1. Introduction
As one of the most important living environments for human beings [
1], grasslands cover approximately 24% of the world’s land surface area [
2]. Grassland ecosystems accumulate up to 18% of the total global terrestrial carbon sink each year and play an important role in the global carbon cycle [
3]. However, due to global warming and unreasonable human disturbance [
4], they are subjected to serious progressive degradation [
5] so that the dynamic balance of the carbon cycle in grassland ecosystems has been broken [
6]. Dynamically monitoring the ecological environment changes of grassland ecosystems is critical to understand the role of grasslands in the global carbon cycle and is desirable for the local government to manage the grasslands resource.
Primary production represents the major input of carbon and energy into ecosystems [
7] and above-ground net primary production (ANPP) is considered as an integrative variable of the function of terrestrial ecosystems because of its relationships with animal biomass, secondary productivity, and nutrient cycling [
8,
9]. Grassland ANPP is one of the most direct indicators of grassland ecological environment status [
10]. Methodologically, grassland ANPP could be estimated through the ecosystem models, which have always been regarded as essential and indispensable tools in simulating net primary production (NPP), owing to their ability to describe the response of the grassland ecosystem to changing environmental conditions and human disturbances [
11]. Since the 1980s, a variety of process-based ecological models have been developed to estimate grassland ANPP [
12]. Among them, denitrification and decomposition (DNDC) is considered as one of the most widely used and successful biogeochemical models [
13], and it has been applied to simulate the amount and dynamics of carbon for almost all terrestrial ecosystems [
14]. At the International Workshop on Global Change for Asia Pacific Region in 2000, the DNDC model was designated as one of the biogeochemical models applicable for the Asia Pacific region [
15]. Although the DNDC model provides a powerful tool to estimate ANPP, significant uncertainties still exist when it is used to simulate the regional ANPP for grasslands because of the human disturbances, especially the grazing impact.
Grazing could significantly influence primary production, vegetation composition, and root biomass [
16], and it would have a more direct and rapid impact on standing biomass than other factors, such as management practices, edaphic conditions, and climate in the short term [
17]. Therefore, how to quantify the variation of the grazing effects on grassland ANPP is critical to simulate regional grassland ANPP. Despite a lot of debate about whether or not grazing can increase grassland ANPP at the individual site [
18], due to the lack of sufficient historic grazing data, there have not been enough systematic and comprehensive investigations conducted on the regional grazing effects by far, especially in the nomadic or semi-nomadic pastures. Although the grazing data derived from the “Gridded Livestock of the World” (GLW) data could be used to model the grazing effect on dry grassland carbon cycling, it was too coarse to reflect the spatial variation of grazing intensity [
19]. Additionally, because of the large area for livestock grazing, the relatively long duration of active interaction between animals and plants, and the difficulty encountered in measuring the forage consumed by free-ranging animals [
20], it is even hard to assess regional grazing effects in free grazing systems. A significant obstacle to quantifying the regional grazing effects is the difficulty in getting the data regarding the grazing locations and stocking rates through conducting grazing experiments at a large enough scale [
21]. Moreover, there is a lack of accurate and repeatable techniques to obtain the details of grazing intensity and grazing period at regional scale [
22]. Limited regional monitoring makes it difficult to pinpoint the regional impact of livestock on the grassland ANPP [
23].
With the development of satellite and computer technologies, remote sensing techniques have become highly promising tools in monitoring spatial and temporal changes of the grassland ecosystem at regional scales with rapid data acquisition and at lower cost [
24]. In view of the different vegetation status between grazing and non-grazing areas, and the good relationships between the vegetation indices and vegetation coverage, the vegetation indices are usually used to explore the ability of remote sensing data to quantify the grazing intensity or evaluate the grazing effects. For example, Kawamura
et al. (2005) investigated the spatial distribution of grazing intensity based on the Normalized Difference of Vegetation Index (NDVI) and the tracking data recorded by global positioning system (GPS) [
25]. Blanco
et al. (2009) applied the NDVI to detect the spatial and temporal patterns of vegetation in two different grazing systems (
i.e., continuous and two-paddocks rest-rotation) [
22]. Yu
et al. (2010) estimated the grazing capacity using the NDVI, above-ground biomass data, and theoretical livestock carrying capacity [
26]. Li
et al. (2014) identified the mowing and grazing regions from the NDVI data and then obtained grazing intensity through the grazing steppe area and annual sheep units [
27]. These studies indicated that the spatial pattern of the vegetation indices (e.g., NDVI) could be used to identify regional changes resulting from livestock grazing that may not be apparent from local monitoring [
23]. Therefore, it is feasible to employ the vegetation indices to quantify the spatial distribution of grazing intensity at regional scale [
28,
29]. However, there has been little research on the grazing period and the variation of grazing intensity within and between seasons [
30]. To simulate regional grassland ANPP in free grazing systems, especially for a short time, it is necessary not only to quantify the spatial distribution of regional grazing intensity, but also to obtain the grazing period of grazing pastures.
