Grazing intensity monitoring in Northern China steppe: Integrating CENTURY model and MODIS data

Feng, Xiaoming; Zhao, Yannan

doi:10.1016/j.ecolind.2009.07.002

Cited by 57 publications

(29 citation statements)

References 21 publications

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“…Grazing Intensity (SUD/ha) Grazing Period (day) Simultaneously, the time-series grazing intensity and the grazing period, which had been hardly reported in previous studies [8,17,25,27,28] and could not be provided by FAO (Figure 6a), were also provided in this study. It allows us to quantify the temporal changes of grazing intensity in nomadic grazing patterns and analyze the types of pastures.…”

Section: Land Cover Typesmentioning

confidence: 96%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Wang

Bian

2016

Remote Sensing

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Abstract:Measuring the impact of livestock grazing on grassland above-ground net primary production (ANPP) is essential for grass yield estimation and pasture management. However, since there is a lack of accurate and repeatable techniques to obtain the details of grazing locations and stocking rates at the regional scale, it is an extremely challenging task to study the influence of regional grazing on the grassland ANPP. Taking Zoige County as a case, this paper proposes an approach to quantify the spatial and temporal variation of grazing intensity and grazing period through time-series remote sensing data, simulated grassland ANPP through the denitrification and decomposition (DNDC) model, and then explores the impact of grazing on grassland ANPP. The result showed that the model-estimated ANPP while considering grazing had a significant relationship with the field-observed ANPP, with the coefficient of determination (R 2 ) of 0.75, root mean square error (RMSE) of 122.86 kgC/ha, and average relative error (RE) of 8.77%. On the contrary, if grazing activity was not considered in simulation, a large uncertainty was found when the model-estimated ANPP was compared with the field observation, showing R 2 of 0.4, RMSE of 211.51 kgC/ha, and average RE of 32.5%. For the whole area of Zoige County in 2012, the statistics of the estimation showed that the total regional ANPP was up to 3.815ˆ10 5 tC, while the total regional ANPP, without considering grazing, would be overestimated by 44.4%, up to 5.51ˆ10 5 tC. This indicates that the grazing parameters derived in this study could effectively improve the accuracy of ANPP simulation results. Therefore, it is feasible to combine time-series remote sensing data with the process model to simulate the grazing effects on grassland ANPP. However, some issues, such as selecting proper remote sensing data, improving the quality of model input parameters, collecting more field data, and exploring the data assimilation approaches, still should be considered in the future work.

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Section: Land Cover Typesmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Wang

Bian

2016

Remote Sensing

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“…Productivity is the basis for the formation of biomass, and biomass is the manifestation of productivity. The currently available methods for the estimation of grassland productivity and biomass primarily include field surveys, statistical modeling [4][5][6][7], process modeling [8][9][10][11] and parameter modeling [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

et al. 2014

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The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005-2012 and the -accumulated‖ MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and -accumulated‖ MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005-2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g•m −2 ; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western OPEN ACCESSRemote Sens. 2014, 6 5369 desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005-2012, ranging from 10.56-17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert.

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“…Soil degradation and water and wind erosion were perceived to be the most pressing problems for land use in Inner Mongolia. Three reasons for these problems could be identified: first, increasing herd densities have increased grassland utilization beyond its threshold of natural resilience, resulting in grassland degradation, the appearance of uncovered land surface, and a respective increase in soil erosion (Feng & Zhao 2011. Second, the initial impacts of climate change have increased the vulnerability of the grassland ecosystem because of more severe droughts .…”

Section: Scientific Focus and Research Needsmentioning

confidence: 99%

Confronting international research topics with stakeholders on multifunctional land use: the case of Inner Mongolia, China

König¹,

Podhora²,

Helming³

et al. 2014

iForest

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© iForest -Biogeosciences and Forestry IntroductionLand use in the Inner Mongolia Autonomous Region ("Inner Mongolia") has a long tradition of nomadic pastoralism (Zhizhong & Wen 2008). With economic development, population growth, and increasing demand for natural resources globally and particularly in China, regional land use preferences have changed toward an increased preference for commodity production (Zhen et al. 2010(Zhen et al. , 2014. Thus, land use in Inner Mongolia is experiencing a period of transition, which affects natural resources and regional development in different ways. Because Inner Mongolia is increasingly facing severe land degradation problems, regional land use changes were triggered by the Chinese government with the introduction of several land conservation policies, such as the "Wind and sand source control around Beijing and Tianjin project" (2001 to 2010), which focused on afforestation and grassland maintenance ; the "Sloping land conversion program" (SLCP -initiated 1999) to convert arable land to grassland or forests in the agro-pasture zone (Yin & Yin 2010, König et al. 2012b; and the "Grazing prohibition" policy (since 1987), which requires livestock fencing and bans livestock from degraded areas (Jun Li et al. 2007, Li & Huntsinger 2011. Simultaneously, the government has excluded herders from vast areas of land and has attempted to move them into "minority villages", where they are expected to survive by producing milk for the dairy industry using a limited and fixed area of grassland. These developments resulted in the abandonment of nomadic pastoralism in favor of individual farming, with fenced herding and increasing herd densities per unit of land (Zhizhong & Wen 2008).With the increasing demand for land-based resources and ecosystem services, land use is no longer an issue that is of interest only to local land managers. Instead, society places a complex portfolio of demands on land utilization. Some of these demands are met by commodities for which farmers and land managers are paid, such as food, fiber, energy, and timber. Other demands exhibit the character of public goods, e.g., habitats, biodiversity, clean water and air, greenhouse gas mitigation, the buffering of weather extremes, cooling, flood control, cultural assets, and recreational and human health assets, and they contribute to the social and environmental good (Costanza & Daly 1992, de Groot et al. 2010.The concept of multifunctional land use was developed to encompass the multitude of services that land use provides (Wiggering et al. 2003). The underlying rationale for multifunctional land use is to simultaneously and interactively consider the social, economic, and environmental effects of any land use action including the effects of commodity production and those of uses for the public good. The multifunctionality of land use reflects the wide spectrum of land use options and their interaction with regard to sustainable development (Wiggering et al. 2006). Thus, scientific support requires the inclusio...

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Grazing intensity monitoring in Northern China steppe: Integrating CENTURY model and MODIS data

Cited by 57 publications

References 21 publications

Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

Confronting international research topics with stakeholders on multifunctional land use: the case of Inner Mongolia, China

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