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Search Results: 1 - 10 of 36847 matches for " Gengxing Zhao "
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Estimating Chlorophyll Content of Apple Leaves Based on Different Scales in Differential Window  [PDF]
Zhaoying Han, Xicun Zhu, Zhuoyuan Wang, Gengxing Zhao, Ling Wang
Agricultural Sciences (AS) , 2015, DOI: 10.4236/as.2015.69106
Abstract: The aims of this study are to explore the effect of different scales in the high spectral data on the estimation of chlorophyll content of apple leaves, to find out the optimal differential window scale and to establish a model for estimating the chlorophyll content of apple leaves. Taking the apple leaves as the research object, the actual spectral reflectance of the leaves was determined by the ASD Field Spec 3 spectrometer and the chlorophyll contents of the leaves were measured in the laboratory. Firstly, the differential transformations from 1 to 30 window scales were done for actual spectral data respectively, and correlation analyses were done between apple leaf chlorophyll content and differential data, then two sensitive wavelengths were chosen under each window. Secondly, taking five consecutive differential windows as a group, the best differential window was selected in each group. Lastly, after the conversion of two sensitive wavelengths in six differential windows, relationship analyses between chlorophyll content of apple leaves and two sensitive wavelengths were done, then two new parameters with the largest correlation coefficient were chosen to establish estimation model. Results showed that with increasing differential window, the determination coefficient (R2) of estimation model decreased after an initial increase, the tipping point was at the 13th differential window scale. Testing the partial least squares (PLS) model and the stepwise regression (SR) model established under differential window scale of the 13th, the R2 of the SR model was higher than that of the PLS model. The RMSE and RE% of the SR model were lower than that of the PLS model, which showed that SR model was more suitable to estimate chlorophyll content.
Characteristics and Spatial Variability of Saline-Alkaline Soil Degradation in the Typical Yellow River Delta Area of Kenli County, China  [PDF]
Zhuoran Wang, Gengxing Zhao, Mingxiu Gao, Chunyan Chang, Jichao Jia, Jin Li
Journal of Environmental Protection (JEP) , 2014, DOI: 10.4236/jep.2014.512104
Abstract: As an important area of reserve land resources, the Yellow River Delta is faced with the problem of soil salinization. Grasping the characteristics of soil salinity as well as its spatial variation patterns is an important foundation of prevention, control and utilization of saline soil. This study selected Kenli County of the Yellow River Delta, obtained soil salinity data through field survey and lab experiment, and used statistical, GIS interpolation and buffer analysis methods to analyze the characteristics of soil salinity and its spatial variation patterns. Our results showed that the general soil salinity in the study area was mainly moderate and there was a significant positive correlation between different soil layers of 0 - 15 cm, 15 - 30 cm and 30 - 45 cm and soil salinity increased with the increase of soil depth. The areas with high soil salinity in each soil layer mainly distributed in the east near the Bo Sea in the county, while the areas with lower soil salinity mainly distributed in the southwest, centre and the two sides of the Yellow River in the northeast. Soil salinity showed a trend of decrease with the increase in distance to the Bo Sea, while stretching from the Yellow River, it showed increase tendency with the increase in distance to the Yellow River. The order from high soil salinity to low of different vegetation types was naked land → suaeda glauca → tamarix → vervain → reed → couch grass → paddy → cotton → winter wheat → maize; the order for different geomorphic types was depression → slightly sloping ground → slow hillock → high flood land. This study preliminary delineated the characteristics of soil salinity as well as its spatial variation patterns in the study area, and provided scientific basis for soil resource sustainable utilization in the Yellow River Delta.
Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery  [PDF]
Tongrui Zhang, Gengxing Zhao, Chunyan Chang, Zhuoran Wang, Ping Li, Deyu An, Jichao Jia
Agricultural Sciences (AS) , 2015, DOI: 10.4236/as.2015.61006
Abstract: In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.
Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy  [PDF]
Lulu Gao, Xicun Zhu, Cheng Li, Lizhen Cheng, Ling Wang, Gengxing Zhao, Yuanmao Jiang
Agricultural Sciences (AS) , 2016, DOI: 10.4236/as.2016.710061
Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746; the validation R2 was 0.720; and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.
Monitoring Soil Nitrate Nitrogen Based on Hyperspectral Data in the Apple Orchards  [PDF]
Yu Wei, Xicun Zhu, Cheng Li, Lizhen Cheng, Ling Wang, Gengxing Zhao, Yuanmao Jiang
Agricultural Sciences (AS) , 2017, DOI: 10.4236/as.2017.81002
Abstract: This paper is aimed to monitor the soil nitrate nitrogen content in the apple orchards rapidly, accurately and in real time by making full use of the effective information of soil spectra. The 96 air-dried soil samples of the apple orchards in Qixia county, Yantai city, Shandong province were used as the data source. Spectral measurements of soil samples were carried out by ASD Fieldspec 3 in the darkroom, and the content of the soil nitrate nitrogen was determined by chemical method. Then the hyperspectral reflectance of soil samples were preprocessed by Multivariate Scatter Correction (MSC) and First Derivative (FD), the correlation analysis was carried out with the soil nitrate nitrogen content. The sensitive wavelength of soil nitrate nitrogen was screened. Finally, the Support Vector Machine (SVM) model for the soil nitrate nitrogen content was established. The results showed that the selected sensitive wavelength were 617 nm, 760 nm, 1239 nm, 1442 nm, 1535 nm, 1695 nm, 1776 nm, 1907 nm and 2088 nm. Hyperspectral monitoring model was established by SVM, in which the prediction set R2 was 0.959, RMSE was 0.281, RPD was 3.835; the correction set R2 was 0.822, RMSE was 0.392, RPD was 2.037. The SVM model could be used to monitor the soil nitrate content accurately.
