| Technology Application |
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Application of fused data to grassland biomass estimation |
| YIN Xiaoli1,2, ZHANG Li2, XU Junyi1, LIU Liangyun2 |
1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China |
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Abstract In order to realize the real-time and high-precision monitoring of grassland biomass, the authors established a biomass estimation model for grasslands based on the fused data from the spatial and temporal adaptive reflectance fusion model (STARFM) in this study. Firstly, the authors introduced the STARFM model to fuse the MODIS and the Landsat TM data in Xilin Hot, Inner Mongolia. It was found that NDVI is a better input data for STARFM to achieve high-precision NDVI through comparing reflectance data and NDVI data. The most efficient statistical model, an exponential model, was chosen for estimating biomass based on the high-precision NDVI and the field survey data. Finally, two exponential models were set up respectively, with the fused NDVI and the original MODIS NDVI as independent variables. It was found that R2 increased from 0.761 to 0.832 and RMSE decreased from 32.521g/m2 to 28.653 g/m2 after using the fused NDVI. The results obtained by the authors prove that the fused NDVI can improve the accuracy of the grassland biomass estimation.
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| Keywords
color image
image retrieval and image classification
texture
gray level co-occurrence matrix (GLCM)
texture feature
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Issue Date: 21 October 2013
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2013,
Vol. 25
