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*Corresponding author (email: wubf@irsa.ac.cn)

• RESEARCH PAPER • doi: 10.1007/s11430-014-4883-7

Remote sensing based monitoring of interannual variations in vegetation activity in China from 1982 to 2009

LI Fei1,2, ZENG Yuan1, LI XiaoSong1, ZHAO QianJun1 & WU BingFang1*

1 Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing 100101, China; 2 University of Chinese Academy of Sciences, Beijing 100049, China;

Received August 7, 2013; accepted October 10, 2013

Terrestrial vegetation is one of the most important components of the Earth’s land surface. Variations in terrestrial vegetation directly impact the Earth system’s balance of material and energy. This paper describes detected variations in vegetation activ-ity at a national scale for China based on nearly 30 years of remote sensing data derived from NOAA/AVHRR (1982–2006) and MODIS (2001–2009). Vegetation activity is analyzed for four regions covering agriculture, forests, grasslands, and Chi-na’s Northwest region with sparse vegetation cover (including regions without vegetation). Relationships between variations in vegetation activity and climate change as well as agricultural production are also explored. The results show that vegetation activity has generally increased across large areas, especially during the most recent decade. The variations in vegetation activ-ity have been driven primarily by human factors, especially in the southern forest region and the Northwest region with sparse vegetation cover. The results further show that the variations in vegetation activity have influenced agricultural production, but with a certain time lag.

vegetation activities, AVHRR, MODIS, NDVI, China

Citation: Li F, Zeng Y, Li X S, et al. 2014. Remote sensing based monitoring of interannual variations in vegetation activity in China from 1982 to 2009. Sci-ence China: Earth Sciences, doi: 10.1007/s11430-014-4883-7

Terrestrial vegetation is an important component of the bi-osphere and plays a leading role in the maintenance of the Earth’s material and energy balance. Because of this, the fields of ecology, botany, and earth sciences are all con-cerned with this topic (Chapin et al., 2002; Schulze et al., 2005). With global temperatures increasing as a result of global climate change and with continued growth in human economic activities, variations in vegetation activities have become a key area of research (Nemani et al., 2003; IPCC, 2007).

In China, the economic development over the last 30 years has led to noteworthy achievements. However, the rapid economic development has also led to a deterioration

of the ecological environment, which is affecting people’s livelihoods and has become a serious issue (Sun et al., 2012). At the same time, people are becoming more aware of the enormous impacts of environmental change on social and sustainable development, and relevant agencies are try-ing to restore the deteriorating ecological environment through various efforts, including afforestation, water di-version and irrigation, the construction of new wetlands, and the Grain for Green project.

Satellite remote sensing provides us with a new approach to understand the Earth’s ecological environment from a multi-dimensional perspective and on a macro scale (Cracknell et al., 1993; Gould, 2000). Terrestrial vegetation, as the primary information in remote sensing records, is the main objective of environmental remote sensing research (Jensen et al., 2007). Vegetation detection using remote

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sensing is based mainly on the difference in spectral reflec-tance, especially the strong absorption in the visible range and the high reflectance in the near-infrared range (Jiang et al., 2008). The widely-used vegetation index (VI) just uses these characteristics to monitor variations in vegetation (Tueller, 1989). As the VI has a good relationship with bi-ophysical parameters, it is also called biomass index (Yang et al., 2009). Currently, hundreds of VIs are applied in the field of earth sciences (Barati et al., 2011). The most wide-ly-used one is the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974), for which it is also the easiest to obtain remote sensing data. This study uses long-term NDVI time series to monitor variations in vegetation activ-ity.

In the previous studies of vegetation activity using re-mote sensing data, Myneni et al. (1997), using 1982 to 1991 AVHRR-NDVI data, showed an increase in global vegeta-tion activity and an earlier onset of green-up for spring veg-etation, a phenomenon particularly obvious in the higher latitudes of the Northern Hemisphere (Myneni et al., 1997). Fang et al. (2003) and Piao et al. (2003), using 1982–1999 AVHRR-NDVI data, showed an increase in vegetation ac-tivity in China. Liu et al. (2012) showed that the fraction of green vegetation cover in China has increased, based on MODIS-NDVI data for 2000 to 2010 and using the methods of least squares and least absolute deviation. Finally, Zhang et al. (2013), using AVHRR, SPOT-VGT, and MODIS NDVI data for 1982–2011, showed that the start time of the growing season for alpine vegetation on the Tibetan Plateau has continued to advance.

