Guangdong Province Engineering Research Center for Land Information Technology

Guangzhou, China

Guangdong Province Engineering Research Center for Land Information Technology

Guangzhou, China
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Zhang R.,South China Agricultural University | Zhang R.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Yao Y.-B.,Wuhan University | Hu Y.-M.,South China Agricultural University | And 3 more authors.
Journal of Geodesy | Year: 2017

The Global Navigation Satellite System presents a plausible and cost-effective way of computing the total electron content (TEC). But TEC estimated value could be seriously affected by the differential code biases (DCB) of frequency-dependent satellites and receivers. Unlike GPS and other satellite systems, GLONASS adopts a frequency-division multiplexing access mode to distinguish different satellites. This strategy leads to different wavelengths and inter-frequency biases (IFBs) for both pseudo-range and carrier phase observations, whose impacts are rarely considered in ionospheric modeling. We obtained observations from four groups of co-stations to analyze the characteristics of the GLONASS receiver P1P2 pseudo-range IFB with a double-difference method. The results showed that the GLONASS P1P2 pseudo-range IFB remained stable for a period of time and could catch up to several meters, which cannot be absorbed by the receiver DCB during ionospheric modeling. Given the characteristics of the GLONASS P1P2 pseudo-range IFB, we proposed a two-step ionosphere modeling method with the priori IFB information. The experimental analysis showed that the new algorithm can effectively eliminate the adverse effects on ionospheric model and hardware delay parameters estimation in different space environments. During high solar activity period, compared to the traditional GPS + GLONASS modeling algorithm, the absolute average deviation of TEC decreased from 2.17 to 2.07 TECu (TEC unit); simultaneously, the average RMS of GPS satellite DCB decreased from 0.225 to 0.219 ns, and the average deviation of GLONASS satellite DCB decreased from 0.253 to 0.113 ns with a great improvement in over 55%. © 2017 Springer-Verlag Berlin Heidelberg


Song Y.-Q.,South China Agricultural University | Yang L.-A.,Northwest University, China | Li B.,South China Agricultural University | Li B.,Guangdong Province Engineering Research Center for Land Information Technology | And 7 more authors.
Sustainability (Switzerland) | Year: 2017

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0-20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg-1). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale. © 2017 by the authors.


Zhao X.,South China Agricultural University | Zhao X.,Guangdong Province Engineering Research Center for Land Information Technology | Zhao X.,Guangdong Province Key Laboratory for Land Use and Consolidation | Ye Y.,South China Agricultural University | And 13 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2017

Rapid industrialization and urbanization have brought a series of environmental problems related to soil pollution in the Pearl River Delta region. A systematic and synthetic evaluation of the cultivated land quality is of great significance to guide the rational utilization and protection of cultivated land, as well as to realize the comprehensive balance and management of cultivated land. However, the soil pollution factors have seldom been considered in cultivated land quality evaluation system, which was also short of a sensitivity analysis of the index weight. Based on GIS (geographic information system) multi-objective decision analysis, the paper conducted a study on the evaluation of cultivated land quality and the sensitivity analysis of index weight by taking Zengcheng District of Pearl River Delta as an example. With soil fertility factor and environmental assessment index taken into consideration, the comprehensive evaluation index system of cultivated land quality was constructed from 4 aspects: Soil physical and chemical properties, agricultural production conditions, location conditions and soil environment, so as to analyze the general characteristics of cultivated land quality and the spatial layout rule in Zengcheng District, and the weight sensitivity analysis of OAT (one-at-a-time) method was adopted to evaluate the influence degree of each index weight's uncertainty on the evaluation results. In order to reflect the trend and regularity of the changon cultivated land quality and its spatial pattern, the weight value of only one factor changed at a time while the other factors remained unchanged. When the value of RPC (range of percent change) and IPC (increment of percent change) were at ±30% and ±2% respectively, and every criterion factor was applied to all the evaluation indicators as the main change factor, a total of 420 groups of weight values were generated, among which every group of weights were calculated to produce a new evaluation result of the comprehensive quality of cultivated land. The main results of the study were as follows: 1) The cultivated land in Zengcheng District had a good overall quality, but high quality cultivated land was lacked. The grades of cultivated land quality were mainly concentrated at the 2nd and 3rd grade, which accounted for 30.88% and 31.69%, respectively. The proportion of middle and low yield field was relatively large. 2) From the perspective of the spatial distribution, the cultivated land with better quality was mainly distributed in the southern plains, the basins with better irrigation conditions like Zengjiang River basin, East River basin and Xifu River basin, as well as North River valley. The comprehensive quality of most cultivated land in Zhengguo, Xiaolou and Paitan was not high, and the overall quality of cultivated land in Xintang Town was poor. 3) The changes of weights had a certain influence on the quality distribution of cultivated land. Seen from the spatial distribution of change rate of cultivated land quality generated from different weight distribution, the spatial differences of the calculated results were rather big. When the weight increased or decreased by the same value, the same factor showed the same sensitivity to the quality evaluation results of cultivated land. 4) The maximum MACR (mean absolute change rate) value of 3.558 2% was much lower than the corresponding weight change rate of 30%, showing that the evaluation results were relatively stable, and the quality of cultivated land in Zengcheng District was overall stable. In conclusion, in the comprehensive evaluation of cultivated land quality, the soil environmental factors should be taken into consideration, and a quantitative analysis of micro-data of the soil pollution situation should be conducted to make the evaluation system more comprehensive and more scientific. The sensitivity analysis of the index weight can verify the reliability of the evaluation results, which can help the relevant departments make better decisions, and reduce the uncertainty in the spatial multi-criterion decision-making. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


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.

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