Since being highly correlated with vegetation phenology and easily available [
31], a time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product (MOD13Q1) was chose to develop an approach to directly estimate the regional grazing intensity and grazing period. Taking the grassland-wetland ecosystems on the Zoige Plateau in China as a case, we aimed to simulate grassland ANPP under grazing conditions based on DNDC model and, thereby, evaluated the spatial and temporal variation of grazing effects and the impact of grazing on grassland ANPP. The specific objectives of this paper were to (1) develop a methodology to quantify regional grazing parameters, including grazing intensity, and grazing period; and (2) simulate the grassland ANPP and investigate its response to grazing activity.
2. Study Area
The Zoige Plateau (101°30′–103°30′ E, 32°20′–34°00′ N, 3400 m a.s.l), located at the eastern edge of the Qinghai-Tibetan Plateau in China (
Figure 1a), covers an area of approximately 4600 km
2 of peatlands [
32]. It is regarded as one of the largest alpine peat swamp in the world with about 40 percent of peat stocks in China [
33]. This paper takes Zoige County as a case area which is a major and typical region on Zoige Plateau (
Figure 1b) [
34]. The study area is mainly covered by wetlands and grasslands and there is a national nature reserve established in the center region of Zoige County to serve as functions for ecological conservation (
Figure 1c). Due to global climate change and human disturbance (e.g., drainage, grazing, and peat harvest), since 1970s, the wetlands and grasslands in this area had experienced degradation, and the typical landscape degradation gradient was characterized by wetlands, moisture grasslands, dry grasslands and deserts. Over the past decades, the wetland area had been decreasing at a rate of about 3.85% per year [
35]. After the middle of 1980s, human activities like livestock grazing has become one of most critical factors influencing the ANPP in this region [
36]. These situations would make Zoige County representative and suitable for carrying out simulation of the grazing effect on alpine grassland ANPP.
The climate of Zoige County is typical of alpine regions, with low temperature and high humidity. Based on the meteorological data measured at Zoige meteorological station from 1957 to 2012, the annual mean temperature was 1.21 °C and the average annual precipitation ranged from 464.8 to 862.9 mm (mainly concentrated in May–August). The growing period is from late March to Early September [
34], and the NDVI would be up to the maximum value at the end of July to early August. The native grasslands are divided into summer pastures, winter pastures and all-year pastures by local people [
37]. In the summer pastures, continuous livestock grazing will last from June to October, which is the best season for forage production. The grazing pastures from November to May of the next year are defined as the winter pastures, where grazing period would last seven months or even longer. In different kinds of pastures, the grazing systems are characterized by nomadic grazing.
6. Conclusions
Taking the Zoige Plateau grassland-wetland ecosystem of China as a case, this study proposed an approach to simulate the grazing effects on ANPP using time-series remote sensing data and a process-based ecosystem model. Compared with the ANPP simulation results without considering grazing, the ANPP estimated had more significant relationship with the observed ANPP, with R2 of 0.75, RMSE of 122.86 kgC/ha, and average RE of 8.77%. Therefore, this method can successfully estimate the grassland ANPP under grazing conditions at regional scale.
In Zoige County, the total regional ANPP in 2012 was up to 3.815 × 105 tC. If grazing activity was not considered in simulating the ANPP, the regional ANPP would be overestimated by 44.4%, up to 5.51 × 105 tC. For the whole study area, livestock grazing and ANPP would change with the land cover types. For instance, peatlands had remained almost pristine marsh due to less human activity, with average ANPP of 794.97 kgC/ha. Herbaceous wetlands and wet meadows also had higher above-ground productivity (more than 800 kgC/ha). For lower grazing intensity (<2.6 SUD/ha) and shorter grazing period (less than 82 days), the ANPP of herbaceous wetlands and wet meadows were 680.4 kgC/ha and 733.32 kgC/ha, respectively. Meadows, prairies, and sparse pastures, accounting for 75% of the study area, were considered to be major grazing pastures, with the annual average grazing intensity more than 3.3 SUD/ha and the grazing period more than 159 days.
In conclusion, the combination of the time-series remote sensing data and a process-based ecosystem model could provide high resolution data about the regional grazing intensity and grazing period, monitor the spatial variations of the grazing intensity, identify the kinds of the pastures, simulate the grassland ANPP under grazing conditions, and investigate the response of grassland ANPP to grazing. If following our method, it is worth paying close attention to how to obtain the perfect vegetation indices’ curves for non-grazing areas without the disturbance of the environment factors. Meanwhile, it is essential to try to use the proper NDVI data and high quality drive data (including meteorological, soil and vegetation data) at the regional scale in simulating regional above-ground biomass. In addition, the grazing data and the grassland ANPP at regional scale should be further verified through the field experimentations or by other means (e.g., remote sensing). Moreover, the mechanisms about ANPP and grazing still need to be explored by combining the remote sensing with the field samples in future work. Lastly, the data assimilation approaches should be explored to obtain accurate simulation results in the future work.