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Technical Report on
Cost – Effectiveness of Remote Sensing for
Agricultural Statistics in Developing and Emerging
Economies
Technical Report Series GO-09-2015
December 2015
Technical Report on
Cost-Effectiveness of Remote Sensing for Agricultural
Statistics in Developing and Emerging Economies
Table of contents
Figures……………………………………………………………………………………………………………… 4 Tables………………………………………………………………………………………………………………. 5 Acronyms and Abbreviations……………………………………………………………………………. 6 Preface…………………………………………………………………………………………………………….. 10 Executive Summary………………………………………………………………………………………….. 12 Acknowledgments……………………………………………………………………………………………. 16 1. Introduction…………………………………………………………………………………………………. 17 2. Application Domains…………………………………………………………………………………….. 18
2.1. Land cover mapping…………………………………………………………………………. 18 2.2. Census and survey frame construction……………………………………………… 20 2.3. Production of field survey documents………………………………………………. 21 2.4. Estimation of crop areas…………………………………………………………………… 21 2.5. Forecasting and monitoring crop yields……………………………………………. 23 3. Who does what?............................................................................................. 24
3.1. International investments…………………………………………………………………. 24 3.2. National remote sensing activities in developing and transition Countries…………………………………………………………………………………………..
27
4. The Cost-Efficiency Literature……………………………………………………………………….. 47 5. Cost case Studies………………………………………………………………………………………….. 56
5.1. Haiti: Point Area Frame Sampling……………………………………………………... 56 5.2. Morocco: Area Frame Sampling………………………………………………………… 68 5.3. China: Area Frame Sampling and Regression Analysis………………………. 74 5.4. India: Area Frame Sampling and Pixel Counting………………………………… 84
6. What’s next?................................................................................................... 94 6.1. Imagery access: the 2015 situation…………………………………………………… 94 6.2. The immediate future of imagery and software………………………………… 99 6.3. The use of drones for field surveys: Malawi and China……………………... 100
References………………………………………………………………………………………………………… 104
4
Figures
Figure 1 - Haiti, RENOP Land – Use map……………………………………………………………….. 58 Figure 2 - Comparison of the standardized sampling variances: a) Haiti survey, b) French TerUti survey…………………………………………………………………………
64
Figure 3 - Stratification Efficiency versus proportion of Non- Sampled Area in a Province……………………………………………………………………………………………….
71
Figure 4 - Administrative Divisions of the People’s republic of China………………….. 75 Figure 5 - China, relation between wheat CVs and areas at county level in Anhui province…………………………………………………………………………………….
78
Figure 6 - China, relation between middle rice CVs and areas at county level in Anhui province…………………………………………………………………………………….
79
Figure 7 - CV as a Function of Rice Crop Area, by State……………………………………….. 89
5
Tables
Table 1 - Main uses of Remote Sensing in the 31 countries analyzed…………………… 27 Table 2 - Haiti, Point Area Frame Land Covers Percentages…………………………………. 57 Table 3 - Haiti, Stratification plan…………………………………………………………………………. 58 Table 4 - Haiti, Sampling Plan for the 2013 growing Season…………………………………. 60 Table 5 - Haiti, Estimated Crop Areas (2013, first growing season)………………………. 63 Table 6 - Sample Allocation by Stratum………………………………………………………………… 66 Table 7 - Variance Gains at the Department and National Levels…………………………. 66 Table 8 - Morocco Estimated Crop Areas……………………………………………………………… 70 Table 9 - Morocco, stratification relative efficiencies at national level…………………. 71 Table 10 - Morocco, stratification relative efficiencies at province level……………… 73 Table 11 - Area Frame Surveys in China……………………………………………………………….. 76 Table 12 - China, Official Statistics for 2013 in Area Frame Provinces…………………… 77 Table 13 - China, design level crop areas in Anhui province…………………………………. 78 Table 14 - Comparison of ER Calculation Methods……………………………………………….. 80 Table 15 - China Efficiencies at the County Level………………………………………………….. 81 Table 16 - India Annual Forecasts based on Remote Sensing……………………………….. 85 Table 17 - Rice Sampling Plan, CV Stratification Efficiency……………………………………. 88 Table 18 – Punjabi State LISS III Data (24 Sept 2014)……………………………………………. Gujarat State Landsat Data (31 Dec 2014)……………………………………………
90 91
6
Acronyms and Abbreviations
AAIC Agricultural Assessments International Corp. ADB Asian Development Bank AFDB African Development Bank AF Area Frame AFSIS Asian Network of Country Agricultural Statisticians AGHRYMET Centre Regional de Formation et d'Application en Agrométéorologie et
Hydrologie Opérationnelle AGRICAB Framework for Enhancing Earth Observation Capacity for Agriculture
and Forest Management in Africa AGRIMONIS Tarbil Integrated Agricultural Monitoring System ALOS Advanced Land Observing Satellite AMIS Agricultural Market Information System ASPRS American Society of Photogrammetry and Remote Sensing AVHRR Advanced Very High Resolution Radiometer AWIFS Advanced Wide Field Sensor BAS Bureau of Agricultural Statistics, Philippines BELSPO Belgian Scientific Policy BFAP Bureau for Food and Agricultural Policy, South Africa BIOMA Biophysical Models Applications BPS Badan Pusat Statistik, Indonesia CAAS Chinese Academy of Agricultural Sciences CAERS Cropland Acreages Estimation by using Remote Sensing and Sample
Survey CBA Cost/Benefit Analysis CCD Charge-Couple Device CDL Crop Data Layer CGMS Crop Growth Monitoring System CHARMS Chinese Agricultural Remote Sensing Monitoring System CIMMYT Centro Internacional de Mejoramiento de Maíz y Trigo CNDVI Corine Normalized Difference Vegetation Index CNIGS Centre National d’Information Geo-Spatiale, Haiti CNT Centre National de la Cartographie et de la Télédetection, Tunisia CONAB Companhia Nacional de Abastecimento, Brazil CORINE Coordination of Information on the Environment CSA Central Statistical Agency, Ethiopia CSE Centre de Suivi Ecologique, Senegal CSIR Council of Scientific and Industrial Research, India CSIRO Commonwealth Scientific and Industrial Research Organization CV Coefficient of Variation CVM Contingent Valuation Method DAFF Department of Agriculture, Forestry and Fisheries, South Africa DAPS Direction de l'Analyse de la Prévision et des Statistiques, Senegal
7
DGEDA Direction Générale des Etudes et du Développement Agricole, Tunisia DIMPE Direction de Metodología y Producción Estadística, Colombia DMC Disaster Satellite Monitoring DMN Direction de la Météorologie Nationale, Morocco DRSRS Department of Resource Surveys and Remote Sensing, Kenya DSNA National Directorate for Agrarian Services, Mozambique DSS Direction de la Stratégie et des Statistiques, Morocco EC European Commission EC-FED EC Fond Européen de Développement EMPRABA Empresa Brasiliera de Pesquisa Agropecuària ERMEX-NG Estación de Recepción México Nueva Generación ESA European Space Agency ESA LC-CCI European Space Agency Land Cover–Climate Change Initiative EUMETSAT European Organization for the Exploitation of Meteorological
Satellites EUSI European Space Imaging FAO Food and Agriculture Organization of the United Nations FAO-GIEWS FAO Global Information and Early Warning System on Food and
Agriculture FAORAP FAO Regional Office for Asia and the Pacific FAS Foreign Agriculture Service, USDA FASAL Forecasting Agricultural Outputs using Space, Agro-Meteorology and
Land-Based Observations FEWS-NET Famine Early Warning Systems Network Geoglam Global Agricultural Geo-Monitoring GeoSAS GeoSAS Consulting Service PLC, Ethiopia GEOSS Group on Earth Observation System of Systems GISTDA Geo-Informatics and Space Technology Development Agency, Thailand GMES Global Monitoring for Environment and Security, European Union GMFS Global Monitoring for Food Security; ESA project GPS Global Positioning System GSD Ground Sampling Distance IBGE Instituto Brasiliero de Geografia e Estatística ICALRD Indonesian Centre for Agricultural Land Resources Research and
Development ICRISAT International Crops Research Institute for the Semi-Arid Tropics IGN Institut Géographique National, France ILRI International Livestock Research Institute IKI Russian Space Research Institute, Russia INAM Instituto Nacional de Meteorologia, Mozambique INE Instituto Nacional de Estadística, Guatemala INEI Instituto Nacional de Estadística e Informática, Peru INPE Instituto Nacional de Pesquisas Espacias, Brazil INRA Institut National de la Recherche Agronomique, Morocco IRRI International Rice Research Institute ISPRS International Society for Photogrammetry and Remote Sensing ISRO Indian Space Research Organization
8
ISTAT Istituto Nazionale di Statistica, Italy ITA Consorzio Italiano per il Telerilevamento dell’Ambiente, Italy ITC Faculty of Geo-Information Science and Earth Observation, University
of Twente, Netherlands JFPR Japan Fund for Poverty Reduction JAXA Japan Aerospace Exploration Agency JECAM Geoglam Joint Experiment for Crop Assessment and Monitoring JFPR Japan Fund for Poverty Reduction JPL Jet Propulsion Laboratory, NASA JRC Joint Research Centre, European Commission JRC-MARS JRC Monitoring Agricultural Resources unit KKU Khon Kaen University LF List Frame LISS Linear Imaging Self Scanner LPSA Levantamento Sistemàtico de Produçào Agricola, Brazil LUCAS Land Use/Land Cover Area Frame Survey MAGyP Ministerio Agricultura, Ganadería y Pesca, Argentina MARNDR Ministère de l’Agriculture, des Ressources Naturelles et du
Développement, Haiti MARS Monitoring Agricultural Resources, EC MERIS Medium Resolution Imaging Spectrometer MF Multiple Frame MNCFC Mahalanobis National Crop Forecast Centre, India MODIS Moderate Resolution Imaging Spectroradiometer MS Multispectral Imagery NASA National Aeronautics and Space Administration NASS National Agricultural Statistics Service, USA NBS National Bureau of Statistics, China NDVI Normalized Difference Vegetation Index NGCC National Geomatics Centre of China NISR National Institute of Statistics, Rwanda NRCS Natural Resources Conservation Service, USA OSS Observatoire du Sahara et du Sahel PAN Panchromatic Imagery PICES Producer Independent Crop Estimates System (South Africa) PITDD Programme d'Informations Territoriales pour le Développement
Durable, Haiti PPS Probability Proportional to Size PREVS Pesquisa de Previsão e Acompanhamento de Safras, Brazil PRISM Philippine Rice Information System PROBAV Project for Onboard Autonomy (satellite) – Vegetation PSU Primary Sampling Unit RADI Institute of Remote Sensing and Digital Earth, China RCMRD Regional Centre for Mapping Resources for Development, Kenya RENOP Référentiel National d’Observations Ponctuelles, Haiti RESTEC Remote Sensing Technology Center of Japan RGA Recensement Général de l'Agriculture, France
9
RIICE Remote Sensing-Based Information and Insurance for Crops in Emerging Economies
RISAT Radar Imaging Satellite RMA Risk Management Agency, USDA RS Remote Sensing SAC-ISRO Space Applications Centre – Indian Space Research Organization SAGARPA Segretería de Agricultura, Ganadería, Desarollo Rural, Pesca y
Alimentación, Mexico SANSA South African National Space Agency SAR Synthetic Aperture Radar SDSA Sous-Direction des Statistiques Agricoles, Tunisia SEDENA Secretaría de la Defensa Nacional, Mexico SIAP Servicio de Información Agroalimentaria y Pesquera, Mexico SPOT Satellite pour l’Observation de la Terre SRS Simple Random Sampling SSU Secondary Sampling Unit STARS Spurring a Technology for Agriculture through Remote Sensing SUPARCO Pakistan Space and Upper Atmosphere Research Commission Tarbil Tarımsal İzleme ve Bilgi Sistemleri, Turkey THEOS Thailand Earth Observation Satellite TNAU Tamil Nadu Agricultural University UBA University of Buenos Aires UCL Université catholique de Louvain UEM Universidade Eduardo Mondlane UMD-GEOSS University of Maryland – Global Earth Observation System of Systems UNEP-DEWA United Nations Environment Programme – Division of Early Warning
and Assessment UNSTAT United Nations Statistics Division UPM Universidad Politécnica de Madrid USAID United States Agency for International Development USDA United States Department of Agriculture USGS United States Geological Survey VEGETATION Part of SPOT System VOI Value of Information WAOB World Agriculture Outlook Board (USDA) WASDE World Agricultural Supply and Demand Estimates (USDA) WFP-VAM World Food Programme – Vulnerability Analysis and Mapping
10
Preface
This Technical Report on Cost-Effectiveness of Remote Sensing for
Agricultural Statistics in Developing and Emerging Economies was prepared in
the framework of the Global Strategy to Improve Agricultural and Rural
Statistics. The Global Strategy is an initiative that was endorsed in 2010 by the
United Nations Statistical Commission. It provides a framework and a blueprint
to meet current and emerging data requirements and the needs of policy makers
and other data users. Its goal is to contribute to achieving greater food security,
reduced food price volatility, higher incomes and greater well-being for rural
populations, through evidence-based policies. The Global Strategy consists of 3
pillars: (1) establishing a minimum set of core data; (2) integrating agriculture
in National Statistical Systems (NSS); and (3) fostering statistical systems’
sustainability, through governance and statistical capacity building.
