Zhang Lifu

The Characteristic Analyses of Images from the UAV Remote Sensing System

Comparing with the satellite and the common aerial vehicle, the UAVRSS (unmanned aerial vehicle remote sensing system) is a new type of aviation remote sensing for earth observation. It can work in an ultrahigh (10~30km) or ultra-low space (150~400m) for its long time, instantaneous transmission, low-cost, loading ability, etc. In this paper, through the characteristic analyses of the UAVRSS and the images/data from two aspects, some new applications are developed for the UAVRSS images.

Spatial scale issue in temperature and emissivity separation from thermal hyperspectral imager

This paper analysis the scale issue using four scale transformation methods (center pixels method (CPM), simple average method (SAM), point spread function method (PSF), wavelet transform method (WTM)). The results show that Center pixels method is not fit for scale transformation; PSF method can keep best the spatial autocorrelation of images, and lowest standard deviation. The optimal spatial resolution for hyperspectral thermal infrared is 6.25m, using semi-variance.

A study of aerosol changing rule in Beijing during a decade

Basing on the Aerosol Product data from the AERONET website, We analyzed the 550nm aerosol optical depth(AOD), Angstrom exponent(AE), aerosol volume concentration and aerosol size distribution from 2003 to 2013, excluding the year 2008 due to lacking of data. The research shows: high AOD often occurred in spring and summer. The coarse particle such as dust particle dominates in the spring and the fine particle dominates in the summer. On the interannual variation, there is an obvious trend: From 2003 to 2007, the AOD was often above 1.5 due to the dust pollution from north areas in spring and took the second place in summer. Before and after the Beijing Olympic Games, the atmospheric condition improved. Nevertheless, the AOD rised again till 2012 and 2013. In addition, fine mode aerosol optical depth is closed concerned with PM2.5 and the fine particle volume concentration affects the AOD more. In a word, the study of aerosol changing rule in Beijing from 2003 to 2013 can reflect some environment issues and contribute to atmospheric improvement as a related reference.

Analysis on the spatial-temporal variations of methane over China using SCIAMACHY data

Methane is an important anthropogenic greenhouse gas contributing to global climate change, and its warming effect is just second to carbon dioxide. Satellite remote sensing technique can obtain large scale distribution of trace gases, and it has been an important tool in the field of atmospheric observation. This paper presents the spatial-temporal variations of methane in China based on the vertical columns of methane measured by the SCIAMACHY sensor on board ENVISAT. The distribution of annual averaged CH4 vertical columns in China during 2003-2005 shows that the spatial gradients of CH4 vertical column look like a three-level ladder, and low terrain areas such as plains and basins tend to have high CH4 level, but the surrounding terrain also matters. The variation curves of monthly average CH4 vertical columns in China during 2003-2005 show that the peak centers of CH4 are found in summers, and the trough centers in winters. The month where the yearly seasonal component has its maximum in CH4 of Northern China is earlier than that of Southern China. Most parts of China have its minimum in winter and spring except Hainan, whose minimum emerges in summer. The Emissions Database for Global Atmospheric Research (EDGAR) showed that during 2003 to 2008 the total emissions of CH4 in China were increasing year by year, which were high consistency with the trend of the average concentration of atmospheric methane in China. It is estimated that the growth of methane concentration was partly caused by the increase of total emissions of CH4.