Study on the vegetation dynamic change using long time series of remote sensing data

Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)—radiometric characteristics of which do not change with time—are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.

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Photosynthesis, carbon acquisition and primary productivity of phytoplankton: a review dedicated to Colin Reynolds

Hydrobiologia ◽

10.1007/s10750-020-04321-y ◽

2020 ◽

Vol 848 (1) ◽

pp. 77-94 ◽

Cited By ~ 2

Author(s):

Martin T. Dokulil ◽

Kuimei Qian

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Time Series ◽

Primary Productivity ◽

Light Harvesting ◽

Algal Growth ◽

Long Time Series ◽

Methods And Techniques ◽

Long Time ◽

The Subject

AbstractThe review intends to give an overview on developments, success, results of photosynthetic research and on primary productivity of algae both freshwater and marine with emphasis on more recent discoveries. Methods and techniques are briefly outlined focusing on latest improvements. Light harvesting and carbon acquisition are evaluated as a basis of regional and global primary productivity and algal growth. Thereafter, long-time series, remote sensing and river production are exemplified and linked to the potential effects of climate change. Lastly, the synthesis seeks to put the life achievements of Colin S. Reynolds into context of the subject review.

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Time-series processing of large scale remote sensing data with extreme learning machine

Neurocomputing ◽

10.1016/j.neucom.2013.02.051 ◽

2014 ◽

Vol 128 ◽

pp. 199-206 ◽

Cited By ~ 8

Author(s):

Jiaoyan Chen ◽

Guozhou Zheng ◽

Cong Fang ◽

Ningyu Zhang ◽

Huajun Chen ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Extreme Learning Machine ◽

Large Scale ◽

Remote Sensing Data ◽

Sensing Data ◽

Learning Machine

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