The Action Plan to Implement the Global Strategy includes an important
Research programme, to address methodological issues for improving the
quality of agricultural and rural statistics. The envisaged outcome of the
Research Programme consists in scientifically sound and cost-effective methods
that will be used as inputs in preparing practical guidelines for use by country
statisticians, training institutions, consultants, etc.
To enable countries and partners to benefit, at an early stage, from results of the
Research activities, it was decided to establish a Technical Reports Series, to
allow widespread dissemination of the technical reports and advanced draft
guidelines and handbooks available. This would also enable countries to
provide earlier and more feedback on the papers.
The Technical Reports and draft guidelines and handbooks published in this
Technical Report Series are prepared by Senior Consultants and Experts, and
reviewed by the Scientific Advisory Committee (SAC) of the Global Strategy,
the Research Coordinator at the Global Office and other independent Senior
Experts. For some of the research topics, field tests are organized before final
results can be included in the relevant guidelines and handbooks.
The main purpose of this Technical Report on Cost-Effectiveness of Remote
Sensing for Agricultural Statistics in Developing and Emerging Economies is to
carry out a cost-efficiency analysis on the use of remote sensing through a
literature review and the analysis of case studies in a series of 31 developing
11
and emerging countries. In particular, the report reviews the main uses of
remote sensing such as frame construction, stratification, production of survey
documents, crop area estimation, forecasting and monitoring of crop yields. The
report also provides an analysis of the current situation of imagery access and
future developments in the sector.
12
Executive Summary
The use of Remote Sensing for agricultural statistics began in the 1970s, with
the launch of the Landsat MSS satellite. At that time, NASA (in the United
States) as well as other space agencies, such as ISRO (India) launched research
programs aiming to use remote sensing for agricultural statistics. Later, many
other space centres became active (Brazil, Canada, China, EU, France, Russia).
Today, approximately 200 earth-observation satellites are operational.
High-resolution optical and radar data are now readily available from these
satellites. The main constraints limiting their use are: i) cost – very high
resolution imagery costs between US$20 and US$40 per km2; ii) data size – the
terabyte is the storage unit when working at country level; and iii) the
technological limitations upon using GIS and of image-processing software.
The current policy of the National Aeronautics and Space Administration
(NASA) and the European Space Agency (ESA) is to make imagery from the
Landsat 8 and Sentinels satellites available free of charge and downloadable
within 24 hours as processed products such as vegetation indexes, soil moisture
and land-cover maps. This opens the way to innovative uses, especially in
developing countries.
It is worth noting that the private sector is also now involved in the field of
satellite remote sensing: government-supported companies such as
DigitalGlobe deliver imagery, and start-ups such as Skybox, Planetlab and
Satellogic are developing micro-satellites, of which about 400 will be launched
in 2015 and 2016. The question of the cost of high-resolution imagery from
micro-satellites is not yet settled.
The use of remote sensing for agricultural statistics has various facets.
In censuses and surveys, the first stage consists in elaborating a population
frame. Satellite imagery is of the utmost value for this purpose, as provides a s
table reference when elaborating the population frame; in particular, it may be
used to subdivide areas of interest into enumeration areas in which list frames
of holdings are defined, and it helps with the definition of area frames
composed of primary and secondary sampling units, which are readily
identifiable in the digital imagery. Remote sensing also supports new
approaches to population stratification: it enables, for example, the use of the
13
crop intensity classes derived from archived imagery, which in association with
various sampling fractions can significantly reduce sampling variances.
Whatever the census or survey approach, imagery will enhance the efficiency of
field surveyors by enabling optimized displacements, facilitating the location of
the various survey elements and providing more precise information for farmers
as to the fields surveyed.
The classified imagery can deliver crop data layer maps, which are issued
annually before harvests to provide an initial estimation of current crop areas
and which also provide information for private-sector organizations that will
enable them to improve the agricultural business. When sufficiently reliable, the
imagery is merged with ground survey data in the estimation process.
Calibration estimators will usually help to reduce sampling variance.
Low-resolution imagery, such as that available from MODIS, AVHRR and
PROBA-V satellites, can provide seamless coverage of the target area. This
imagery can be merged with crop-yield models and meteorological information
to give pre-harvest crop yield forecasts, which in turn enhances the possibilities
for sound management of national food security.
The literature on cost-efficiency is unbalanced with regard to agricultural
cost/benefit publications and the image value analyses carried out by space
agencies.
Information on the costs relating to agriculture applications remains scarce. In
precision agriculture, the maximum acceptable cost for farmers is estimated at
US$10/ha. In the context of statistical surveys, the Space and Upper
Atmosphere Research Commission (SUPARCO) project in Pakistan, however,
has reduced survey costs from US$7 million to US$300,000 and staff from
3,500 to 18 by moving from classical list frame surveys to area frame surveys
using satellite imagery.
Image value analyses are abundant, and usually attribute high value to Landsat
data for agriculture: remote sensing applications in the state of Iowa are valued
at US$858 million per year; the value of Landsat imagery in the world
agricultural supply and demand estimates is thought to be US$3 million; and
USDA Crop Data Layer at US$4 million. The recommended way to evaluate
the efficiency of remote sensing is the derivation of stratification or regression
relative efficiencies, expressed as ratios of variances. These are discussed in the
four case studies in Chapter 5.
14
To select case studies for cost-efficiency analysis, 31 developing or emerging
countries were reviewed in terms of their uses of remote sensing.
Stratification based on remote sensing – land cover mapping and point screen
photo-interpretation – was encountered in Argentina, Brazil, China, Ethiopia,
Guatemala, Haiti, India, Kenya, Malawi, Morocco, Mozambique, Nigeria,
Pakistan, Peru, Rwanda, Senegal and South Africa.
Crop area estimation based on remote sensing – regression analysis and pixel
counting – was being tested in Argentina, Bangladesh, Brazil, China, India,
Kenya, Malawi, Mexico, Morocco, Mozambique, Pakistan, the Philippines,
Russia, Senegal, South Africa, Thailand, Tunisia and Turkey.
Crop yield forecasts were made in Argentina, Brazil, China, India, Kenya,
Morocco, Russia, Senegal, Thailand, Tunisia and Turkey.
Haiti, Morocco, China and India were selected for case studies.
In Haiti, the point frame survey by the Centre National de l’Information Géo-
Spatiale (CNIGS) was analysed. The cost of stratification reflected increased
field survey costs of 3 percent, but decreases in variances of as much as
50 percent at the regional level were obtained.
In Morocco, the stratification based on land-cover maps of 66,000 km2 derived
from expensive Satellite Pour l’Observation de la Terre imagery increased
annual survey costs by 30 percent. However, in view of the efficiencies
obtained, the investment is worthwhile.
In China, remotely sensed stratification covered 1.65 million km2. The cost
increase was only 3 percent, but in Anhui province stratification relative
efficiencies of 2.8 for rice and 1.4 for corn were obtained.
In India, radar and optical imagery is used to monitor 90 percent of the
production of the eight major crops. A stratification efficiency for rice of
between 1.2 and 3.3 was achieved, and bias induced by pixel counting could be
evaluated.
Current conditions of data availability, access, price and pre-processing enable
an efficient use of remote sensing for agricultural statistics. The main use –
regression analysis integrating ground surveys and image classification – is
rarely applied in statistical processes in the countries studied. Instead, it was
15
noted that the use of remote sensing for land-use mapping and point photo-
interpretation supporting frame optimization was widespread; the case studies
also showed that these uses had resulted in improved cost efficiency.
In view of current technical progress, the future of remote sensing in
agricultural statistics looks bright – though care is needed in applying it in areas
such as sub-Saharan Africa, where the small size of the fields, crop mixtures
and heterogeneity of crop phenology are common limiting factors.
16
Acknowledgments
This report was prepared by Jacques Delincé (FAO) and profits from studies
published by the Global Strategy Office –www.gsars.org/category/publications/
– and from support provided by the authorities of the countries where the case
studies were carried out.
The work and support of a large number of experts is warmly acknowledged:
Aguilar, J. (SAGARPA)
Ambrosio, L. Iglesias, L. (UPM)
Arrach, R. and Kamili M. (DSS)
Astrand, P., Games, M., Gallego, J.,
Lemoine, G., Léo, O., Kerdiles, H. and
Rembold, F. (JRC)
Bartalev, S. (IKI)
Bayma-Silva, G. (Embrapa)
Berk, B. (Tarbil)
Beukers, R. and Collette, A. (DAFF)
Boubée, P. (Airbus D.S.)
Brown, M. and Doorn, B. (NASA)
Bolliger, F. (FAO)
Becker, I. (UMD-GEOSS)
Bydekerke, L. (Eumetsat)
Chen, Z. (CAAS)
Conde, M.C. (UBA)
Craig, M., Hanuschak, G. and Vogel, F.
