Influence of meadow changes on net primary productivity: a case ...

Geosciences Journal Vol. 19, No. 3, p. 561  573, September 2015 DOI 10.1007/s12303-014-0057-z ⓒ The Association of Korean Geoscience Societies and Springer 2015

Influence of meadow changes on net primary productivity: a case study in a typical steppe area of XilinGol of Inner Mongolia in China Xiaobing Li

State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China Guoqing Li* Institute of Geography & Planning, Ludong University, Yantai, Shandong 264025, China

Hong Wang Han Wang Jingjing Yu

}

State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China

ABSTRACT: In this research, we monitored the change (degeneration or improvement) in meadow vegetation over an approximately 12-year timespan in the typical steppe area of Inner Mongolia in China. Linear trend analysis (LTA) and the MOD13Q1-NDVI time series data were used to evaluate the changes in the net primary productivity (NPP) during the vegetation growing seasons between 2000 and 2011. The Carnegie Ames Stanford Approach (CASA) model was used, and the relationship between the vegetation change and meadow NPP was analyzed and validated with field data collected in 2011. The results indicate the following: (1) the growth status and NPP of the meadow vegetation in the typical steppe area of Inner Mongolia varied greatly for each year without an obvious linear trend between the change of meadow vegetation and NPP; (2) additional analysis with field measured data, collected in 2011, revealed that the average dry weight of the above-ground biomass in the area where the NPP had increased was less than that in the area where it had decreased; the dry weight of the above-ground biomass of the meadow vegetation that showed degeneration was greater than that of the meadow vegetation that showed improvement; (3) a possible reason for the phenomenon mentioned in (2) was that the government protected the degenerated meadows with less biomass, which led to vegetation growth and increased NPP, whereas the meadows that had not been degenerated or showed only minor degeneration and still received rich biomass were over-grazed, causing the NPP to decline. Key words: NDVI, net primary productivity, meadow change, typical steppe

1. INTRODUCTION The steppe ecosystem is the largest ecosystem in China and covers 41% of the total territorial area (Qi et al., 2003). Under current climatic conditions, the carbon storage of the steppe vegetation accounts for 16.7% (Chang and Tang, 2008) of the total vegetation carbon storage in China, which offsets 77.32% (Lal, 2002) of the industrial carbon emission in China in the year 2000 (1 Pg C). By 2004, there had been nearly 180,000,000 hm2 seriously degenerated steppe in China, which represented approximately 45% of the total steppe *Corresponding author: [email protected]

area. Additionally, the degenerated steppe is expanding at the rate of 2 million hm2 each year in China (China, 2004). According to the 2011 Report on the State of Environment in China, 90% of natural steppe in China is degenerated to different degrees (China, 2010). The steppe in the typical steppe area in Inner Mongolia is expectedly degenerated and had reduced 175,000 hm2 from 1975 to 2000 (Yan et al., 2011). Once the meadow is destroyed, the carbon stored in the meadow would be released into the atmosphere, which would increase the carbon dioxide emission levels and the associated greenhouse effect and global warming (Rey et al., 2011). This research aims to discern the influence of the changes in the meadow vegetation on the vegetation carbon fixation capabilities by the analysis of the change in meadow areas between 2000 and 2011 and to obtain a dynamic change trend of the vegetation carbon fixation capabilities in the studied area. Biomass is regarded as the most fundamental quantitative feature of the steppe ecosystem by certain scholars and is therefore adopted for the analysis of the change in the meadow vegetation’s carbon storage (Piao et al., 2007; Fan et al., 2008; Peri and Lasagno, 2010). Net Primary Productivity (NPP) can play an important role in the evaluation of steppe carbon fixation capabilities. Not only are the biomass living fraction and vegetation litter fraction present as forest vegetation estimates in the NPP of a steppe ecosystem (Larcher, 2003), but also the part eaten by the plant-eating animals (Liu et al., 2007) are included. Besides, as an important indicator of biomass change and the basis of research on transformations between material and energy in a meadow ecosystem (Kajimoto et al., 1999; Nirmal Kumar et al., 2012), NPP directly reflects the productivity of the meadows under natural conditions (Lieth, 1978; Roxburgh et al., 2005) and can therefore better reflect the dynamic changes of the vegetation fixation capabilities when compared with that of the biomass (Zhao and Running, 2010; Morgan et al., 2011). In this research, we used NPP and the biomass to evaluate the meadow vegetation’s capacity for carbon fixation capacity and the meadow vegetation’s carbon storage, respectively.

562

Xiaobing Li, Guoqing Li, Hong Wang, Han Wang, and Jingjing Yu

As the NPP cannot be measured directly and completely on either a regional or global scale, indirect simulations have become an important research approach that is now widely recognized (Cramer et al., 1999). Among the different models, the light use efficiency model based on remote sensing data is notably effective due to its solid theoretical basis and extensive application and is potentially the most robust approach (Running et al., 2004). Carnegie Ames Stanford Approach (CASA) (Potter et al., 1993) model uses a lightuse efficiency (LUE) factor, which is the efficiency of conversion of light energy into dry materials by plant, has been widely applied in the evaluation of the regional land NPP (Hicke et al., 2002; Piao et al., 2005) and the global carbon cycle (Potter et al., 1993, 2003, 2007; Field et al., 1995). These studies indicate that the CASA model can evaluate the dynamic change and spatio-temporal variability of the vegetation’s capacity for carbon fixation capacity at both the regional and global scales (Zhu et al., 2006) modified certain parameters of the CASA model according to the characteristics of vegetation in China, and the improved model was adopted in this research to simulate carbon fixation capacity in the study area. Remote sensing technology has been widely applied in monitoring the change of meadow vegetation and focuses primarily on the use of NDVI data from such sensors as the Advanced Very High Resolution Radiometer (AVHRR), SPOT and Moderate Resolution Imaging Spectrometer (MODIS), among others (Giri and Shrestha, 1996; Hill and Donald, 2003; Holm et al., 2003; Budde et al., 2004; Le Maire et al., 2011). There are already numerous methods of monitoring the meadow change with NDVI data from the above sensors (Singh, 1989; Lunetta and Elvidge, 1999), such as the Principal Component Analysis (PCA) (Byrne et al., 1980), Coefficients of Variation (CoV) (Vicente-Serrano et al., 2006), Linear Trend Analysis (LTA) (Myneni et al., 1997; Fuller, 1998), Image Algebra Change Detection (IACD)(Lyon et al., 1998) and Change Vector Analysis (CVA) (Lambin and Strahler, 1994). Every method has its own set of advantage and weight factors (Singh, 1989; Lunetta and Elvidge, 1999). Therefore, it is necessary to choose judiciously a method to monitor changes in the meadows accurately. LTA is one of the most widely used approaches to monitor vegetation change using the NDVI dataset, since it is a simple, intuitive way to identify continuous inter-annual vegetation change trends (Myneni et al., 1997; Fensholt et al., 2009; Zhao et al., 2012). LTA produces a simple linear regression model between time and vegetation index value to delineate temporal trends within the NDVI dataset. · In this paper, CASA model is used to simulate change of NPP in the typical steppe area of XilinGol of Inner Mongolia during 2000~2011 and further estimate the spatial variation characteristic of the vegetation's carbon fixation capacity. · In addition, MOD13Q1-NDVI data and LTA method is