Overall, there are many studies on variations in vegeta-tion activity at regional scales using remote sensing data. However, little knowledge exists on variations in vegetation activity at a national scale, especially in terms of covering different vegetation systems. Therefore, this paper will in-vestigate this particular aspect—vegetation activity on a national scale—using relatively long records of remote sensing data from 1982 to 2009. The paper specifically will study (1) variations in vegetation activity in four different regions, including agricultural, forest, and grassland regions, as well as China’s Northwest region with sparse vegetation cover; (2) regional differences in the variations in vegeta-tion activity; and (3) sensitivity of vegetation activity to climate change, as well as impacts of vegetation activity on agricultural production.

1 Data and methods

1.1 Data collection and pre-processing

The data used in this study mainly involves remote sensing data, information on vegetation types, meteorological data, and socio-economic statistics.

The remote sensing data were obtained from the NOAA- AVHRR satellite series and MODIS aboard the NASA Ter-

ra satellite. The AVHRR data, which were downloaded from the GLCF website (http://glcf.umd.edu/data/gimms), provided NDVI products for every 15 days based on that period’s maximum value. The time frame covered was from January 1982 to December 2006 with a spatial resolution of 8 km. MODIS data, which were downloaded from the NASA LAADS website (http://ladsweb.nascom.nasa.gov), included products of MOD13A1 based on a maximum value for every 16 days. The time frame was from January 2001 to December 2009 with a spatial resolution of 500 m. In order to make the MODIS-NDVI consistent with the AVHRR-NDVI, all of the MOD13A1 products were resampled to 8 km. Next, the multi-NDVI values in the growing season (from May to September) were averaged to represent a yearly level of vegetation activity.

Vegetation type data were obtained from Chinese vege-tation maps (scale of 1:1 million) compiled by an editorial committee of the Chinese Academy of Sciences in 2001. Vegetation types were combined as needed, with sparse vegetation areas and non-vegetation areas combined to form a “sparse vegetation region”, which is distributed mainly in the northwest of China. Grasslands and meadows were combined to form a “grassland region”, whereas forests and shrubs were combined to form a “forest region” and various croplands were combined as an “agricultural region” (Fig-ure 1).

Meteorological data, including average temperature and cumulative rainfall from May to September, were derived from 756 meteorological stations in China. Data were downloaded from China’s meteorological data sharing ser-vice system (http://cdc.cma.gov.cn). With an exclusion of abnormal values, 605 standard station data from 1982 to 2009 were used in this study, along with the continuous observation data available for the west of the Tibetan Plat-eau for the last 30 years. The meteorological data were in-terpolated with a resolution of 8 km using the method of Kriging.

1.2 Methods

For three ten-year periods (1982–1990, 1991–2000, and 2001–2009), the slope and coefficient of determination (R2) for the changes in NDVI over each period were used to de-tect the variations in vegetation activity for the four regions dominated by a certain vegetation type (forest, grassland, agriculture, and sparse vegetation).

To determine the sensitivity of vegetation activity to cli-mate change, the method of correlation analysis was used.

First, by taking five years as a period, the slopes of the temperature, rainfall and NDVI curves were obtained using the formula:

, i i i iy a t b (1)

where ti is a time variable; yi is a climate factor (e.g., tem-perature or rainfall) or NDVI; i is a time period; ai is a

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Figure 1 Distribution of meteorological stations and vegetation types.

regression coefficient (and slope) and bi is the intercept of regression line.

Next, the relationships between the slope of the NDVI (Va) curve and both temperature (Ta) and rainfall (Pa) were calculated using the formula’s:

, . a a a aV aT c V aP c (2)

Finally, the coefficient of determination of eq. (2) was used to explore the sensitivity of vegetation activities to climate change.