(formerly NASS)
Defourny P. (UCL)
Du Preez, E. (SiQ Ltd.)
Ferreira, F. (GeoterraImage)
Genovese, G. and Massart, M. (EC)
Giovacchini, A. (ITA)
Hardjo, H. (BPS)
Imala, V. (DRSRS)
Harris, M., Johnson, D., Miller,
M. & Mueller, R. (NASS)
Justice, C. (UMD)
Latham, J. (FAO)
Laur, H. (ESA)
Maligalig, D.S. (ADB)
Manzi, S. (NISR)
Medfai, A. (DGEDA)
Meyer, F. and Van der Burgh,
G. (BFAP)
Nelson, G. (formerly IFPRI)
Ngendakumana, V. (AfDB)
Parihar, J.S. (ISRO)
Piffer, T. (CONAB)
Ray, S. (MNCFC)
Recide, R. (BAS)
Rojas, J. (FAOSLM)
Schichor, P (EUSI)
Singh, D. (FAORAP)
Tenorio, J. (SIAP)
Van Speybroek, D. (BELSPO)
Verstraete, M. (SANSA)
Wigton, W. (AAIC)
Yu, X. and Zhou, W. (NBS)
17
1
Introduction
Since the launch of the Landsat series in July 1972, agriculture has been a
major beneficiary of satellite imagery. Among the first applications of the
technology in this field was that occurring in the efforts to improve agricultural
statistics (Hanuschak and Delincé, 2004; Taylor et al., 1997), even though
constraints posed by lack of the required expertise in statistics, image software
and budget availability often limited its adoption.
With spatial resolution brought down to 0.5 m (Marchisio, 2014), farmers’
declarations could be checked (Kay et al., 1997) and precision farming
(Schumpeter, 2014) became feasible, because the economic value of these
operations easily justified the expense. On other scales, land-cover mapping
(Defourny et al., 2011; Chen, 2014) and early-warning systems (Brown and
Brickley, 2012; Rembold et al., 2006 & 2013) became a reality. It is worth
noting that the cost of imagery and software followed a kind of Moore’s law,
and today’s free access to images from Landsat 8 and Sentinel 1 and 2 and
image analysis software such as Skybox opens up new possibilities.
This study concentrates on the cost-efficiency of using remote sensing for
agricultural statistics in developing and emerging economies. Western Europe
and North America are therefore not considered, though many of the examples
discussed herein are the result of close collaboration with agencies from these
areas.
The reader interested in the state of African statistical systems will find detailed
information in FAO (2010) and AFDB (2014); however, these do not report on
the use of remote sensing techniques by national statistical services.
This report has six chapters. After an introductory chapter, Chapter 2 considers
various applications of remote sensing for agricultural statistics; Chapter 3
looks at “who does what” with imagery in 31 countries; Chapter 4 reviews the
publication and study methods of cost-efficiency aspects over the last 20 years;
and Chapter 5 describes case studies of the use of imagery for creating area
frames in Haiti and Morocco and the use of imagery at the estimation level in
China and India. Chapter 6 examines the future developments in the imagery
sector.
18
2
Application Domains
2.1. Land cover mapping
The daily availability of low-resolution imagery from the AVHRR (1 km),
VEGETATION (1 km), MODIS (500–250m), MERIS (250m) and PROB-V
(100 m) instruments enables the derivation of cropland masks at the global
scale. With the free access to current Landsat 8 acquisitions and to the entire
Landsat archive, mapping at 30 m resolution is feasible provided that adequate
computing power is available. Such digital maps at resolutions of 1 km to
30 metres provide global-level references for total cropland, which may be split
between rain-fed and irrigated land (Portmann et al. 2009). The reader
interested in cropland mapping should consult Basso et al. (2014b). For Africa
only, recommended reading is Vancutsem et al. (2013), for an analysis of ten
cropland data sets. Current data sets come from AVHRR, VEGETATION,
JERS-SAR (Thenkabail et al., 2009), MODIS (Pittman et al., 2010; Friedl et al.,
2010 and Fritz et al., 2015), MERIS (Arino et al., 2008) and Landsat (Yu et al.,
2013).
Although these data sets do not fully agree as to total available cropland or
exact locations, their use can at least enable stratification in terms of
agricultural and non-agricultural land and hence prevent wastage of samples
where crop finding probabilities are near zero. Assuming, for example, a non-
agricultural stratum occupying a third of an administrative area of interest,
reallocation of the entire sample to the remaining cropland area will enable
stratification with relative efficiency of 1.51 at almost no cost. The fact that the
crop mask contains errors will have no effect on bias, but it will reduce relative
efficiency if too many samples are wrongly classified. Such cropland maps are
not uniformly accurate, and regions that are less intensively cultivated are
relatively poorly classified. The efficiency of stratification will in general be
lower in developing countries than in intensively farmed regions.
1 Without stratification the variance of the mean will be
2/(2/3)*n – 33 percent of sample size
is lost in a region without crops. With stratification the variance will become 2/n, so the
relative efficiency is Er= {2/(2/3)*n}/{
2/n} =3/2 =1.5.
19
Examples of national land-cover maps include the Sudan (Latham, 2011 and
2012) and Pakistan (SUPARCO, 2012), where the maps have a minimum
polygon size of 1 ha. In northern Sudan alone – an area of 1.9 million km2 –240
man-months were needed for photo-interpretation to regroup the 83 original
classes into seven major classes: this shows that the stratification of an
agricultural frame cannot by itself justify the adoption of land-cover
classification. Nonetheless, once they have been created for public decision-
making land-cover maps are a unique tool for optimal stratification.
Google Earth should also be considered for stratification when very high
resolution imagery is required. Although image geometry is not guaranteed and
acquisition dates may be uncertain or unknown, the system provides a cost-
efficient way of envisaging stratification, especially point area frame sampling.
Experience in Europe with black-and-white orthophotos and in Kenya, Malawi
and Mozambique, shows that an operator using the appropriate tools could
classify between 600 and 800 points per day. Hence a team of five could
stratify 1 million points in less than a month, which shows that stratification of
an entire country can be done efficiently. In Europe, the observed stratification
relative efficiency with black-and-white orthophotos was 1.6 for cereals and 3.0
for olives (Gallego and Delincé, 2010).
Under the Global Strategy, FAO is seeking to improve methods for using
existing land-cover and land-use databases for agricultural statistics (Hill,
2014). To evaluate the potential of satellite imagery in land-cover mapping, test
sites of 400 km2 were identified in Bangladesh, Ethiopia, Indonesia, Malawi,
Pakistan and Tanzania. Images with resolutions from MODIS at 250 m to
Worldview3 at 31 cm have been ordered and will be used to construct land-
cover maps. Cost efficiency will be assessed in terms of accuracy and related
cost. Final reports are expected in 2016.
The European Space Agency (ESA) has launched the Land Cover Climate
Change Initiative (LC-CCI), which offers world-wide land-cover imagery at a
resolution of 300 m derived from MERIS for 1998–2002, 2003–2007 and
2008–2012. Three consistent global land-cover products are supplemented by
climatological seven-day time series – NDVI, snow cover and burnt areas. With
Sentinel-2 now operational, coverage in Africa will be refined to 20 m
resolution with monthly updates (Defourny et al., 2014).
20
2.2. Census and survey frame construction
In its 2009 Handbook on Geospatial Infrastructure in support of Census
Activities, the United Nations Statistics Division (UNSD) began to recommend
the use of satellite and aerial imagery for census activities. This was taken up
by several census offices, which used the integration of image-analysis
functions into GIS software and open-source software running on personal
computers. Among the countries implementing or considering this approach are
Botswana, Ethiopia, The Gambia, Ghana, Kazakhstan, Lesotho, Malawi,
Namibia, Nigeria, the Russian Federation, the Seychelles, South Africa, South
Sudan, Swaziland, Tanzania and Zambia (Geospace, 2015; Eze, 2009).
The imagery has several advantages, especially in the absence of digital maps:
Generally falling within a well-defined projection system, the imagery
provides a cartographic framework with accurate representation of
territory; it facilitates observance of the usual enumeration area
properties such as completeness, non-overlap, respect of administrative
divisions, accurate delineation and allocation of attributes.
Updating and verification require less time and fewer staff because the
high-resolution imagery provides adequate detail; the counting and
categorization of dwellings could largely become an office task.
Using personal digital assistant (PDA) devices will minimize the need
to print maps and will facilitate planning, because imagery will be
downloaded as required for daily needs.
Surveyors will work more efficiently because image-based maps will
make navigation easier.
The imagery will favour the adoption of integrated frames, especially if
a survey area frame approach is adopted.
Area frame construction is another major opportunity to use remote sensing in
agricultural statistics.
At the design level, the existence of a positive spatial correlation will favour the
selection of numerous small secondary sampling units (SSUs), usually
regrouped into primary sampling units (PSUs). A territory may be subdivided
on topographical maps by superimposing a grid or by delineating blocks, but
frame design will be enhanced if imagery is incorporated. The NASS has
always used frames composed of PSUs and SSUs with physical boundaries
extracted from Landsat imagery: only a sample of PSUs will be observed, but
21
the sampling variance associated with PSUs can be minimized if they are made
as similar as possible – that is, they are each as representative as possible of the
stratum they belong to. Photo-interpretation of satellite imagery is an efficient
way to achieve this.
Imagery is essential for the stratification of an area frame. Photo-interpretation
or automatic classification of imagery into land-cover classes enables the
definition of homogeneous contiguous or non-contiguous sets within which
sampling variance will be minimized. As the sampling variance between strata
reaches zero because all strata are sampled or considered to contribute a zero
value to the total estimate, the more the strata differ as a result of sound image
analysis and the better the outcome. If the area reduces to a point, the reasoning
remains valid: the SSU size varies for each point and equals the area of the
selected field; if points have been stratified from imagery, their land uses within
strata will be more similar than between strata. This ensures that the sampling
error will be minimized.
2.3. Production of field survey documents
Whether list or area frames are used, surveyors will profit from the availability
of imagery. Aerial photographs or very high-resolution satellite images will
help them to access land and locate farmers. Imagery will minimize major
declaration and measurement errors. They will also motivate farmers by giving
them free access to the most recent technology.
2.4. Estimation of crop areas
GEOSS (2009) and Craig and Atkinson (2013) review the literature on the
estimation of crop areas. Duveiller and Defourny (2010) present an attractive
conceptual framework for considering the effectiveness of imagery in function
of the agricultural landscape.
Two main methods derive crop area statistics from remotely sensed data: the
pixel counting method and the calibration method.
Pixel counting is the more direct way: although often criticized (Gallego et al.,
2010), this approach is almost the only current operational use of remote
sensing.
