The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002–2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days, respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation and increasing temperature at the early growing period because of global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.
Land degradation in arid and semiarid regions, such as the Sahel in Africa and temperate grasslands in Australia, has become a critical threat (Gisladottir and Stocking, 2005; Prober and Thiele, 2005; Sop and Oldeland, 2013). China's vast grassland has also suffered from land degradation, mainly in the northern and western cold areas over long periods (Chen and Tang, 2005; Li et al., 2014; Wang et al., 2013). The Inner Mongolia Autonomous Region (IMAR), which is located along the northern border of China, is 68 % grassland (Kawamura et al., 2005a). Typical and meadow steppes, which are mainly used for grazing and animal husbandry, are the primary grassland ecosystems found in the IMAR (Kang et al., 2007). In recent decades, the Chinese government has implemented a “return-farmland-to-grassland” strategy to reverse the land degradation in pastures (Shang et al., 2014; Wang et al., 2010). Vegetation restoration has been used to protect diverse degraded landscapes. A considerable number of studies suggest that vegetation cover helps mitigate soil erosion by stabilizing soil aggregation, reducing wind speeds and water erosion, improving soil porosity and increasing biological activities in the soil (Fattet et al., 2011; Florentine et al., 2013; Lee et al., 2002; Lieskovský and Kenderessy, 2014).
To evaluate the vegetation restoration effect, anthropogenic and climatic impacts should be considered. Vegetation cover change represents the most direct response of vegetation to climate changes and human activities (Zhao et al., 2012). Akiyama and Kawamura (2003) analyzed the land cover change over 1979–1997 and indicated that the areas with productive grasslands decreased while low-productivity grasslands increased. The seasonal change in vegetation (phenology) is a key parameter for studying and analyzing climate change and vegetation responses (the feedback between the land surface and the atmosphere), which can improve the simulation quality of carbon, water, and energy exchanges between the atmosphere and the land surface (Ma et al., 2013). The vegetation productivity and phenology in the temperate region of China has already changed in response to the dramatic climatic changes (Jeong et al., 2011; Piao et al., 2006, 2010; Peng et al., 2011). Earlier studies indicated that the recovery of vegetation from long-term degradation is related to the increase in precipitation (Eklundh and Olsson, 2003; Sop and Oldeland, 2013). The growing body of evidence suggests that climate warming has advanced the biological spring in temperate China (Chen et al., 2005; Piao et al., 2006; Zheng et al., 2002). Additionally, longer growing seasons, particularly earlier spring vegetation green-up, may significantly enhance the vegetation productivity in temperate and boreal regions (Cong et al., 2013; Hu et al., 2010; Kimball et al., 2004).
As traditional fieldwork is time-consuming and costly, remote sensing methods have been utilized as cost-effective approaches to detect vegetation changes at large spatial scales. Monitoring landscapes through satellite-derived vegetation indices (VIs), such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), is a successful method for assessing vegetation conditions and phenology (Glenn et al., 2008; Zucca et al., 2015). Previous studies suggested that time series satellite data can reliably detect the phenology, forage quantity, and quality of grassland areas using the VIs derived from Advanced Very High Resolution Radiometer (AVHRR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images (Kawamura et al., 2003, 2005a, b, c). Significant delays in the vegetation green-up during 1982–1991 in the desert steppe and in part of the typical steppe of Inner Mongolia were detected using AVHRR NDVI (Yu et al., 2003). Moreover, although the signs of the trends in the vegetation green-up dates detected by various methods were broadly consistent spatially and for different vegetation types, large differences occurred in the magnitudes of the observed trends. The large variance obtained using different methods is particularly apparent for arid and semiarid vegetation types (Cong et al., 2012, 2013; Zhao et al., 2012).
Because of the complex vegetative species, diverse vegetation coverage and vast observational areas in semiarid continental areas, limited research has been conducted in the IMAR to analyze the phenology shift using Terra MODIS NDVI time series. In this study, the objectives are to (1) evaluate the changes in land use and land cover, including vegetation cover and NDVI trends; (2) detect the vegetation phenology (the start/end dates, the maximum vegetation productivity dates and the length of the growing season) using Terra MODIS NDVI data; and (3) relate the trends of the NDVI and phenology to changes in the climate (precipitation and temperature) at 18 meteorological stations throughout the permanent grassland between 2002 and 2014.
The IMAR (Fig. 1), the third-largest province and autonomous region with
plateaus in the country, occupies a total area of 1.18 million km
Information on the meteorological stations.
The Inner Mongolia Autonomous Region (IMAR). The gray part represents the permanent grassland area, whereas the black dots represent the selected meteorology stations.
The meteorological data, including monthly precipitation and temperature,
were acquired from the China Meteorological Data Sharing Service System
(
Two types of products derived from Terra MODIS satellite data were employed.
