Brief review of vegetation phenological information extraction software based on time series remote sensing data
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摘要: 物候是植被生理生态过程与环境变化相互作用的体现,研发基于时间序列遥感数据的植被物候信息提取软件具有现实意义。现有软件主要是国外科研人员结合特定遥感数据发展的,集成的数据平滑重建方法不同,服务的对象也有差异。对现有软件功能和特点的比较分析有助于用户在选用软件时更有针对性,也可为研发植被物候软件提供参考。在简述遥感监测植被物候原理和重建时间序列遥感数据常用数据平滑方法后,文章汇总了多款集成重建方法和物候提取方法于一体的植被物候软件。重点介绍了TIMESAT,SPIRITS和DATimeS软件,比较分析了这些软件的功能特点。最后,结合遥感大数据发展和植被物候监测应用需求的背景,针对发展友好图形用户界面且汉化版的应用软件进行了展望。Abstract: Vegetation phenology reflects the interactions between the physiological and ecological processes of vegetation and environmental changes and thus it is practically significant to research and develop the software used to extract the vegetation phenological information based on time series remote sensing data. The existing pieces of software mainly include those developed by foreign R&D staff based on specific remote sensing data. They integrate different methods for data smoothing and reconstruction and serve different users. The analysis and comparison of the functions and characteristics of the existing pieces of software will assist users to select more targeted software and can also provide references for the R&D of the software for vegetation phenology monitoring. This paper first briefly introduces the monitoring principles of vegetation phenological information using remote sensing data and commonly used data smoothing methods for the reconstruction of time series remote sensing data. Then it summarizes multiple pieces of professional software for vegetation phenology monitoring that integrate the reconstruction methods and phenological information extraction methods. Most especially, it introduces the software TIMESAT, SPIRITS, and DATimeS in detail and compares and analyzes their functions and characteristics. Finally, it puts forward the prospect of developing localization application software with user-friendly graphical user interfaces according to the development of remote sensing big data and the demand for vegetation phenology monitoring.
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Key words:
- residual noise /
- time-series reconstruction /
- TIMESAT /
- SPIRITS /
- machine learning /
- DATimeS /
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