To create crop data layers (CDLs), NASS uses pixel counting in remotely
sensed images, having abandoned pre - harvest calibration methods ten years
22
ago. Using multiple sensors and images of various dates from Landsat,
Resourcesat and Disaster Satellite Monitoring (DMC), supervised automatic
image classification using See5 software is based on current year ground truth
provided by the Farm Service Agency Program (FSA) (Mueller and Harris,
2013). A major advantage of a CDL is its availability at end June, when the
June enumerative survey results are issued. On the basis of the accuracies given
by Johnson (2010), it is evident that results are highly dependent on crop
diversity. In Iowa, where corn and soybeans are the dominant crops,
classification accuracy reaches 95 percent and area bias can be calculated as
less than 1 percent. In North Dakota, classification accuracy is 70 percent, and
in Idaho 80 percent, but bias for corn is acceptable at 2 percent in North Dakota
and 2 in Idaho; bias for spring wheat is of the same order of magnitude at
4.5 percent in North Dakota and 2 percent in Idaho. In the three states
mentioned, the largest bias encountered was 10 percent for 93,000 ha of canola
in North Dakota. It is worth nothing that the literature does not provide any
evidence as to the necessity of imposing random sampling for collecting the
training sets, but considers cost to fall within the first selection criteria. Readers
interested by the bias aspects of pixel counting should consult Marley et al.
(2014).
Since 2012 the Mahalanobis National Crop Forecasts Centre in India has
forecast crop areas for 11 major crops (Ray et al., 2014) on the basis of current-
year ground truth from 5,680 field data items and the classification of optical
and radar satellite imagery. Preferential prices for Resourcesat and Risat images
make the project particularly cost-efficient. The system may be described as a
forecast system, because the initial results (on crop area and production) are
available before harvest. Results are available several months before the official
statistics of the Directorate of Economics and Statistics are issued. The
discrepancies – 4 percent for rice on 37 million ha in 2013/14 – are the result of
imprecision in both approaches.
Statistics Canada joined the AgriFood Canada agriculture monitoring
framework in 2009 and has since carried out annual crop classification using
Landsat imagery (Daneshfar et al., 2014). Accuracies in 2013 varied from
74 percent to 90 percent in the nine provinces involved. In 2011 the derived
crop areas were compared with census figures, with mixed results. CDLs were
derived in JECAM 2013 from Rapideye 5-metre and Radarsat2 images with
accuracy above 95 percent, which was considered equivalent to field
observations.
23
Calibration methods (Benedetti, 2014) are used to integrate field survey data
and image classification results. The United States Department of Agriculture
(USDA) is the only agency that utilizes a regression estimator based on remote
sensing, although limited to the February post-harvest county crop area
estimates, when the imagery contains enough information, and to the
administrative level where the high field survey variances justify the additional
work. Argentina, China and Pakistan are carrying out trials in which estimators
reduces sampling errors in ground surveys by integrating auxiliary information
known for entire populations: if the auxiliary variable is well correlated with
crop areas, the ratio or regression estimators remain unbiased and gain from
reduced sampling variance. Coverage by satellite imagery on two dates usually
enables classification with an accuracy of between 60 percent and 70 percent
depending on crop phenology, field size and landscape complexity. The use of
freely downloadable Landsat and Sentinel imagery and open-access software
reduces costs and increases the chances that a relative efficiency above 2,
guarantees cost-efficiency.
2.5. Forecasting and monitoring crop yields
The forecasting and monitoring of crop yields is effectively supported by daily
delivery of weather information and images from satellites. Basso et al. (2014a
and 2014b) review the projects and methods in detail. They first describe crop
simulation models based on statistical, mechanical or process-based
approaches; these rely on soil, weather and farming conditions that are not
readily picked up by remote sensing. They list 26 remote sensing vegetation
indexes commonly used in models as proxies for soil, crop development and
stress conditions but their conclusions are not optimistic, especially in the case
of small fields and mixed cropping. Early-warning systems are among major
users of this kind of monitoring to detect crop sowing, development stage and
harvests and the effects of droughts or floods or population changes. The most
active systems are FEWS-NET, FAO-GIEWS, UNEP-DEWA, WFP-VAM and
JRC-MARS.
National Crop-monitoring systems based on remote sensing include Brazil’s
CONAB, China’s RADI, India’s MNCFC, Morocco’s DMN, Mozambique’s
DSNA, Pakistan’s SUPARCO, Senegal’s CSE and Tunisia’s CNT.
A review of the scope, evaluation criteria, current approaches, evolution and
organization of crop yield and production forecasting frameworks can be found
in Delincé (2015).
24
3
Who does what?
3.1. International investments
Many international projects promote the use of remote sensing for agricultural
statistics. Rowland et al. (2007) list 66 projects for Africa and the 43 active
remote sensing entities located in Africa. This section looks at the more recent
projects, which are not listed in the 2007 paper.
In June 2011 the G20 launched the Global Agricultural Geo-Monitoring
(Geoglam) and Agricultural Market Information System (AMIS) initiatives
with a view to improving crop production forecasts and increasing transparency
through a global agricultural monitoring “system of systems” based on satellite
and earth-based observations. The Geoglam crop monitor provides a multi-
source assessment of crop growing conditions, status and agro-climatic factors
likely to affect global production. It covers the four primary crops (wheat,
maize, rice and soy) in the main crop-producing regions of the AMIS countries.
The assessments, which have been produced since September 2013, are
published in the AMIS Market Monitor bulletin.2 Representatives from 30
organizations are participating in the assessments.
The EC-FP7 SIGMA project (Gilliams, 2014) focuses on multi-annual changes
such as variations in cultivation practices and extension, reduction or
abandonment of agricultural land. Using the Geoglam Joint Experiment for
Crop Assessment and Monitoring (JECAM) test sites, Sigma monitors sites in
Argentina, Brazil, Burkina Faso, China, Ethiopia, France, Madagascar, the
Russian Federation, Spain, Tanzania, the Ukraine, the USA and Viet Nam.
The ASIA-Rice project led by JAXA in collaboration with the Asian Network
of Country Agricultural Statisticians (AFSIS) provides monthly crop forecasts
for Geoglam (Okumara et al., 2014) covering Indonesia, Thailand and Viet
Nam. The project uses the Jasmin software provided by RESTEC to monitor
rice growing through six weather indicators derived from MODIS and
meteorological satellites.
2 http://www.amis-outlook.org/fileadmin/user_upload/amis/docs/AMIS_brochure
25
The Global Monitoring for Food Security (GMFS) project funded by ESA from
2003 to 2013 as part of the Global Monitoring for Environment system focused
on Ethiopia, Kenya, Malawi, Mali, Mozambique, Niger, Senegal, the Sudan and
Zimbabwe,3 providing seven services: crop yield and vegetation monitoring,
FAST precipitation and evapo-transpiration, soil moisture monitoring, support
for the national agricultural survey, agricultural mapping, SAR knowledge
transfer and support for crop and food supply assessment missions.
The AGRICAB EC-FP7 research project, which started in 2011 and ended in
2015, focused on agriculture and forestry management with a view to
improving data access, agro-meteorological modelling, early warning,
agricultural statistics, livestock monitoring and forest mapping. These were
developed in case studies in Kenya, Mozambique, Niger, Senegal, South Africa
and Tunisia. Africa was represented by ILRI, RCMRD, INAM, AGHRYMET,
CSE, DRSRS, UEM, OSS, CSIR and GeoSAS, and South America by INPE.
The 2011–2014 E-AGRI EC-FP7 project sought to support the uptake of
European ICT research results in China, Kenya and Morocco. Its six activities
were crop yield monitoring using the CGMS and BIOMA platforms, crop yield
forecasting and area estimation using remote sensing indicators and statistical
analysis, and capacity-building.
Funded by the Swiss Agency for Development and Cooperation (Nelson et al.,
2014), the Remote Sensing-Based Information and Insurance for Crops in
Emerging Economies (RIICE) project tested SAR-based mapping of rice areas
in Cambodia, India, Indonesia, the Philippines, Thailand and Viet Nam between
late 2012 and early 2014. The main actors were IRRI, the Swiss company
Sarmap, PhilRice, ICALRD, TNAU and GISTDA. The mapping was done with
X-SAR imagery from ten dates, mostly SLC 3-metre resolution from
Cosmoskynet and Terrasar on 13 sites covering an area of 4,780km2. For site-
level parametrization of the decision rule algorithm, field measurements were
made on eight dates at 20 fields per site. For validation, 100 locations were
selected providing 1,338 GPS-located measurement points, of which 50 percent
were rice fields and 50 percent areas used for other purposes. At the site level,
mapping accuracy varied between 86 percent and 97 percent. Most errors
occurred in river zones for which crop masks could be created; omission errors
resulted from rice-crop calendars that were unsuited to some problem areas.
3 Documentation can be found at: http://www.gmfs.info/
26
The Asian Development Bank’s 2014 Innovative Data Collection Methods for
Agricultural and Rural Statistics project is funded by the Japan Fund for
Poverty Reduction (JFPR); the technical adviser and development partner is the
Japan Aerospace Exploration Agency (JAXA) (ADB, 2014). It will be piloted
in the Lao People’s Democratic Republic, the Philippines, Thailand and Viet
Nam with a view to promoting the use of satellite technology in formulating
and monitoring food security policies. The agencies entrusted with the mandate
of compiling agricultural statistics should be the national project implementing
agencies. IRRI and GISTDA, which have experience with remotely sensed
monitoring of rice, are potential partners.
The 2014–2016 Spurring a Technology for Agriculture through Remote
Sensing (STARS) project4 is financed with US$7.5 million from the Bill &
Melinda Gates Foundation and led by ITC-Twente in collaboration with
CSIRO, ICRISAT, UMD and CYMMIT. The project is “... a coordinated set of
activities, designed to learn, identify opportunities and challenges, and test
hypotheses around the potential exploitation of remote sensing technology in
improving the productivity of crop-based smallholder production systems and
livelihoods of smallholder farmers in sub-Saharan Africa and South Asia.”
4 http://www.stars-project.org
27
3.2. National remote sensing activities in developing and
transition countries
Table 1 below summarizes the findings for the 31 screened countries in this
chapter.
Table 1: Main uses of Remote Sensing in the 31 countries analysed
LF = list frame AF = area frame MF = multiple frame Admin = registers OP = operational Exp = experimental
LF&AF Exp Exp Exp
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3.2.1. Argentina
The Ministry for Agriculture, Breeding and Fisheries produces the official
agricultural statistics. A new survey system initiated in 2010 consists of an area
frame with stratification based on classified MODIS multi-temporal images
with an ISODATA algorithm (MAGyP, 2010). The clusters are re-grouped on
the basis of their temporal profiles into arable, pasture and non-agriculture
classes. By superimposing a hexagonal tessellation of 2,500 ha, four strata are
created as functions of agriculture intensity. The sample is obtained by
superimposing a regular grid of points on the area frame, each point generating
a rectangular segment of 400 ha. For accessibility, the points are moved along
the perimeter of an expandable circle to the point at which they come into
contact with the road network. In each segment, land uses are drawn on a
reference document for further area computation. Additional research evaluates
the use of classified imagery for regression and calibration estimations.
