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E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Remote Sensing of Crop Acreage and Crop Mapping in the E-Agri Project
Chen ZhongxinChen Zhongxin
Institute of Agricultural Resources and Regional PlanningInstitute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesChinese Academy of Agricultural Sciences
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Outline
• I. The Objectives for WP5
• II. Main Tasks in WP5
• III. Research Plan and Activities
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
• Adapt and design in-situ segment sampling method set up crop area extrapolation models for the study areas (sampling and scaling-up)
• Select the optimal remote sensing classification options for crop area in spectral and temporal terms
• Generate crop area estimates with in-situ sampling and remote sensing
• Analyze errors (sampling and non-sampling) and costs for crop area monitoring with remote sensing
• Demonstrate the selected technology in the study areas
I. The Objectives for WP5
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
II. Main Tasks in WP5
• To adapt and design segment sampling method• To establish the crop area spatial extrapolation model for
the study area• To execute the segment sampling and track sampling in
the study areas
• To collect the remote sensing data .• To pre-process and classify the satellite images• To select the best classification option in both spectral and
temporal terms• To generate the area estimates using the ground sampling
dataset
WP51
WP53
WP52
WP54
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
• To generate the area estimate using best classification option
• To generate the area estimate combining regression and remote sensing
• Analysis of sampling and non-sampling errors• Analysis of mapping costs
• to evaluate what is the impact on the mapping accuracy when no or very limited ground survey (for example based on the track sampling) is conducted.
II. Main Tasks in WP5
WP52
WP54
WP55
WP56
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Participating Institutions
• VITO (WP51,52, 53, 54, 55,56)
• CAAS (WP51,52)
• AIFER (WP51, 52)
• INRA (WP53, 54)
• DRSRS (WP56)
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
III. Preliminary Research Plan
• Data Preparation and Collection
• In-situ sampling and extrapolation
• Remote Sensing Classification of Crop
• Error analysis
• Generate crop acreage estimates from in-situ and remote sensing data
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Data collection and preparation
• Background data– GIS maps (land use, administrative, road, soil, vegetation,
contour, crop, geology, geomorphology, hydrology)– Socio- economic statistical data for 10 yr– Crop calendar and phenology– Climate data
• In-situ data: field segments and tracks• Remote sensing imagery
– Time series of LR images– HR images
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Data collection and preparation
• Remote sensing imagery– Time series of LR images: MODIS, AVHRR,
AWiFS, VEGETATION, – HR images: TM, ALOS, SPOT, IRS, HJ-1– VHR images: QB, IKONOS, Aerial
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
平原 丘陵 山区平原
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Crop Mapping for Winter Wheat in Anhui, 2009
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
In-situ data from field segments
• 50 samples @ 1km x 1km
• With 25 km intervals
• Winter wheat and maize
• Existing samples 500m x 500m
• Study region size 40000km2?
E-Agri Project Kick-off Meeting, Mol, 24-25, 201113Technical flow of spatial sampling scheme
Construction of survey unit
Data preparation (cropland plots and Agricultural Census)
Process of cropland plots
Design of spatial sampling scheme
Simple Randon
sampling
Two stage sampling
Based on Agricultura
l Census and landuse
data
Regular grid as PSU
PPS
Samples selection
Field survey
Population inference
E-Agri Project Kick-off Meeting, Mol, 24-25, 201114
Samples Spatial distribution in Faku county Samples Spatial distribution in Fengtai county
Samples Spatial distribution in Dehui County
E-Agri Project Kick-off Meeting, Mol, 24-25, 201115
Fig 4.2 Distribution of sample village Fig 4.3 Distribution of sample plots in sample village
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
In-situ Segments2008 2009
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
In-situ sampling and extrapolation
• Selection of sampling frame• spatial vs. non-spatial
• Sampling methods:– Random– Systematic– Stratification
• Remote sensing sampling• Extrapolation (scaling-up)– Relevant to sampling method– Regression with remote sensed info
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Remote Sensing Classification of Crop
• Hard classification vs. soft classification– Hard for HR images– Soft for LR time-series data with sub-pixel
classification
• Automation vs. visual interpretation
• Supervised vs. unsupervised classification
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
The sub-pixel classification result
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
ALOS : 10m,2009-3-20
QuickBird : 0.61m,2009-3-25
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Error analysis
• Sampling error
• Non-sampling error
• Cost analysis
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Generate crop acreage estimates
• From in-situ segment and track sampling– Get crop acreage estimate based on statistics
• HR remote sensing info– Direct pixel count for full coverage– Regression if sampled
• LR remote sensing– Regression with HR or in-situ samples– Sub-pixel classification
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Activities
• Define the research regions (C, M, K)• Background data collection• Remote Sensing data collection/ processing• Field survey (2-3 times)• Sampling and extrapolation model• Remote Sensing classification• Error analysis• Generate crop estimate• WP5.6?
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Define the research regions (C, M, K)
• China – Huaibei, Anhui
• Moroco - ?
• ? Kenya?
• Time: asap (1 month? Before April 30)?
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Background data collection (research regions)
– Socio- economic statistical data for 2001-10– Climate data for 2001-10– GIS maps (land use, administrative, road, soil,
vegetation, contour, crop, geology, geomorphology, hydrology)
– Crop calendar and phenology
• Time: 6 months (before September 30)
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Remote Sensing data collection
– Time series of LR images: MODIS, AVHRR, AWiFS, VEGETATION,
– HR images: TM, ALOS, SPOT, IRS, HJ-1– VHR images: QB, IKONOS, Aerial
• Time:– 3 months for first datasets– progressively
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Remote Sensing Image Processing
– Geometric correction– Radiometric correction– Time series preparation– Derived parameters (VIs, Ts, etc.)– Phenology
• Time:
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Field surveys
• 2-3 times for winter wheat and maize
• 50 samples 1kmx1km (500m x 500m?)
• Track servey
• Time: April, August of 2011, 12 and 13 for China– For Moroco?
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
• Sampling and extrapolation model
• Remote Sensing classification
• Error analysis
• Generate crop estimate
• WP5.6?
E-Agri Project Kick-off Meeting, Mol, 24-25, 2011
Thanks for Your Attentions!