Generating high spatiotemporal resolution LAI based on MODIS/GF-1 data and combined Kriging-Cressman interpolation

Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.

Monitoring behavior of poultry based on RFID radio frequency network

Poultry behavior monitoring is an important basis for the poultry disease warning.Manual monitoring is mostly used nowadays.In this work,the automatic monitoring system for assisting manual monitoring was examined.Sophisticated data mining techniques were used to leverage the data collected by RFID devices.Specifically,(1)weighing sensors and wireless networks of Multiple RFID-tag-collector groups were used to monitor the poultry behavior;(2)RFID tags were putted on individual poultry so that the moving time of the poultry between two RFID-tag-collectors could be recorded.Thus,the characteristic functions of poultry behaviors such as speed,ability to snatch food and resting time could be extracted based on the distance between two RFID-tag-collectors and the relevant time parameters;(3)the sick,normal,active and other poultry groups were categorized by using the K-means method which utilizing the behavior characteristics and poultry weight data in data mining.The results demonstrated that accurate classifications could be obtained according to the poultry characteristics,and the clustering results matched with the results obtained by manual method to identify the poultry groups.Consequently,the technique in this paper has great potential for large-scale poultry disease warning and poultry classification.