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Chen F.,South China Agricultural University | Chen F.,Guangdong Province Key Laboratory for Land Use and Consolidation | Chen F.,Guangdong Province Engineering Research Center for Land Information Technology | Hu Y.,South China Agricultural University | And 13 more authors.
Journal of Information and Computational Science | Year: 2015

The soil heavy metal contamination and relevant eco-environmental problems have been concerned for a long time. The assessments of soil heavy metal pollution usually fails to reflect the real pollution level because of the uncertainty process of predicting spatial distribution of soil properties, which is based on the observation data of sampling sites. However, the use of Sequential Indication Simulation (SIS) method can effectively evaluate the uncertainty of heavy metal in soil. The purpose of this study is to evaluate the high soil lead content in a county scale based uncertainty evaluation in Zengcheng District, Guangzhou. It demonstrates that the local uncertainty evaluation value of the soil lead content is very high while the spatial uncertainty evaluation value is relatively low. Hence, more attention should be paid to the misclassification rate of simulation when evaluating the uncertainty of high risk areas. In order to provide contamination prevention measures, this study gives several feasible resolutions for the prevention administrative department of heavy metal in this county: 1) Pay attention to the classification error rate in the simulation when making the decision of fertilization and prevention of pollution risk and evaluating the uncertainty of soil nutrients and areas of high risk of heavy metal in soil, then rationalize the final decision; 2) The delineation of high-standard farmland should make full use of the accurate and detailed spatial results of nutrients and heavy metal contents in soil, and take the spatial uncertainty assessment values of different attributes into consideration to make the results more scientific. Copyright © 2015 Binary Information Press.

PubMed | South China Agricultural University, Wuhan University, Guangdong Province Key Laboratory for Land use and consolidation and Shenzhen University
Type: | Journal: Scientific reports | Year: 2016

Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation monitoring applications. However, because of the influence of various observing errors, the accuracy of PPP is generally at the cm-dm level, which cannot meet the requirements needed for high precision deformation monitoring. For most of the monitoring applications, the observation stations maintain stationary, which can be provided as a priori constraint information. In this paper, a new PPP algorithm based on a sliding window was proposed to improve the positioning accuracy. Firstly, data from IGS tracking station was processed using both traditional and new PPP algorithm; the results showed that the new algorithm can effectively improve positioning accuracy, especially for the elevation direction. Then, an earthquake simulation platform was used to simulate an earthquake event; the results illustrated that the new algorithm can effectively detect the vibrations change of a reference station during an earthquake. At last, the observed Wenchuan earthquake experimental results showed that the new algorithm was feasible to monitor the real earthquakes and provide early-warning alerts.

Zeng Q.,South China Agricultural University | Zhu T.,South China Agricultural University | Zhuang X.,South China Agricultural University | Zheng M.,South China Agricultural University | And 3 more authors.
Multimedia Tools and Applications | Year: 2015

Plant species identification is one of the most important research branches of botanical science. In this paper, a novel shape descriptor, namely Periodic Wavelet Descriptor (PWD) of plant leaf, is firstly presented. Then based on the PWDs of the leaves of different plant species, we constructed a database of PWDs. At last, a Back Propagation Neural Network (BPNN) is trained to fulfill the experiment of plant species identification. The experimental results show that the proposed algorithm combined the PWD of plant leaf with BPNN is effective with a correct identification rate about 90 %. © 2015 Springer Science+Business Media New York

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