2 Results

Figure 2 shows the interannual change in NDVI for the four different vegetation regions. In the region with sparse vege-tation, vegetation activity has continuously increased (Fig-ure 2(a)). The average value of AVHRR-NDVI in this re-gion has risen to 0.21, higher than the AVHRR-NDVI value of deserts, which is 0.057. For the period 2001–2009, the regional average value of MODIS-NDVI increased up to 0.48, significantly higher than the MODIS-NDVI value of bare soil, which is 0.17 (Guerschman et al., 2009), or that of deserts, which is 0.15. This means that the vegetation cover in the region with sparse vegetation has continued to in-crease over the last 30 years. For the agricultural region, Figure 2(b) shows that vegetation activity first increased significantly in the 1980s, then decreased remarkably in the 1990s, and again started to increase in the period 2001–

2009. Figure 2(c) shows that variations in forest vegetation activity were similar to those in crop vegetation activity in the agricultural region. Only the change rate of forest vege-tation activities was relatively gentle. As for grassland veg-etation, Figure 2(d) shows that vegetation activity has in-creased significantly in the 1980s and during 2001–2009, but has remained stable in the 1990s.

With regard to the regional differences, Figure 3(a) and (b) shows that in the 1980s NDVI increased significantly in the agricultural areas of North China and the country’s Northeast Plain. Grassland NDVI in the Qinghai-Tibet Plateau and the Tianshan Mountains areas also increased dramatically, while it decreased in eastern Inner Mongolia. Comparing variations in forest vegetation activity in the north of China with those in the south shows that forest vegetation activity in the northeast has increased while it decreased in the southern forest area. However, a clear trend could not be observed. As for the sparse vegetation areas in the northwest of China, Figure 3(b) shows that vegetation activity increased across a broad area.

Figure 3(d) shows that in the 1990s, vegetation activity in China generally decreased, especially in the agricultural and forest regions. In the southeastern coastal areas, vegeta-tion activity increased dramatically (Figure 3(c)). In contrast, vegetation activity in China’s northwest has tended to in-crease. In eastern Inner Mongolia, grassland vegetation ac-tivity has continuously decreased since the 1980s.

In terms of NDVI changes from 2001 to 2009, Figure 3(e) and (f) shows that crop vegetation activities in the agricultural

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Figure 2 NDVI interannual changes for different vegetation systems from 1982 to 2009. (a) sparse vegetation; (b) crop vegetation; (c) forest vegetation; (d) grassland vegetation.

region have tended to increase. Forest vegetation activity in the southwest increased significantly, while in the southeast it has continued to decrease since the 1990s. In the grass-land region, vegetation activity on the Tibetan Plateau clearly decreased during this period. Notably, while grass-land vegetation activity in eastern Inner Mongolia has con-tinuously decreased since the 1980s, vegetation activity in the northwest region with only sparse vegetation has in fact continued to increase since that time.

3 Discussion

3.1 Sensitivity of vegetation activity to climate change

Analysis of the relationship between the rate of temperature change and the rate of NDVI change shows the agricultural vegetation activity was most sensitive to temperature change. This was especially significant in the northern ag-ricultural areas. In addition, grassland vegetation activity in the Tibetan Plateau and eastern Inner Mongolia, as well as forest vegetation activity in the southeastern coastal areas, were also sensitive to temperature change. In contrast, areas where vegetation activity was more influenced by changes in rainfall occurred mainly within the grassland areas in Hulun Buir and eastern Inner Mongolia (Figure 4).

3.2 Driving forces of variations in vegetation activity

Comparing variations in vegetation activity among the dif-ferent regions shows that vegetation activity in the north-west region with sparse vegetation increased significantly,

especially in the period 2001–2009. Figure 4 shows vegeta-tion activity in this region was not sensitive to climate change. Human factors such as the Grain for Green project might be the primary driving force of variations in vegeta-tion activity. For the agricultural region, vegetation activity was more sensitive to temperature change than rainfall. It can be inferred that crop vegetation activity is constrained by temperature conditions when the water condition is met through human interventions such as irrigation. For the for-est region, vegetation activity was not sensitive to climate change. The trend of variations in vegetation activity in the northeast areas over the last 30 years was not clear. In con-trast, vegetation activity in the south decreased in the years before the year 2000. After the year 2000, vegetation activ-ity in the southwestern forest area increased significantly, while southeastern forest vegetation activity continued to decrease. This is probably related to the implementation of forest protection in the southwest areas and the continued growth of economic activities in the southeast. As for grassland, Figure 4 shows that vegetation activity in some areas such as the Tibetan Plateau and eastern Inner Mongo-lia was sensitive to climate change. It can be concluded that the ongoing decrease of vegetation activity in eastern Inner Mongolia is caused primarily by climate change. In addition, it is also affected inevitably by overgrazing.

