IGARSS 2011 Vancouver Jul 26th , 2011 Chun-Sik Chae and Joel T. Johnson ElectroScience Laboratory
2856 IGARSS 2011- CHARMS.ppt
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Transcript of 2856 IGARSS 2011- CHARMS.ppt
Zhongxin Chen , Qingbo Zhou, Jia Liu, Limin Wang, Jianqiang Ren, Qing Huang, Hui Deng, Li Zhang, Dandan Li1Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA
2Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 1000813Research Department, Remote Sensing Application Center, MOA, Beijing [email protected] 2011, Vancouver, 24-29 July, 2011
CHARMS - CHINA AGRICULTURAL REMOTE CHARMS - CHINA AGRICULTURAL REMOTE SENSING MONITORING SYSTEMSENSING MONITORING SYSTEM
Outline
• Bcakgrounds• System Structure of CHARMS• System Implementation• System Components
– Crop acreage monitoring– Soil moisture monitoring– Crop growth and yield– Information dissemination
• Conclusions and Perspectives
BackgroundsBackgrounds
3
Global Food SecurityGlobal Food SecurityFood
Security
Climate Change
Global mean temperature increased by 0.7℃ in 20 th century, and increased another 0.1℃ recently
Population
Booming ~ 7billion9 billion in 2050
Land
Decreasing and degradation
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
– ….
China Agriculture Monitoring with 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.
System structure of CHARMSSystem structure of CHARMS
Remotely Sensed Data
Info Inversion Models or Algorithms
Ag-Info Monitored
by RS
Validation Data
Ground In situ Data
Met. Obs.
Ground Truthing
Agro-Info Monitoring Network
Theory of Physics & Agronomy
Expert Knowledge
Info Distribution & Service
Ag. Management & Policy-making
Image Processing
Geom. Corr
Atmos. Corr
Mosaic
Data fusion
……
Auxiliary Information
Other RS Info
Basic Database工作站
多个服务器
服务器
Logic of Crop Monitoring System with Remote Sensing
System Components
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 m
anagem
ent
layerF
unction
layerA
pplication layer
Highlights of the system
• A set of standards or protocols• Workflow-driven machanism• Modular structure• Distributed C/S and B/S hybrid system
System ImplementationSystem Implementation
Organization of CHARMS Activities
In-situ Crop Monitoring Sites
System componentsSystem components
Crop monitoring• Data
– TM, 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
Remote sensing data for 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
accuracycontrol
Cropland map
Monitoring results
for previous year
Crop spatial sampling frame
Landsat TM & Validation in Huabei Plain
2003 , RGB:432 2004 , RGB:432
Zouping, Shandong
WheatBuilt-up
Zhangqiu, Shandon
20062006 20072007
In-situ investigation
Deferential GPS , quadrat size 500*500m2
• 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
Wanning, HainanPaddy Rice
2008 2009
Time Span: Nov 24, 09 – Dec 7, 09Data Source: EOS/MODIS
Growth Monitoring for Winter Wheat
Legend
BetterNormalWorse
Could/ND
Soil Moisture Status of Cropland Time : May 5-23, 2005
Date Source : EOS-MODS
LegendHeavy
ModerateLight
NormalMoistDesertCloudWaterSnow
Frosted
China Agriculture Remote-sensing Monitoring System (CHARMS) -Yield
26
Grassland Productivity in 2009 vs 2008
Grassland Productivity in 2009 vs 5-yr Mean
(a) (b) (c)
Phenology of Winter Wheat
Turning-green(a) 、 Heading(b) 、 Maturity(c)
Phenology of Wheat and Maize vs. Observation
Crop Mapping
Flood in South China, July 2003 (Dongting Hu)
监测时间: 2004 年 3 月 28 日数 据 源: EOS/MODISSand Storm, 2004-3-28
气团中心
越冬作物
无沙尘区域
沙尘区域
云
水体
Snow Harm in Feb 2008
Earthquake Impact on Cropland in Wenchuan 2008
Agro-Information Distribution Calendar
Soil MoistureCrop GrowthCrop AcreageCrop Yield
Conclusion and PerspectivesConclusion and Perspectives
Conclusion and Perspectives
• CHARMS 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
Short-term warning Agricultural Production MonitoringMarket information system
Monitoring Vulnerable GroupsNutrition Surveillance System
Cropping patterns monitoring Crop growth monitoring and yield estimation
Assessment of yield increase potentials Cropping patterns dynamics modeling
Warning System of Food SecurityMedium and long-term warning
Early Warning System on Food Security
Acknowledgements
The research was 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
Thanks for Your Thanks for Your Attention!Attention!