Satellite Data Reduction Using Entropy-preserved Image Compression Technique

In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.

Data release of the LAMOST pilot survey

This paper describes the data release of the LAMOST pilot survey, which includes data reduction, calibration, spectral analysis, data products and data access. The accuracy of the released data and the information about the FITS headers of spectra are also introduced. The released data set includes 319 000 spectra and a catalog of these objects.

A regional suspended load yield estimation model for ungauged watersheds

Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques(DRTs), e.g., principal component analysis(PCA), Gamma test(GT), and stepwise regression(SR), have been used to select the most effective variables. The artificial neural network(ANN) and multiple linear regression(MLR) are also common tools for SLY modeling. We conducted this study(1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and(2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region(in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient,relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.

South Galactic Cap u-band Sky Survey(SCUSS):Project Overview

The South Galactic Cap u-band Sky Survey （SCUSS） was established in 2009 in order to provide a photometric input catalog for target selection of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） project. SCUSS is an international cooperative project between National Astronomical Observatories, Chinese Academy of Sciences, and Steward Observatory at the University of Arizona, using the 90 inch （2.3 m） Bok telescope on Kitt Peak. The telescope is equipped with a prime focus camera that is composed of a mosaic of four 4096 × 4096 CCDs and has a field of view of about 1 deg2. From 2009 to 2013, SCUSS performed a sky survey of an approximately 5000 deg2 field of the South Galactic Cap in u band, including the Galactic anticenter area and the SDSS-IV extended imaging area. The limiting magnitude of SCUSS is deeper than 23 mag （at a signal-to-noise ratio of 5）. In this paper, we briefly describe the goals of this project, method of observations and data reduction, and we also introduce current and potential scientific activities related to the SCUSS project.