Detecting changes in biomass productivity in a different ... · Monitoring changes in biomass productivity as caused by land management are essential to guide appropriate management

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.


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.


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.



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.


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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