Download to read offline
![Zhongxin Chen , Qingbo Zhou, Jia Liu, Limin Wang, Jianqiang Ren, Qing Huang, Hui Deng, Li Zhang, Dandan Li 1 Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA 2 Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081 3 Research Department, Remote Sensing Application Center, MOA, Beijing 100081 [email_address] IGARSS 2011, Vancouver, 24-29 July, 2011 CHARMS - CHINA AGRICULTURAL REMOTE SENSING MONITORING SYSTEM](https://image.slidesharecdn.com/2856-igarss-2011-charms-110728105432-phpapp02/75/2856-IGARSS-2011-CHARMS-ppt-1-2048.jpg)
More Related Content
Similar to 2856 IGARSS 2011- CHARMS.ppt
2856 IGARSS 2011- CHARMS.ppt
- 1. Zhongxin Chen ,Qingbo Zhou, Jia Liu, Limin Wang, Jianqiang Ren, Qing Huang, Hui Deng, Li Zhang, Dandan Li 1 Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA 2 Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081 3 Research Department, Remote Sensing Application Center, MOA, Beijing 100081 [email_address] IGARSS 2011, Vancouver, 24-29 July, 2011 CHARMS - CHINA AGRICULTURAL REMOTE SENSING MONITORING SYSTEM
- 2. Outline Bcakgrounds SystemStructure of CHARMS System Implementation System Components Crop acreage monitoring Soil moisture monitoring Crop growth and yield Information dissemination Conclusions and Perspectives
- 3.
- 4. 3 Global FoodSecurity Food Security Climate Change Global mean temperature increased by 0.7℃ in 20 th century, and increased another 0.1℃ recently Population Booming ~ 7billion 9 billion in 2050 Land Decreasing and degradation
- 5. Overview Since 1983,monitoring yield of winter wheat in North China Plain (pilot study) Key research projects during each 5-year plan Several (quasi-)operational Crop Remote Sensing Monitoring systems CMA CAS MOA Emerging SSB … .
- 6. China Agriculture Monitoringwith Remote Sensing (CHARMS system in MOA) Since 1998, run every year Operational running in the whole nation Monitoring key crops and Grassland Acreage change Growth Yield & productivity Environment & disasters, etc. Grassland degradation Grass-livestock balance, etc.
- 7. System structure ofCHARMS
- 8. Logic of CropMonitoring System with Remote Sensing
- 9.
- 10. Professional data processing(index, information extration) Basic data handeling （ inqury, subset, merge) Agriculture Remote Sensing Monitoring Data management (input, edit, organize) management tools Database engine kernal data modules Local files Local database Remote database Remote files Data management layer Function layer Application layer
- 11. Highlights of thesystem A set of standards or protocols Workflow-driven machanism Modular structure Distributed C/S and B/S hybrid system
- 12.
- 13. Organization of CHARMSActivities
- 14. In-situ CropMonitoring Sites
- 15.
- 16. Crop monitoring DataTM, CBERS, SPOT, IRS, HJ-1, Aster, Envisat IKONS, QUICKBIRD EOS-MODIS, NOAA-AVHRR, AWiFS Methodology Change detection for acreage Stratified sampling and scaling up method Ground truthing Monitored crops: wheat, maize, rice, soy bean, cotton, canola, sugar-cane
- 17. Remote sensing datafor 2 consecutive years Common areas Subsetting for basic monitoring units Omit non-cropland Non-supervised classification Supervisved classification mannual modification in-situ samples crop acreage change accuracy control Cropland map Monitoring results for previous year Crop spatial sampling frame
- 18. Landsat TM &Validation in Huabei Plain
- 19. 2003 ， RGB:4322004 ， RGB:432 Zouping, Shandong Wheat Built-up
- 20.
- 21. In-situ investigation DeferentialGPS ， quadrat size 500*500m 2 Winter wheat ： 2299 Maize ： 1024 Spring Wheat ： 273 Soy Bean ： 431 Cotton ： 694 Early Paddy Rice ： 476 Late Paddy Rice ： 960 Total sampled quadrats (in 2009): 6157
- 22. Paddy Rice 20082009 Wanning, Hainan
- 23. Time Span: Nov24, 09 – Dec 7, 09 Data Source: EOS/MODIS Growth Monitoring for Winter Wheat Legend Better Normal Worse Could/ND
- 24. Soil Moisture Statusof Cropland Time ： May 5-23, 2005 Date Source : EOS-MODS Legend Heavy Moderate Light Normal Moist Desert Cloud Water Snow Frosted
- 25. China Agriculture Remote-sensingMonitoring System (CHARMS) -Yield
- 26. Grassland Productivity in2009 vs 2008 Grassland Productivity in 2009 vs 5-yr Mean
- 27. Phenology of WinterWheat Turning-green(a) 、 Heading(b) 、 Maturity(c)
- 28. Phenology of Wheatand Maize vs. Observation
- 29.
- 30. Flood in SouthChina, July 2003 (Dongting Hu)
- 31. 监测时间： 2004 年3 月 28 日 数 据 源： EOS/MODIS Sand Storm, 2004-3-28 气团中心 越冬作物 无沙尘区域 沙尘区域 云 水体
- 32. Snow Harm inFeb 2008
- 33. Earthquake Impact onCropland in Wenchuan 2008
- 34. Agro-Information Distribution CalendarSoil Moisture Crop Growth Crop Acreage Crop Yield
- 35.
- 36. Conclusion and PerspectivesCHARMS is an extendable remote sensing agriculture monitoring system Further new functions or components can be added to the system upon new demands Future system will not only focus on agriculture monitoring from remote sensing, but also contribute more in decision making in agricultural management and food security
- 37. Early Warning Systemon Food Security Short-term warning Agricultural Production Monitoring Market information system Monitoring Vulnerable Groups Nutrition Surveillance System Cropping patterns monitoring Crop growth monitoring and yield estimation Assessment of yield increase potentials Cropping patterns dynamics modeling Warning System of Food Security Medium and long-term warning
- 38. Acknowledgements The researchwas supported by the NSFC project (no. 40930101), and MOST the international corporation project (2010DFB10030), MOA 948 program project(no. 2010-S2, and 2009-Z31), and EU FP-7 E-Agri project with contract no. 270351. Thanks Pei Zhiyuan, Xu Bin, Yang Peng, Wu Wenbin for providing related information
- 39. Thanks for YourAttention!