19 October 2005 MODIS-based seasonality and distribution of Leaf Area Index of grass land of Gonghe Basin in Qinghai-Tibet plateau
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Abstract
Leaf area index (LAI) is a key variable of up-scaling or down-scaling in global climate change research. The Qinghai-Tibetan Plateau is an ideal place to study and model interactions between natural ecosystem and climate change because there are unique interactions between ecosystems and environments on the extremely high plateau where vegetation remains undisturbed. In this study, we present field data for leaf area index(LAI) in 42 field plots located along an altitudinal gradient from 2800m to 4000m around Gonghe basin in Qinghai-Tibetan plateau using a global positioning system during 2003-2004. The vegetation types of these field plots included grasslands and grass-shrub mixed lands. We also acquired MODIS data (product MOD13Q1) over the study area between January and December, 2003. These products consisted of 15-days composite of vegetation indices (EVI and NDVI) at 250m spatial resolution. We developed a MODIS-based Leaf Area Index Model of Grass land using 20 site-specific measurement data from the 42 field plots, and validated the model using other 22 field plots measurement data. Using MODIS-derived Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), Data analyses have shown that EVI had a stronger linear relationship (R2=0.8226, n=20) with LAI at Gonghe Basin than did the NDVI (R2=0.7885, n=20). The simulation of the model was conducted at Gonghe Basin of Qinghai-Tibetan plateau. The predicted LAI values agreed well (EVI model R2=0.621, n=22) (NDVI model R2=0.612, n=22) with observed LAI of grassland at Gonghe Basin. This study demonstrated the potential of the model for scaling-up of LAI of grasslands in China.
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Huazhong Zhu, Huazhong Zhu, Tianxiang Luo, Tianxiang Luo, Yaping Yang, Yaping Yang, } "MODIS-based seasonality and distribution of Leaf Area Index of grass land of Gonghe Basin in Qinghai-Tibet plateau", Proc. SPIE 5976, Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, 597616 (19 October 2005); doi: 10.1117/12.626368; https://doi.org/10.1117/12.626368
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