also adopted to determine areas where the vegetation degradation or restoration in the study area. Later, the variation characteristics of NPP and dry weight of the above-ground biomass in such areas are studied. · The influences of vegetation change on carbon storage of above-ground biomass and vegetation carbon fixation capacity are analyzed by combing with the field survey data. 2. STUDIED AREA AND DATA SOURCE 2.1. Profile of Study Area The typical steppe area in the XilinGol League of Inner Mongolia is located in Abag Banner, Xilin Hot City, East and West Ujimqin Banner of Inner Mongolia in China (Fig. 1). The steppe area is located in a continental arid and semi-arid climate with an average annual range of temperature from –1 °C to 4 °C and an average annual range of precipitation from 150 mm to 450 mm. There are several types of landforms, with the high plain being the dominant kind. The altitude is from 800 m to 1800 m, and it is high in the south and east and low in the north and west. The various soil types are chestnut soil, chernozem, brown soil, moisture soil, aeolian sandy soil, boggy soil and saline-alkali soil. The various steppe types are mainly temperate bunch grasses of the typical steppe, temperate grasses, forbs meadow steppe and certain temperate grasses and forbs salt meadow. The vegetation is mainly xerophytic grass, such as Stipa grandis P. Smirn, Stipa krylovii Roshev, Leymus chinensis Tzvel and Cleistogenes squarrosa Keng. 2.2. Data Sources (1) Meteorological data: Average monthly temperature and total monthly precipitation data from April to November of each year from 2000 to 2011, as provided by 51 weather stations in Inner Mongolia and total monthly solar radiation data provided by 98 weather stations in China (China Meteorological Data Sharing Service System, CMDSSS: http:// new-cdc.cma.gov.cn). (2) Remote sensing data: MOD13Q1 data from the 113th day to the 305th day of each year between 2000 and 2011 provided by the MODIS sensor (The Earth Observing System Data and Information System, EOSDIS), website: http://reverb. echo.nasa.gov/reverb). (3) Land-use map: 1:100,000 land-use maps of 2000 (The Environmental and Ecological Science Data Center for West China, website: http://westdc.westgis.ac.cn), 2005 and 2010 (classified by the author of this article with the Landsat5TM data downloaded from the following institutions: Scientific Data Service Platform, Computer Network Information Center, Chinese Academy of Sciences, website: http://datamirror.csdb.cn). (4) Field survey data: vegetation community composition, vegetation coverage and above-ground biomass data collected

Influence of meadow changes on net primary productivity: a case study in China

563

Fig. 1. Location of the study area.

by the author from field surveys between July 15th and August 2nd in 2011. 3. METHODS 3.1. Basic Treatment of Data The technology roadmap of the study is shown in Figure 2 and mainly includes the evaluation of vegetation carbon fixation capabilities and the monitoring of meadow changes. The CASA model is adopted to simulate the NPP (vegetation carbon fixation capabilities); subsequently, the LTA method is used to analyze the relation between the meadow changes and NPP changes.

3.1.1. Interpolation of meteorological data As there are only 5 weather stations in the studied areas, and only 1 of them is capable of collecting total monthly solar radiation; direct interpolation of the data provided by these weather stations would produce erroneous results. To reduce the interpolation error caused by the lack of weather stations as much as possible, meteorological data from 51 weather stations in Inner Mongolia were used in this research for the interpolation of the average monthly temperature and total monthly precipitation, and data from 98 weather stations in China were used for the interpolation of the monthly solar radiation. All interpolations were performed with the Kriging method. Finally, the average monthly temperature, the average monthly precipitation and the total monthly solar

564


radiation within the abovementioned period were obtained for the study area. 3.1.2. Reconstitution of the NDVI data Although de-noising and cloud removing have been conducted on the MOD13Q1-NDVI data through the Maximum Value Composite (MVC), it is still difficult to guarantee that all the pixels in each of the 16-day images are cloudless for the entire study period (Chen et al., 2004). We judged the quality of the NDVI data with MOD13Q1 image quality layer (250 m 16 days VI Quality), and the results indicated that none of the 16-day images could guarantee that the NDVI values for all the pixels in the study area were reliable, and for certain images, more than 40% of the pixels were deemed as unreliable. Therefore, it is difficult to analyze the change in the meadows with the raw MOD13Q1-NDVI data, and further processing of the images is required. Harmonic Analysis of Time Series (HANTS) is a combination of smoothing and filtering that can acquire amplitude and phase information with non-zero frequencies through Fourier transformations. A least square curve-fitting model is implemented and pixels that have lower values than the model are regarded as cloudy and excluded from the dataset by assignment of zero weights. A new curve is fitted to the remaining pixel values. Through repeated iteration of the above algorithm, images are reconstituted and the influence of cloud cover can be removed (Roerink et al., 2000; Jakubauskas et al., 2001). Through HANTS filtering, the author reconstituted the MOD13Q1NDVI data with a resolution of 16 days into a NDVI series with a one-month resolution, for the growing season of the vegetation in the studied area (from April to November) and used it as the input data for NPP (Table 1). 3.1.3. Determination of the meadow distribution area This research focuses on the meadow type of land-use and disregards other land-use or vegetation types. Therefore, it is necessary to determine the precise distribution of the meadows. Aided with the land use maps of 2000, 2005 and 2010, the author worked out the public area of the meadows to within above 3 periods and took the overlap area as the meadow area for the purpose of this research. 3.2. Setting of the Field Sample Plots and Treatment of the Samples To analyze the current state of the area where the vegetation has changed, the author acquired samples of vegetation

community compositions, vegetation coverage and aboveground biomass from July 15th to August 2nd in 2011. There were 65 meadow sample plots along the road at intervals that ranged from 15 km to 20 km. To correspond with the MODIS pixels, the size of sample plots was fixed at 500 m × 500 m with three quadrats (1 m × 1 m) in each plot. The sample plot coverage was measured with a photographic method (Li, 2011), and the above-ground biomass was collected by the harvest method, which involved steaming for 15 minutes at 105 °C and drying for 24 hours at 85 °C. The average coverage and the average biomass of the 3 quadrats in a sample plot were regarded as the coverage and biomass of the sample plot. 3.3. Calculation of NPP Following the application of the CASA model in China, the method mentioned in the literature (Zhu, 2005) is adopted for the calculation of NPP. The overall design of the NPP estimation model is illustrated in the “evaluation of vegetation carbon fixation capabilities” part of Figure 2. The NPP is regarded as a function of irradiance, the absorbed photosynthetically active (400~700 nm) solar radiation (APAR), and an actual light utilization efficiency (Potter et al., 1993). NPP is calculated as follows: NPP  x t  = APAR  x t     x t  ,

