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Research on Estimation of NPP Based on HJ-1 Data in Beijing Area

Zhang Shu-fa

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Abstract

In this study,using area weighted average method to revise the maximum energy efficiency with farmland,woodland,grassland in six feature types which classified by land-use-1 map based on HJ-1/CCD remote sensing data.Estimating NPP of Beijing in 2009 with CASA model,and the result shows that the highest NPP of Beijing is appeared in July and August,and the general trend of the distribution is high in northern and west,low in southern and east in Beijing;The estimates values are close to measured values,with correlation coefficient is 0.8.As a result,the method used in this study was reasonable and reliable.

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What this paper is about

In this study,using area weighted average method to revise the maximum energy efficiency with farmland,woodland,grassland in six feature types which classified by land-use-1 map based on HJ-1/CCD remote sensing data.Estimating NPP of Beijing in 2009 with CASA model,and the result shows that the highest NPP of Beijing is appeared in July and August,and the general trend of the distribution is high in northern and west,low in southern and east in Beijing;The estimates values are close to measured values,with correlation coefficient is 0.8.As a result,the method used in this study was reasonable and reliable.

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Available abstract

In this study,using area weighted average method to revise the maximum energy efficiency with farmland,woodland,grassland in six feature types which classified by land-use-1 map based on HJ-1/CCD remote sensing data.Estimating NPP of Beijing in 2009 with CASA model,and the result shows that the highest NPP of Beijing is appeared in July and August,and the general trend of the distribution is high in northern and west,low in southern and east in Beijing;The estimates values are close to measured values,with correlation coefficient is 0.8.As a result,the method used in this study was reasonable and reliable.

Key concepts: Beijing, Woodland, Correlation coefficient, Remote sensing, Grassland, Environmental science, Computer science, Estimation

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