforestation purposes. Photographs credit:
A. Mussery.
© 2014 British Society of Soil Science, Soil Use and Management
2 D. Helman et al.
can be done because much of the rainfall in the region is
orographical and the elevation of the study area is about the
average for these stations.
Soils in the study area are a sandy loam or sandy clay
loam. Native vegetation consists of scattered dwarf shrubs
and patches of annual herbaceous vegetation. Grasses and
legumes are dominant (Danin & Orshan, 1990). Vegetation
growth starts in the late autumn (November), with a
maximum during early spring (February–April) followed by
a senescent period to the beginning of the summer (June). In
summer, the herbaceous vegetation dries out and only the
shrubby and woody vegetation is present.
Sites description
Three sites were analysed in this study; two sites that were
managed since 1992 and a Control site representing the
traditional land use (Figure 1):
Conservation site: ca. 50-ha fields of sustainably managed
shrubland savanna in a privately owned farm established
in 1992. This site includes a 2-ha Acacia victoria
woodland (250 trees per ha). Supervision ensures that
grazing is at <1 livestock unit per ha (Figure 1b).
Control site: ca. 25 ha of land that had been managed by
traditional agricultural practices, that is, tilled and
unsupervised grazing for centuries (Figure 1c).
Contour–trenched site: ca. 25-ha fields planted with about
100 trees per ha during 1992 using the contour trenching
technique. This technique consists in digging trenches by
using heavy machines along contour lines. Trenches are
used to slow down and collect runoff water which then
infiltrates into the soil (Figure 1d). It was previously
argued that the use of heavy machinery in such low
productivity areas causes soil erosion decreasing the
productivity of the native vegetation (Mussery et al.,
2013).
All sites have similar climatic, topographical, edaphic and
vegetation characteristics.
Field sampling
The biomass of the annual herbaceous vegetation was
sampled between 2008 and 2010 in seven plots within the three
sites (Figure 1a) at the end of the rainy season (April–May).
Five pairs of 20 by 30 cm quadrats were randomly selected in
each plot for Contour-trenched and Control sites as described
in Wright et al. (2006). In the Conservation site, because the
biomass of annual vegetation is usually higher near trees
(Mor-Mussery et al., 2013), samples were taken from the area
underneath the tree canopy and from the ‘open’ areas. Then,
it was averaged to represent the entire site considering their
relative fraction cover from the total area. Dry biomass
weight for each sample was determined after 2 days of drying
at 60 °C. Average biomass was calculated in g/m2 including
the associated standard errors. This method means that
biomass can be estimated without using large-scale destructive
sampling and is more representative in low-biomass
heterogeneous areas such as the study area.
At the same plots, six soil samples from the top 20-cm
layer were randomly taken after removal of plant litter. To
account for the effect of trees on SOM in the Conservation
site, samples were taken in a similar manner to that
described above for biomass. Sampling was during three
different seasons (spring – March, summer – June and
autumn – October). To calculate SOM, each sample was
dried overnight at 105 °C and ignited for 4 h at 500 °C.SOM estimates are given as percentage of oven dry soil, that
is, SOM = (dry-burned)/dry (Sparks, 2003).
Data derived from satellites
To assess changes in biomass production during a longer
period (2001–2010), we used a time series of the Normalized
Difference Vegetation Index (NDVI) as derived from
MODIS on NASA’s TERRA satellite (MOD13Q1). This
product is freely available (https://lpdaac.usgs.gov/products/
modis_products_table/mod13q1) in time intervals of 16 days
at a spatial resolution of 250 m and has high quality
temporal resolution. MODIS products have been available
from March 2000. To expand the time span of the analysis,
we also used a 30-m Landsat data set (Landsat-5 TM and
Landsat-7 ETM+) which has been available since 1984. To
account for the effect of management on annual vegetation,
Landsat images at the peak of the growing period were
selected (February–April). Only four Landsat images (1989–
1991 and 1998) from this period were available for this area.
Because the spatial resolution of MODIS is quite coarse
with the Conservation site represented by seven pixels,
Contour-trenched by three and Control by four (Figure 1), we
examined the variability within MODIS pixels using Landsat.
Mean NDVI and standard deviations of the Landsat image
were calculated for the three sites (Conservation, Contour-
trenched and Control). Results from a two-sample t-test
showed that the variability within the same site was smaller
than between sites (P < 0.01), justifying the use of the 250 m
MODIS pixels to compare NDVI between sites.