(1)

where NPP(x,t) stands for the NPP of the pixel x in month t; APAR(x,t) stands for the photosynthetic active radiation (MJ/m2/month) absorbed by the pixel x in month t; (x,t) stands for the actual light utilization efficiency (gC/MJ) of the pixel x in month t. 3.3.1. Estimation of APAR APAR(x,t) = SOL(x,t) × FPAR(x,t) × 0.5,

(2)

where SOL(x,t) stands for the total solar radiation (MJ/m2/ month) of the pixel x in month t; FPAR(x,t) stands for the proportion of photosynthetic active radiation absorbed by a vegetation layer (no unit); constant 0.5 refers to the proportion of solar radiation that can be used by the vegetation (wavelength: 0.38~0.71 μm) expressed as total solar radiation. There is a linear relation between FPAR and NDVI within a certain range (Ruimy et al., 1994), which can be determined by the maximum and minimum NDVI values of a certain vegetation type.

Table 1. Reconstitution of NDVI series in growing season Month April May June July August September October November The starting Julian day of 113 113,129 145,161 177,193 209,225 241,257 273,289 305 MOD13Q1 data (day n) For 1 scene data for a month, the NDVI value of the scene is taken as the NDVI value of the month, and for 2 scenes data for a month, the average value of the 2 scenes data is taken as the NDVI value of the month.


565

Fig. 2. Technology roadmap.

FPARmin are 0.001 and 0.95, respectively, and they are independent of the vegetation type. SR is determined by the following formula. SRi,max and SRi,min are the 95th and 5th percentile of the NDVI of vegetation type i, respectively.

FPAR(x,t)NDVI =  NDVI  x y  – NDVIi min    FPARmax – FPARmin  ------------------------------------------------------------------------------------------------------------------- NDVIi max – NDVIi min  + FPARmin .

(3)

NDVIi,max and NDVIi,min are, respectively, the maximum and minimum NDVI value of vegetation type i for the study period. Furthermore, research has indicated that there is a linear relation between FPAR and SR (LOS et al., 1994), as well, which can be represented by formula (4): FPAR(x,t)SR =  SR  x t  – SRi min   FPARmax –FPARmin  ---------------------------------------------------------------------------------------------------- +FPARmin . (4)  SRi max – SRi min  In the above two formulae, the values of FPARmax and

1 + NDVI  x t  SR  x t  = ---------------------------------- . 1 – NDVI  x t 

(5)

NDVI(x,t) is the NDVI of the pixel x in month t. On comparison of the results estimated by the FPAR-NDVI and the FPAR-SR, we found that the FPAR estimated by the NDVI was higher than the measured value, whereas the FPAR estimated by the SR was lower than measured value, although the error was less than that of the results estimated by the NDVI directly. Thus, (Los, 1998) combined the two methods and took the average value as the estimation result of FPAR, which had the least error. In this research, we combined the above two formulae to obtain the average value (Zhu, 2005).

566


FPAR  x t NDVI + FPAR  x t SR -. FPAR  x t  = ----------------------------------------------------------------------2

(6)

To remove the inherent error in the vegetation classification and the NDVI data, (Zhu et al., 2006) introduced the concept of vegetation classification precision such that the maximum value of NDVI would vary with the classification precision. The maximum and minimum values of NDVI are determined in the following three steps: 1) obtain probability distribution diagram of the maximum NDVI value of a meadow at an interval of 0.0001; 2) according to the vegetation classification precision x, select pixels of the meadow in the probability distribution range of [(1  x)/2, (1 + x)/2]; 3) calculate probability distribution of the NDVI on the selected pixels and then the NDVI value corresponding to the 95th percentile of this probability distribution is the maximum NDVI value and the value corresponding to the 5th percentile is the minimum value. The first and second steps aim to rectify the error caused by the vegetation misclassification and the 95th percentile adopted in the third step corrects the error caused by the noise in the remote sensing images to a certain extent. The average value of the classification precision of the land-use map used in this research is 85%. 3.3.2. Calculation of actual light utilization efficiency (Potter et al., 1993) argued that vegetation enjoys the maximum light utilization efficiency under ideal conditions, whereas as evidenced from practical situation, maximum light utilization efficiency is primarily influenced by the ambient temperature and moisture. The formula used in (Potter et al., 1993) is the following:   x t  = T1  x t   T2  x t   W  x t   max ,

(7)

where T1  x t  and T2  x t  represents, respectively, the stress of low temperature and high temperature on light use efficiency (°C); W  x t  is the stress influence coefficient of moisture (no unit); max is the maximum light utilization efficiency under ideal conditions (gC/MJ). Different vegetation types have different maximum light utilization efficiency (εmax). As the value of the maximum light utilization efficiency in the region has a pronounced influence on the estimation result of the plants’ growth, estimation of the maximum light utilization efficiency is extremely important. εmax of Chinese grassland was estimated to be about 0.542 gC/MJ according to specific condition of vegetation in Inner Mongolia (Zhu et al., 2006). Therefore, this value is adopted in this paper. Temperature stress factor, Tε1(x,t), reflects the limit of the vegetation’s inherent biochemical action at low and high temperatures on photosynthesis, which lowers NPP (Field et al., 1995). 2

T1  x t  = 0.8  0.02  Topt  x  – 0.0005   Topt  x   . (8)

