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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Research on the Optimal Thresholds for Crop Start and End of Season Retrieval from Remotely Sensed Time-Series Data Based on Ground Observations
摘要： Crop phenology is of great importance to crop growth monitoring, crop classification and yield prediction. Obtaining accurate phenological information is still difficult due to the complexity of cropping systems. A variety of methods have been utilized in retrieving crop phenology from remotely sensed time-series vegetation index data, among which the so-called threshold method is the mostly used. However, currently the thresholds are set empirically and a validation of the obtained phenological information is often lacking. This paper attempted to investigate the optimal thresholds for retrieving the Start of Season (SOS) and the End of Season (EOS) of different crops from MODIS EVI time-series data by using the dynamic threshold method. The crop growth and development dataset from National Meteorological Information Center of China were used to select analysis samples. The recorded green-up dates or three-leaf dates were used as reference SOS, and the recorded maturity dates were used as reference EOS. Four indicators, Bias, Biassign, Root Mean Square Error(RMSE) and Correlation Coefficient(R) were used to assess the accuracy of the retrieved phenology. Results show that: (1) the often used 20% threshold is not optimum in retrieving crop SOS and EOS. (2) Optimum thresholds are 24% and 48% for retrieving SOS and EOS, respectively. (3) Accuracy assessment results indicate that it is better to set different thresholds in retrieving crop SOS and EOS.
关键词： Dynamic threshold method,Optimal threshold,Remote sensing,Phenology retrieval
更新于2025-09-10 09:29:36
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Events detection and recognition by the fiber vibration system based on power spectrum estimation
摘要： One of the important successes of optical fiber sensor established for the security system is the detection and the recognition of any type of events. The performance parameters (event recognition, event detection position, and time of detection) are unavoidable and describe the validity of any perimeter detection system. An event recognition is any signal detected within the protected area, and it is related to a non-intrusion event and an intrusion event. To achieve the detection and the recognition events at the real time, an effective two-level vibration recognition method and a technique are proposed and presented in this article. The signal characteristics (short-term energy and short-time over-threshold) have been used and compared to the dynamic threshold to judge the type of event. Then the extraction of the power distribution features on the frequency domain through power spectral estimation on the suspected intrusion signal samples is carried out and finally combined with the time-domain characteristics as feature vector through Support Vector Machine to determine the efficiency and effectiveness of the proposed vibration recognition method. The experimental simulation results show that the proposed method is effective and reliable. With collected data, it can detect and recognize the type of event in real time.
关键词： Support Vector Machine,event detection and recognition,power spectral estimation,Optical fiber sensor,dynamic threshold,short-time over-threshold,short-term energy
更新于2025-09-09 09:28:46