Satellite Crop Monitoring System and Yield Assessment

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Satellite Crop Monitoring System and Yield
Assessment
By JWAN M.ALDOSKI
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Geospatial Information Science Research Center
Faculty of Engineering, Department of Civil
Engineering, Jalan UPM ,43400,
Serdang, Selangor, Malaysia
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Presentation Outline 

• Introduction
• Remote Sensing RS Basic for Crop Monitoring
• Problem
• Why Crop Monitoring System
• Crop Monitoring System Requirements
• Fundamental and Resources Requirements
• Budget Requirements
• Methodology
• Examples

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Introduction

• Nowadays agriculture and food security have
  become an serious issue throughout the world. And
  since the population is growing, the importance
  of food production is increasing as well. New
  ways of cropland monitoring and management should
  be introduced in order to meet food demand in the
  future.
• Remote sensing data is a highly recommended by
  many researchers and user as a useful tool for
  cropland monitoring and for an assessment of
  spatial variability of the crop productivity

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Remote sensing basic in crop monitoring
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Problem

• Generally in Iraq, public organizations or
  governments officer provide the crop information
  and statistics. The data provided are available
  only at the end of the season and generally lack
  temporal and synoptic character. In a number of
  countries now, satellite remote sensing and GIS
  technologies have supported crop data collection
  and provide crop information and statistics
  successfully but not in Iraq. Therefor it is
  necessary to build a crop monitoring system in
  Iraq base on remote sensing data (starting the
  system in Ninawa, Iraq ) .

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Objective is Presenting fundamental, criteria,
resources, budget and business plan requirements
and for any organization starting to use remote
sensing for producing crop monitoring system
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Why RS Crop Monitoring System

• It is digital, temporal, and synoptic in
  character and can reach inaccessible areas ,It
  will provide
• Crop area estimation
• Crop monitoring and yields forecasting
• Crop type identification, mapping of cropland
  boundaries
• Monitoring crop growth process
• Provide timely and accurate information on crops
  during the growing season 

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Requirements for using RS in crop monitoring
system

• Fundamental requirements
• Sustainable access to satellite image collections
• Resources and competencies for data collection
  and processing
• Robust geospatial and statistical methodologies
• Comprehensive capacity building programmes
• Collaboration among statistical services and
  mapping agencies
• Interaction with stakeholders
• Budget and business plan

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Resources

• Resources required to start producing crop
  monitoring system
• Qualified manpower examples
• Remote Sensing and GIS analysts are responsible
  to undertake the assignment of image processing
  and construction of Area Frame
• Statisticians are responsible for sample design,
  extrapolation and final estimation
• Image analysts calculates the area based on
  pixel/object based classification
• Field staff are responsible to carry out field
  survey and crop signature collection.
• Hardware / Software
• Input
• Funding

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Resources

• Resources required to start producing crop
  monitoring system
• Qualified manpower examples
• Hardware / Software
• A laboratory (mostly 20,000 sq ft) would be
  required to accommodate the manpower and
  equipment
• Hardware for data processing (workstation/laptop),
  data collection (GPS/smartphone/tablet), input
  and output in digital format (scanner/printer),
  storage and dissemination are required
• Software for Statistical analysis, GIS/RS
  processing, mobile based data collection,
  Computer Assisted Personal Interviewing (CAPI)
  and metadata / data dissemination will be needed.
• Input data
• Funding

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Resources
Modis data

• Resources required to start producing crop
  monitoring system
• Qualified manpower examples
• Hardware / Software
• Input data
• The UN has developed a series of purchase
  agreements with key image providers e.g.
  MacDonald Dettwiler (MDA) (QuickBird, IKONOS,
  WorldView-1, WorldView-2, GeoEye-1, WorldView-3,
  KOMPSAT-2, KOMPASAT-3, ZY-3 and RADARSAT-2) and
  Airbus DS Geo (derived from TerraSAR-X, SPOT 6/7
  and Pleiades)
• In the context of humanitarian actions the image
  sources are available through the International
  Charter for Space and Major Disasters .
• Integration of national agricultural monitoring
  with the FAO/WFP CFSAM assessment missions
  provides technical assistance to crop production
  forecasts.
• Funding

Digital Globe
SAR image example
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Acquisition of Satellite Data

• SPOT-6/7
• Sentinel-1 2
• Landsat 8
• IR imagery

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Resources

• Resources required to start producing crop
  monitoring system
• Qualified manpower examples
• Hardware / Software
• Input data
• Funding
• Integrating remote sensing into crop monitoring
  system will necessitate allocation of appropriate
  levels of funds
• Optimization of imagery acquisition to processing
  and field data collection and validation.
  Potential to share costs with other applications
• Costs of verification using high and very high
  resolution data may reduce the costs of field
  validation
• The cost of hardware and software are mostly one
  time