Analysis of satellite data and validation
Normalized Difference Vegetation Index was calculated as:
NDVI ¼ qNIR� qRqNIRþ qR
where qNIR and qR are clear sky partially atmospheric
corrected surface reflectance in the near infrared
(841–876 nm) and the visible red (620–670 nm), respectively.
The difference between these two is divided by the sum of
the two bands to normalize the index between �1 and 1.
© 2014 British Society of Soil Science, Soil Use and Management
Detecting productivity changes using remote sensing 3
NDVI has been shown to be suitable for studying vegetation
in semi-arid regions with a large range in values (Huete
et al., 2002).
We calculated NDVI integrals over the MODIS time series
as a surrogate for biomass productivity (Paruelo et al., 1997;
Jobb�agy et al., 2002). First, we excluded the possibility of
temporal variations in NDVI due to the soil background
(Montandon & Small, 2008). A constant NDVI value during
the year (NDVI ca. 0.08) was measured in nearby open areas
that represented bare soils. Then, this value was selected to
represent the contribution of soil to NDVI for all three sites
due to the similar physical properties of their soils (Mussery
et al., 2013). Smoothing using the LOWESS technique
(Cleveland, 1979) was performed to eliminate outliers due to
cloud-contamination.
To calculate the contribution of the annual herbaceous
vegetation to the NDVI from MODIS, we decomposed the
time series to trend and seasonality following Roderick et al.
(1999). The only difference was that we used the minimum
NDVI at the end of each summer to draw the trend instead
of using moving averages. This could be done because
woody (perennial) and herbaceous (annual) vegetation in this
area have distinct and time-separate phenological cycles
(Karnieli, 2003). The trend of the time series represents the
contribution of soil background and perennials (Roderick
et al., 1999) to the NDVI, while the seasonality represents
the contribution of annual vegetation to the NDVI. We used
the integral (the area underneath the curve) of the seasonal
signal of NDVI as a surrogate for biomass of annual plants
as suggested by Paruelo et al. (1997).
Integrals of NDVI from selected plots within the three
sites (Figure 1a) were regressed linearly against biomass of
annual herbaceous vegetation sampled in field in several
years. The NDVI integrals showed strong positive
correlation (r = 0.83, P < 0.01) with the biomass of annual
vegetation (Figure 2). This allowed us to calculate the
NDVI-based precipitation use efficiency (PUE) for the entire
MODIS period (2001–2010) by dividing the NDVI integrals
of each site by the total precipitation.
To estimate the change in biomass productivity in the
Conservation and Contour-trenched sites with respect to the
Control site since the management began in 1992, we used
NDVI from both Landsat and MODIS images (1989-2010)
from the peak of the growing season (maximum NDVI). The
NDVI of the Control site has been reduced from that of the
Conservation and the Contour-trenched sites for each year.
Then, we divided the results by the NDVI of the Control site
to neutralize the effect of precipitation to allow comparison
between years. The total change for 1989 to 2010 was then
calculated in two different ways: first, as the difference
between the average during the period before management
began (1989–1991) and after then (1998–2010); and second,
using the slope of the linear relationship between percentage
change and year (Figure 5).
Results
Normalized Difference Vegetation Index and precipitation
use efficiency
The data for 10 yr MODIS NDVI (2001–2010) showed
negative trends with r = �0.41, �0.36 and �0.37 for the
Conservation, Contour trenched and Control sites, respectively
(not shown). All trends were highly significant (P < 0.001 for
all sites, using the two-tailed probability test). Annual
precipitation and PUE also showed negative trends
(Figure 3). PUE showed very large interannual variations as
a result of a strong positive correlation with precipitation
(average r = 0.92) (Figure 3). No significant differences in
trends of PUE between sites were found. However, the
Conservation site had the highest PUE, whilst the Contour-
trenched site had the lowest PUE in all years.
The precipitation–biomass relationship was assessed
through a positive correlation between NDVI integrals and
precipitation (Figure 4). It was highly correlated in all three
sites. The Pearson correlation coefficient (r) was r = 0.97 for
Conservation, r = 0.95 for Contour-trenched and r = 0.96 for
the Control sites (P < 0.001 for all sites). The Conservation
site showed the highest biomass response to precipitation,
that is, greater positive slope in Figure 4 with a = 0.32. The
Contour-trenched site showed the lowest response to
precipitation with a = 0.20 compared with the Control site
with a of 0.25.