Topt (x) is the average temperature (°C) of the month when the NDVI in a certain area reaches the maximum value for the year. Many researchers have shown that the value of NDVI and its change can reflect the growth in the plants. When NDVI reaches the maximum value, plants grow the fastest, and the corresponding temperatures can approximately represent the most suitable temperature for the plants’ growth. In this study, Topt (x) is the average of the average monthly temperatures in July and August. Tε2(x,t) represents the gradually lowering trend of the plant’s light use efficiency when the temperature changes from the most suitable temperature Topt(x) to higher or lower temperatures (Potter et al., 1993; Field et al., 1995). As more respiration consumptions at high or low temperatures would certainly reduce the light use efficiency (Potter et al., 1993), the plant’s light use efficiency is reduced accordingly. T2  x t  = 1.184/  1 + exp  0.2   Topt  x  – 10 – T  x t      1/  1 + exp  0.3   –Topt  x  – 10 – T  x t     ,

(9)

where T(x,t) is the average temperature of a certain month (°C). Moisture stress factor: moisture stress influence coefficient Wε(x,t) reflects the influence of active moisture conditions, which can be used by plants for light use efficiency. With the increase in active moisture in the environment, Wε(x,t) increases gradually, and its range is from 0.5 (extremely dry) to 1 (very humid) (Piao et al., 2001): W  x t  = 0.5 + 0.5  E  x t  /E p  x t  ,

(10)

where E(x,t) is the actual evapotranspiration in the region (in mm), which can be obtained with a regional actual evapotranspiration model (Zhou and Zhang, 1995). Ep(x,t) is the potential evapotranspiration in the region (mm), which can be calculated according to the complementary relationship mentioned in the literature (Zhou and Zhang, 1996). E  x  t  =  P  x t   R n  x  t   2

2

  P  x t   +  Rn  x t   + P  x t   Rn  x t    / 2

2

 P  x t  + Rn  x t     P  x t c  +  Rn  x t    ,

(11)

where P(x,t) is the precipitation of a pixel x in month t (mm) and Rn(x,t) is the surface net radiation of a pixel x in month t (mm). As there are usually no surface net radiation data at the weather station and most other meteorological elements needed in the calculation of surface net radiation data are difficult to obtain, the empirical model of Zhou Guangsheng (Zhou and Zhang, 1996) is adopted in this research. 0.5

Rn  x t  =  Ep0  x t   P  x t     Ep0  x t   - ,  0.369 + 0.598  -----------------P  x t   

(12)


Ep  x t  =  E  x t  + Ep0  x t  /2 ,

(13)

where EP0(x,t) is the potential evapotranspiration in a partial region, which can be obtained with the vegetation-climate relation model developed by Thornth Waite (Zhang, 1989a; Zhang, 1989b). 10  T  x t  Ep0  x t  = 16  -------------------------Ix 

x

3

,

(14)

2

  x  =  0.6751  I  x  – 77.1  I  x  + 17920  I  x  –6

+ 492390  10 , 12 T  x t  I  x  =  --------------5 i=1

(15)

1.514

,

(16)

where I(x) is the heat index for March, April and May (°C) and α(x) is a constant which varies with region and is a function of I(x). This relationship is only effective from 0 °C to 26.5 °C. Thornth Waite set the possible evapotranspiration rate at 0 for temperatures less than 0 °C. When the temperature is greater than 26.5 °C, evapotranspiration will only increase with temperature and is independent of the value of I(x). After all parameters required for CASA model are obtained with Formula (2) to (16), NPP can be calculated with Formula (1).

567

ear regression model (Eq. 19): y = a* x + b,

(19)

where x is time, y is the modelled change indicator (e.g., vegetation index), a is the slope of the regression line and b is the intercept. The slope a, estimated by an ordinary least-squares method Equation (20), represents the vegetation change trend:  12    12 x 12 y   x y  –   12 i=1 i i i = 1 i i = 1 i a = ------------------------------------------------------------------------------, 2 2 x  –   12  12    12 x  i=1 i i=1 i

(20)

where xi is 1 for the first year, 2 for the second year and so on; yi is the SINDVI of each pixel (when LTA is applied at the pixel level) or ASINDVI of all vegetated pixels(when LTA is applied for the entire study area) for year i. a < 0 indicates a decrease trend of SINDVI and a > 0 indicates an increase trend; a < 0 indicates a degraded vegetation condition and a > 0 indicates an improved vegetation condition. Similarly, we calculated the change in trend of NPP with formulas (17–20) to obtain the SINPP and ASINPP of NPP in the studied area and the change trend slope of NPP (aNPP) in the past 12 years. We defined the trend slope calculated with SINDIV as aSINDVI to avoid confusion. 4. RESULTS AND ANALYSIS

3.4. Remote Sensing Monitoring of Meadow Change 4.1. Verification of the Simulated Value of the NPP The seasonally integrated NDVI (SINDVI) can effectively reflect the change of surface vegetation (Bai et al., 2008; Budde et al., 2004; Giri and Shrestha, 1996; Hill and Donald, 2003; Holm et al., 2003; Le Maire et al., 2011; Yang et al., 1998). SINDVI data were used for the inter-annual time series. SINDVI of each pixel is calculated (Stow et al., 2003) as in Equation (17): e

SINDVI =  MNDVIi j p , j=s

(17)

where SINDVIi,p is the SINDVI for pixel p in year i; MNDVIi,j,p is the monthly NDVI for pixel p in month j for year i; and s and e are growing season starting and ending months, respectively. The spatially averaged SINDVI (ASINDVI) of a given region is calculated (Zhou et al., 2001) as in Equation (18): 1 N ASINDVIi = ----  SINDVIi p , Np = 1

(18)

where N is the total number of pixels for a given region. Using MDDVI data from 2000 to 2011, the SINDVI of each pixel and ASINDVI of all vegetated pixels were calculated (Eqs. 17 and 18). For the study area, April was identified as the start and November as the ending month of the growing season (Li, 2011). Evaluating general change trends using linear trend analysis. LTA is widely used for evaluating general trend (Ma and Frank, 2006). It is based on the lin-

The simulation accuracy of this model has been validated in Inner Mongolia by the author, as well as other researchers, and the results have been published elsewhere. A linear relationship is observed between the measured NPP value and the simulated value, with the linear correlation coefficient being 0.93 (p < 0.0001) (Gang et al., 2007; Li, 2011; Zhang et al., 2008). The Mean Absolute Error (MAE) between the simulated ANPP and the measured ANPP is 17.49 g C·m–2·a–1, and the Mean Relative Error (MRE) is 17.45%, both of which are considered to be small (Zhang et al., 2008). 4.2. The Relation between Meadow Change and NPP To determine the relation between meadow change and vegetation carbon fixation capabilities, 748 test spots are selected from northeast to southwest and northwest to southeast profiles (Fig. 3a) The corresponding relation diagram of aNPP and aSINDVI is shown in Figure 3b. When the vegetation state is improved (aSINDVI > 0), 524 spots of aNPP are greater than 0, and 224 spots of aNPP are less than 0. When the vegetation quality deteriorates (aSINDVI < 0), 239 spots of aNPP are greater than 0, and 539 spots of aNPP are less than 0. Figure 3b indicates that the growth status and NPP of the meadow vegetation in the typical steppe area of Inner Mongolia varied greatly for each year without an obvious linear trend between the change