The 500 m spatial resolution MCD12Q1 product (MODIS Land Cover Type Yearly
L3 Global 500 m, Version 5 – which is generated by the International
Geosphere–Biosphere Programme (IGBP) global vegetation classification scheme
from 2002 to the latest available year, 2012 – was used to detect the land
use/cover change and to extract the permanent grassland area in the IMAR.
MOD13Q1 (Vegetation Indices 16-Day L3 Global 250 m, Version 5) data from
2002 to 2014 were used to extract the NDVI for estimating trends in vegetation cover and phenology changes over
the 11 years. The NDVI, which is a nonlinear combination of red and
near-infrared (NIR) spectral radiances (NIR
After the preprocessing procedures (mosaic and reprojection), the data
quality of the NDVI data was assessed using the corresponding quality
assessment (QA) information that describes the utility of the VI values.
When the VI usefulness
The original and smoothed MODIS NDVI time series from the
Chifeng meteorology station from 2002 to 2014 (42
Then the NDVI time series were temporally smoothed by a Savitzky–Golay filter
(Chen et al., 2004), which provides a simplified least-squares-fit
convolution for smoothing and computing derivatives of a set of consecutive
values (such as a spectrum) (Fig. 2). The S–G filter performs best in most
situations when smoothing different vegetation types using various
satellite data (Geng et al., 2014). The Savitzky–Golay filter is computed as
follows:
The start and end dates of a growing season are usually selected as the indicators of phenology shifts. The globally constant NDVI threshold may be suitable for forested ecosystems but may also surpass the peak value of the NDVI in semiarid grasslands (White et al., 2003; White and Nemani, 2006). Thus, we determined the start/end dates (SOS/EOS), peak vegetation productivity date of growing season (POS), and the length of growing season (LOS) from the MODIS NDVI time series data by modifying the method applied by Butt et al. (2011): (i) the inflection point (the maxima of the second derivative) during the spring (from March to May) was identified as the SOS, while another inflection point (the maxima of the second derivative) during the autumn (from August to October) was identified as the EOS; (ii) the POS was defined as the date of the maximum NDVI during the growing season (Ma et al., 2013); and (iii) the LOS was defined as the difference between the EOS and SOS (Fig. 3). The trends of the phenological stages were estimated by regressing the SOS, POS, EOS and LOS against years over the study period.
Previous research indicated that only the precipitation and temperature in the spring and early summer determine vegetation growth (Bai et al., 2004). In the present study, the relationships between climate data (monthly and cumulative precipitation and mean monthly temperature) and the phenology dates (SOS, EOS, POS, LOS) were assessed using the population regression function.
The data-processing procedures were conducted using Interface Data Language (IDL) ver. 8.3 (Exelis Visual Information Solutions, Colorado, USA) and MATLAB software ver. 7.12 (MathWorks Inc., Sherborn, USA). The thematic maps were created using ArcGIS ver. 9.3 (ESRI, California, USA).
The primary land use and land cover change from 2002 to 2012 according to
the latest MODIS land cover data are shown in Table 2. Furthermore, the standard deviation
(SD) and coefficient of variance (CV
Land cover changes in the Inner Mongolia Autonomous Region
(2002–2012). The area unit is
The extraction of phenology dates from NDVI. SOS is the start of growing season; POS is the maximum NDVI date during the growing season; EOS is the date of the end of season; LOS is the length of growing season.
Grassland was the most extensive land cover type in the IMAR, occupying
49.67
The permanent grassland (2002–2012) was then extracted, yielding an area of
37.57
The cumulated NDVI trend in permanent grassland from 2002 to 2014. The green parts represent the increasing area, while the red parts represent the decreasing area.
The annual total precipitation and mean temperature of 18 meteorology stations.
Yearly changes in the annual precipitation and mean temperature at 18 stations are shown in Fig. 5. The mean annual precipitation ranged from
221.90 to 404.38 mm year
The statistical results of the average phenology dates (SOS, POS, EOS, LOS)
at all 18 meteorological stations in various years are shown in Table 3.
Overall, the SOS at the 18 stations varied from mid-March to May, with a
mean annual value of 122.21
After applying a linear analysis procedure, the average SOS and POS of the 18 stations advanced by 5.79 and 2.43 days, respectively, from 2002 to 2014. The average EOS of the stations was delayed by 5.07 days. Finally, the average LOS of the stations was extended by 10.86 days from 2002 to 2014 (Fig. 6).
The selected regression models at different phenological stages (SOS, POS,
EOS, and LOS) between precipitation (monthly and accumulated value during
different periods) and temperature (monthly and mean value during different
periods) are presented in Table 4. The delayed effect from climate in SOS
was obviously detected. Generally, the SOS negatively correlated with the
cumulative precipitation, especially during the growing season in the last
year (March–September,
The trend of phenology dates from 2002 to 2014. SOS is the start of growing season; POS is the maximum NDVI date during the growing season; EOS is the date of the end of season; LOS is the length of growing season.
