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Zhao Q.,Shihezi University | Zhao Q.,Geospatial Information Engineering Research Center | Liu W.,Shihezi University | Liu W.,Geospatial Information Engineering Research Center | And 4 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

With the rapid development of unmanned aerial vehicle (UAV), it is widely used in the field remote sensing which is different from the satellite remote sensing, and has many advantages such as more convenient, lower cost, and shorter revisit cycle. However, effective information cannot be extracted from the multispectral images of UAV easily because of the high-resolution multi-band redundant data which can increase the complexity of data processing and consume a lot of computational resources. Therefore, the purpose of this research is to study the optimum bands combination which can be extracted by multispectral image. Manas's riverside in Shihezi, Xinjiang was selected as research area. Fixed-wing UAV equipped with Micro MCA12 Snap was used to obtain high-resolution multispectral images. Based on this system, a method was proposed to select the optimum bands combination for topographical objects classification. First, the standard deviation and correlation coefficient of the multi-spectral image's gray value were analyzed; the original bands combinations were got with the OIF method. Then, the most informative spectral feature bands and texture feature bands were determined respectively by using variety methods, such as vegetation and water index, principal component analysis, and GLCM. Finally, the original bands combination, spectral feature bands and texture feature bands were combined to obtain the final result. According to the analysis, bands 1, 6, 11, NDVI, NDWI and the mean parameter of GLCM combination of Micro MCA12 Snap multi-spectral sensors were selected as the optimum bands combination for topographical objects classification. After the selection of the bands combination, unsupervised classification and supervised classification methods were used to verify the classification accuracy with the optimum bands combination respectively. The classification accuracy with IsoData of ROI (region of interest) was increased from 83.57% to 89.80%, when it comes to SVM, the accuracy was increased from 95.58% to 99.76%. In addition, the study also provides effective reference for the selection of optimum bands combination with UAV multispectral images. © 2016, Chinese Society of Agricultural Machinery. All right reserved.


Wang C.,Shihezi University | Wang C.,Geospatial Information Engineering Research Center | Wang C.,Geospatial Information Engineering Laboratory | Wang W.,Shihezi University | And 12 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2016

Overgrazing is one of the main reasons of grassland degradation. How to monitor the temporal and spatial distribution of feed intake rapidly and effectively is very important for formulating scientific and rational grazing plan and protecting the grassland. Due to Xinjiang's remote geographic location and vast area, traditional ways of collecting data about feed intake such as recording the data manually is not efficient. With the rapid development of global navigation satellite system, the grazing trajectory data of heads can be collected conveniently. Grazing trajectory records the information of feeding process such as time, position and velocity. In this paper, a temporal and spatial distribution model of feed intake based on trajectory data of grazing is proposed. The establishment of the proposed model consists of 2 steps: constructing the distribution of feed intake with the minimum temporal and spatial granularity, which is used to calculate the feeding intake distribution of the herds in one day, and generating the distribution of feed intake with the multiple temporal and spatial granularities, which is used to describe the feeding intake distribution of the herds in a bigger temporal and spatial granularity. To obtain the distribution of feed intake with the minimum temporal and spatial granularity, the grazing region is computed by building buffer zone using grazing trajectory data. Then the theoretical feeding intake of herds is allocated upon the grazing region evenly to acquire the temporal and spatial distribution model of feed intake. To generate the distribution of feed intake with the multiple temporal and spatial granularities, the target grassland is divided into grids and the distribution of every feed intake with the minimum temporal and spatial granularity is mapped into the grids, and then the multiple temporal and spatial granularities are computed by overlapping the feeding intake grids. The model is subsequently utilized to compute the feed intake distribution. We collected grazing trajectory data of sheep in the studied grassland from July to October in 2015. Taking these grazing trajectory data as input, the model gives the corresponding feed intake distribution. The model result shows that: 1) Feed intake is relatively higher in those places where trajectory points are intensive; 2) Feed intake distribution varies with the different period, which is highly consistent with rotational grazing cycle; 3) There is a significant negative correlation between feed intake and slope of terrain. To verify the accuracy of the proposed model, we randomly chose 59 sample areas of 1 m2 in grazing region and computed the forage surplus of each sample area. We analyzed the correlation between feed intake and forage surplus of each sample area. The result shows the modeled feed intake is significantly negatively correlated with forage surplus, with a correlation coefficient of -0.704, and the accuracy of the model is 86.2%. Thus, the model is useful for rapid acquisition of feed intake's distribution information in vast area, and it also provides an efficient method to monitor feed intake distribution in grazing grassland. © 2016, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


Zhao Q.,Shihezi University | Zhao Q.,Geospatial Information Engineering Research Center | Zhao Q.,Geospatial Information Engineering Laboratory | Jin G.,Shihezi University | And 11 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

In order to reduce the losses caused by cotton pests and diseases, the prevention and control of cotton pests and diseases has become an important link in the process of cotton planting. Therefore, a timely and effective collection of information about cotton pests and diseases naturally becomes the premise and basis of monitoring and further forecasting. However, traditional way of collecting data by filling in paper manually presents several defects, such as the error caused in space and time, the cumbersome statistics and its time-consuming characteristic. Moreover, temporal resolution and spatial resolution of remote sensing monitoring can hardly reach the standard of monitoring, and the inversion model usually fails in achieving the ideal monitoring effect in terms of different cotton varieties and different growth stages. In comparison, information collection based on Mobile GIS has such advantages as its portability, public participation, instantaneity and accuracy in space and time. Focusing on the process of data collection, transmission, storage, analysis and service provision of cotton pests and diseases, this paper proposes a business solution of cotton pests and diseases information service based on Mobile GIS. The method tries to combine GPS positioning with off-line map loading in the mobile terminals to help acquire the precise location data. Users can mark the location where cotton pests and diseases occur by plotting points or polygon graphics on the map, the deviation of which is within 15 meters. Through the development of Android/IOS mobile terminal, collection time and grade of cotton pests and diseases are offered, which thus help gather the attribute information. By defining JSON data collection module, the unity of different platforms' data collection is achieved. And through network, the data are further sent to the spatial-temporal database in the service terminal. Since the database of this system is Oracle Spatial database, when the server side receives data, the system automatically writes them to the database by using JDBC technology through Oracle API (application programming interface). Then the integrating and storing of spatial data and attribute data are realized. The whole process can be finished in 5 seconds. By using spatial interpolation based on inverse distance weighted method, the information of cotton pests and diseases is visualized, with the accuracy of 70%. Meanwhile, monitoring and forecasting services are provided to the mobile terminal through the server side by using ArcGIS Server. Users can browse thematic map of cotton pests and diseases released by administrator timely, which simplifies the process of information transmission. Through application and practice, it shows that this system has characteristics of instantaneity, accurate positioning, and public participation. The fact that users play the roles of both data collectors and service receivers propels the forming of new mode of geographical information service. In addition, the system also provides effective reference for information collection and agricultural condition monitoring. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved.

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