Estimating Spatiotemporal Patterns of Aboveground Biomass Using

Agricultural and Forest Meteorology 200 (2015) 119–128

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

j ournal homepage: www.elsevier.com/locate/agrformet

Estimating spatiotemporal patterns of aboveground biomass using

Landsat TM and MODIS images in the Mu Us Sandy Land, China

a a b,∗

Feng Yan , Bo Wu , Yanjiao Wang

Institute of Desertiﬁcation Studies, Chinese Academy of Forestry, Beijing 100091, China

National Climate Center, China Meteorological Administration, Beijing 100081, China

a r a

t i b s

c t

l e i n f o r a c t

Article history: Aboveground biomass (AGB) in areas of desertiﬁcation cannot only represent the status of vegetation but

Received 23 September 2013

can also provide evidence to evaluate the effects of ecological restoration and help land managers realize

Received in revised form 9 September 2014

sustainable development of desert ecosystems. Current research estimating AGB by remote sensing has

Accepted 18 September 2014

mainly focused on forest, grasslands and crops, and has infrequently been applied to desert ecosystems.

Available online 10 October 2014

We used Landsat Thematic Mapper (TM) images and Moderate Resolution Imaging Spectro-Radiometer

(MODIS) data to estimate AGB and its spatiotemporal patterns from 2000 to 2012 in the Mu Us Sandy

Keywords:

Land of China. Results showed that: (1) AGB varied from 2000 to 2012 and total AGB showed an increasing

Aboveground biomass (AGB)

trend of 0.1743 Tg per year. The lowest total AGB was observed in 2000 and 2001 and the highest in 2012,

The Mu Us Sandy Land

with slightly less in 2007. (2) AGB spatial extent (percent of ground covered) had a decreasing trend of

Spatiotemporal pattern

5.37% during the study period and AGB was mainly in the southwestern and eastern parts of the study

area. AGB had no change in 2.23% of this area, and areas of no change were mainly in the northwestern

and southwestern parts. There was an increasing AGB trend in 92.40% of the area, which was mainly in

large areas of the middle, northeastern, and southern parts of the Mu Us Sandy Land. (3) In the sandy

land from 2000 to 2012, areas with mild and moderate ﬂuctuations and increasing AGB made up the

largest part of the study area. Those two types of ﬂuctuations accounted for 74.60% of the total area, and

were widely distributed in the northeastern, eastern, central, and southern portions of the sandy land.

Areas with severe and extremely severe ﬂuctuations and decreasing AGB were relatively small. These

two types represented 0.86% of the total area and were scattered in the northwestern and western parts

of the sandy land. (4) With the increase of temperature and precipitation, total AGB tended to increase

from 2000 to 2012, somewhat in agreement with precipitation (r = 0.595). However, precipitation was

not the only factor affecting AGB. Human factors such as population, livestock, and particularly positive

policies also impacted the spatiotemporal patterns of AGB.

1. Introduction living conditions (Bakr et al., 2012; Helldén and Tottrup, 2008;

Kassas, 1995; Reynolds et al., 2007; Rubio and Bochet, 1998). China

Since the start of the 21st century, the world’s social econ- is one of the most seriously desertiﬁed countries in the world. A

omy has developed rapidly with improvement in many people’s bulletin related to the status of desertiﬁcation and sandiﬁcation

standard of living; the human impact on the earth’s ecosystems in China issued by State Forestry Administration, P. R. China in

has reached a new level. The great social progress made by civiliza- 2011 showed there was 2623,700 km of desertiﬁed land by the

tion is also associated with a series of ecological problems, such as end of 2009, which accounted for 27.33% of the national terri-

desertiﬁcation, soil erosion, and environmental pollution. Among tory. In the new century, desertiﬁcation is still the most critical

these problems, desertiﬁcation is one of the most serious threats factor restricting sustainable social, economic, and environmental

to arid and semiarid environments and has seriously inhibited development in China. The Mu Us Sandy Land lies on the farming-

the development of local economies and improvement of people’s pastoral ecotone of northern China, in a forest–grassland–desert

transition zone. Affected by natural and anthropogenic factors, the

Mu Us Sandy Land has been considered as an important ecological

∗ barrier in China, whose environment is very sensitive and vulner-

Corresponding author. Tel.: +861 062 824 079.

E-mail addresses: [email protected], [email protected] (Y. Wang). able to ecological changes. Studying the spatiotemporal patterns

http://dx.doi.org/10.1016/j.agrformet.2014.09.010

120 F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128

of vegetation from 2000 to 2012 is vital for land managers to vast areas. Estimating AGB in desert ecosystems is of great signiﬁ-

understand the process of desertiﬁcation and to comprehensively cance for the comprehensive evaluation of the global carbon cycle.

evaluate carbon sinks in the Mu Us Sandy Land in the new century. Hence, in this research we chose the Mu Us Sandy Land, located on

Biomass has been widely used to evaluate productivity and is the farming-pastoral ecotone of China, as an area to study methods

an important indicator in carbon cycle studies related to vegeta- for estimating AGB using MODIS and TM data. Also, spatiotemporal

tion growth (Burrows et al., 2003; Crow, 1978; Fang et al., 2001; patterns of AGB from 2000 to 2012 were analyzed, providing data

Houghton et al., 2000). Monitoring AGB dynamically in desertiﬁed that expands our scientiﬁc knowledge of desert ecosystem recovery

areas can not only show the status of the growth of local vegetation and deserts as carbon sinks.

but also provide evidence that ecosystem managers and scientists The objective of this research was to estimate AGB and its spa-

can use to evaluate the effects of ecological restoration in deser- tiotemporal patterns in semiarid regions using TM images and

tiﬁed areas, as well as to study the carbon cycle and to realize MODIS data from 2000 to 2012. We therefore analyzed the scale

sustainable development of desert ecosystems. In the past, tradi- transformation of vegetation index between MODIS and TM, and

tional methods for estimating AGB mainly involved ﬁeld inventory, transferred an AGB estimation model established with vegetation

which suffered from many shortcomings such as having many areas index and in situ AGB samples from TM images (Yan et al., 2013)

poorly represented and being time consuming, labor intensive, and to MODIS data. Then, the MODIS data of 2000–2012 were used to

inefﬁcient. Remote-sensing technology has the advantages of hav- estimate AGB, and trend and ﬂuctuation were used as indicators

ing a macro scale, providing dynamic data, and being economic and of AGB spatiotemporal patterns in the Mu Us Sandy Land of China.

efﬁcient, and lacks the inadequacies of more traditional methods. Finally, we thoroughly analyzed the causes of AGB variations from

Estimation of AGB has been done for many years using remote- natural (e.g. temperature, precipitation, and deﬁcit of precipitation)

sensing reﬂectance and various vegetation indices (Boyd, 1999; and human factors (e.g. population and policies).

Hame et al., 1997; Huete et al., 2002; Moreau et al., 2003; Todd

et al., 1998; Zhang et al., 2007). Ikeda et al. (1999) used Landsat TM

2. Study area and data

observations in 1984–1990 and climate data to estimate AGB with a

growth model, as well as revealed that TM2/TM3 was more success-

2.1. Study area

ful at accurately estimating biomass than the normalized difference

vegetation index (NDVI), and that the ratio TM4/TM5 performed ◦ ◦ ◦ ◦

This study area lies at 37 27 –39 22 N, 107 20 –111 30 E and

best. Curran et al. (1992) applied TM data to study the linear rela-

includes the southern part of Erdos (Inner Mongolia, China), the

tionship that existed between NDVI and the leaf area index (LAI),

northern part of the Yuyang District sandy area (Yulin, Shannxi),

demonstrating the potential use of TM data for studying seasonal

and the northeastern part of Yanchi County (Ningxia), covering

dynamics in the forest canopy. Todd et al. (1998) used TM images 2

about 40,000 km (Fig. 1). Elevations vary from 950 to 1600 m above

to show the relationship between biomass and the tasseled cap

mean sea level. The climate varies from middle to warm temperate

green vegetation index (GVI), brightness index (BI), wetness index ◦

zones with mean annual temperature varying from 6.0 to 8.5 C,

(WI), NDVI, and the red waveband (RED) on grazed and ungrazed

and mean annual precipitation ranging from 250 to 440 mm. Pre-

rangelands in north-central Colorado. Zheng et al. (2004) coupled

cipitation in the Mu Us Sandy Land is mainly concentrated from

AGB values in Wisconsin, USA, with various vegetation indices

July to September, and especially in August, which accounts for

derived from Landsat Enhanced Thematic Mapper Plus (ETM+) data

60–75% of annual precipitation. Precipitation varies in a south-

through multiple regression analysis. They found that AGB for pine

east to northwest direction from 440 to 250 mm, respectively. The

forests was strongly related to the corrected NDVI, and that sepa-

soil and vegetation types of the Mu Us Sandy Land show a tran-

rating hardwoods from pine forests substantially improved the AGB

sitional pattern: in the northwest a semi-desert calcic brown soil

estimations compared with those obtained with overall regres-

zone occurs, while to the southwest in Yanchi County a semi-desert

sion. Muukkonen and Heiskanen (2005, 2007) applied Advanced

sierozem zone is found; also, to the southeast in the Loess Plateau,

Spaceborne Thermal Emission and Reﬂection Radiometer (ASTER)