Annual mean and standard deviation (SD) of phenological
stages at 18 meteorological stations during 2002–2014. The units are day of year (DOY); SOS, POS, EOS and LOS are the start, peak,
end and length of the growing season; Mean is the mean value of the
phenology dates; SD
The climate variables most strongly correlated with phenology, and the corresponding parameters of its linear model. SOS is the start of growing season; POS is the maximum NDVI date during the growing season; EOS is the date of the end of season; LOS is the length of growing season. Prec and Temp represent the precipitation and temperature, respectively.
This study investigated the change in the cumulative annual NDVI and phenology during the growing season of the permanent grassland in the IMAR. The phenological dynamics were correlated with the local meteorological variations.
Our results indicated that the cumulative annual NDVI had a positive trend mainly in the northern and western regions (Fig. 4). In the west, desert steppes are dominant. Previous research has reported a close relationship between vegetation changes and climate factors, particularly precipitation and temperature (Cao et al., 2013). The upward trend in the annual precipitation is considered the main meteorological factor that led to the cumulative annual NDVI increase in the desert steppes. However, the area with a negative trend was mainly found in the central and east IMAR, where typical steppes dominate. Similar trends have been reported by Cao et al. (2013): in the central IMAR, the annual NDVI trend reverses when applying NDVI data from 1998 to 2008 such that significant relationships with temperature and precipitation exist. Chuai et al. (2013) used the annual mean NDVI during the growing season (April to October) and found a moderate decrease in the steppes of the IMAR from 1998 to 2007.
Regarding the phenology changes (Table 3) and trends (Fig. 6), the extension of the growing season was detected, as also revealed by other recent research in temperate China (Bai et al., 2004; Cong et al., 2013). An earlier onset of the start is most prominent (Linderholm, 2006). Referring to previous studies, the SOS was obviously earlier in the present study period (from mid-March to May). As Lee et al. (2002) reported that the onset of green-up for typical and desert steppes occurred from early May to early June (1982 to 1990), the SOS since 2002 had advanced by approximately 1 month compared with the previous 20 years. This finding was consistent with the previous suggestion that the SOS advanced between the 1980s and the 2000s in major biomes in China, according to various approaches (Cong et al., 2013; Ma and Zhou, 2012). However, limited research had investigated the senescence date of the growing season. Our results have provided the valuable finding that, besides of the advance in SOS, the delay in EOS (5.07 days) also contributed the extension of the growing season.
Some researchers have indicated that the phenology could be influenced by the climate several months before (Estrella and Menzel, 2006; Miller-Rushing and Primack, 2008). In our results, this delayed effect has been found. Our results were in agreement: global warming could promote the vegetation growth and extend the growing season (Linderholm, 2006). The temperature has increased significantly, particularly since the 1980s (Ding and Chen, 2008; Gao et al., 2009). However, from 2002 to 2014, the IMAR grassland tended to be slightly colder in the spring (from January to May). We speculated that the increasing precipitation might be the main driving factor of the advance in SOS and the delay in EOS. Nevertheless, previous work revealed that precipitation decreased slightly over the last 50 years compared with the obvious interannual change. Thus, the precipitation appeared to increase over the recent decade. Rather than the change in temperature, the wetter weather conditions were considered the main reason for the phenology change in the IMAR.
Our results indicated that plant productivity in the IMAR increased, but in some areas with grazing, land degradation is ongoing. As different vegetation types have various response to the climate change (Yuan et al., 2007), the spatial heterogeneity of the phenology dynamics needs to be detected. Meanwhile, the present study period only covered the last 13 years, limiting its conclusiveness regarding the change in the IMAR grassland ecosystem during a longer period. Thus further research should be conducted to identify the correlation between changes in phenology and meteorology.
In this study, we examined recent trends in the NDVI and phenology changes
in the Inner Mongolia grassland using Terra MODIS time series data. The
relationships between phenology change (SOS, EOS, POS and LOS) and climate
data (precipitation and temperature) were also evaluated. The following
conclusions can be drawn from the study:
The positive trends of the cumulative annual NDVI (77.10 %) could be
interpreted as an increase in plant productivity in the Inner Mongolia
permanent grassland. The advance in the SOS and the delay in the EOS extended the LOS in
Inner Mongolia between 2002 and 2014. The increase in precipitation is the main factor for the extension of
the growing season.
Overall, the results reveal recent trends in the Inner Mongolia grassland
and their correlation with climate data. Further analysis using long-term
satellite data and climate data should be conducted.
We are grateful to Yae Kimura and Wakana Kyuno of the University of Tsukuba, Japan, and all of the staff members of the Rangeland Survey and Design Institute of Inner Mongolia, China, for their assistance with the field experiments. This work was supported by the Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (B) (no. 21405033). Edited by: A. Cerdà