The Normalized Difference Vegetation Index at the peak
of the growing season for the entire period (1989–2010)
showed nonsignificant trends in all sites (P > 0.1, not
A9
B8
C8
D8
C10
D10E10
F10
G10
B10
y = 0.08x + 5.79r = 0.83P < 0.01
5
10
15
20
25
30
0 50 100 150 200 250
Biomass [g/m2]
ND
VI IN
TE
GR
AL
Figure 2 Linear relationship between Normalized Difference
Vegetation Index (NDVI) integrals derived from MODIS and
biomass of annual vegetation from field sampling at the
Conservation (green), Contour-trenched (red) and Control (orange)
sites. Plots are denoted with letters as in Figure 1a. Numbers
indicate the year of sampling (8–10 for 2008–2010). Error bars are
for standard error. This figure is available in colour online at http://
wileyonlinelibrary.com.
© 2014 British Society of Soil Science, Soil Use and Management
4 D. Helman et al.
shown). However, NDVI changes in Conservation and
Contour-trenched sites with respect to Control site were
significant (P < 0.05) with notable positive and negative
trends (Figure 5). The Conservation site showed a positive
change with time (r = 0.52), while the Contour-trenched site
displayed negative change (r = �0.50). Although both sites
differed from the Control site (percentage change greater/
smaller than zero) even before management began
(intersection with start of management line in Figure 5), the
difference was found to be not significant for both sites
(P > 0.1, one-tailed paired t-test). The average change for
the entire period was between +18 and +37% for the
Conservation site and �18 to �12% for the Contour-trenched
site with respect to the Control field as calculated from
average and linear regression, respectively, as described in
the methods’ section.
The NDVI time series of the seasonal component that
represents the annual vegetation is shown in Figure 6. The
differences between sites are distinct where the Conservation
site had the highest NDVI compared with the other two.
Contour trenched had the lowest NDVI with an exception of
1 yr (2003/4) where it was comparable with the variability
within the Control site (shaded confidence bands in
Figure 6). The largest differences in NDVI between sites
were recorded when NDVI reached its maximum during the
peak of the growing season (between February and April).
Variability in biomass distribution within a site was noted
for some years (2002/3, 2005/6 and 2008/9) (shaded
confidence bands in Figure 6). The largest deviations from
the average NDVI of the site was during 2002/3 when NDVI
varied within all sites by ca. �0.05.
Soil organic matter
Figure 7 shows the differences in SOM between sites. SOM
in the Conservation site was significantly greater than in the
other two sites (P < 0.005, using the two-tailed student
t-test) in all three seasons (spring – March, summer – June
and fall – October). Differences between Contour-trenched
and Control sites were significant (P < 0.05) only in the early
0
10
20
30
40
50
60
70
150 175 200 225 250 275 300 325
Contour trenchedControl
ND
VI In
tegr
al
Precipitation [mm]
r = 0.97 P < 0.001
r = 0.96 P < 0.001r = 0.95 P < 0.001
Conservation
Figure 4 Relationship between Normalized Difference Vegetation
Index (NDVI) integrals and total annual precipitation. Linear
regressions for the three sites have positive slopes (a) of 0.32, 0.20
and 0.25 for Conservation (green), Contour-trenched (red) and
Control (orange) sites, respectively. Bars indicate maximum and
minimum values within each site. This figure is available in colour
online at http://wileyonlinelibrary.com.
Start ofmanagement
(1992)
L a n d s a t M O D I S
60
45
30
15
0
–15
–30
–45
Cha
nge
[%]
1986 1991 1996 2001 2006 2011
Figure 5 Percentage of change in Normalized Difference Vegetation
Index (NDVI) at the peak season for the Conservation (green) and
Contour-trenched (red) sites with respect to the Control field (see
explanation in text). Data used is from Landsat (1989–1998) and
MODIS (2001–2010) satellites. Negative and positive trends are
significant at P < 0.05 (using one-tailed probability for Pearson’s
coefficient of correlation). This figure is available in colour online at
http://wileyonlinelibrary.com.