satellite data to estimate the biomass of boreal forest stands in

Finland. They put forward a method coupled with standwise for-

est inventory data, ASTER, and MODIS satellite data to estimate

biomass and verify the possibility of using remote sensed data to

conduct carbon inventories. Verbesselt et al. (2006) used Systeme

Probatoire d’Observation de la Tarre (SPOT) vegetation data to con-

struct a time series designed to monitor herbaceous biomass in

South Africa. Propastin et al. (2012) chose ground biomass data

and net primary production (NPP) data calculated from the sea-

viewing wide ﬁeld-of-view sensor (Sea WiFS) to test the reliability

of their modiﬁed light use efﬁciency (LUE) model in the grasslands

of Kazakhstan. Claverie et al. (2012) used Formosat-2 data to sim-

ulate a time series of green area index (GAI) and dry aboveground

biomass (DAM) of maize and sunﬂower; actual crop biomass was

estimated very well especially considering that biomass measure-

ments were not used for calibration. Yan et al. (2013) used Landsat

TM/ETM+ images from 1988 to 2007 and 283 in situ AGB samples in

August 2007 to estimate the AGB for the Mu Us Sandy Land of China

over the past 30 years. By reviewing current international research

studies on biomass estimation by remote-sensing techniques, we

found most studies mainly focused on forest, grasslands, and crops,

with relatively few applications for desert ecosystems. Although

vegetation is sparser in a desert ecosystem than in forest, grassland,

and crop ecosystems, total AGB of deserts is still considerable across Fig. 1. Sketch map of the location of the Mu Us Sandy Land.

F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128 121

Table 1

a grey cinnamonic soil occurs with a warm temperate climate in a

Red–near infrared (R–NIR) vegetation indices selected in this study and associated

forest-steppe zone. The main vegetation cover type is sandy grass-

references.

land, which covers more than 80% of the sandy area and Artemisia

VI* Reference

Ordosica is a dominant species. The other natural vegetation types

include steppe, meadow, and shrubs (Zhang, 1994). In addition, NDVI = (NIR − R)/(NIR + R) Rouse et al. (1973)

there are farmlands distributed along the river or scattered in the RVI = NIR/R Pearson and Miller (1972)

DVI = NIR − R Jordan (1969)

sandy grasslands, and artiﬁcial forest and shrubs (Wu and Ci, 2002).

SAVI = (1 + L)(NIR − R)/(NIR + R + L) Huete (1988)

Grassland in the northwestern part of the Mu Us Sandy Land is 2

MSAVI = (NIR + 1) − (1/2) × [(2NIR + 1) − Qi et al. (1994)

mainly used for grazing and in the eastern and southern parts of the ½

8(NIR − R)]

sandy land, some grassland areas are reclaimed into farmland. Land *

Deﬁnitions: NDVI, normalized difference vegetation index; RVI, ratio vegetation

use forms complex patterns across the landscape with staggered

index; DVI, difference vegetation index; SAVI, soil adjusted vegetation index; MSAVI,

distributions of different types being very common and agricultural modiﬁed soil adjusted vegetation index; NIR and R, the reﬂectance of near infrared

land taking the largest proportion. and red bands, respectively; L, a soil adjustment factor (Qi et al., 1994).

surface reﬂectance from sensors mainly using red and near infrared

2.2. Data

spectral bands. To quantitatively study vegetation coverage and

growth, dozens of vegetation indices have been designed. Among

In this research, MODIS and Landsat TM satellite data were

these indices, the NDVI, Ratio Vegetation Index (RVI), Difference

used to estimate AGB in the Mu Us Sandy Land. MODIS is a

Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI) and

key instrument aboard the Terra and Aqua satellites, which pro-

Modiﬁed Soil Adjusted Vegetation Index (MSAVI) have been com-

vides high radiometric sensitivity (12 bit) in 36 spectral bands

monly used (Table 1).

ranging in wavelength from 0.4 to 14.4 ␮m, covers a 2300-

Among dozens of available vegetation indices, MSAVI has been

km wide swath, and provides global coverage every 1–2 days

widely used as a remote-sensing technique to study vegetation in

(http://modis.gsfc.nasa.gov). Compared with other frequently used

various research studies (Epting et al., 2005; Rogan and Yool, 2001;

data such as TM, ETM+, and National Oceanic and Atmospheric

Veraverbeke et al., 2012; Wu et al., 2007; Yan et al., 2013; Zheng

Administration (NOAA)–Advanced Very High Resolution (AVHRR)

et al., 2004), because it has been shown to increase the dynamic

images, MODIS data have higher spectral and temporal resolu-

range of the vegetation signal while further minimizing the inﬂu-

tions. Both MODIS vegetation indexing products (MOD13Q1 (DOY:

ence of soil as background data, resulting in greater sensitivity to

209) from 2000 to 2012) and MODIS surface reﬂectance prod-

the signal received from vegetation (Qi et al., 1994). Yan et al. (2013)

ucts (MOD09GQ (DOY: 224) in 2007) were ordered from the

chose the Mu Us Sandy Land of China as a study area and analyzed

National Aeronautics and Space Administration (NASA). MOD13Q1

correlations between in situ AGB samples and multiple vegetation

and MOD09GQ data are provided at a 250-m spatial resolution in

indices derived from Landsat 5 TM and found correlations of AGB-

a Sinusoidal projection with 16-d and 1-d temporal resolutions,

MSAVI was relatively higher than those of the SAVI, DVI, RVI, and

respectively (https://lpdaac.usgs.gov/products/). The Modis Repro-

NDVI. And as a result, an AGB-MSAVI model (Eq. (1)) for estimating

jection Tool (MRT) was used for the MODIS titled land products

AGB in August in the Mu Us Sandy Land of China was established.

to perform subset and projection transformation. Albers Conical

In this study we continued to use Eq. (1) to estimate a time series

Equal Area (ellipsoid is Krasovsky, longitude of central meridian is

◦ ◦ ◦

of AGB from 2000 to 2012.

105 E, latitude of standard parallels are 25 N and 47 N) map pro-

jection was employed. Aside from the MODIS data, two Landsat = 2

AGB 12.733 × MSAVI − 1.315 R = 0.612 (1)

5 TM images with the path/rows 127/33 and 127/34 acquired on

August 12, 2007 (DOY: 224) were also used. After applying atmo-

3.2. Scale transformation of vegetation index between MODIS

spheric correction by the Fast Line-of-sight Atmospheric Analysis of

and TM

Spectral Hypercubes (FLAASH) atmospheric correction model from

ENVI software, the digital number (DN) of the TM image was con-

Landsat 5 TM and MODIS data were used for estimating AGB in

verted to surface reﬂectivity. Then, topographic maps at a scale of

our study. Compared with MODIS, TM has higher spatial resolution

1:50,000 were used to spatially locate the ground control points for

(30 m) but a longer revisit period (16 days) and a smaller swath

geometrically correcting the satellite images, and the mean error

(185 km). The availability of TM images is often limited by weather

after geometric correction was controlled within 0.5 pixel.

conditions and the longer revisit period across large areas. While

Historical record of monthly temperature and precipitation

MODIS has some advantages such as the shorter revisit period (at

(1980–2012) of nine meteorological stations located in the Mu Us

least 2 times/day), the larger swath (2330 km) and moderate spatial

Sandy Land, including the stations of Uxin Ju, Shenmu, Uxin, Yulin,

resolution (250, 500, and 1000 m) can make up for the inadequacies

Otog Qian, Hengshan, Henan, Dingbian and Jingbian (Fig. 1), were

of TM images. Scale transformation must be carried out during the

used to calculate the average annual temperature and precipitation

course of estimating AGB by MODIS data with Eq. (1) derived from

and standardized precipitation index (SPI).

