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Remote Sensing for
agricultural statistics
Main uses and cost-effectiveness
in developing countries
Pietro Gennari,
Food and Agriculture Organization of the United Nations
RS and Big Data
• RS is one of the key component of Big Data
• RS data is big = volume criterion
• RS use large-scale analytics to extract information and
respond to scientific questions of global nature
• RS is not new, but it is continuing to grow (opportunities
provided by the new generations of satellites)
• Improvement of agricultural statistics among the initial
applications, since the launch of Landsat in early ‘70s.
• Research programme undertaken by the Global Strategy to
Improve Agriculture and Rural Statistics aimed at verifying
the conditions for cost-effective use in developing countries
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Main uses of RS data for agricultural statistics
• Monitoring Land cover/Land use
• Area Frame construction
• Support to field work of censuses/surveys
• Crop acreage estimation
• Crop yields monitoring/forecasting
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Land cover mapping
• Daily availability of low-resolution imagery (up to 30 m)
allow the derivation of cropland masks at world-level
• Cropland mapping algorithms
• Land cover/use classification
• Example: MDG indicator of forest cover
Area frame construction
Design level:
• Definition of the physical boundaries of PSU’s and SSU’s from photointerpretation of Landsat imagery.
Stratification level:
• Distinction between agricultural and non agricultural land avoiding to
select PSU’s located where the probability of finding a crop are close
to zero => Increase in relative efficiency by 50%, at almost zero cost
• Reduction of the sampling variance within each stratum through
photo interpretation or automatic classification of imagery into land
cover classes
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Crop acreage estimation
• Pixel counting: nearly unique example of direct operational use of RS
for crop area estimation (USDA/NASS, the Indian Mahalanobis
National Crop Forecasts Centre, Statistics Canada)
• Calibration methods: reducing the sampling error obtained from a
survey by integrating auxiliary information (regression estimator) or
calibration of RS data on the basis of ground-truth data
• Example: joint work FAO-USGS/NASA to increase the reliability of
individual crop area estimates. Four information products: 1) cropland
areas; 2) cropping intensities; 3) irrigated versus rainfed cropland; 4)
area by crop types
Tanzania as pilot country to test and roll-out the new methodology
Products and statistics disseminated through USGS & FAO platforms
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Crop yield monitoring/forecasting
• Mechanistic models: use mechanisms of plant and soil
processes to simulate the growth of specific crops. Involve
fairly detailed and computation-intensive simulations
• Functional models: Simplified simulation of complex
processes
• Statistical models: based on yield info for large areas;
combine a secular trend and variations due to weather
conditions (Remote Sensing)
• Many national initiatives: China, India, Morocco, Pakistan,
Mozambique, Senegal, Tunisia, etc.
• Several intern. initiatives: FEWSNET, GIEWS, MARS, VAM
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Cost-effectiveness of RS in agricultural statistics
• Recent FAO study on 31 developing & transition countries
• Conclusions: use of RS for agricultural statistics can be
cost-effective (given current conditions in terms of data
availability, access, price and preprocessing), but it
depends on the use that is foreseen.
• Main uses of RS and photo-interpretation in developing
countries: design and optimization of sampling frames;
land use mapping
• Regression analysis integrating ground survey and image
classification results is instead rarely used
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For Land Cover Maps
• Stratification of an agricultural sampling frame, alone,
cannot justify expenses for a land cover classification
(Example of North Sudan: 240 man months necessary for
photo interpretation into 7 main land classes with an area
of 1.9 M. km2).
• The cost of photo-interpretation of LU map at 1/25,000
was estimated at 3.5 $/km2 in Morocco (Spot)
• The expected stratification efficiency is in general lower in
developing countries (less intensive cropping) compared
to more intensive agriculture regions
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For Area Frame
• Relative efficiency depends on the complexity of the
landscape and the crops diversity
• Analysis done for 3 major cereals in Morocco (soft wheat,
durum wheat and barley), shows that the relative efficiency
varies widely from province to province (from 1.4 to 14)
• At the national level and for the most important crops, the
relative efficiency gain is of 300%.
• The variance reduction is higher the larger the percentage
of the area excluded from the sample due to the use of land
use maps produced with satellite imageries
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Conditions for the effective use of RS
• The effective use of RS data is quite demanding on NSOs
• Sustainable access to satellite image collections
• Photo-interpretation capacity, image processing and geographic
information systems (GIS) software applications
• Robust geospatial and statistical methodologies
• Capacity to combine ground reference with RS data
• Highly trained, multi-disciplinary and motivated team with the
capacity to produce accurate estimates and defend them
• Unequal capacity of countries to harness the potential of RS data
• A comprehensive programme of technical assistance on the use of
RS for agricultural statistics is needed to bridge this knowledge gap
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Thank you for your attention