3.2.2. Bangladesh
In pilot studies in Bangladesh involving the Ministry of Agriculture, the Bureau
of Statistics and the Space Research and Remote Sensing Organization, rice
production estimates were made in Aman and Boro regions 2011–2012
(Rahman, 2014) by merging area estimates from satellite imagery with ground
measurements of yields. Crop masks of non-agriculture land are derived from
Landsat images every three years, and SAR data are acquired on three dates
during the growing season in parallel with weekly MODIS data.
3.2.3. Brazil
Agricultural statistics are issued in Brazil by the Instituto Brasileiro de
Geografia e Estatistica (IBGE). In addition to the ten-year census based on
enumeration areas and interviews – the last was in 2006 – annual agricultural
statistics are produced by the Levantamento Sistemàtico de Produçào Agricola
(LSPA) survey, in which statistics are derived from expert opinions (Bolliger,
2014). In view of the disadvantages of the LSPA approach, IGBE and INPE
implemented the PREVS project from 1987 to 2000 (IBGE, 2005) in which
photo-interpretation of Landsat TM images led to the construction, using a
NASS approach, of an area frame for the states of Paraná, Santa Catarina, São
Paulo and the Federal District. Although results and costs were satisfactory –
66 percent of the estimates had coefficients of variation (CVs) below 25 percent
– it was seen mainly to confirm the reliability of the expert estimates, which
were often in the confidence interval of the area frame results.
29
Additional results come from the new Project of Land Use and Coverage,
developed by the IBGE Geosciences Directorate, based on ten classes of land-
cover mapping with MODIS images every two months (Freitas et al., 2013).
INPE is testing the Monitoring Agriculture by Brazilian Remote Sensing
(MoBARS) approach, which uses Landsat-like images in the states of Rio
Grande do Sul, Paraná and São Paulo for soybean, corn and sugarcane. Crop
areas are estimated every two months during the growing season; in parallel,
MODIS data provide crop-layer maps virtually without the need for fieldwork.
In terms of data distribution, Embrapa has created a GIS website called Soma
Brazil, in which maps of agricultural themes and the results of IBGE surveys
can be visualized. IBGE is considering improving LSPA surveys by
constructing a nation-wide master frame that could be based on administrative
registers of farmers and family farms.
At the same time, the Companhia Nacional de Abastecimento (CONAB)
produces monthly statistical information on areas of cotton, rice, beans, castor
oil, millet, soya, oats, canola, rye, barley, wheat and triticale (CONAB, 2014).
The Geosafras project is discussed in Da Silva (2013), GEOSS (2010) and
Fontana et al. (2008). National multi-temporal MODIS coverage is used for
grain areas and yields; Landsat and Spot data are used for coffee and citrus
crops.
3.2.4. Chile
The Ministry of Agriculture and the National Statistics Institute organized their
last census in 2007. Since 2013, the entire territory of Chile has been covered
with an area frame (Ambrosio, 2014) using a stratified two-stage sampling plan.
After exclusion of urban areas, rural areas are stratified according to land use
and agricultural intensity. The census results were preferred to imagery for
stratification due to the complexity of the landscape. Square segments of 25 ha
are used, with blocks in which five replicates are chosen systematically. The
sampling fraction of 1.25 percent was chosen to achieve a CV of 15 percent on
major production sectors at the regional level.
3.2.5. China
In China, official agricultural statistics are the responsibility of the National
Bureau of Statistics (NBS). Remote sensing replaced the list frame approach in
the grain-producing counties in Liaoning, Jilin, Jiangsu, Anhui, Shandong,
Henan and Hubei provinces in 2003. The sampling plan (Pan et al., 2012) uses
30
a stratified approach in which a 5x5 km grid is superimposed upon
province/county boundaries, images are classified and grids are clustered into
three strata as functions of total arable land and the three main crop areas. A
sample of 150 grid cells per province is selected by PPS, and five segments are
randomly selected per grid cell; replicates are used to estimate accuracy. The
Rapideye and Spot satellite imagery used until 2013 has been replaced with
Chinese GF-1 imagery, taken with a P/MS sensor at 2/8 metres and a 16-metre
WFV sensor.
The Cropland Acreages Estimation using Remote Sensing and Sample Survey
final sample size targets a 5 percent CV for major categories at the province
level, covering wheat, corn, rice, cotton and soybeans. Of the 226 villages in
Beizheng district in Liaoning province (Yu, 2014), for example, 24 were field
surveyed in a total of 120 segments using direct expansion estimators giving a
CV for rice of 6.9 percent and for corn 5.4 percent; using regression estimators,
the CV for rice was reduced to 5.3 percent and for corn 3.7 percent. This
corresponds to relative efficiencies of the regression estimator of 2.2 for rice
and 3.1 for corn.
The Ministry of Agriculture also uses remote sensing to monitor agriculture.
The Chinese Academy of Agricultural Science (CAAS) started the Chinese
Agricultural Remote Sensing Monitoring System (CHARMS) in 1999. The
system monitors changes in crop areas, yields, production, growth and drought
for wheat, rice, maize, soybeans, cotton, canola and sugarcane. It reports the
monitoring information to the Ministry of Agriculture and agriculture-
management organizations more than 100 times per year.
The CAAS was a partner in the E-AGRI project in which Mengcheng county –
2,149 km2 – and Guoyang county – 2,107 km
2 – were the objects of an area
frame survey (Chen et al., 2013). The methods tested were physical boundary
segments and circular segments: in both, a regular 4 km grid enables the
selection of points on Google Earth for photo-interpretation. In the first case,
average segment size was 5.9 ha; in the second 4.6 ha; sample sizes were 100
and 193 segments. A CV of 3 percent was obtained for wheat on 148,000 ha in
Mengcheng. Classification of a merged SPOT5/Landsat TM image was carried
out. The observed relative efficiency for wheat was 2.6 percent, for soybeans
2.7 percent and for maize 2 percent.
Based on the CAAS Institute of Remote Sensing and Digital Imagery, the
CROPWATCH system (Wu, 2014a) uses high-resolution images from IRS,
31
Landsat, SAR and HJ-1 and medium-resolution images from FY-3A, MODIS
and AVHHR to issue forecasts for crop areas, yields and production. Four
bulletins per year review the world’s main production zones (Wu et al., 2014b)
in terms of the supply and demand balance for rice, wheat, soybeans and maize.
Crop area estimation is implemented in Argentina, Brazil, China, France,
Germany, the Ukraine and the United States; crop-condition indicators are
derived for the 31 main food-producing countries. The team is composed of 15
remote sensing analysts, one agro-meteorologist and a communications officer.
For China (Wu, 2014c), the crop areas are derived as the product of total
farmland and the planted proportion with the crop-type proportion. The planted
proportion is derived from unsupervised classification of high-resolution
satellite images from Landsat TM, IRS P6 AWIFS and HJ-1 CCD, with
accuracy above 95 percent. The crop-type proportion comes from thousands of
photographs taken along transects – the GVG system. The yield estimate comes
from three approaches based on remote sensing indicators or agro-
meteorological indicators.
Global Land Cover 30 (Chen, 2014) is a product delivered by the National
Geomatics Centre of China (NGCC). It was created for reference years 2000
and 2010, but only the 2010 land cover is publicly available on the web. The
classification system includes ten land-cover types – cultivated land, forest,
grassland, scrubland, wetland, water bodies, tundra, artificial surfaces, bare land
and permanent snow and ice. Derived from Landsat and HJ-1 imagery, the
raster land-cover product is rendered with 30-metre resolution and structured
into 853 tiles.
3.2.6. Colombia
The frame used by the National Department of Statistics for the national agro-
pastoral survey was established in 1985/86 (DIMPE, 2013) following FAO
recommendations. It merges a list frame for large farms and an area frame of
6,000 physical-limit segments averaging 200 ha of an estimated total of
179,000 SSUs and 61,733 PSUs. The sampling plan based on PSUs and SSUs
is stratified into six strata in 23 of the 33 departments. Stratification and
PSU/SSU delineation based on aerial photographs at 1/20 k to 1/50 k were
transferred to topographical maps. Samples are extracted, as in Morocco and
Rwanda, by replicated PPS systematic sampling of PSUs, with one SSU
randomly chosen by PSU leading to two-stage self-weighting sampling. The
frame needs adjustment (Saa-Vidal et al., 2012): in addition to doubling the
32
sample size to reach the 15 percent CV target at the department level,
stratification and segment delineation should be revised in accordance with the
latest imagery.
3.2.7. Ethiopia
In preparation for its next census in 2018 Ethiopia used using remote sensing
from 2008 to 2013 (Tariku, 2014; Srivastava, 2014) to test an area frame
approach in Oromia region – 284,000 km2–. The frame is composed of PSUs –
enumeration areas containing 200 households – from the CSA list frame used
for crop production surveys, and SSUs – segments of 40 ha. Using SPOT
satellite images, the PSUs were stratified as functions of agricultural land use;
239 PSUs were selected with probability proportional to the number of
segments and hence population. Two segments were chosen per PSU. The CVs
were usually above 30 percent for crops covering 50,000 ha. No use was made
of image classification by the CSA.
3.2.8. Guatemala
In 2013, INE conducted a multiple-frame survey to estimate national crop areas
and production (Barrientos, 2014). For the area frame, a 1 km2 grid was used in
which the squares were subdivided into segments of 6.25 ha, 25 ha, 50 ha or
100 ha, for a total of 190,100 segments. Depending on the stratum involved, the
squares were subdivided into segments of 6.25 ha, 25 ha, 50 ha or 100 ha over
190,100 ha. The sample was finally composed of 1,500 segments structured in
five replicates. Additional information on the area frame characteristics can be
found in Ambrosio (2014). No use of image classification is made in estimating
crop areas.
3.2.9. Haiti
The National Network of Points Observation in Haiti has provided annual crop-
area estimates at the district level since 2012. Organized by the Centre National
de l’Information Geo-Spatiale in collaboration with the Ministry of Agriculture
and the Institute for Statistics, the survey consists of multi-purpose field surveys
during the two growing seasons and one yield survey for maize, beans, yams,
bananas and rice. The area frame was created by superimposing a regular
125x125 metre grid on to a map of the country; the resulting 1,728,000 points
were photo-interpreted with the support of IGN aerial imagery. A sample was
then drawn by Bethel allocation and 20,000 points were visited on the ground.
The 2013 measurements at the national level go from CVs of 2 percent for
33
maize on 214,500 ha to 7 percent for rice on 16,314 ha. At the district level,
accuracy varies according to the extent of a crop: for maize, CVs go from
5 percent in Centre district, where maize covers 51,000 ha, to 11 percent in
North-East district, where the area is only 6,000 ha.
3.2.10. India
Official agricultural statistics produced by the Department of Economics and
Statistics integrate estimation based on remote sensing with field surveys.
Parihar and Oza (2006) describe a method used until 2012 and explain the roles
of state agricultural statistics authorities and the Directorate of Economics and
Statistics in the Department of Agriculture and Cooperation.
The Mahalanobis National Crop Forecasts Centre was created in 2012 by the
Ministry of Agriculture by transforming the former Forecasting Agricultural
Outputs using Space, Agro-Meteorology and Land-Based Observations
(FASAL) programme ( Parihar and Oza, 2006) into operational activities
(Pariharand and Manjunath, 2013; Ray et al., 2014). State estimates of areas
and yields are produced at several dates for rice, jute, rapeseed, potatoes, wheat,
cotton and sugarcane, ensuring 90 percent coverage of national production.