568


tivity among the vegetation communities, which is followed by the meadows with moderate degeneration and finally by the seriously degenerated meadows, which exhibit the lowest productivity among the vegetation communities. However, this characteristic is not typical for different vegetation functional communities (Bao et al., 2004; Li and Liu, 2011). Therefore, we should analyze the influence of meadow changes in the vegetation carbon fixation capabilities with due consideration to the steppe types in the studied area. 4.3. Ground Verification

Fig. 3. (a) Distribution of the test spots and (b) the corresponding relation diagram of aNPP and aSINDVI.

of meadow vegetation and NPP. Results from a recent study on Chinese meadows by Tsuyoshi Akiyama and Kensuke Kawamura (2007) show that with progressive meadow degeneration, the NPP, as well as the vegetation carbon fixation capabilities, of a community typically decrease (China General Administration of Quality Supervision, 2003; Akiyama and Kawamura, 2007). However, from the observations of meadows at different stages of degeneration, several researchers have found that the vegetation would grow for a medium disturbance, which resulted in an increase in the biomass and NPP after a mild degeneration of the steppe (Liu et al., 2005; Cheng et al., 2007; Zhao et al., 2010) and with a subsequent increase in the vegetation carbon fixation capabilities. In a study of the alpine meadow, Li (2011) found that meadows with low degeneration have the highest produc-

To further analyze the relation between meadow change and vegetation carbon fixation capabilities, we validated it with field data collected in 2011 and the field sampling points are shown in Figure 4. And in Figure 4 we divide aSINDVI into two categories, namely, increasing (+) and declining (–), which represent the vegetation changes in the improved and degenerated pixels, respectively. Additionally, aNPP is also divided into increasing (+) and declining (–) categories, which represents the “increase” and “decline” of the vegetation carbon fixation capabilities, respectively. When the above two permutations are combined, we obtain four kinds of relation between vegetation change and carbon fixation capabilities change (Fig. 4 and Table 2). In the remote sensing images, the degeneration or improvement of the vegetation mentioned in this article is defined as the change of a pixel at different times and not as the degeneration or improvement of the vegetation community composition, coverage or biomass of a pixel in comparison to other pixels in the same year. However, we argue that a community in a currently improved area was derived from a community that was worse, and a community in a currently degenerated area was derived from a community that was better, at an earlier period of time. The combination of (− +) for aSINDVI and aNPP indicates degeneration of meadows but an increase in the vegetation carbon fixation capabilities. (Liu et al., 2005) argued that the reason for the above was the marked difference in the biomass and its variation in different vegetation communities at different degeneration stages. At a low degeneration stage, the proportion of high-quality gramineous forage grass would increase, but the leguminous forage grass and other forbs would not change. The meadow was in good health, and the biomass and NPP would most likely increase after a slight degeneration of the meadow. (Zhao et al., 2010) conducted rotational grazing experiment in the summer and autumn on the Stipakrylovii steppe in the central part of the Inner Mongolia steppe region and also assessed the compensatory growth of plants under medium disturbance. In some degenerated sample plots, biomass and NPP increased, which resulted in an increase of the vegetation carbon fixation capabilities; therefore, the vegetation community at this moment would be in the stage of “being stable → slight degeneration”. In contrast, the combination of (+ −) for aSINDVI and aNPP


569

Fig. 4. Distributions of field verification points and four categories between aNPP and aSINDVI.

indicates an improvement of the meadow conditions but a decrease in the vegetation carbon fixation capabilities. According to the above logic, we argue that at this moment the vegetation is in the stage of “slight degeneration → being stable”. Without the “compensatory growth” of plants, the vegetation carbon fixation capabilities decline. However, Table 2 indicates that although the vegetation carbon fixation capabilities declines in this stage, the minimum value, maximum value and average value of the dry weight of the above-ground biomass in the field sample plots are all larger than those in the combination of (− +) for aSINDVI and aNPP. Thus, although the vegetation carbon fixation capabilities decline in this stage,

the carbon storage of the vegetation is larger than in the stage “being stable → slight degeneration”. The combination of (− −) for aSINDVI and aNPP indicates degeneration in the meadow quality and a decline of vegetation carbon fixation capabilities. It is generally believed that with progressive meadow degeneration, the quality of the meadows worsens. In this situation, the comparative biomass of high quality gramineous forage grass and leguminous forage grass decrease sharply, but the forbs with little nutritive value and even certain poisonous grasses increase (Li et al., 2008). A clear decline in the dominant plants, vegetation coverage, biomass and NPP results in the decline of the vegetation

570


Table 2. Relationships among vegetation change, dry weight of above-ground biomass and field verification aSINDVI/aNPP

Sample Plot No.

Composition of Main Species

13, 17, 18, 19, 22, 24, 28, 34, 39, 44−49, 51−53, 55−57, 59, 68−71, 73, 74

Stipa grandis P. Smirn, Leymus chinensis Tzvel, Stipa krylovii Roshev, Cleistogenes squarrosa Keng, Artemisia frigida Willd, Allium mongolicum Regel

Vegetation Above -ground Dry Percentage in Total Area (%) Coverage (%) Weight (g/m2) Range

+/+ (medium or serious degeneration → slight degeneration)

14.53–60.11

13.5–187.87

Average 33.12

70.31

47.12

Standard Deviation 12.17

41.52 Range

+/− (slight degeneration → stable)

Stipa krylovii Roshev, Allium ramosum Linn., 1−3, 7, 23, 26, 27, Cleistogenes squarrosa Keng, 30, 36, 41, 43, 54, Leymus chinensis Tzvel, 61, 65−67 Salsola collina Pall., Allium mongolicum Regel, Agropyron cristatum (Linn.) Gaertn.

11.02–53.9

26.35–190.37 Average

32.49

82.5

21.92


49.17 Range

−/+ (stable → slight degeneration)

4, 12, 35, 37, 38, 50, 62, 72

Stipa krylovii Roshev, Cleistogenes squarrosa Keng, Allium mongolicum Regel, Stellera chamaejasme L., Artemisia frigida Willd, Chenopodium glaucum Linn., Agropyron cristatum (Linn.) Gaertn.