TM, because of differences in spectral and spatial resolutions in the

two sensors.

3. Methodologies Scale transformation is the process of transforming information

and knowledge among different scales. In remote sensing, one basic

3.1. Relationship between vegetation indices and AGB characteristic of an image is the spatial resolution or the size of an

area on the ground from which the measurements that compose

Vegetation index derived from satellite data by combining two the image are derived (Woodcock and Strahler, 1987). Some pixel-

or more spectral bands is one of the primary sources of information based scale transformation methods have been proposed such as

for operational monitoring of the earth’s vegetation cover (Gilabert mathematical statistical analysis, fusion, and classiﬁcation trans-

et al., 2002). A good relationship exists between the amount of formations (Aman et al., 1992; Berterretche et al., 2005; Braswell

biomass on the ground and the spectral vegetation index since et al., 2003; Feng et al., 2012; Gao et al., 2006; Kim and Barros, 2002;

biomass can be reﬂected by the index (Tucker, 1979). A vegeta- Mayaux and Lambin, 1995; Teillet et al., 1997; Xu and Zhang, 2013).

tion index is generally deﬁned as mathematical transformations of Gao et al. (2006) used a spatial and temporal adaptive reﬂectance

122 F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128

fusion model (STARFM) to predict Landsat-scale reﬂectance using Reﬂectivity data of TM3 and TM4 (Path/Row: 127/33 and

the 500-m daily surface reﬂectance product (MOD09GHK) with 127/34) on August 12, 2007 (DOY: 224) were used to calculate

Landsat and MODIS images. Hilker et al. (2009) applied method MSAVI hereinafter expressed as MSAVITM. Next, the nearest neigh-

of STARFM to map seasonal changes in vegetation at a Landsat spa- bor interpolation method was used to resize the resolution of

tial resolution and 8-day time intervals with Landsat and MODIS MSAVITM from 30 m to 250 m. FVC (DOY: 224) of the Mu Us

(MOD09GHK) images. Berterretche et al. (2005) compared different Sandy Land was then divided into ﬁve grades using a 0.2 step;

methods to predict leaf area index (LAI) in a boreal forest and found 300 corresponding samples were randomly selected for each grade

aspatial orthogonal regression analysis (reduced major axis) was from MSAVITM and MSAVIMODIS; then spatial scale transforma-

the most practical option for regional NPP modeling with coarse tion regression equations were established (see Eqs. (3)–(7)) for

resolution inputs. Feng et al. (2012) adopted a simple linear regres- each step. Based on the scale transformation method for MSAVI

sion model to measure the agreement between Landsat and MODIS from TM and MODIS images, we used reﬂectivity data derived

images and assessed the quality of Landsat surface reﬂectance from MOD13Q1 (DOY: 209) to calculate FVC and MSAVIMODIS and

products using MODIS data. Xu and Zhang (2013) used a cross- acquired a time series of MSAVITM in 2000–2012.

comparison method based on relationship between sampled pixels

MSAVITM = 0.1689MSAVIMODIS + 0.0748,

of ASTER data and those of corresponding ETM+ data to assess con-

sistency in forest-dominated vegetation observations. Among the note : R = 0.9143 and 0 ≤ Fc ≤ 0.2 (3)

pixel-based scale transformation methods, mathematical statisti-

cal analysis bases on a function of pixel-based parameters, does

not need to input too much weight on the physical mechanism MSAVI = 0.6582MSAVI + 0.0104,

TM MODIS

of remote sensing. And it has been wildly used for transformation

note : R = 0.8462 and 0.2 < Fc ≤ 0.2 (4)

among different remote sensed images with different pixel-scales

(Berterretche et al., 2005; Feng et al., 2012; Xu and Zhang, 2013). In

this research, we chose mathematical statistical analysis transfor-

MSAVITM = 0.5434MSAVIMODIS + 0.0227,

mations method to transform MSAVI values between Landsat TM

and MODIS images. 2

note : R = 0.8352 and 0.4 < Fc ≤ 0.6 (5)

Fractional vegetation cover (FVC) refers to the percentage of

the vertically projected area covered by vegetation as a fraction

of the entire area under study (Gitelson et al., 2002; Purevdorj

MSAVITM = 0.8936MSAVIMODIS − 0.1599,

et al., 1998); FVC is an important parameter used to character-

R = . . < ≤ .

ize land surface vegetation cover and growth conditions. With note : 0 8275 and 0 6 Fc 0 8 (6)

the development of remote sensing, several FVC retrieval meth-

ods have been proposed, such as empirical, vegetation index, and

= . + . ,

sub-pixel unmixing models (Asrar et al., 1992; Choudhury, 1987; MSAVI 0 2828MSAVI 0 1932

TM MODIS

Zhou and Robson, 2001) and the pixel decomposition method has 2

note : R = 0.8437 and 0.8 < Fc ≤ 1.0 (7)

also been widely used (Gutman and Ignatov, 1998; Mu et al., 2013;

Tømmervik et al., 2003; Zhang et al., 2013). The pixel decompo-

sition method assumes that each pixel consists of vegetation and To evaluate error of AGB estimation caused by scale trans-

bare soil and the spectral value is a linear combination of the two formation of vegetation index between Landsat TM and MODIS

parts. So, FVC can be expressed as images, we randomly chose four samples from southern to north-

ern Mu Us Sandy Land, according to TM data coverage (Path/Row:

−

NDVI NDVImin 127/33 and 127/34). The selected samples were mainly in an area

FVC = (2)

−

NDVImax NDVImin with varying degrees of desertiﬁcation, rather than with homoge-

neous land cover. The area of each sample was 4.5 km × 4.5 km,

where NDVImin is the minimum NDVI corresponding to 0% vege- exactly corresponding to 18 pixel of MODIS vegetation index prod-

tation cover or bare soil and NDVImax is the maximum one 100% uct MOD13Q1 and 150 pixel of TM images, (Fig. 1). Transformation

vegetation cover, respectively. Because of landscape heterogeneity relationship from Eqs. (3) to (7) were used to retrieve MSAVITM from

in the sandy land, selecting “pure” bare and vegetation pixels from MSAVIMODIS, and AGB on August 12, 2007 was estimated from TM

a lower spatial resolution remote sensing image is very difﬁcult, but and MODIS images. Minimum, maximum, mean, standard devia-

relatively pure pixel selection is easy. In this research, an interactive tion (SD), and relative error (RE) of the estimated AGB from two

method was used to select such pixels from MODIS images. We ﬁrst sensors were analyzed (Table 2). Results showed that minimum

chose a large area of pure pixels from TM data and then used this values of AGB based on TM data were 0 t/ha for all four samples,

area to obtain the relatively pure pixels from MODIS images. In the whereas those of AGB based on MODIS imagery were usually larger

northeastern part of Mu Us Sandy Land is the largest area of original than 0 t/ha, especially for S1 and S4 samples. Maximum and SD

Sabina vulgaris in China, about 9333 ha. As an evergreen dominant values of AGB based on TM were also larger than those of AGB

shrub, S. vulgaris tends to grow so densely in the sandy land that based on MODIS. These differences are mainly owing to image spa-

it can cover sand dunes completely, likes “a vast green blanket” tial resolution and landscape heterogeneity in the Mu Us Sandy

(Dong and Zhang, 2000). The relatively pure vegetation pixels were Land. The spatial resolution of MODIS was coarser than that of TM

◦ ◦

selected near a site (109 17 37.74 E, 38 58 52.65 N) within the S. and it was more difﬁcult to ﬁnd “pure” pixels from MODIS data. For

vulgaris Natural Reserve of Mu Us, Inner Mongolia. Relatively pure the mean values, however, there was little difference between each

◦ ◦

soil pixels were selected near a site (107 38 31.20 E, 38 46 8.40 N) pair of estimated AGB from both TM and MODIS images, which was

covered by large areas of drifting sand. Inserting MODIS surface mainly caused by the method of scale transformation of vegetation

reﬂectance product MOD09GQ (2007; DOY: 224) data of the Mu index between MODIS and TM. Considering the spatial resolution,

Us Sandy Land to NDVI and MSAVI equations (Table 1), we calcu- we chose TM data as “true” values and calculated RE of the AGB

lated those two indices, hereinafter expressed as NDVIMODIS and estimated from MODIS imagery. Error analyses showed that the

MSAVIMODIS. Then, NDVIMODIS was entered in Eq. (2) to calculate smallest RE values of estimated AGB were in S3 and S1, which cor-

FVC. responded to the minimum and maximum mean values of AGB

F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128 123

Table 2

Statistical results of estimated AGB from MODIS and TM images (unit: t/ha).