The country is covered with a regular square 5 km grid, with each segment
classified by satellite imagery; any segment which comprises more than
5 percent of agricultural area is included in the definition of agriculture strata
(Parihar et al., 2012). Four crop strata are defined on the basis of crop intensity
in the classified images using the Dalenius-Hodges method. For area
estimation, a 15 percent sample is randomly selected in each stratum to enable
computation of standard errors. Ground visits are used for training in the
classification rule, but regression and calibration are not applied. Classified
SAR RISAT imagery enables estimation of rice and jute areas (Chakraborty et
al., 2013). In the same way, AWIFS and LIS III images lead to estimations for
other crops. Crop areas result from the multiplication by stratum of the
percentage of pixels classified as the target crop with population segment size
and area per segment.
Yield estimates for 11 crops are made either (i) at the beginning of the crop
season through regression models linking vegetation indexes and remote
sensing or (ii) during the growing season with DSSAT biophysical models and
remote sensing spectral models integrating low-resolution imagery from
INSAT-3 and meteorological information (Tripathy et al., 2013).
34
3.2.11. Indonesia
Indonesia has been testing point area frame sampling since 1998 (Mubekti,
2014) to determine rice areas in different stages of development; 25 ha square
segments have been used since 2005 as PSUs, with four grid replicates per
10x10 km block and with SSUs located on a 5x5 grid. The aim is to observe
phenological stages of rice at each sample point with a view to estimating the
area of rice at each stage. Stratification results from maps of land used for rice,
arable upland and non-arable land and imagery enables the creation of field
documents on a scale of 1/5,000. PSUs are chosen (f=1 percent) on a square
grid, and sample size is set to produce estimates at the district level. In
Indramayu district – 2,000 km2 – the 2012 survey was based on 54 segments
with 1,300 points observed and completed within a week including SMS
transmissions and real-time revision of the estimates.
The Ministry of Agriculture and the Presidential Office for Development
Monitoring are among the institutions supporting use of the method for
national-level implementation in 2015.
3.2.12. Kenya
The Department of Resource Survey and Remote Sensing has provided annual
statistical estimates of crop areas and yields for the Food Security Steering
Committee (FSSC) since 1984. The approach, described in Imata (2013a),
involves aerial surveys of points to estimate areas of maize and wheat.
Stratification covers approximately 14,500 km2 annually on the basis of
Landsat imagery from 2007 and the aerial surveys: about 10,000 colour
photographs scaled at 1/22,000 with GPS localization are interpreted on a 100-
point grid to estimate the wheat and maize areas. Three months before harvest,
crop radiometry is collected in a second aerial survey to predict yields through
regression equations of yield versus vegetation indexes.
The Department of Resource Survey and Remote Sensing has joint the
AGRICAB FP7 project (Tote et al., 2013) to seek technical improvements.
Following a LUCAS approach (Gallego and Delincé, 2010), 10,000 points have
been photo-interpreted on Google Earth, leading to 615 field visits in 2012 in
Kakamega and Batere Mumias (Imata, 2013b). Rapideye 5-metre resolution
images are used for crop classification, but results were discouraging as a result
of the high variability of crop development. In addition, the Regional Centre for
Mapping of Resources for Development (RCMRD) was associated with the
35
GMFS ESA project (Gilliams et al., 2012), whereas CSE was part of the E-
AGRI EC project (de Wit et al., 2013) on crop yield forecasting
Independent crop-production estimates are produced by international initiatives:
FEWS-NET issues monthly reports, but according to Brown and
Brickley (2012) they rarely refer to NDVI vegetation indexes.
MARS issues periodic maize production forecasts (Rojas, 2009) based
on analysis of CNDVI indexes of SPOT vegetation.
The Regional Centre for Mapping of Resources for Development in
Nairobi supports its 20 member states in eastern and southern Africa
with problem-solving and natural resource and environmental
applications, integrating remote sensing, surveying and cartography.
3.2.13. Malawi
The Ministry of Agriculture collects the agricultural statistics through its
Agriculture Production Estimation Survey system (APES), composed of three
parts: field crops production estimation, horticulture crops production
estimation, and livestock production estimation.
Based on a farm household list frame, from February it delivers four estimates
of the areas and production of the major crops; however, the current paper
approach does not enable survey precision to be derived.
Malawi is a member of the Nairobi Regional Centre for Mapping of Resources
for Development, and has trained 18 people in remote sensing and
telecommunications between 2000 and 2005 (Rowland et al., 2007). After tests
of a list frame approach to estimating crop production in 2011, the ESA GMFS
project developed an area frame for the whole country in 2012 (Ceccarelli and
Remotti, 2012). A grid set of 350,000 points 500 metres apart was photo-
interpreted on Google Earth imagery and stratified into five land-use classes. In
a field survey in Dowa and Ntchisi districts – 5,000 km2
– 74 percent of the
points were reached in six weeks by 20 Ministry of Agriculture surveyors,
leading to CVs for the five main crops of between 3 percent and 20 percent.
SPOT imagery had an overall accuracy of 41 percent, insufficient for sound
image classification.
A survey in Central region in 2013 using the national area frame covered 4,500
points. Partial results for September 2013 (Ceccarelli et al., 2013) show that in
36
two districts for which 70 percent of the survey data were available, maize areas
had CVs of 9 percent and 15 percent.
The World Bank financed two agricultural survey projects in 2015 for the
Ministry of Agriculture. One (ITA-EFTAS 2015) consisted in a national point
area frame survey; the results are not yet fully available, but technical details
are given in section 6.3.1. The other project (ASTRIUM 2015) consisted in an
estimation of corn acreages and yields at national and district level using SPOT
imagery.
3.2.14. Mexico
The National Institute of Statistics and Geography produces the official
agricultural statistics. The last agriculture census took place in 2007; it was
followed in 2012 by an agriculture survey of 33 products based on 97,000
holdings (Pérez Cadena, 2013). PDA devices were used to facilitate data
collection and access satellite imagery during interviews to compare crop plot
polygons defined by farmers with areas plotted by GIS.
SIAP and SAGARPA run projects to integrate remote sensing technologies into
agricultural statistics. With the creation of ERMEX-NG by SAGARPA and
SEDENA in 2013, Mexico now has a SPOT reception station. Salvidar and
Sanchez (2014) describe the main activities in agricultural statistics as:
cropland mapping based on 600,000 images in ten years of archives; a
land-use map has been created for the whole country;
crop area estimation: in 2010, 2011 and 2012, areas under maize,
wheat, sorghum, sugarcane and beans were estimated by integrating
ground data with supervised image classification; results are available at
the state level;
checking that areas are sown with maize, sorghum and beans according
to subsidy agreements; 5,642 fields were checked in 2014 with remote
imagery;
and area frame sampling using satellite imagery, starting in 2007.
3.2.15. Morocco
The Division of Strategy and Statistics (DSS) of the Ministry of Agriculture
renewed the national area frame in 2008 on the basis of SPOT-5 imagery (DSS,
2011; Arrach et al., 2014; Ballaghi et al., 2014,): the sample is now composed
of 3,000 segments of 4 to 30 ha for arable and permanent crop strata and 200 ha
37
for forest and pasture strata. Using SPOT5 imagery, all provinces were
stratified in ten strata; bare soil and water strata are not sampled. The strata
were subdivided into square PSUs: agricultural strata have PSUs from 500 ha to
4,000 ha. Each PSU is subdivided into SSUs; the initial squares were changed
to irregular polygons to cover intersections with strata boundaries, and during
field visits the segments are expanded to include entire intersected crop plots.
The PSUs are selected with PPS – the number of theoretical segments – and the
SSUs from randomly chosen replicates. Although the DSS does not use current-
year imagery to classify crops, it participated in the E-AGRI FP7 project
(Mahyou et al., 2014), in which Landsat 5 and Landsat 7 were used in the 2007,
2011, 2012 and 2013 marketing years. For rain-fed crops strata in an area of
18,000 km2, cereal areas were extrapolated from the classified segments, with
accuracy of the order of 95 percent.
The National Meteorological Division has adapted the JRC CGMS approach of
the E-AGRI EC project for crop yield forecasts (De Wit et al., 2014). CGMS-
Morocco includes the DMN, INRA and DSS institutes, which issue bulletins
every two months based on meteorological and satellite information.
3.2.16. Mozambique
Agricultural statistics are the responsibility of the Department of Agriculture
Statistics in the Ministry of Agriculture (Amade, 2012). The agricultural
censuses in 2000 and 2010 were interspersed with agricultural surveys. The
ministry’s early-warning System issues periodic bulletins on seven crop areas,
yields and production. The first international project involving Mozambique
was the ESA GMFS project: some training took place but little else, and
collaboration in the AGRI-CAB EC project was agreed to reconcile annual list
survey estimates and early-warning system forecasts. Tote et al. (2013) describe
the current forecasts systems: the Ministry of Agriculture’s Trabalho de
Inquérito Agrícola or TIA and Aviso Prévio, INAM, MARS, FEWS-NET,
GIEWS and SADC-AMESD. With regard to agricultural statistics, AGRI-CAB
and the Ministry of Agriculture implemented a point frame survey in Inharrime
district – 2,149 km2 – in 2014 (AGRICAB, 2015) using a 500x500 metre grid
and STAT-AGRI software for stratification, sampling, estimation, image
classification and crop association analysis. The methodology adopted was that
described for Senegal (see below). The 11,000 points area were stratified on
Google Earth into ten land-cover classes, and resulted in a sample of 980
points. The CV obtained for cassava, the most widely grown crop covering
12,000 ha, was 11 percent. Comparison with official statistics revealed
38
discrepancies because information was available only at the province level, and
the AGRICAB survey was late, so many fields were erroneously allocated as
fallow land.
3.2.17. Nepal
Nepal conducted its 2012 agriculture census on the basis of a list frame derived
from the population census (Bashyal, 2014): farmers on 130,000 holdings
selected from 5,200 PSUs were interviewed. The sampling fraction per PSU
was defined as the function of the number of agro-holdings obtained in the
population census. No use of area frame or imagery was used.
3.2.18. Nicaragua
An area frame was created in 1995 by USDA-NASS in collaboration with the
Ministry of Agriculture and the Central Bank of Nicaragua, UNDP and USAID
(Hoffman, 2014). In view of the cost of satellite imagery at that time,
stratification was based on topographical maps and identified intense, moderate
and low agricultural land use, urban areas and non-agricultural land. A sample
of 5,600 points was randomly selected across the national territory of
130,370 km2 to give a sampling probability proportional to farmland. Observed
CVs were a function of the extent of the crop areas, enabling the estimation of
corn areas with a CV of 3 percent at the national level. No follow-up took
place.