0
40
80
120
160
200
240
280
320
360
0.00
0.05
0.10
0.15
0.20
0.25
0.30
2000 2002 2004 2006 2008 2010
PU
E
Precipitation [m
m]
PrecipitationContour trenchedControlConservation
Figure 3 Patterns of precipitation use efficiency (PUE) based on
Normalized Difference Vegetation Index (NDVI) integrals and
annual precipitation for Conservation (green), Contour trenched (red)
and Control (orange) sites. Total annual precipitation is also
presented (blue). Negative trends are significant at P < 0.05 (using
one-tailed probability for Pearson’s coefficient of correlation). This
figure is available in colour online at http://wileyonlinelibrary.com.
© 2014 British Society of Soil Science, Soil Use and Management
Detecting productivity changes using remote sensing 5
summer (June). SOM measured at the beginning of the
summer (June) was significantly greater than in the autumn
(October) and early spring (March) in the Conservation and
Contour trenched sites (P < 0.05). In the Control site, there
was no significant difference between seasons (P > 0.1). The
relative differences in SOM between the Conservation and
the Control sites ranged from 40 to 70% depending on the
season.
Discussion
Variability in biomass production, SOM and PUE
Biomass productivity in the study area varied markedly
through time with water availability as the main driving
factor similar to other semi-arid regions (Noy-Meir, 1973).
During 2001–2010, the biomass productivity at the season
peak, as estimated from the NDVI data, was 40–360 g/m2, a
range typical for semi-arid environments. PUE was relatively
low (0.3–1.2 g/m2/mm), similar to that reported for this
region (Tadmor et al., 1974), and within the broad range
(0.05–1.81 g/m2/mm) reported for the rest of the world’s arid
and semi-arid ecosystems (Le Houerou, 2000). Differences
between sites were evident where the Conservation site had
the greatest PUE and Contour-trenched site the least. The
same differences were found also in biomass production as
assessed by NDVI integrals.
However, great or small biomass productivity is not
necessarily indicative of a land management effect. To
confirm that these differences were due to human activity,
sites were compared with the Control field during the period
before and after management began. The relative change
between the managed sites and the Control site was shown to
increase with time (Figure 5). Conservation through
supervised grazing increased biomass productivity of the site
by an average of ca. 30%, while afforestation using contour
trenching reduced biomass productivity of the annual
vegetation by ca. 15%.
A supervised grazing regime has been reported to be
beneficial to the annual plant community in semi-arid
environments under certain conditions (Osem et al., 2002).
Moreover, it was demonstrated that livestock grazing could
be an effective management tool in rehabilitating lands when
limited in duration and livestock per area (Zaady et al.,
2001). In the case of the Conservation site, this managed
grazing was shown to be effective in increasing biomass
productivity in a substantial manner (ca. 30%).
Afforestation using the contour trench technique reduced
biomass productivity (by 15%), probably because it causes
deterioration in soil stability with negative effects on water
availability for the annual plants (Eldridge et al., 2000;
Mussery et al., 2013).
Biomass variability within sites was evident in the
deviations from the average NDVI of the site. This was
especially apparent during 2002/3, but also to some extent
during 2005/6 and 2008/9 (shaded bands in Figure 6). For
the other years, biomass distribution within the site was
relatively uniform. This can be explained by variability in
precipitation. During 2002/3 and 2005/6, the coefficient of
variation (CV) in daily precipitation was greater by ca.15%
from the mean CV for the entire 2001–2010 period, while in
0
0.1
0.2
0.3
0.4
0.5
0.6
S DM J S DM J S DM J S DM J S DM J S DM J S DM J S DM J S DM J S DM J S
ND
VI
Date
Conservation
ControlContour trenched
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 6 The seasonal component of the
Normalized Difference Vegetation Index
(NDVI) time series representing the annual
vegetation (see explanation in text) from the
Conservation (green), Contour-trenched (red)
and Control (orange) sites. Confidence bands
are maximum and minimum NDVI values
within the same site (shadowed areas). This
figure is available in colour online at http://
wileyonlinelibrary.com.
0
1
2
3
4
5
6
7
March June Oct
ControlContour-trenched
SO
M [%
]
a
cb
c
a
a
Conservation
Figure 7 Soil organic matter (SOM) sampled in three different
seasons: early spring (March), early summer (June) and mid-fall
(Oct). Error bars represent the standard error. Letters above
columns represent significance levels between Conservation versus
Control (left), and Contour-trenched versus Control (right). Letters
mean: ‘a’ for highly significant (P < 0.005), ‘b’ for significant
(P < 0.05), and ‘c’ for nonsignificant difference (P > 0.1), using two-
tailed student t-test. This figure is available in colour online at
http://wileyonlinelibrary.com.