Sample Minimum Maximum Mean SD RE

(%)

MODIS TM MODIS TM MODIS TM MODIS TM

S1 0.362 0 4.054 7.622 2.360 2.296 1.235 1.755 2.76

S2 0 0 3.755 8.091 1.235 1.190 0.756 1.350 3.84

S3 0 0 2.258 7.880 0.589 0.600 0.495 0.742 1.80

S4 0.248 0 3.960 7.410 1.366 1.322 0.741 1.324 3.31

among the four samples. RE values in S2 and S4 (with moderate

CV = , (10)

mean values of AGB) were as high as 3.84% and 3.31%, respectively.

According to error analyses of AGB estimation from TM and MODIS

where is SD, is the mean value of an array, x is the array (in this

images, we found that RE could be generally controlled within 4%.

study is AGB), i and n are the numbers of years from 2000 to 2012.

Thus, Eqs. (3)–(7) could be effectively used to estimate AGB from

TM and MODIS images of the Mu Us Sandy Land.

4. Results and discussion

4.1. Temporal variations of AGB

3.3. Trends and ﬂuctuations of AGB

By inserting scale-transformed MSAVI derived from MODIS

Trends of a series of numbers in chronological order can be

products from 2000 to 2012 into Eq. (1), we calculated a series

expressed as the slope of a line. For measurements of AGB, a posi-

of AGBs in the Mu Us Sandy Land. Statistical results of area per-

tive slope indicates that the series tends to increase, and a negative

centages of various AGB levels showed that these areas ﬂuctuated

slope indicates decreasing AGB. For the present study of AGB trends

dynamically since 2000 (Table 3). Areas with AGB percentage not

in the Mu Us Sandy Land, a sequence of AGB spectra from 2000 to

more than 1.5 t/ha occupied more than 57.8% of the total sandy land

2012 was expressed as a 13-dimensional AGB data matrix. Linear

in each year. The percentage of area with 0 ≤ AGB ≤ 0.5 t/ha peaked

ﬁtting by the least-squares method was performed for this matrix

at 54.44% in 2000 and was smallest in 2012, at 10.37% of the total

at pixel level (see Eq. (8)) and the spatial distribution of AGB slope

study area. The percentage of area with 0.50 t/ha < AGB ≤ 1.0 t/ha

was calculated.

maximized in 2006 (32.22%) and was smallest in 2012 (16.23%).

x − x y − y ≤ ( i ¯) ( i ¯) The percentage of area with 1.0 t/ha

= i=1 i

= , , . . .n

slope n ( 1 2 3 ) (8)

x − x 2 (39.70%) and was smallest in 2000 (13.94%). The percentage of area

i=1( i ¯)

with 1.5 t/ha < AGB ≤ 2.0 t/ha maximized in 2012 (21.93%) and was

smallest in 2000 (3.17%). The percentage of area with AGB > 2.0 t/ha

where slope is the slope of the line with the best linear ﬁt, x is the

peaked in 2012 (20.31%) and was smallest in 2000 (3.39%).

independent variable (in this study is year), i and n are the numbers

Total AGB in the sandy land from 2000 to 2012 (Fig. 2) was

of years from 2000 to 2012, and y is the dependent variable (in this

lower than average in 2000, 2001, 2004, 2005, 2006, and 2008.

study is AGB).

There were minima in 2000 and 2001, at 2.26 and 2.54 Tg, respec-

SD and coefﬁcient of variation (CV) were used to portray ﬂuctua-

tively. Total AGB was greater than average in 2002, 2003, 2007 and

tions of AGB in the sandy land. SD and CV are important and widely

2009–2012, peaking in 2007 and 2012 with values 4.70 and 5.62 Tg,

used indicators for quantitative evaluation of an array’s degree of

respectively. Overall, from 2000 to 2012, total AGB in the entire

dispersion. SD represents the deviation between a set of data and

sandy land increased by 0.1743 Tg annually, indicating generally

their mean value. CV is deﬁned as the ratio of SD, and the mean

improved conditions for vegetative growth.

value and can be used to eliminate inﬂuences of different units or

averages on comparison of the dispersion of two or more sets of

4.2. Spatial variations of AGB

data. SD and CV are expressed in the following equations:

4.2.1. Characteristics of trends and ﬂuctuations

n 2

1 Statistical results of AGB trends showed that its average annual

= (xi − x¯) (i = 1, 2, 3. . .n) (9)

n growth rate was from −39.5 to 38.5. The peak value of the slope

i=1

distribution histogram is 3.62, accounting for 4.40% of the total area.

Table 3

Percentages of areas with different amounts of AGB from 2000 to 2012 (unit: t/ha).

Year Area percentage (%)

AGB = 0 0 < AGB ≤ 0.5 0.5 < AGB ≤ 1.0 1.0 < AGB ≤ 1.5 1.5 < AGB ≤ 2.0 AGB > 2.0

2000 26.31 28.13 25.05 13.94 3.17 3.39

2001 24.32 24.32 26.73 16.49 3.96 4.18

2002 11.42 10.92 20.03 30.27 15.45 11.92

2003 8.65 13.64 24.48 34.31 11.15 7.77

2004 9.11 16.39 26.48 33.71 7.96 6.35

2005 9.17 18.08 29.82 28.32 6.99 7.63

2006 8.81 17.17 32.22 29.15 6.32 6.33

2007 6.60 9.37 22.76 34.22 15.26 11.79

2008 9.70 18.31 30.15 26.97 7.07 7.80

2009 5.95 9.91 23.73 35.67 12.38 12.36

2010 4.72 10.15 30.03 39.70 8.48 6.92

2011 5.16 10.72 24.44 36.21 12.38 11.10

2012 4.39 5.98 16.23 31.16 21.93 20.31

124 F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128

Table 4

Classiﬁcation system for spatial patterns of AGB.

Fluctuation characteristic Trend characteristic

CV Level Slope Trend Slope Trend

≤

0 < CV 0.3 Mild Slope > 0 Increasing Slope < 0 Decreasing

0.3 < CV ≤ 0.6 Moderate

0.6 < CV ≤ 0.9 Severe

CV > 0.9 Extremely severe

found that CV was mainly in the range of 0–0.9 at pixel level from

y = 0.1743x + 2.7657

R² = 0.5736 2000 to 2012, representing 91.72% of the study area. The maximum

CV was 0.28 and the area with 0 ≤ CV < 0.3 constituted 34.47% of

4 the total area. Regions with 0.3 ≤ CV < 0.6 and 0.6 ≤ CV ≤ 0.9 made

up 46.85% and 10.40% of the total area, respectively. The area of

CV > 0.9 embraced 8.28% of the area and was mainly in northwest-

AGB (Tg)

ern (northwestern Uxin Banner and southeastern Otog Banner) and

southwestern (southern Otog Qian Banner) parts of the sandy land.

0 4.2.2. Spatial pattern

2 0 1 2 4 5 6 7 8 9 0

3 1

Trend characteristics may reveal the entire trend of AGB devel- 201 200 200 200 200 200 200 200 200 200 201

200 201

Year opment in the future, which is equivalent to the direction of a space

vector. AGB ﬂuctuations may show the dispersion of an AGB array,

Fig. 2. Variations of total AGB from 2000 to 2012. which is equivalent to the module of the space vector. The combi-

nation of trend and ﬂuctuation characteristics can express both the

2 direction and module at the same time, and is able to describe the

Of that area, 5.37% or 2059.31 km had a decreasing AGB trend with

characteristics of AGB variation very well. In this research, we cou-

negative slope; this area was mainly in the southwestern (eastern

pled the two characteristics to quantitatively classify spatial AGB

Otog Banner and southwestern Uxin Banner) and eastern (central

patterns in the Mu Us Sandy Land from 2000 to 2012 (Table 4). In

Yuyang District) parts of the Mu Us Sandy Land (Fig. 3). In an area of

2 the regions with CV = 0 or slope = 0, AGB was never less than zero,

856.63 km or 2.23% of the study area, AGB showed no signiﬁcant

although the mean annual AGB was zero. The corresponding land

change with zero slope; this area was primarily in the northwestern

surface was generally water bodies, drifting sandy land, bare soil,

(northwestern Uxin Banner, eastern and southeastern Otog Banner)

and artiﬁcial construction. Hence, both CV = 0 and slope = 0 were

and southwestern (southeastern of Otog Qian Banner) parts of the

not considered in the classiﬁcation system of AGB spatial pattern. In

sandy land. AGB tended to have an increasing trend with positive

2 that system, given that CV was mainly between 0 and 0.9 for 91.72%

slope over a large area of 35,460.00 km , or 92.40% of the study area.