3.2.19. Nigeria
In 2010, Nigeria tested with USAID support an agricultural area frame survey
in the 44,000 km2 Kaduna region (Hoffman, 2014). Using SPOT-5 imagery, the
stratification – intense, moderate and low agricultural land use, non-agricultural
and urban land and water – used an intermediate level of blocks with physical
limits. A sample of 600 points was randomly drawn, with various sampling
fractions per stratum. At each point, the farm was surveyed for crops, livestock
and fisheries areas and production: the overall results indicated that a slightly
larger sample size would be needed to produce reliable statistics with
10 percent CV for the top ten crops. Taking an average farm size of 5 ha, the
sample accounted for 0.1 percent of farmland. Political instability prevented a
repeat or extension of the project.
39
3.2.20. Pakistan
Official crop statistics in Pakistan are generated through a village list frame
survey. The parallel Agricultural Information System, started in 2005, supports
the Government in integrating remotely sensed data into existing data
collection, analysis and dissemination systems and promotes the combination of
satellite data and improved field estimates for crop areas and yields and to
monitor crop status to improve the accuracy and timeliness of agricultural
statistics (Ahmad, 2014). SPOT 5-metre imagery was used to create a national
area frame. After elimination of non-agricultural areas, Sindh and Punjab
provinces were divided into PSUs of 1,000 ha, each of which was assigned a
stratum label based on crop intensity, SSUs of 400 ha and TSUs of 30 ha; the
sample size was 314. Province-level crop area estimates are derived as the sum
of strata averages weighted with the number of population segments for each
stratum. The survey covers wheat, rice, cotton, sugarcane and sunflowers.
The accuracy of subsequent classification of multi-date Landsat coverage was
too low for the use of a regression estimator or to create a CDL (Wigton, 2012),
but the current classification of bi-temporal SPOT images is promising in that
regression between field and classification wheat percentages gives r2 above
0.95. Yield estimates are derived from NDVI profiles and periodic e-bulletins
are published.5
CVs of 3 percent to 5 percent are obtained for major crops, and values of 7
percent to 10 percent for mixed cropping. Results are issued in April and
August. The annual costs of the project, which involves 18 staff, amount to
PKR30 million; the CRS departmental costs for the surveys, which involve
3,500 staff, reach PKR700 million.
3.2.21. Peru
The 2000 agricultural census was based on an area frame consisting of PSUs of
10 km2 and SSUs of 2 km
2 systematically sampled with replicates (Otànez,
2004). Stratification was based on Landsat imagery and aerial photographs.
Visits were made to 12,277 physical limit segments and the farmers were
interviewed. Of these segments, 20 percent had to be subdivided to
accommodate the large number of fields in each. A list frame of large holdings
was used to create a multiple frame on the basis of which an annual survey sub-
sample of 2,000 segments was designed, with a vi141ew to calibrating the
5 See: http://dwms.fao.org/~test/ais_ebulletin4_en.asp
40
annual survey against the census with ratio or regression estimators. The current
ENA annual production survey (INEI, 2014) appears to make no reference to
the calibration approach.
3.2.22. The Philippines
The Bureau of Agricultural Statistics first tested the use of remote sensing in
the 1980s (Garcia and Esquivias, 2012) in Pangasinan province. Rice and corn
crops were the subject of an area frame survey of 72 segments of 25 ha using
Landsat imagery, but results were inconclusive. Further tests were carried out in
2006 in preparation for the 2012 census. Quirino municipality in Isabela
province was surveyed, using stratified point sampling based on Landsat and
Google Earth imagery. The system was evidently practicable, but was not
adopted because of the cost and irregular availability of the imagery, small field
size, mixed cropping and asynchronous cropping.
The agricultural statistics currently produced by the bureau are based on a list
frame, but pilot remote sensing projects continue, as described in the next two
paragraphs.
The ADB Innovation Project covering the Lao DPR, the Philippines, Thailand
and Viet Nam encourages statistical offices to use remote sensing to
supplement data from surveys or administrative reporting systems. Operations
started with estimations of rice areas and production. The user-friendly software
developed by the Japan Aerospace Exploration Agency and RESTEC converts
optical and radar data into rice maps and area estimates. The project also uses
results from crop-cutting experiments and growth models with a view to
estimating rice yields and output.
The four-year PRISM project, under the Food Staples Sufficiency Program, is
supported by the Department of Agriculture, the Philippines Rice Research
Institute, Sarmap and IRRI. It aims to establish a nationwide information
system that will provide timely and accurate data on rice cultivation and
harvests in each province, and serves as a platform for developing consistent
assessments of rice production, crop health and losses due to natural disasters
and epidemics. Using SAR radar imagery, PRISM is operational in seven
regions, where municipalities and rice fields are selected for ground truth.
41
3.2.23. The Russian Federation
Agricultural statistics are produced by the Russian Federation Statistical
Service.6 For the 2006 agricultural census, the IKI Space Research Institute7
was contracted to assess the use of MODIS and Landsat imagery. In 2013, a
second contract with IKI was signed to process the southern regions of
Semikarakorsky district in Rostov oblast (province) and Liskinsky district in
Voronezh oblast for the 2014 census, which was postponed to 2016. The
deliverable was a land-use agricultural map showing arable land, hay, pastures
and gardens, fallow and abandoned land at a scale of 1:50,000 based on the
Landsat and MODIS archives up to 1988 and Russian satellite images of 2.1-
metre resolution. Following a feasibility test at the regional level comparing
census results with the land-use map, the IKI recommendation was to extend
the product to the whole country and to use it during the interviews to eliminate
obvious errors as to crop areas and land use. However, because of budget
restrictions and the complexity of the remote sensing, the approach will not be
used in the 2016 census.
The Ministry of Agriculture also has contracts with the Centre for Monitoring
of Agricultural Land for remote sensing to monitor agriculture.8 Agricultural
areas are not estimated, but agricultural field limits have been delineated for
most of Russia, although without classification, using Ikonos and Rapideye
imagery. 9
Land-cover maps have been produced, and validated through field
work.
Crop yield forecasts are produced by the Hydro-Meteorological Centre. The IKI
NDVI profiles available in the VEGA-Geoglam platform are part of the
process.
Vega-Geoglam10
is part of the G20 Geoglam initiative and financed by the FP7
SIGMA project. It covers the whole of Eurasia and test sites in Argentina,
Belgium, Brazil, Burkina Faso, China, Ethiopia, France, Madagascar, the
Russian Federation, the Ukraine, Tanzania and Viet Nam. The website provides
access to daily and archived satellite imagery from the Modis, Landsat and
Russian satellites, land-cover maps showing arable land, winter crops and
summer crops, and field-level information such as biomass. The tools provide
6 http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/en/main/
7 http://www.iki.rssi.ru/eng/
8 http://www.rosagroland.ru/about/
9 http://atlas.mcx.ru/
10 http://vega.geoglam.ru/
42
for supervised classification of Landsat with Modis land-cover maps. The
Russian beta version provides tools for image classification by oblast. The tools
have been adapted for monitoring forest resources and contain images for all
sites. From a research point of view, JRC (Fritz et al., 2006) compared the costs
and accuracy of various classification methods for vegetation, Modis and Meris
data for part of the Rostov oblast.
3.2.24. Rwanda
The National Institute of Statistics collaborates with the Ministry of Agriculture
and Animal Resources, the Rwanda Agricultural Board and the National
Agricultural Export Board. The agricultural statistics system (Manzi, 2014)
consists of a programme of seasonal agricultural surveys based on multiple
frame area and list sampling methods and an agricultural reporting system
based on a network of experts organized at the district level.
The area frame is composed of 10,560 PSUs in the five strata sampled – the
seven non-agriculture strata are not sampled – and 8,556 physical limit
segments in the 540 PSUs sampled. The sampling rate varies according to the
importance of crops and livestock and expected variances; 25,000 tracks – the
part of a field falling within a segment – are surveyed. Segment size is 10 ha
except for the rangeland stratum, where a 50 ha size was chosen. PSUs are
selected in a systematic manner, with probability proportional to size (PPS).
This is achieved by ordering potential SSUs by the PSUs arranged according to
their proximity. By systematically selecting four sets of SSUs, a PPS selection
of PSUs is achieved. The selected PSUs are then subdivided into SSUs, from
which one is selected at random.
The approach uses ortho-rectified images at 2-metre resolution and colour
imagery at 25 cm resolution to enable stratification and the delineation of PSUs
and segments. At the national level, the precision of the 2014 estimates varied
by between 5 percent and 15 percent according to crop extent.
3.2.25. Senegal
Ten-year censuses – the latest was in 2013 – are interspersed with annual list
frame production surveys and a structure survey every three years, all carried
out by DAPS. Of 500,000 rural households, 6,300 are surveyed (f=1.3 percent)
in 900 of the country’s 9,000 districts, each equivalent to a PSU.
43
Senegal is part of the AGRI-CAB project, which was implemented in an
administrative unit of 2,500 km2 in collaboration with the Centre de Suivi
Ecologique, which has had a satellite receiving station since 2005; DAPS is the
final user (Tote et al., 2013). The area point frame was obtained by overlaying a
500x500 metre grid over the territory of interest. Stratification into six classes
was derived from photo-interpretation of Google Earth imagery covering
10,000 points. The sampling fraction was 10 percent and was systematic by
bloc with non-aligned replicates. Field documents were printed from the
satellite imagery – Rapideye 5-metre in Senegal and Kenya, Google Earth in
the other countries. In Senegal, locally recruited surveyors using hand-held
devices fed a central database in real time. Although the CVs for the main crop
areas were below 5 percent, the project reports relative efficiency of the
stratification close to 1 as a result of the multi-purpose objective, and relative
efficiency of the use of satellite imagery also close to 1 because the same crop
can be at any stage from sowing to harvest at any location. In view of the
US$3,000 cost of a Rapideye image and the €20 per day paid to field workers,
the cost-efficiency of remote sensing is questionable.
The CSE produces bulletins based on NOAA rainfall estimates and vegetation
data, and uses SPIRITS to analyse the stage of the season to identify the year
most similar to the current year to forecast yields at the end of August, when
vegetation stops growing, and to estimate biomass. The bulletins advise
pastoralists as to movement to minimize overgrazing. Fires are monitored on
the basis of MODIS data.
3.2.26. South Africa
Statistics SA produces agricultural statistics through list frame surveys covering
commercial companies active in the field. Its crop area and production
estimates therefore do not cover smallholder farmers. Another recurrent
problem is delays in their availability.
In 2000, the Department of Agriculture, Forest and Fisheries established the
Crop Estimates Committee, which is responsible for official annual estimates of
crop areas and yields. Statistics SA is one of its six members. The approach
used is described in Beukers (2014). Among various data sources, the
committee uses data provided by the National Crop Statistics Consortium,
established in 2002 and consisting of the Agricultural Research Council,
GeoterraImage Ltd and SiQ Ltd.
44
Its first activity was a subjective area frame survey consisting of visits or
telephone contact with farmers on the basis of an area frame established
through classification of Landsat 5 images. Blocks of 5x5 km were classified
for stratification into classes of agriculture intensity. Samples were defined by
overlaying a 225-metre square grid and randomly selecting a subset of points to
vary sampling intensity by stratum. Data were collected at the point, field and
farm levels, but decreasing farmers’ answer rates posed problems. Objective
yield measurements were realized through a PPS sub-sample.