3.3 The impact of variations in vegetation activity on agricultural production

The changes in vegetation activity would inevitably also have an impact on agricultural production, especially for the

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Figure 3 Interannual changes in NDVI in China for 1982–1990 ((a), (b)), 1991–2000 ((c), (d)), and 2001–2009 ((e), (f)). (a), (c), (e) represent the levels of significance quantified with R2 for each 10-year period; (b), (d), (f) represent the strengths of the vegetation activity quantified with the slope for each 10-year period.

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Figure 4 Correlations between the NDVI change rate and the temperature (a) and rainfall change rates (b). Correlations for each are quantified with R2.

Figure 5 Relationships between annual changes in NDVI and agricultur-al production expressed as grain yield (a) and number of sheep (b) from 1982 to 2009.

production of grain and animal husbandry. Figure 5(a) shows the relationship between grain yield and the interan-nual change in NDVI. Overall, their trajectories from 1982 to 2009 were consistent. Especially in the late 1990s, crop vegetation activity decreased, resulting in a decrease in grain yield. With the enhancement of vegetation activity, grain yield began to increase. The inflection nodes show that there was a certain time lag between this change in NDVI and the change in grain yield, which might have been caused by lagged human decision-making.

Figure 5(b) shows the relationship between the annual change in NDVI and the number of sheep at the end of the

year. Trends in NDVI interannual changes were consistent with those for sheep livestock production. For example, a decrease in NDVI resulted in a decrease in the number of sheep at the end of 2004. Similar to the findings for crop vegetation, a time lag existed between the two events.

3.4 Uncertainties of remote sensing in monitoring veg-etation activities

Although satellite remote sensing is a powerful means in the research of terrestrial ecosystem monitoring, there are some uncertainties. For example, the saturation of NDVI in the case of dense vegetation inevitably limits its role in vegeta-tion monitoring (Baret et al., 1991; Huete et al., 2002; Wang et al., 2005). In addition, the spatial resolution of remote sensing data used in this study was 8 km. Mixed pixels therefore also have an impact on the monitoring results.

Land use and land cover change (LUCC) is an important driver of variations in vegetation activity. Because the veg-etation type data used in the study are from before the year 2000, this might also have influenced monitoring results.

Meteorological data are a primary source of climate change monitoring. The methods of interpolation are com-monly used to process climate data. Due to the limited number of meteorological stations and surface heterogeneity, it is difficult to reflect the real climate characteristics at the local scale, compared to the large scale. As a result, there were some uncertainties in the analysis of the relationship between variations in vegetation activity and climate fac-tors.

4 Conclusions

This paper uses the long-term remote sensing time series to monitor variations in vegetation activity for different vege-

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tation systems, leading to several conclusions. Vegetation activities in China have tended to increase

across broad regions, especially over the last ten years. Spe-cifically, vegetation cover in the northwest region with sparse vegetation cover has continually improved. Vegeta-tion activity of grasslands has also generally increased, in particular showing dramatic increases in the 1980s and dur-ing 2001–2009. In contrast, in the 1990s grassland vegeta-tion activity was relatively stable. Trends in vegetation ac-tivity in agricultural and forest areas were similar. In the 1980s and during 2001–2009, both increased significantly, whereas in the 1990s, both decreased significantly.

Regarding the regional differences, vegetation activity in the southeastern forest areas has continuously decreased since the 1980s. In contrast, vegetation activity has in-creased since 2001. For grasslands, vegetation activity in eastern Inner Mongolia has continuously decreased over the last 30 years. In the Tibetan Plateau area, it has been de-creasing since 2001.

Regarding the relationship between the change in NDVI and climate factors, the strength of vegetation activity in the agricultural region was closely related with temperature change. Grassland vegetation activity in some areas, such as Hulun Buir, eastern Inner Mongolia, and the Tibetan Plat-eau, were sensitive to climate change. Vegetation activity in the northwestern region with sparse vegetation and that in the southwestern forest areas was not sensitive to climate change. However, monitoring results show that vegetation activity in both regions increased. This was probably a re-sult of the implementation of the Grain for Green project and forest protection policies. The decrease in vegetation activity in the southeast was related to the ongoing growth in economic activities. The relationship between the NDVI of crop vegetation and grain production shows that vegeta-tion activities were related to grain production but with a certain time lag.

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Climate Change: Carbon Budget and Relevant Issues (Grant No. XDA05050100).

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