Aboveground biomass estimate methods for typical grassland types in the Tibetan Plateau
CHU Duo1,2, DEJI Yangzong2, JI Qiumei3, TANG Hong2
1. Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
2. Tibet Institute of Plateau Atmospheric and Environmental Sciences, Lhasa 850000, China;
3. Tibet Institute of Animal Husbandry, Lhasa 850000, China
The estimation of aboveground biomass (AGB) is necessary for studying productivity, carbon cycles, nutrient allocation, fuel accumulation in terrestrial ecosystems, and rangeland management and monitoring. In this study, AGB estimate models were developed for 3 major grassland types (alpine meadow, alpine steppe, and temperate steppe) in the Tibetan Plateau by integrating AGB data collected from 6 sites in central Tibet in 2004 and concurrent vegetation index (VI) derived from MODIS data sets. The results show that MODIS VI is more suitable for estimates of alpine meadow and alpine steppe. The cubic polynomial regression based on NDVI is the best estimate model for alpine meadow with the correlation of 0.82, while for alpine steppe the model is EVI based cubic polynomial regression model with the correlation of 0.83; due to strong spatial heterogeneity of temperate steppe in central Tibet, the relationship between AGB and VI for temperate steppe is poorer than that for alpine grassland (alpine meadow, alpine steppe). The MODIS VI based estimates of AGB during the growing season is better than the total biomass; during the growing season the correlation between AGB of alpine grassland (alpine meadow and alpine steppe ) and MODIS VI is higher than 0.8 with a maximum value of 0.92, and for temperate steppe it is above 0.67 also. In contrast, NDVI is the best vegetation index for AGB estimates of alpine meadow and temperate steppe while EVI is the best for alpine steppe. Due to unique spectral response of green vegetation, MODIS VI is more suitable for estimates of AGB during the growing season, and the accuracy of AGB estimates will decrease during the non-growing season. For the same types of grassland with less difference in AGB, the linear or polynomial regression model is more suitable for modeling or estimating AGB in the Tibetan Plateau than other estimate models.
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2013, Vol. 25 
