Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in...Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta(YRD) region using moderate resolution imaging spectroradiometer(MODIS) time-series data. The normalized difference vegetation index(NDVI) was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.展开更多
Sampling frequency is an important factor to be considered during the design of a water monitoring network,and the cost-effective selection of possible ways and means for the optimization of sampling frequency is stil...Sampling frequency is an important factor to be considered during the design of a water monitoring network,and the cost-effective selection of possible ways and means for the optimization of sampling frequency is still needed.This paper introduces water pollution index deviation ratio comparison(WPI DRC),a procedure for the optimization of sampling frequency for a routine river water quality monitoring system.Sampling frequency optimized using WPI DRC at monitoring station X5 in the mainstream of Xiangjiang River is compared with that established using the traditional Statistical Algorithm method.The result of comparison indicates that WPI DRC is more feasible than the traditional one.And then,the sampling frequencies for other 16 monitoring stations also have been optimized,and the results show the sampling frequencies of all the stations except that X4 are reduced,and there is no unacceptable difference between water quality evaluation results at 17 stations before and after the optimization.Therefore,it is concluded that WPI DRC is an effective optimization process with operable results,which can be used to fulfill the requirement of practical monitoring work.展开更多
基金supported by the National Natural Science Foundation of China (41471335, 41271407)the National Remote Sensing Survey and Assessment of Eco-Environment Change between 2000 and 2010, China (STSN-1500)+2 种基金the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2013BAD05B03)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050601)the International Science and Technology (S&T) Cooperation Program of China (2012DFG22050)
文摘Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta(YRD) region using moderate resolution imaging spectroradiometer(MODIS) time-series data. The normalized difference vegetation index(NDVI) was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.
基金the funding from the National Water Pollution Control and Management Technology Major Projects of China(2012ZX07503-002)the Special Research Funding for the Public Benefits sponsored by Ministry of Environmental Protection of PRC(201309067)
文摘Sampling frequency is an important factor to be considered during the design of a water monitoring network,and the cost-effective selection of possible ways and means for the optimization of sampling frequency is still needed.This paper introduces water pollution index deviation ratio comparison(WPI DRC),a procedure for the optimization of sampling frequency for a routine river water quality monitoring system.Sampling frequency optimized using WPI DRC at monitoring station X5 in the mainstream of Xiangjiang River is compared with that established using the traditional Statistical Algorithm method.The result of comparison indicates that WPI DRC is more feasible than the traditional one.And then,the sampling frequencies for other 16 monitoring stations also have been optimized,and the results show the sampling frequencies of all the stations except that X4 are reduced,and there is no unacceptable difference between water quality evaluation results at 17 stations before and after the optimization.Therefore,it is concluded that WPI DRC is an effective optimization process with operable results,which can be used to fulfill the requirement of practical monitoring work.