Hyperspectral Inversion of Potassium Content in Apple Leaves Based on Vegetation Index  [PDF]
Xiaoyan Guo, Xicun Zhu, Cheng Li, Yu Wei, Xinyang Yu, Gengxing Zhao, Houxing Sun
Agricultural Sciences (AS) , 2017, DOI: 10.4236/as.2017.88061
The aim of this study is to establish the estimation model of potassium content in apple leaves by using vegetation index. A total of 96 fresh apple leaves were collected from 24 orchards in Qixia County, Shandong Province. The spectral reflectance of the leaves was measured by ASD FieldSpec4. The difference vegetation index (DVI), ratio vegetation index (RVI) and normalized vegetation index (NDVI) were used to make the contour map through Matlab platform, and the combination of high correlation wavelength was selected to establish the random forest (RF) regression model of potassium content. The hyperspectral reflectance increased with the increase of leaf potassium content. The correlation between DVI and the content of potassium is higher than NDVI and RVI. The optimal vegetation index was DVI (364,740), the correlation coefficient was 0.5355. The random forest regression model established with DVI selected vegetation index was the best. R2 was 0.8995, RMSE and RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the random forest regression model to reverse the potassium content of apple leaves has achieved good results. It is important to determine the growth status of apple in hyperspectral and to determine the potash fertilizer of apple trees.
Eco-environmental evaluation and spatial-temporal collocation of regional land consolidation

WANG Ailing,ZHAO Gengxing,WANG Ruiyan,YUAN Xiangming,

应用生态学报 , 2006,
Abstract: Eco-environmental evaluation and spatial-temporal collocation of land consolidation is the basis of regional land consolidation. Taking Qingzhou County of Shandong Province as an example, this paper established the eco-environmental evaluation index system and evaluation model of land consolidation, based on the systematic analysis of land consolidation characteristics and natural and social conditions of Qingzhou County. The comprehensive score of each evaluation unit was obtained by integrated index evaluation method and GIS techniques, and the spatial- temporal collocation of regional land consolidation was proposed accordingly. The results indicated that in Qingzhou County, the total area of cultivated and un-utilized land was 1 446 km2 , among which, the land consolidation area in near future mainly distributed in the northwest part of plain region, occupying 15. 35% of the total, mid-phase land consolidation area mainly distributed in the northwest and central parts of plain region, occupying 13.58%, land consolidation area in specified future mainly distributed in the north part of plain region, occupying 40. 71%, and non- land consolidation area mainly distributed in hilly region, occupying 30. 36%. These results could provide scientific instruction for the land consolidation planning and implement in Qingzhou County.
A Framework of Research and Practice: Relationship between Work Engagement, Affective Commitment, and Turnover Intentions  [PDF]
Liyu Zhao, Jingchao Zhao
Open Journal of Social Sciences (JSS) , 2017, DOI: 10.4236/jss.2017.510019
There is a growing research interest in the topic of work engagement over the past years. In reference to Schauefeli, Salanova, Gonzalez-Roma & Bakker (2002) [1], work engagement is described as “a positive, fulfilling work-related state of mind that is characterized by vigor, dedication and absorption”. As compare to the researches based on the relationship between work engagement and organizational commitment and job performance, the existing researches on the relationship between work engagement and turnover intentions are far fewer. We theoretically discussed the relationship among work engagement, affective commitment and turnover intentions. Research results show that work engagement is negatively related to turnover intentions whereby affective commitment plays a regulating role. Affective commitment moderates the relationship between work engagement and turnover intentions whereby employees’ affective commitment is stronger and employees are more willing to invest effort in their work; hence, employees’ turnover intentions are reduced.
Study on Business English Practical Teaching from the Perspective of Economics of Language  [PDF]
Cuiling Zhao, Yanan Zhao
Creative Education (CE) , 2019, DOI: 10.4236/ce.2019.104054
Abstract: The theory of Economics of Language holds that the relationship of language learning and language application is just like investment and benefit. Business English teaching is an investment that can bring economic benefits, so we should try to get a higher return with a lower cost. With an analysis of economic value of business English practical teaching, this paper puts forward some principles and strategies for business English practical teaching. Optimizing the allocation of education resources and establishing a practical teaching system highlighting both language skills and business practice can maximize the economic benefit of Business English education.
Estimating the Size of an Injecting Drug User Population  [PDF]
Yang Zhao
World Journal of AIDS (WJA) , 2011, DOI: 10.4236/wja.2011.13013
Abstract: This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to use the information from harm reduction programs, especially, Needle Exchange Programs (NEPs). The approach involves using respondent-driven sampling design to collect a sample of injecting drug users who appear at site of NEP in a certain period of time and to obtain retrospective self-report data on the number of friends among the IDUs and number of needles exchanged for each sampled injecting drug user. A methodology is developed to estimate the size of injecting drug users who have ever used the NEP during the fixed period of time, and which allows us to estimate the proportion of injecting drug users in using NEP. The size of the IDU population is estimated by dividing the total number of IDUs who using NEPs during the period of time by the estimated proportion of IDUs in the group. The technique holds promise for providing data needed to answer questions such as “What is the size of an IDU population in a city?” and “Is that size changing?” and better understand the dynamics of the IDU population. The methodology described here can also be used to estimate size of other hard-to-reach population by using information from harm reduction programs.
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