of areas with measured CV, we divided the degree of AGB ﬂuctua-

This included most areas of the central, northeastern, and southern

tion in the study period into mild, moderate, severe, and extremely

parts of the sandy land.

severe, or four levels at steps of 0.3. Positive and negative slopes

To enhance the comparability of degree of AGB ﬂuctuation in

indicated increasing and decreasing AGB trends, respectively. Using

various areas, we used the CV as an indicator to quantitatively

the classiﬁcation system, the AGB spatial patterns were calculated

evaluate those ﬂuctuations in the sandy land. During analysis of

and classiﬁed (Fig. 5).

the distribution of the CV of AGB from 2000 to 2012 (Fig. 4), we

Fig. 3. Distribution of slope of AGB trends from 2000 to 2012. Fig. 4. Distribution of the coefﬁcient of variation of AGB from 2000 to 2012.

F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128 125

Table 5

Areas and percentages of different AGB variation character from 2000 to 2012.

Number Type Area (km ) Percentage (%)

−4 Decreasing with extremely severe ﬂuctuation 214.00 0.56

−3 Decreasing with severe ﬂuctuation 115.88 0.30

−

2 Decreasing with moderate ﬂuctuation 703.81 1.83

−

1 Decreasing with mild ﬂuctuation 1025.63 2.67

0 No change 856.63 2.23

1 Increasing with mild ﬂuctuation 11356.06 29.59

2 Increasing with moderate ﬂuctuation 17274.00 45.01

3 Increasing with severe ﬂuctuation 3874.44 10.10

4 Increasing with extremely severe ﬂuctuation 2955.50 7.70

AGB spatial patterns were analyzed, including the development mainly in the western (southern Otog), southeastern (northern

of statistics related to the area and percentage of the sandy land Jingbian), and eastern (northern Hengshan) parts. Areas with

with various AGB trends and ﬂuctuations (Table 5). The results extremely severe ﬂuctuations and increasing trends of AGB covered

show that areas with extremely severe or severe ﬂuctuations 2955.50 km (7.70%), largely in the northwestern (southeastern

and decreasing trends of AGB covered 214.00 and 115.88 km , Otog and northwestern Uxin) and southwestern (southern Otog

respectively, or 0.56% and 0.30% of the study area. These areas Qian) parts. Among the various AGB spatial patterns across the

were scattered in the northwestern (southeastern Otog Qian and entire sandy land, the largest area had a combination of moderate

northwestern Uxin) and western parts of the sandy land. Areas ﬂuctuations and increasing trends of AGB. The second largest area

of moderate ﬂuctuations and decreasing trends of AGB covered had mild ﬂuctuations and increasing trends. Combined, these two

703.81 km (1.83% of study area), which were mainly in the south- types of areas made up 74.60% of the study area. The smallest cover-

western (central and eastern Otog Qian and northern Dingbian) age was for areas with severe and extremely severe ﬂuctuations and

parts of the sandy land. Areas with mild ﬂuctuations and decreasing decreasing trends of AGB. These two types of areas constituted only

trends of AGB covered 1025.63 km (2.67%), which were largely 0.86% of the total area and were scattered across the northwestern

in the northern (southern Ejin Horo and western Shenmu), east- and western parts of the sandy land.

ern (western Yuyang), and southwestern (eastern Otog Qian and

southwestern Uxin) parts of the sandy land. Areas with no change

4.3. Causes of variations of AGB

covered 856.63 km (2.23%), mainly in the northwestern (south-

eastern Otog and northwestern Uxin) and southwestern (central

The variations of total AGB in the Mu Us Sandy Land dur-

and southern Otog) parts. Areas with mild ﬂuctuations and increas-

ing 2000–2012 were mainly controlled by changes of vegetation

ing trends of AGB covered 11,356.06 km (29.59%), primarily in the

growth conditions and vegetation distribution. In semiarid and

northeastern (southern Ejin Horo, western Shenmu, and northern

arid regions, growth conditions of natural vegetation are mainly

Yuyang), central (eastern and central Uxin), and southern (northern

affected by temperature and precipitation. For vegetation distri-

Dingbian and northwestern Jingbian) parts. Areas with moderate

bution, in addition to the limitation of climate factors, human

ﬂuctuations and increasing trends of AGB covered 17,274.00 km

activities on the landscape such as grazing and forestation are also

(45.01%), mainly in the western, southwestern, southeastern, and

important. Thus, causes of AGB variations in the sandy land are

northeastern parts of the sandy land. Areas with severe ﬂuctua-

mainly addressed with respect to natural and human factors.

tions and increasing trends of AGB covered 3874.44 km (10.10%),

4.3.1. Natural factors

Temperature and precipitation are the most important natural

climate factors affecting the growth of vegetation in semiarid and

arid regions. In this research, historical records of nine meteoro-

logical stations were used to calculate average annual temperature

and precipitation for the Mu Us Sandy Land. By analyzing changes

of these two variables from 1980 to 2012 (Fig. 6), we found that

◦

both gradually increased, at 0.046 C/a and 1.669 mm/a, respec-

tively. The trend of increasing temperature was more signiﬁcant

y = 0.0 456x + 7.5771 600 R² = 0.3669 10

500 )

8 ºC mm) n(

400 ure( o 6 t ati 300 ipit

4 empera

200

y = 1.6 69x + 317.35 Precipitation

R² = 0.047 100 Temperature 2

0 0

2 2 6 0 0 2 4 8 0 4 6 8 0 4 8 2 6 198 198 199 199 199 199 200 200 200 200 201 201

198 198 198 199 200

Fig. 5. Spatial pattern of AGB from 2000 to 2012 (AGB: negative and positive num- Year

bers show decreasing or increasing ﬂuctuation, respectively; numbers indicate level

− − −

of ﬂuctuation; 4, extremely severe; 3, severe; 2, moderate; –1, mild; 0, no Fig. 6. Variations of annual average temperature and precipitation from 1980 to

change; 1, mild; 2, moderate; 3, severe; 4, extremely severe). 2012.

126 F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128

6 600 3

5 500 2 )

4 400 1 (mm on

3 300 I ati 0 SP l AGB (Tg) ipit ota

2 200 ec T

Pr -1 Total AGB Henan

1 100

Precipitation -2 Uxin

Ux in Ju 0 0 2 9 2 1 3 5 6 7 1 0 4 0 8 -3

1 3 6 7 9 1 2 0 2 4 5 8 0 200 200 201 200 200 200 200 200 200 201 201 200 200 200 200 200 200 200 201 201 Year 200 200 200 200 200 201

Year

Fig. 7. Variations of total AGB and precipitation from 2000 to 2012.

Fig. 8. Variations of annual standardized precipitation index from 2000 to 2012.

had precipitation classiﬁcations (and numbers of corresponding

than that of precipitation. Fluctuations of total AGB and precipita-

years) as follows: extremely wet (1), very wet (1), near normal (8),

tion revealed an almost uniform trend across the sandy land, with

moderately dry (1), severely dry (1), and extremely dry (1). Both

their correlation coefﬁcient r = 0.595 (Fig. 7). Precipitation in the

extremely wet and extremely dry years at Uxin and Uxin Ju were

sandy land was 212.5 mm in 2000, which was 38.53% below aver-

zero. The extremely wet and dry years made Henan the station with

age and the minimum in the above study period. Precipitation in

the greatest AGB ﬂuctuation of the three. Moreover, near-normal

1999 was 223.9 mm, which was 35.24% below average. Considering

precipitation years at Uxin Ju were 11, the highest among the three

that there is a time lag between vegetation growth and meteoro-

stations in 2000–2012. There were two severely dry years, the same

logical factors (Nezlin et al., 2005; Roerink et al., 2003; Schmidt and

as Uxin, which gave the AGB spatial pattern at Uxin Ju an increasing

Karnieli, 2000), there were more or less continuous severe droughts

with mild ﬂuctuation character. At Uxin, the precipitation classiﬁ-

in 1999 and 2000. This had a negative effect on the growth of natu-

cations (and numbers of corresponding years) were very wet (2),

ral vegetation, which is the most important reason for the smallest

moderately wet (1), near normal (8), and severely dry (2). Number

AGB in 2000. Precipitation in 2001 and 2002 was 426.4 mm and

of near-normal years was the same as that at Henan but less than

467.8 mm, respectively, which were 23.34% and 35.31% above aver-

that at Uxin Ju, respectively. This gave the AGB spatial pattern in the

age. With the increase of precipitation, total AGB in 2001–2002 was

Uxin region an increasing with moderate ﬂuctuation characteristic.

