Abstract
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 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.
Highlights
Grassland ecosystems are one of the most important types of terrestrial ecosystems on the planet.They provide the ecosystem functions of soil and water conservation, wind erosion prevention, sand fixation and air purification
The aboveground biomass data used in this study came from multi-year field survey data collected by our research group, and a large-scale field campaign organized by the Grassland Monitoring and Supervision Center Ministry of Agriculture of China (GMSC), primarily in July and August from
The unitary linear regression model was selected for estimating the aboveground biomass of the Xilingol grassland for the years 2005–2012 (Table 2)
Summary
Grassland ecosystems are one of the most important types of terrestrial ecosystems on the planet They provide the ecosystem functions of soil and water conservation, wind erosion prevention, sand fixation and air purification. Due to the simple calculations involved and the high accuracy of the approach, statistical regression models using remote sensing data have been widely applied for the estimation of grassland biomass. For different types of grassland areas, they established relational models between NDVI and field survey biomass data that allowed them to estimate the distribution of grass production in China. In the present study, based on this strategy, we used ground survey data from the Xilingol grassland for the years 2005–2012 and MODIS productivity data for the same time period to establish statistics-based models for estimating biomass. We further tested the accuracy of the models and selected the optimal model for estimating the aboveground biomass of the Xilingol grassland during the growing period
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