16.18–49.45

24.51–120.29

Average 35.20

77.08

10.04


30.72 Range

−/− (slight degeneration → medium or serious degeneration)

5, 6, 11, 14, 15, 20, 21, 25, 29, 40, 60, 64, 75

Leymus chinensis Tzvel, Allium ramosum Linn., Stipa krylovii Roshev, Cleistogenes squarrosa Keng, Artemisia frigida Willd, Chenopodium glaucum Linn.

14.06–51.22

Average 33.36

79.52

20.92


carbon fixation capabilities (Jiang et al., 2010). Therefore, we argue that the vegetation community is currently in the stage “slight degeneration → medium or serious degeneration”. From the perspective of vegetation carbon storage and carbon fixation capability of the vegetation community, we classify the combinations of (− +) and (+ +) as vegetation carbon fixation capabilities increasing area, and the combinations of (+ −) and (− −) are classified as vegetation carbon fixation capabilities declining area. In the vegetation carbon fixation capabilities increasing area, the dry weight of the above-ground biomass ranges from 13.5 g/m2 to 187.87 g/m2 with an average value of 73.70 g/m2, and in the vegetation carbon fixation capabilities declining area, the dry weight of above-ground biomass ranges from 26.35 g/m2 to 234.48 g/m2 with an average value of 80.01 g/m2. The maximum value, minimum value and average value of the dry weight of the above-ground biomass in sample plots in the vegetation

37.20–234.48

51.13

carbon fixation capabilities declining area are all larger than those in the vegetation carbon fixation capabilities increasing area. Therefore, the carbon storage of the vegetation community in the vegetation carbon fixation capabilities increasing area is stronger than that in vegetation carbon storage declining area. From the aspect of the relation between meadow change and vegetation carbon storage, we classify the combinations of (+ +) and (+ −) as improvement of meadows, and (− −) and (− +) as degeneration of meadows. In the areas where meadow quality is improved, the dry weights of above-ground biomass in all the sample plots range from 13.5 g/m2 to 190.37 g/m2 with an average value of 76.4 g/m2, and in areas where the meadow quality is degenerated, the dry weights of aboveground biomass in all sample plots range from 24.51 g/m2 to 234.48 g/m2 with an average value of 78.3 g/m2, which indicates that the carbon storage of the vegetation community in the meadows in degeneration is larger than that observed in


the meadows in improvement. We argue that the reason for this phenomenon is that the government invests in the protection of seriously degenerated meadows, which leads to an improvement in the meadow quality (69% of the total area in studied region). However, as it takes years to restore the vegetation, the carbon storage of such meadows is not high yet. In contrast, the studied area is also a traditional pastoral area and the meadows that are of good quality enjoy large carbon storage and are consumed by animals, which results in the degeneration of the meadows (accounting for 31% of the total area in studied region). 5. CONCLUSIONS AND DISCUSSION 5.1. Discussion The results indicate that CASA models developed for estimating NPP based on standwise grassland inventory data and moderate resolution MODIS data (Gang et al., 2007; Zhang et al., 2008; Li, 2011) can be utilized also to the coarse resolution NOAA-NDVI data. The demonstrated approach can be used as a costeffective tool to produce preliminary NPP estimates for large areas where more accurate national or large scale grassland inventories do not exist. However, although the estimates were reasonable when averaged for large areas, the pixel level estimates could have low accuracy. Furthermore, the method requires a reliable grassland mask, which is not always available. In this study, although the state of the meadows is divided into “degeneration” and “improvement”, there is in fact an intermediate or “stable” state. The “degeneration”, “stable” and “improvement” states discussed in this article refer to the change of a pixel over time; therefore, we should accurately determine the variation range of SINDVI under the state of “degeneration”, “stable” and “improvement” states. First, we need a large number of regular field observation spots over a considerable number of years, which is difficult to realize because of the lack of historical data. Several scholars classify the range of SINDVI according to the above three or more meadow change states from a statistical viewpoint (Zhao et al., 2012). However, such a classification is also unable to represent the actual state of the meadow change accurately (Tong et al., 2004). In addition, as the study area in this research is a traditional pastoral area with high grazing intensity and obvious human influence (Chen, 2000), it is difficult to find pixels that have never been disturbed by human activity for a period of 12 years (from 2000 to 2011). Even if the enclosed samples plot in reserve areas, the hydrothermal condition, altitude, soil types and other factors are likely to change (Li et al., 2000; Wilson, 2009). All of the different areas have significant influence on the vegetation community composition, meaning that there is still no consensus on whether the variation range of SINDVI for the vegetation community should be enclosed in sample plots or natural reserves for the normal variation range of all the

571

community types in the studied area. If a sufficient quantity of field sampling data is collected, meadow change grades should be further sub-divided. 5.2. Conclusions (1) the growth status and NPP of the meadow vegetation in the typical steppe area of Inner Mongolia varied greatly for each year without an obvious linear trend between the change of meadow vegetation and NPP (2) additional analysis with field measured data, collected in 2011, revealed that the average dry weight of the aboveground biomass in the area where the NPP had increased was less than that in the area where it had decreased; the dry weight of the above-ground biomass of the meadow vegetation that showed degeneration was greater than that of the meadow vegetation that showed improvement (3) a possible reason for the phenomenon mentioned in (2) was that the government protected the degenerated meadows with less biomass, which led to vegetation growth and increased NPP, whereas the meadows that had not been degenerated or showed only minor degeneration and still received rich biomass were over-grazed, causing the NPP to decline. ACKNOWLEDGMENTS: Supported by the National Basic Research Program of China (973 Program: 2014CB138803), Key Program of National Natural Science Foundation of China (41030535) and the National Key Technology R & D Program of the Ministry of Science and Technology (2011BAC07B01-3).

REFERENCES Akiyama, T. and Kawamura, K., 2007, Grassland degradation in China: methods of monitoring, management and restoration. Grassland Science, 53, 1–17. Bai, Z.G., Dent, D.L., Olsson, L., and Schaepman, M.E., 2008, Global assessment of land degradation and improvement 1: identification by remote sensing. Report, ISRIC-World Soil Information, 122 p. Bao, Y.J., Li, Z.H., and Zhong, Y.K., 2004, Compositional dynamics of plant functional groups and their effects on stability of community ANPP during 17 yr of mowing succession on Leymus chinensis steppe of Inner Mongolia, China. Acta Botanica Sinica, 46, 1155–1162. Budde, M.E., Tappan, G., Rowland, J., Lewis, J., and Tieszen, L.L., 2004, Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis. Journal of Arid Environments, 59, 481–498. Byrne, G.F., Crapper, P.F., and Mayo, K.K., 1980, Monitoring landcover change by principal component analysis of multitemporal Landsat data. Remote Sensing of Environment, 10, 175–184. Chang, R. and Tang, H., 2008, Sensitivity analysis on methods of estimating carbon sequestration in grassland ecosystem of Inner Mongolia, China. Journal of Plant Ecology, 32, 810–814. Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., and Eklundh, L., 2004, A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky – Golay filter.