greater than that in 2000. From 2002 to 2005, both precipitation

and total AGB had decreasing trends. The same time lag occurred

in 2005–2006 when precipitation was 225.7 mm and 262.2 mm, 4.3.2. Human factors

34.72% and 24.16% below average, respectively. This might have Aside from natural driving forces such as precipitation and tem-

produced the smaller AGB in 2005–2006. Affected by the continu- perature, human driving forces such as population, production

ous severe drought, AGB in 2006 was even less than that in 2005. type, policy, and others were also important factors causing varia-

In 2007–2008 and 2009–2012, variations of AGB and precipitation tions of AGB (Dai, 2010; Jiang, 2005; Mu et al., 2013; Wang et al.,

were more stable. 2006; Zhou et al., 2009). Uxin Banner is the only region in the

Fluctuations of total AGB and precipitation showed an almost remotest area of the Mu Us Sandy Land. Statistical data of human

uniform trend. A precipitation deﬁcit somewhat affects levels of factors in Uxin Banner gave a better representation and they could

vegetation growth and AGB (Zhao and Running, 2010). It was nec- be used to represent the socioeconomic situation of the sandy land.

essary to quantitatively evaluate this deﬁcit for comprehensively (1) Population and livestock

understanding AGB spatial variations. Among dozens of drought In Uxin Banner, the population was 93,752 in 2000 and 108,797

indices, the standardized precipitation index (SPI) developed by in 2012, an annual average increase of 7.04%. Total livestock num-

McKee et al. (1993, 1995) has been widely used for agricultural bers, especially sheep and goats, were 605,465 to 1.34 million head

drought, water supply, and water management interests (Caccamo in 2006, and 1.02 million in 2012, an annual average increase of

et al., 2011; Ji and Peters, 2003; Narasimhan and Srinivasan, 2005; 5.68%. Continuous increases of population and number of breed-

Quiring and Papakryiakou, 2003). The SPI can be calculated by ing animals boosted pressure on the land and water resources. This

ﬁtting long-term precipitation data to a Gamma probability dis- could have led to the changes of land use and AGB.

tribution function and transforming that distribution to a normal (2) Policies

one, which ensures a mean of zero for SPI on any timescale. Pos- AGB was also affected by the environmental education of local

itive SPI values indicate greater than median precipitation, and people and by national and regional policies, which resulted in

negative ones less than median precipitation. McKee et al. (1993) interventions and interest-driven impacts that improved AGB,

proposed a seven-category classiﬁcation for the SPI: extremely wet especially in grazing areas. With the implementation of the House-

(2.00 or greater); very wet (1.50 to 1.99); moderately wet (1.00 to hold Responsibility System (HRS) in the 1980s, livestock and

1.49); near normal (−0.99 to 0.99); moderately dry (−1.49 to 1.00); pasture were allocated to small operators and farmers, and the

severely dry (−1.99 to −1.50); and extremely dry (−2.00 or less). length of a grassland contract was extended from an indeﬁnite term

Considering the locations of the nine meteorological stations and to a period of 30–50 years. Stimulated by the HRS, farmers began

their AGB characteristics, we chose Henan, Uxin, and Uxin Ju, from to plant more trees, shrubs, and grass. In Uxin Banner, tree and

south to north of the Mu Us Sandy Land, as typical stations. SPI on shrub planting in 2000 brought the forest area to 225,000 ha, an

a 12-month scale for the three stations was calculated to address increase around fourfold from 1975 (Jiang, 2005). In 2012, forest

the causes of AGB spatial patterns from 2000 to 2012 (Fig. 8). Fig. 5 area reached 376,867 ha. Areas of natural and artiﬁcial grassland

shows that the main AGB spatial pattern around Henan, Uxin, and in 2000 were 606,000 and 10,000 ha, respectively. The ﬁgures in

Uxin Ju stations at a radius of 5 km was increasing, with severe, 2010 were 667,000 and 42,000 ha. Over-cultivation, overcutting,

moderate, and mild ﬂuctuations, respectively. At Henan, SPI values and over-grazing behaviors of farmers have been greatly restricted.

F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128 127

In addition to the HRS, a series of favorable policies were devel- had moderate and mild AGB ﬂuctuations with increasing trends,

oped and implemented to address environmental degradation respectively. These two types of areas constituted 74.60% of the

in China beginning in the late 1990s, which were very impor- total area and were widely distributed in the northeastern, east-

tant for afforestation and combating desertiﬁcation in the Mu ern, central, and southern portions of the sandy sand. Areas with

Us Sandy Land during 2000–2012. The Grain-to-Green Program severe and extremely severe ﬂuctuation combined with decreasing

(GTGP) began in 1999, which subsidized farmers for afforestation AGB made up the smallest portion of the study area, with these two

on marginal croplands. Grazing intensity declined under the GTGP types combined representing only 0.86% of the total area. These

program, which led to vegetation recovery and an improvement areas were scattered in northwestern and western parts of the

in the sandy land (Dai, 2010). As the only one in a sandy area sandy land.

among 174 demonstration counties of GTGP in China, Uxin Ban- Under the background of global climate warming with increas-

ner converted 3300 ha grain plots to forestry and grass in 2000. ing temperature and precipitation, the total AGB tended to increase

From 2001 to 2005, GTGP areas in Uxin Banner were 2000 ha, in 2000–2012, almost in line with precipitation. The relationship

6600 ha, 7330 ha, 8067 ha, and 3667 ha, respectively, which may between AGB and precipitation (especially precipitation deﬁcit)

have greatly improved the ecological environment and provided showed that precipitation was an important natural factor that

better growth conditions for vegetation in the Mu Us Sandy Land. signiﬁcantly affected AGB variation. However, precipitation was

Several other projects had signiﬁcant impacts in combating not the only effect on AGB. Other sources of interference were

desertiﬁcation and improving vegetation recovery, including the human, such as population, livestock, and policies. These altered

“Two Policies that Limited the Effects of Goat Grazing”, the fourth AGB spatiotemporal patterns. Increasing populations of humans

phase of the “Three-North Shelterbelt” project in 2001, and the and livestock exert more pressure on land and water resources in

“Aerial Seeding Afforestation” project in the Mu Us Sandy Land. sandy land ecosystems, which can modify AGB patterns. However,

With implementation of the “two policies” project, the proportions positive policies can somewhat reduce land and water resources

of goats and sheep were 13.4% and 86.6% in 2003, respectively, pressure and promote vegetation restoration and reforestation.

and corresponding ﬁgures in 2010 were 10.1% and 89.9%. The hog Affected by such positive human factors, although precipitation

number also increased considerably, from 70,316 head in 2003 to declined in 2008–2009, total AGB still increased. Positive policies

132,892 in 2010. As a result of the policy, the sheep population such as the “Aerial Seeding Afforestation” and “Three-North Shel-

showed a steady recovery, hogs had a sharp increase, but goats reg- terbelt” projects may be very important in the Mu Us Sandy Land.

istered an abrupt decline (Dai, 2010). Adjustment of animal feeding

structure can reduce grazing pressure on grassland, which would

Acknowledgements

be favorable for vegetation restoration. With implementation of the

Aerial Seeding Afforestation project, the associated afforestation

This research was funded by National Nonproﬁt Institute

area in Uxin Banner has increased 118,774 ha since 2000. Of this,

Research Grant of Chinese Academy of Forestry (grant number

17,334 ha was new afforestation in 2007 and 2008. Furthermore,

CAFYBB2011003) and National Science Foundation of China (grant

during implementation of the fourth phase of the Three-North Shel-

number 41301458 and 40801173). The authors would like to thank

terbelt project, 3067 and 7200 ha of afforestation areas were added

NASA for sharing MODIS data through Reverb and we thank two

in Uxin Banner during 2008 and 2009, respectively. This may be

anonymous reviewers for their valuable comments and sugges-

the major reason for total AGB increase, despite the decrease of

tions.

precipitation from 2008 to 2009.