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Resources

• Work plan
• The acquisition time period of the satellite
  imagery depends upon their phenological stages

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Methodology

• Following techniques have being used
• Satellite image classification
• Satellite based area frame sampling technique
• Regression estimator

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Training Requirements

• Monitoring crops using Remote Sensing and GIS
  requires a range of multi-disciplinary team and
  integrated skill set. Here is a summary of
  required training curricula
• Basic concept of RS, GIS, Statistics and Agronomy
• RS and image processing, classification, analysis
  and reporting
• Land cover classification approaches and database
  development
• Integration of agri-environmental parameters and
  ground based information for yield forecasting
• RS/GIS for Area Sampling Frame
• GPS operating and field data collection for
  enumerators
• Statistical sampling techniques

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Training Requirements

• Trainings should be regularly provided to staff
  by specialized national and international
  organizations
• The number of trainings is to be determined
  according to the requirements/ composition of the
  team.
• E-learning materials should also be developed to
  support the application in crop area and yield
  estimation

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Case examples of implementation
Ethiopia
land cover database
Master sampling frames for agricultural and rural
statistics
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Punjab South Zone broken down into sampling units
(red)
Sampling distribution in Pakistan
Sampling unit from satellite
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Pakistan
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Pakistan
Crop Portal
GLAM Pakistan
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Rice Crop Estimates
Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17 Final Rice Crop Area Estimates(Ha) 2016-17
Province District MAIL MAIL MAIL MAIL IC AF Hybrid
Province District 2012-13 2013-14 2014-15 2015-16 2016-17 2016-17  2016-17
Baghlan   46,005 45,431 32,555 32,196 16,227 17,325 17,842
Kunduz   43,100 86,011 90,310 40,210 26,981 23,314 28,171
Takhar   23,320 32,628 40,523 35,532 15,041 14,238 16,263
Sub Total   112,425 164,070 163,388 107,938 58,249 54,877 62,276
                 
Badakhshan Keshem         1,837 2,425 1,986
Badakhshan   6,000 6,000 5,500 4,850 1,837 2,425  
Balkh Sholgareh         1,361 1,390 1,472
Balkh   10,500 2,100 1,900 2,000 1361 1390  
Nangarhar Shinwar         0 1,468  
Nangarhar Beshud         998 1,468 1,079
Nangarhar Kama         1,064 1,468 1,151
Nangarhar   13,410 8,410 21,958 24,371 2,062 1,468 2,230
Grand Total Grand Total 112,425 164,070 163,388 107,938 63,509 60,160 67,964
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Iraq- Ninawa
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Olive Mapping
Study area and Data
The study area is Bashiqa town in Ninawa .
Data 1- Landsat
Landsat 5 Thematic Mapper Landsat-8 OLI at a
resolution of 30 m of 22-4-2009 and 17-4 2019
respectively
2- Sentinel-2A MSI imagery acquired on 12 April
2019
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L8-OLI 30 m
Google Earth imagery
Sentinel-2 MSI imagery 10 m
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Methodology Test one
Field survey
The objective is to evaluate the potential of
Sentinel imagery to map olive plantations in
Bashiqa using various classification methods
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Olive Map
Test One Results

• K-means
  SVM Quest

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Methodology Test two
Aim is to map out the status of land use/cover
of Bashiqa town in view to detect the changes
that has taken place during the last decade
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Test 2 results
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Conclusions

• Integration of GIS and Remote Sensing will make
  crop monitoring simpler, quicker and more
  accurate
• Satellite imagery and derived products such as
  land cover have will make crop area estimation
  and yield forecast effective and cost efficient
• Implementations such as the Iraq Agriculture
  Information System are extraordinary examples of
  improvement of national ag statistical systems
  through the integration of geospatial
  technologies in traditional approaches and
  methodologies
• This system requires multi-disciplinary
  institution to integrate information from
  satellite remote sensing, GIS, statistics,
  agronomy, agro-meteorology and economics.
• Test 1 SVM had the ideal performance with overall
  accuracy ranging from 71 to 94 and a Kappa
  coefficient from 0.65 to 0.93, depending upon the
  training sample size (ranging from 20 to 500
  pixels per class). The advantage of SVM was more
  obvious when the training sample size was smaller
• Test 2 results indicate that during the last
  decade, olive, vegetation and built-up land have
  been increased by 12.1, 10.6, and 5.7 while
  barren and agriculture have decreased by 18, and
  10.4, respectively.

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Thank You
Any questions?