572


Remote Sensing of Environment, 91, 332–344. Chen, Z., 2000, China typical steppe ecosystem. China science press, Beijing, 322 p. Cheng, X., An, S., Chen, J., Li, B., Liu, Y., and Liu, S., 2007, Spatial relationships among species, above-ground biomass, N, and P in degraded grasslands in Ordos Plateau, northwestern China. Journal of Arid Environments, 68, 652–667. China General Administration o Quality Supervision, I.A.Q., 2003, Parameters for degradation, sandification and salification of rangelands. Beijing, 128 p. China, M.O.E.P., 2004, State of Environment in China in 2004. Report, 46 p. China, M.O.E.P., 2010, State of Environment in China in 2010. Report, 57 p. Cramer, W., Kicklighter, D.W., Bondeau, A., Iii, B.M., Churkina, G., Nemry, B., Ruimy, A., Schloss, A.L., and Intercomparison, T.P.O.F., 1999, Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology, 5, 1–15. Fan, J., Zhong, H., Harris, W., Yu, G., Wang, S., Hu, Z., and Yue, Y., 2008, Carbon storage in the grasslands of China based on field measurements of above-and below-ground biomass. Climatic Change, 86, 375–396. Fensholt, R., Rasmussen, K., Nielsen, T.T., and Mbow, C., 2009, Evaluation of earth observation based long term vegetation trends - Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113, 1886–1898. Field, C.B., Randerson, J.T., and Malmstrom, C.M., 1995, Global net primary production: combining ecology and remote sensing. Remote Sensing of Environment, 51, 74–88. Fuller, D.O., 1998, Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987–1993. International Journal of Remote Sensing, 19, 2013–2018. Gang, L., Xiaoping, X., Daolong, W., and Ruixiang, S., 2007, Application of improved CASA model in productivity evaluation of grassland in Inner Mongolia. Chinese Journal of Ecology, 26, 2100–2106. Giri, C. and Shrestha, S., 1996, Land cover mapping and monitoring from NOAA AVHRR data in Bangladesh. International Journal of Remote Sensing, 17, 2749–2759. Hicke, J., Asner, G.P., Randerson, J.T., Tucker, C., Los, S., Birdsey, R., Jenkins, J.C., Field, C., and Holland, E., 2002, Satellitederived increases in net primary productivity across North America, 1982–1998. Geophysical Research Letters, 29, 61–69. Hill, M.J. and Donald, G.E., 2003, Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series. Remote Sensing of Environment, 84, 367–384. Holm, A.M., Cridland, S.W., and Roderick, M.L., 2003, The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of Western Australia. Remote Sensing of Environment, 85, 145–158. Jakubauskas, M.E., Legates, D.R., and Kastens, J.H., 2001, Harmonic analysis of time-series AVHRR NDVI data. PE & RS-Photogrammetric Engineering and Remote Sensing, 67, 461–470. Jiang, H., Bi, X., Huang, J., and Bai, Y., 2010, Patterns and drivers of vegetation degradation in Xilin River Basin, Inner Mongolia, China. Chinese Journal of Plant Ecology, 34, 1132–1141. Kajimoto, T., Matsuura, Y., Sofronov, M.A., Volokitina, A.V., Mori, S., Osawa, A., and Abaimov, A.P., 1999, Above-and belowground biomass and net primary productivity of a Larix gmelinii stand

near Tura, central Siberia. Tree Physiology, 19, 815–822. Lal, R., 2002, Soil carbon sequestration in China through agricultural intensification, and restoration of degraded and desertified ecosystems. Land Degradation & Development, 13, 469–478. Lambin, E.F. and Strahler, A.H., 1994, Change—vector analysis in multitemporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment, 48, 231–244. Larcher, W., 2003, Physiological plant ecology: ecophysiology and stress physiology of functional groups (4th edition). Springer Verlag Press, Berlin, 513 p. Le Maire, G., Marsden, C., Nouvellon, Y., Grinand, C., Hakamada, R., Stape, J.L., and Laclau, J.P., 2011, MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass. Remote Sensing of Environment, 115, 2613–2625. Li, B., Yang, C., and Lin, P., 2000, Ecology. China Higher Education Press, Beijing, 421 p. Li, L. and Liu, W., 2011, Relationship between plant functional groups, species richnessand community’s productivity in degraded grassland. Acta Agrestia Sinica, 6, 917–921. Li, X., 2011, The impact of climate change on temperate grassland in Inner Mongolia and their response. China Science Presss, Beijing, 232 p. Li, Z., Bao, Y., Wang, H., Xu, T., Cheng, Y., and Gao, J., 2008, The analysis on degeneration status and the driving force of Xilinguole Steppe. Ecology and Environment, 17, 2312–2318. Lieth, H., 1978, Patterns of primary productivity in the biosphere. Dowden, Hutchinson & Ross, Stroudsburg, 211 p. Liu, A., Wang, J., and Han, J., 2007, Study on method of estimating net primary production of rangeland by remote sensing-a case study of Xilingol grassland. Chinese Journal of Grassland, 29, 31–37. Liu, W., Zhou, H., and Zhou, L., 2005, Biomass distribution pattern of degraded grassland in Alpine meadow. Grassland of China, 27, 9–15. LOS, S.O., Justice, C.O., and Tucker, C.J., 1994, A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data. International Journal of Remote Sensing, 15, 3493–3518. Los, S.O., 1998, Linkages between global vegetation and climate: an analysis based on NOAA advanced very high resolution radiometer data. National Aeronautics and Space Administration, 231 p. Lunetta, R.S. and Elvidge, C.D., 1999, Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis Ltd, New York, 119 p. Lyon, J.G., Yuan, D., Lunetta, R.S., and Elvidge, C.D., 1998, A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing, 64, 143–150. Ma, M.G. and Frank, V., 2006, Interannual variability of vegetation cover in the Chinese Heihe river basin and its relation to meteorological parameters. International Journal of Remote Sensing, 27, 3473–3486. Morgan, J.A., LeCain, D.R., Pendall, E., Blumenthal, D.M., Kimball, B.A., Carrillo, Y., Williams, D.G., Heisler-White, J., Dijkstra, F.A., and West, M., 2011, C4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature, 476, 202–205. Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G., and Nemani, R.R., 1997, Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–702. Nirmal Kumar, J.I., Sajish, P.R., Kumar, R.N., and Patel, K., 2012, Biomass and Net Primary Productivity in three different Aged Butea Forest Ecosystems in Western India, Rajasthan. Our Nature,