(3) Other human activities

Among human factors, continuously increasing trends of popu- References

lation, cultivated land area, and number of breeding animals have

Aman, A., Randriamanantena, H.P., Podaire, A., Frouin, R., 1992. Upscale integration

negative effects on the growth of vegetation. However, with the

of normalized difference vegetation index: the problem of spatial heterogeneity.

promotion of environmental education and useful policies, AGB in

IEEE Trans. Geosci. Remote Sens. 30 (2), 326–338.

most of the Mu Us Sandy Land still had moderate or mild ﬂuc- Asrar, G., Myneni, R., Choudhury, B., 1992. Spatial heterogeneity in vegetation

tuations with increasing trends from 2000 to 2012. In contrast, a canopies and remote sensing of absorbed photosynthetically active radiation: a

modeling study. Remote Sens. Environ. 41 (2), 85–103.

rapid expansion of urban area, especially in the southern (Ding-

Bakr, N., Weindorf, D.C., Bahnassy, M.H., El-Badawi, M.M., 2012. Multi-temporal

bian and Jingbian), middle (Uxin), and northeastern (Yuyang and

assessment of land sensitivity to desertiﬁcation in a fragile agro-ecosystem:

Shenmu) parts of the sandy land, was the main cause of vegeta- environmental indicators. Ecol. Indic. 15 (1), 271–280.

Berterretche, M., Hudak, A.T., Cohen, W.B., Maiersperger, T.K., Gower, S.T., Dungan,

tion destruction and AGB decline. Other human factors, such as

J., 2005. Comparison of regression and geostatistical methods for mapping Leaf

mining, factories and supporting highways (Shenmu, Yuyang and

Area Index (LAI) with Landsat ETM+ data over a boreal forest. Remote Sens.

Hengshan), were also reasons for decreasing trends of AGB in the Environ. 96 (1), 49–61.

Boyd, D.S., 1999. The relationship between the biomass of Cameroonian tropical

sandy land.

forests and radiation reﬂected in middle infrared wavelengths (3.0–5.0 ␮m).

Int. J. Remote Sens. 20 (5), 1017–1023.

Braswell, B., Hagen, S., Frolking, S., Salas, W., 2003. A multivariable approach for

mapping sub-pixel land cover distributions using MISR and MODIS: application

5. Conclusions

in the Brazilian Amazon region. Remote Sens. Environ. 87 (2), 243–256.

Burrows, S., Gower, S., Norman, J., Diak, G., Mackay, D., Ahl, D., Clayton, M., 2003. Spa-

This study explored the use of Landsat TM and MODIS images tial variability of aboveground net primary production for a forested landscape

in northern Wisconsin. Can. J. For. Res. 33 (10), 2007–2018.

for estimating AGB in the Mu Us Sandy Land, a farming-pastoral

Caccamo, G., Chisholm, L.A., Bradstock, R.A., Puotinen, M.L., 2011. Assessing the sen-

ecotone of northern China. Spatiotemporal AGB patterns over

sitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sens.

2000–2012 were also analyzed. Environ. 115 (10), 2626–2639.

AGB estimation revealed that areas with various AGB levels ﬂuc- Choudhury, B.J., 1987. Relationships between vegetation indices, radiation absorp-

tion, and net photosynthesis evaluated by a sensitivity analysis. Remote Sens.

tuated. A relatively low average AGB and percentage of area with

Environ. 22 (2), 209–233.

less than 1.5 t/ha AGB accounted for more than half the total area

Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Ducrot, D., Marais-Sicre, C.,

each year. Total AGB increased by 0.1743 Tg annually during the Dejoux, J.-F., Huc, M., Keravec, P., Béziat, P., 2012. Maize and sunﬂower biomass

estimation in southwest France using high spatial and temporal resolution

study period. Spatial variations of AGB based on time series of

remote sensing data. Remote Sens. Environ. 124, 844–857.

retrieved data showed an increasing trend in most of the area (over

Crow, T., 1978. Biomass and production in three contiguous forests in northern

92.40% of the total area). The largest and the second largest areas Wisconsin. Ecology, 265–273.

128 F. Yan et al. / Agricultural and Forest Meteorology 200 (2015) 119–128

Curran, P.J., Dungan, J.L., Gholz, H.L., 1992. Seasonal LAI in slash pine estimated with Pearson, R.L., Miller, L.D., 1972. Remote mapping of standing crop biomass for

Landsat TM. Remote Sens. Environ. 39 (1), 3–13. estimation of the productivity of the shortgrass prairie, Pawnee National Grass-

Dai, Z., 2010. Intensive agropastoralism: dryland degradation, the Grain-to-Green lands, Colorado, Proceedings of the Eighth International Symposium on Remote

Program and islands of sustainability in the Mu Us Sandy Land of China. Agric. Sensing of Envirionment, Ann Arbor, Michigan, USA. 2, 1355-1379.

Ecosyst. Environ. 138 (3–4), 249–256. Propastin, P.A., Kappas, M.W., Herrmann, S.M., Tucker, C.J., 2012. Modiﬁed light use

Dong, X., Zhang, X., 2000. Special stomatal distribution in Sabina vulgaris in relation efﬁciency model for assessment of carbon sequestration in grasslands of Kazakh-

to its survival in a desert environment. Trees 14 (7), 369–375. stan: combining ground biomass data and remote-sensing. Int. J. Remote Sens.

Epting, J., Verbyla, D., Sorbel, B., 2005. Evaluation of remotely sensed indices for 33 (5), 1465–1487.

assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Purevdorj, T., Tateishi, R., Ishiyama, T., Honda, Y., 1998. Relationships between

Sens. Environ. 96 (3), 328–339. percent vegetation cover and vegetation indices. Int. J. Remote Sens. 19 (18),

Fang, J., Chen, A., Peng, C., Zhao, S., Ci, L., 2001. Changes in forest biomass carbon 3519–3535.

storage in China between 1949 and 1998. Science 292 (5525), 2320–2322. Qi, J., Chehbouni, A., Huete, A., Kerr, Y., Sorooshian, S., 1994. A modiﬁed soil adjusted

Feng, M., Huang, C., Channan, S., Vermote, E.F., Masek, J.G., Townshend, J.R., 2012. vegetation index. Remote Sens. Environ. 48 (2), 119–126.

Quality assessment of Landsat surface reﬂectance products using MODIS data. Quiring, S.M., Papakryiakou, T.N., 2003. An evaluation of agricultural drought indices

Comput. Geosci.—UK 38 (1), 9–22. for the Canadian prairies. Agric. For. Meteorol. 118 (1–2), 49–62.

Gao, F., Masek, J., Schwaller, M., Hall, F., 2006. On the blending of the Landsat and Reynolds, J.F., Smith, D.M.S., Lambin, E.F., Turner, B., Mortimore, M., Batterbury,

MODIS surface reﬂectance: predicting daily Landsat surface reﬂectance. IEEE S.P., Downing, T.E., Dowlatabadi, H., Fernández, R.J., Herrick, J.E., 2007. Global

Trans. Geosci. Remote Sens. 44 (8), 2207–2218. desertiﬁcation: building a science for dryland development. Science 316 (5826),

Gilabert, M., González-Piqueras, J., Garcıa-Haro, F., Meliá, J., 2002. A generalized soil- 847–851.

adjusted vegetation index. Remote Sens. Environ. 82 (2), 303–310. Roerink, G., Menenti, M., Soepboer, W., Su, Z., 2003. Assessment of climate impact

Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., 2002. Novel algorithms on vegetation dynamics by using remote sensing. Phys. Chem. Earth, A/B/C 28

for remote estimation of vegetation fraction. Remote Sens. Environ. 80 (1), (1), 103–109.

76–87. Rogan, J., Yool, S., 2001. Mapping ﬁre-induced vegetation depletion in the Peloncillo

Gutman, G., Ignatov, A., 1998. The derivation of the green vegetation fraction Mountains, Arizona and New Mexico. Int. J. Remote Sens. 22 (16), 3101–3121.

from NOAA/AVHRR data for use in numerical weather prediction models. Int. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation sys-

J. Remote Sens. 19 (8), 1533–1543. tems in the Great Plains with ERTS. In: Proceedings of the Third ERTS Symposium,

Hame, T., Salli, A., Andersson, K., Lohi, A., 1997. A new methodology for the estimation NASA, Washington, USA. SP-351, pp. 309–317.

of biomass of coniferdominated boreal forest using NOAA AVHRR data. Int. J. Rubio, J.L., Bochet, E., 1998. Desertiﬁcation indicators as diagnosis criteria for deser-

Remote Sens. 18 (15), 3211–3243. tiﬁcation risk assessment in Europe. J. Arid Environ. 39 (2), 113–120.

