Wu Taixia

Method for time series extraction of characteristic parameters from multidimensional remote sensing datasets

Lots of researches have been increasingly focusing on time series analysis of remote sensing datasets, deriving phenology time and trajectory parameters by carve fitting and detecting changes due to natural or artificial factors. For these applications extraction of various characteristic parameters is an indispensable and fundamental procedure. However, there is a lack of an integrated method currently to manage long time-series remote sensing imagery, meanwhile as a direct access to extracting time series of various characteristic parameters. In this paper we propose a user-friendly program for managing time series of remote sensing datasets, whats more, extracting time series data for areas like point, rectangle and general polygon, according to user-defined formula automatically computing and constructing time series of various characteristic parameters. In addition, spectral data for one day, after a point or scope is selected, is able to be extracted and processed using general spectral analysis methods. This program tries to manage four dimensional (including time, spatial and spectral dimensions) remote sensing datasets, and be applicable to outputting time series of characteristic parameters and spectral data, providing an innovatively fast and flexible tool for time-series studies.

Evaluating different vegetation index for estimating lai of winter wheat using hyperspectral remote sensing data

Leaf area index (LAI) is an important parameter which always be used to estimate vegetation cover and forecast the crop growth and yield. Currently the statistic relationship between LAI and vegetation indices (VI) has been widely applied to predict vegetation LAI. Each vegetation index for inversing LAI has applicable area and conditions, the best vegetation index or spectral parameter of them were not sure for estimating LAI of winter wheat in China. In this paper, PSR-3500 spectrometer and LAI-2200 plant canopy analyser were used to acquire the spectrum and LAI synchronously from April to June in 2013, in xiaotangshan of Beijing. After calculating VIs selected in this study, the correlation relationships between VIs and LAI were established under different spectral widths and center wavelengths. The results show that DVI is the best index with R2 of 0.7. 3nm was verified the best bandwidth with center wavelength of NIR and red was 815nm and 746nm, respectively. The method that multiple VIs were used to inverse LAI synergistically, was proposed in this paper, which established the optimal linear regression model. Finally, the R2 we got between prediction LAI and the measured value reached to 0.9235, which reaffirmed the feasibility of multiple VIs in the estimation of vegetation LAI.

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.