9, 73–82. Peri, P.L. and Lasagno, R.G., 2010, Biomass, carbon and nutrient storage for dominant grasses of cold temperate steppe grasslands in southern Patagonia, Argentina. Journal of Arid Environments, 74, 23–34. Piao, S., Fang, J., and Guo, Q., 2001, Terrestrial net primary production and its spatio-temporal patterns in China during 1982– 1999. Acta Scientiarum Naturalium University Pekinensis, 37, 563–569. Piao, S.L., Fang, J.Y., Zhou, L.M., Zhu, B., Tan, K., and Tao, S., 2005, Changes in vegetation net primary productivity from 1982 to 1999 in China. Global Biogeochemical Cycles, 19, GB2027. doi:10.1029/2004GB002274. Piao, S.L., Fang, J.Y., Zhou, L.M., Tan, K., and Tao, S., 2007, Changes in biomass carbon stocks in China's grasslands between 1982 and 1999. Global Biogeochemical Cycles, 21, GB2002. doi:10.1029/ 2005GB002634. Potter, C., Klooster, S., Myneni, R., Genovese, V., Tan, P.N., and Kumar, V., 2003, Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–1998. Global and Planetary Change, 39, 201–213. Potter, C., Klooster, S., Hiatt, S., Fladeland, M., Genovese, V., and Gross, P., 2007, Satellite-derived estimates of potential carbon sequestration through afforestation of agricultural lands in the United States. Climatic Change, 80, 323–336. Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A., and Klooster, S.A., 1993, Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 7, 811–841. Qi, Y., Dong, Y., Geng, Y., Yang, X., and Geng, H., 2003, The progress in the carbon cycle researches in grassland ecosystem in China. Progress in Geography, 22, 342–352. Rey, A., Pegoraro, E., Oyonarte, C., Were, A., Escribano, P., and Raimundo, J., 2011, Impact of land degradation on soil respiration in a steppe (Stipa tenacissima L.) semi-arid ecosystem in the SE of Spain. Soil Biology & Biochemistry, 43, 393–403. Roerink, G.J., Menenti, M., and Verhoef, W., 2000, Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21, 1911–1917. Roxburgh, S.H., Berry, S.L., Buckley, T.N., Barnes, B., and Roderick, M.L., 2005, What is NPP? Inconsistent accounting of respiratory fluxes in the definition of net primary production. Functional Ecology, 19, 378–382. Ruimy, A., Saugier, B., and Dedieu, G., 1994, Methodology for the estimation of terrestrial net primary production from remotely sensed data. Journal of Geophysical Research, 99, 5263–5283. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M.S., Reeves, M., and Hashimoto, H., 2004, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54, 547–560. Singh, A., 1989, Digital change detection techniques using remotelysensed data. International Journal of Remote Sensing, 10, 989–1003. Stow, D., Daeschner, S., Hope, A., Douglas, D., Petersen, A., Myneni, R., Zhou, L., and Oechel, W., 2003, Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s. International Journal of Remote Sensing, 24, 1111–1117. Tong, C., Wu, J., Yong, S., Yang, J., and Yong, W., 2004, A land-

573

scape-scale assessment of steppe degradation in the Xilin River Basin, Inner Mongolia, China. Journal of Arid Environments, 59, 133–149. Vicente-Serrano, S.M., Cuadrat-Prats, J.M., and Romo, A., 2006, Aridity influence on vegetation patterns in the middle Ebro Valley (Spain). Evaluation by means of AVHRR images and climate interpolation techniques, 66, 353–375. Wilson, S., 2009, Grasses and grassland ecology. Oxford University Press, Oxford, 56 p. Yan, Y., Hu, Y., Liu, Y., Yu, G., and Haimei, W., 2011, The Tendency and its Spatial Pattern of Grassland Changes in the East Xilin Gol from 1975 to 2009. Journal of Geo-information Science, 13, 549– 555. Yang, L.M., Wylie, B.K., Tieszen, L.L., and Reed, B.C., 1998, An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains. Remote Sensing of Environment, 65, 25–37. Zhang, F., Zhou, G., and Wang, Y., 2008, Dynamics simulation of net primary productivity by a satellite data-driven CASA model in Inner Mongolian typical steppe, China. Chinese Journal of Plant Ecology, 32, 786–797. Zhang, X., 1989a, The Potential Evapotranspiration (PE) Index for Vegetation and Vegetation-Climatic Classification (1) – an Introduction of Main Methods and PEP Program. Chinese Journal of Plant Ecology, 13, 1–9. Zhang, X., 1989b, The Potential Evapotranspiration (PE) Index for Vegetation and Vegetation-Climatic Classification (2) – an Introduction of Main Methods and PEP Program. Chinese Journal of Plant Ecology, 13, 197–207. Zhao, D., Liu, Z., Yang, G., Xin, X., Liang, C., Wang, W., and Wang, L., 2010, Grazing impact on distribution pattern of the plant communities and populations in Stipa krylovii steppe. Acta Prataculturae Sinica, 19, 6–13. Zhao, M. and Running, S.W., 2010, Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329, 940. Zhao, Y.Y., He, C.Y., and Zhang, Q.F., 2012, Monitoring vegetation dynamics by coupling linear trend analysis with change vector analysis: a case study in the Xilingol steppe in northern China. International Journal of Remote Sensing, 33, 287–308. Zhou, G. and Zhang, X., 1995, A Natural Vegetation NPP Model. Chinese Journal of Plant Ecology, 19, 193–200. Zhou, G. and Zhang, X., 1996, Study on Chinese Climate-Vegetation Relationship. Chinese Journal of Plant Ecology, 20, 113–119. Zhou, L., Tucker, C.J., Kaufmann, R.K., Slayback, D., Shabanov, N.V., and Myneni, R.B., 2001, Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research, 106, 20069–20083. Zhu, W., 2005, Estimation of net primary froductivity of chinese terrestrial vegetation based on remote sensing and its relationship with global climate change. Beijing Normal University, Beijing, 73 p. Zhu, W., Pan, Y., He, H., Yu, D., and Hu, H., 2006, Simulation of maximum light use efficiency for some typical vegetation types in China. Chinese Science Bulletin, 51, 457–463. Manuscript received January 22, 2014 Manuscript accepted November 28, 2014