Helldén, U., Tottrup, C., 2008. Regional desertiﬁcation: a global synthesis. Global Schmidt, H., Karnieli, A., 2000. Remote sensing of the seasonal variability of vegeta-

Planet. Change 64 (3), 169–176. tion in a semi-arid environment. J. Arid Environ. 45 (1), 43–59.

Houghton, R., Skole, D., Nobre, C.A., Hackler, J., Lawrence, K., Chomentowski, W.H., Tømmervik, H., Høgda, K.A., Solheim, I., 2003. Monitoring vegetation changes in

2000. Annual ﬂuxes of carbon from deforestation and regrowth in the Brazilian Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal

Amazon. Nature 403 (6767), 301–304. Landsat MSS/TM data. Remote Sens. Environ. 85 (3), 370–388.

Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of Teillet, P., Staenz, K., William, D., 1997. Effects of spectral, spatial, and radiometric

the radiometric and biophysical performance of the MODIS vegetation indices. characteristics on remote sensing vegetation indices of forested regions. Remote

Remote Sens. Environ. 83 (1), 195–213. Sens. Environ. 61 (1), 139–149.

Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 Todd, S., Hoffer, R., Milchunas, D., 1998. Biomass estimation on grazed and ungrazed

(3), 295–309. rangelands using spectral indices. Int. J. Remote Sens. 19 (3), 427–438.

Ikeda, H., Okamoto, K., Fukuhara, M., 1999. Estimation of aboveground grassland Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring

phytomass with a growth model using Landsat TM and climate data. Int. J. vegetation. Remote Sens. Environ. 8 (2), 127–150.

Remote Sens. 20 (11), 2283–2294. Veraverbeke, S., Gitas, I., Katagis, T., Polychronaki, A., Somers, B., Goossens, R.,

Ji, L., Peters, A.J., 2003. Assessing vegetation response to drought in the northern 2012. Assessing post-ﬁre vegetation recovery using red–near infrared vege-

Great Plains using vegetation and drought indices. Remote Sens. Environ. 87 (1), tation indices: accounting for background and vegetation variability. ISPRS J.

85–98. Photogramm. Remote Sens. 68, 28–39.

Jiang, H., 2005. Grassland management and views of nature in China since 1949: Verbesselt, J., Somers, B., van Aardt, J., Jonckheere, I., Coppin, P., 2006. Monitoring

regional policies and local changes in Uxin Ju, inner Mongolia. Geoforum 36 (5), herbaceous biomass and water content with SPOT VEGETATION time-series to

641–653. improve ﬁre risk assessment in savanna ecosystems. Remote Sens. Environ. 101

Jordan, C.F., 1969. Derivation of leaf-area index from quality of light on the forest (3), 399–414.

ﬂoor. Ecology 50, 663–666. Wang, X., Chen, F., Dong, Z., 2006. The relative role of climatic and human factors in

Kassas, M., 1995. Desertiﬁcation: a general review. J. Arid Environ. 30 (2), 115–128. desertiﬁcation in semiarid China. Global Environ. Change 16 (1), 48–57.

Kim, G., Barros, A.P., 2002. Downscaling of remotely sensed soil moisture with a Woodcock, C.E., Strahler, A.H., 1987. The factor of scale in remote sensing. Remote

modiﬁed fractal interpolation method using contraction mapping and ancillary Sens. Environ. 21 (3), 311–332.

data. Remote Sens. Environ. 83 (3), 400–413. Wu, B., Ci, L.J., 2002. Landscape change and desertiﬁcation development in the Mu

Mayaux, P., Lambin, E.F., 1995. Estimation of tropical forest area from coarse spatial Us Sandland, Northern China. J. Arid Environ. 50 (3), 429–444.

resolution data: a two-step correction function for proportional errors due to Wu, J., Wang, D., Bauer, M.E., 2007. Assessing broadband vegetation indices and

spatial aggregation. Remote Sens. Environ. 53 (1), 1–15. QuickBird data in estimating leaf area index of corn and potato canopies. Field

McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and Crops Res. 102 (1), 33–42.

duration to time scales. In: Proceedings of the eighth Conference on Applied Xu, H., Zhang, T., 2013. Assessment of consistency in forest-dominated vegetation

Climatology, AMS, Boston, MA, pp. 179–184. observations between ASTER and Landsat ETM+ images in subtropical coastal

McKee, T.B., Doesken, N.J., Kleist, J., 1995. Drought monitoring with multiple time areas of southeastern China. Agric. For. Meteorol. 168 (0), 1–9.

scales. In: Proceedings of ninth Conference on Applied Climatology, AMS, Boston, Yan, F., Wu, B., Wang, Y., 2013. Estimating aboveground biomass in Mu Us Sandy

MA, pp. 233–236. Land using Landsat spectral derived vegetation indices over the past 30 years. J.

Moreau, S., Bosseno, R., Gu, X.F., Baret, F., 2003. Assessing the biomass dynamics of Arid Land 5 (4), 521–530.

Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR. Zhang, L., Wylie, B., Loveland, T., Fosnight, E., Tieszen, L.L., Ji, L., Gilmanov, T., 2007.

Remote Sens. Environ. 85 (4), 516–529. Evaluation and comparison of gross primary production estimates for the North-

Mu, S., Zhou, S., Chen, Y., Li, J., Ju, W., Odeh, I.O.A., 2013. Assessing the impact of ern Great Plains grasslands. Remote Sens. Environ. 106 (2), 173–189.

restoration-induced land conversion and management alternatives on net pri- Zhang, X., 1994. Ecological background, principles of management and optimized

mary productivity in inner Mongolian grassland, China. Global Planet. Change models of the Mu Us sandy grasslands. Acta Phytoecol. Sin. 18 (1), 1–16.

108 (0), 29–41. Zhang, X., Liao, C., Li, J., Sun, Q., 2013. Fractional vegetation cover estimation in arid

Muukkonen, P., Heiskanen, J., 2005. Estimating biomass for boreal forests using and semi-arid environments using HJ-1 satellite hyperspectral data. Int. J. Appl.

ASTER satellite data combined with standwise forest inventory data. Remote Earth Obs. Geoinf. 21, 506–512.

Sens. Environ. 99 (4), 434–447. Zhao, M., Running, S.W., 2010. Drought-induced reduction in global terrestrial net

Muukkonen, P., Heiskanen, J., 2007. Biomass estimation over a large area based primary production from 2000 through 2009. Science 329 (5994), 940–943.

on standwise forest inventory data and ASTER and MODIS satellite data: Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., Ryu, S.-R., 2004.

a possibility to verify carbon inventories. Remote Sens. Environ. 107 (4), Estimating aboveground biomass using Landsat 7 ETM+ data across a managed

617–624. landscape in northern Wisconsin, USA. Remote Sens. Environ. 93 (3), 402–411.

Narasimhan, B., Srinivasan, R., 2005. Development and evaluation of soil moisture Zhou, H., Van Rompaey, A., Wang, J., 2009. Detecting the impact of the Grain for

deﬁcit index (SMDI) and evapotranspiration deﬁcit index (ETDI) for agricultural Green program on the mean annual vegetation cover in the Shaanxi province,

drought monitoring. Agric. Forest Meteorol. 133 (1–4), 69–88. China using SPOT-VGT NDVI data. Land Use Policy 26 (4), 954–960.

Nezlin, N., Kostianoy, A., Li, B.-L., 2005. Inter-annual variability and interaction of Zhou, Q., Robson, M., 2001. Automated rangeland vegetation cover and density esti-

remote-sensed vegetation index and atmospheric precipitation in the Aral Sea mation using ground digital images and a spectral–contextual classiﬁer. Int. J.

region. J. Arid Environ. 62 (4), 677–700. Remote Sens. 22 (17), 3457–3470. 本文献由“学霸图书馆-文献云下载”收集自网络，仅供学习交流使用。

学霸图书馆（www.xuebalib.com）是一个“整合众多图书馆数据库资源，

提供一站式文献检索和下载服务”的24 小时在线不限IP 图书馆。图书馆致力于便利、促进学习与科研，提供最强文献下载服务。

图书馆导航：

图书馆首页文献云下载图书馆入口外文数据库大全疑难文献辅助工具