© 2014 British Society of Soil Science, Soil Use and Management
6 D. Helman et al.
2008/9, the greatest CV was recorded (ca. 30% from the
mean CV). Greater variability in precipitation increases the
topographical effect on biomass production (Abrams et al.,
1986; Swanson et al., 1988). Thus, because some of the
MODIS pixels include both low and high topography
(Figure 1a), the result is considerable variability in biomass
during years with higher CV values.
As observed for biomass, SOM was also greater in the
Conservation site compared with Control and Contour trenched
sites (Figure 7). This is consistent with previous studies in this
region that found the roots of annual vegetation to be the
primary contributors to SOM (Zaady, 2005; Mor-Mussery
et al., 2013). SOM content varies with season and is
significantly greater in the Conservation and Contour-trenched
sites at the beginning of the summer (June). This increase is a
result of SOM accumulation during the wet season when
decomposition of organic matter takes place (Steinberger &
Whitford, 1988). The subsequent decrease to October was
caused by CO2 exchange back to the atmosphere during the
dry season (Austin et al., 2004). The reason for a comparable
SOM content in Contour-trenched and Control sites (and even
greater in Contour-trenched in the early summer, Figure 7) is
not clear. We suggest that tillage of soils in the Control site
exposed organic matter to oxidation, whereas the afforested
Contour-trenched site was not exposed to such tillage because
it is less suitable for agriculture.
Trend analysis for detecting changes in biomass
productivity
The negative trends found in NDVI and PUE during 2001
to 2010 indicate land degradation (Bai et al., 2008), usually
due to human interference (Wessels et al., 2004). However, it
was shown that in regions where water is a limited resource,
precipitation may be the dominant cause for such trends and
not human-induced degradation (Wessels et al., 2007, 2012).
The strong linear relationship (r = 0.96 � 0.01, P < 0.001)
between NDVI and precipitation as observed in the study
area (Figure 4) supports this previous statement. Also, a
similar negative PUE trend in all three sites (Figure 3) is
indicative of a single common factor that affected the entire
area. No significant trends were found when applying the
RESTREND technique (Evans & Geerken, 2004) using
measured and expected NDVI as calculated from the linear
regression in Figure 4. This confirms that precipitation is the
primary factor causing the decrease in biomass productivity.
Human impact on biomass productivity cannot always be
detected using only NDVI and PUE trend analysis. A good
example is the nonsignificant trends in NDVI during the
entire period that included 3 yr before conservation and
afforestation managements began in 1992. The use of lands
that had been managed by traditional agricultural practices
was needed as a reference to detect changes in biomass
productivity due to conservation and afforestation
managements (Figure 5). However, such a reference site will
not always be available. Therefore, we suggest establishment
of standard reference sites under a range of climatic and
environmental conditions in different regions.
Conclusions
A significant linear relationship between satellite-derived
NDVI data and biomass of annual herbaceous vegetation
was found in the low productivity region of the Northern
Negev. The relatively strong correlation was achieved
through decomposition of NDVI time series into their
perennial and annual vegetation components. This
decomposition also eliminated any effect of soils and was
found to be suitable after validation with field measurements.
The strong relationship between rainfall and NDVI in the
study area did not allow for the detection of changes in
biomass productivity using conventional trend analysis.
A further use of traditionally managed lands as a reference
site was needed to account for the impact of land
management on biomass productivity. This emphasizes the
caution needed to interpret trends in NDVI and
precipitation use efficiency. It also indicates that reference
control sites are essential. This can be achieved by examining
reference sites such as abandoned lands from many regions
with different climatic and edaphic features.
Acknowledgements
This work was supported in part by grants from the Israel
Science Foundation (1009/11), Israel Ministry of Science, and
Project Wadi Attir (www.projectwadiattir.com) and the
Sustainability Laboratory (www.projectwadiattir.com). We
thank Prof. Pua Bar from the Department of Geography and
Environmental Development at the Ben-Gurion University of
the Negev for letting us use the environmental lab facility, and
Yaron Michael from the remote sensing laboratory at Bar Ilan
University for assisting in the Landsat data analysis. We also
thank Oren and Eren families from Yatir Farm for hosting
and safeguarding this long-term research site. We finally thank
two anonymous reviewers who helped to improve the
manuscript with insightful comments and Prof. Donald
Davidson for his kind attention.
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