The Producer Independent Crop Estimates System project started in 2005. On
the basis of Landsat and SPOT images, all agricultural field limits are digitized.
A 22.5x22.5 metre square grid of points is overlaid and samples are randomly
selected from the points falling within the digitized fields, with various
sampling fractions for the strata produced from the Landsat 5 imagery.
Fieldwork involves an aerial survey to identify land use and land cover. A slow-
flying aircraft uses point GPS coordinates as waypoints, and the surveyor
registers crop types on a hand-held device, showing field boundaries as
background. If the annual Spot5 composite imagery is subsequently used to
update field boundaries, Landsat 8 multi-temporal coverage is used to track
changes in crop cultivation trends or sudden changes in crop rotation. The
information serves to calibrate the Landsat images. Crop types are classified at
the end of the summer season for the previous summer and winter. The
classified areas give additional credibility to the Producer Independent Crop
Estimates System results, especially when changes occur such as the current
switch from sunflowers to soybeans.
3.2.27. The Sudan and South Sudan
Between 2006 and 2010, the EC-funded Sudan Institutional Capacity
Programme: Food Security Information for Action established a land-cover
map for the Sudan and South Sudan (Latham 2011, 2012). Based on Landsat,
Spot, IRS and Aster imagery, land cover was divided into seven classes after
field validation, covering covered 23.7 million ha in the Sudan and
2.8 million ha in South Sudan. Much of the work was carried out to detect
deforestation; no agricultural statistics were collected.
3.2.28. Tanzania
The 2012 census, which included an agriculture module, used an enumerative
area approach. With USAID support (Hoffmann, 2014), an area frame is under
45
construction; it is expected to use a stratified point sampling approach, as in
Nigeria.
3.2.29. Thailand
In collaboration with JAXA, GISTDA estimated rice areas in the rainy season
of 2011/12 using ALOS Pulsar, Radarsat2 and THEOS data (Rakwatin, 2014;
Okumara et al., 2014). A complete coverage of Thailand with 2009 ALOS 100-
metre resolution ScanSar data was analysed to provide an accuracy of
70 percent in half of the 76 provinces. Tests with ALOS and THEOS on rice-
area data in Khon Kaen province achieved 98 percent accuracy; in Suphan Buri,
accuracy was 76 percent. The KKU crop model based on MODIS and Meteo
ground-sensor information enables the estimation of yields and the derivation of
rice production.
3.2.30. Tunisia
Agricultural crop statistics are derived from surveys of agricultural campaigns,
cereals, farm structure and oases (SDSA, 2001). The annual agricultural
campaign survey relies on an area frame composed of stratified physical limit
segments and a list frame of state-owned land. Administrative units are
stratified using topographical maps – aerial photographs from 1985–1989 were
used for the structure survey – and the area is divided into homogeneous
segments whose size decreases with the intensity of agricultural use, from
200 ha to 2 ha. Samples are selected at the segment level with equal probability
in two independent selections; sampling rates are 4 percent for the arable land
and permanent cropland and 2 percent for woodland and bushland. Photographs
are only used to facilitate the work of field surveyors.
With regard to cereals (Medfai, 2014), the annual survey is based on 4,000
parcels in segments containing cereal fields. The National Remote Sensing
Centre has launched a SCAT project in which square segments are visited and
classified on Deimos and Landsat images. Relative efficiencies of between 1.8
and 3.2 have been observed. The Ministry of Agriculture commissioned CNCT
to pilot wheat and barley monitoring over most of the country during the
2012/13 (Haythem, 2014). Satellite data on vegetation are also used.
Monitoring bulletins issued from March to June present areas and production
forecasts based on NDVI indexes.
46
3.2.31. Turkey
The AGRIMONIS project (Küsek and Üsründağ, 2014), a joint venture of the
Ministry of Agriculture and the Statistical Office, aims to double the value of
the agriculture sector – currently US$120 billion including the food chain.
Relying on a team of 1,000 technicians and a super-computer (200tflops), the
system integrates satellite imagery from 50 cm resolution upward, some 1,200
robot stations, an agro-phenology grid with 1 km2 resolution and an agriculture
database of 28 million parcels. The system, which is intended to serve district
administrations and the private sector, from farmers to traders, will predict crop
yields and has been tested for crop area estimation through classification of
satellite imagery and calibration with ground survey data.
47
4
The Cost-Efficiency
Literature
Methods of cost benefit analysis are described in Renda et al. (2013); standard
approaches have been adopted by major institutions such as the European
Commission (EC, 2008). However, while such an approach may be relevant
when launching a new survey, as we shall see, in the field of agricultural
statistics cost efficiency is rarely considered, though a chapter is devoted to
survey costs in UNSTAT (2005) along with case studies to which we refer in
chapter 5.
A literature review on the cost effectiveness of remote sensing for agricultural
statistics produced for FAO (El Hadani and El Arrach, 2013) considers the use
of remote sensing in stratification, area frame construction, calibration,
estimation and forecasting, and describes experiences in FAO and in China,
Brazil, the European Union, India, Italy and the United States, which use the
standard relative efficiency method (Carfagna, 2001).
Other sources of information are available, but financial issues are not well
documented. Tenkorang and Lowenberg-DeBoer (2008) state: “Out of the
hundreds of agricultural remote sensing documents reviewed, only a few
reported economic benefit estimates. Many of those documents do not provide
details on how the economic benefit was estimated. Clues in the reports and the
fact that the numbers are often much larger than those for detailed studies
suggest that the studies not reporting details are often reporting gross benefits
without deducting the associated cost. Standardizing budgeting methods and
using the reported changes in yield and input application in 12 studies, remote
sensing is estimated to have the potential to improve average farm profits by
about US$31.74/ha Most of the studies based profit estimates on a single crop
season of data. Key improvements needed for studies of the economics of
remote sensing for field crops include: detailed reporting of budget
assumptions, multiple year data sets in the same fields, and replication of
studies of the same technology in different states.”
48
Chapter 12 of Benedetti et al. (2010) deals with the accuracy, objectivity and
efficiency of remote sensing for agricultural statistics, but only one paragraph is
devoted to cost efficiency and the references cited were published over 15 years
ago.
Some United States agencies have published analyses of the value of their
imagery. The National Geospatial Advisory Committee (NGAC) paper titled
“The Value Proposition for Landsat Applications – 2014 Update” notes that
Landsat imagery is the most used by government agencies and non-commercial
entities, and emphasizes the challenges involved in ascertaining the value of
Landsat products, but also refers to the alternative replacement costs and
opportunity costs as possible means to obtain a value estimate. The paper
establishes that the economic value of a single year of Landsat data far exceeds
the cost of building, launching and managing the satellites and sensors. The
economic benefits of Landsat data for 2011 are estimated at US$1.7 billion for
users in the United States and US$400 million for international users. Among
the 16 applications considered, agriculture appears twice: the World Agriculture
Supply and Demand Estimates used Landsat images worth US$3 million, and
the NASS CDL imagery base is valued at US$4 million.
The United States Geological Survey (USGS) evaluated the economic value of
imagery (i) by running partial equilibrium models to derive the additional net
present value provided by imagery of corn and soya in Iowa (Forney et al.,
2012; Raunikar et al. (2013) and (ii) by applying the contingent valuation
method (Miller et al., 2012). The value of such information in Iowa is assessed
in as US$858 million per year. The contingent valuation method involved
asking 6,500 respondents if a Landsat image was worth a particular amount
between US$10 and US$10,000, and according to the response the question
was repeated with a value increased or decreased by 25 percent. On the basis of
the average value obtained and the number of images downloaded, the value of
Landsat imagery in 2011 was calculated at US$2 billion. Of the nine proposed
spheres of application, agriculture (agricultural forecasting, agricultural
management) was third most significant with 7 percent, after environmental
sciences (48 percent) and land use-land cover (25 percent). Vadnais and
Stensaas (2014) report that the USGS evaluated the national land imaging
requirement by using the “value tree” method in which experts are asked to
state the aims and objectives of their organizations and the associated products
and services. Each product is assigned an “impact score”, which is revised to
balance missing inputs. Agriculture, one of the 11 applications studied, was the
sector most affected by non-availability of medium- resolution imagery.
49
With regard to the social benefits of Landsat, a 2013 paper by the Space Study
Board noted: “agricultural forecasting and management – the United States
Department of Agriculture uses Landsat data to monitor global crop supplies
and stocks to forecast shortfalls or gluts of various crops on the market. The
multimillion-dollar United States agricultural commodities market relies on
these crop predictions when conducting futures trading. These important
functions benefit [national] food and economic security as well as security”.
Green et al. (2007) state in a White House report that 38 percent of 1,295
ASPRS survey participants considered that it would not be possible to deliver
the same level of service without Landsat instruments, and quantified the
economic losses caused by non-availability of Landsat imagery at US$935
million annually.
Nelson et al. (2007) studied the use of Landsat remote sensing by five USDA
agencies: the RMA, NASS, the Foreign Agriculture Service (FAS) in support of
the World Agriculture Outlook Board (WAOB), the Forest Service and NRCS,
noting the reasons why anticipated social benefits were not delivered. Their aim
was to orientate future Landsat missions to maximize the economic benefits for
agricultural users. They cite ECON Incorporated (1974) in estimating that the
minimum annual incremental benefits of multi-spectral scanner (MSS) data in
the United States would be US$19.6 million from the wheat forecast, US$106
million in wheat and soybean consumer benefits, and US$53 million from the
accurate world wheat forecast. The reasons why these figures were not achieved
include poor image resolution, limited acquisition capacity, non-availability and
delays, lack of cost-effective processing capacity and the cost of the products –
US$4,500 for a Landsat 5 image in the 1990s. The Risk Management Agency
has won insurance fraud cases on evidence provided by Landsat TM 5 and 7:
for 2005, it claimed a return-on-investment ratio of 8.1, with US$34.4 million
in restitutions or forfeitures and US$3.8 million in USDA satellite image
archive subscription fees. Three activities involve NASS – construction of area
sampling frames, crop-area estimation in ten states, and CDLs – and although
no economic value is proposed, the relative efficiency of remote sensing is
reported as often reaching 3; it is not used at state level, at which results are
published in end June; it is however useful at county level, at which results are
released early in the following year, after the harvest season. The accuracy of
CDLs is reported at over 90 percent. The effects of remote sensing on the
USDA world agricultural supply and demand estimates produced by the
WAOB are quantified in terms of a 1 percent decline in the volatility of market
prices for wheat, corn and soybeans (Isengildina et al., 2005).
50
Hanuschak et al. (2001) and Hanuschak and Mueller (2002) note that crop area
estimation using Landsat MSS data started in 1975 at a cost of US$750,000 for
a single state, which by 1978 had decreased to US$300,000. In the 1980s,
NASS was operational in eight states, and costs declined to US$150,000 per
state. With the introduction of Landsat TM, NASS decided in 1988 to
concentrate on research and to discontinue crop area estimation by remote
sensing. Crop area estimates restarted at the end of the 1990s with CDLs, which
are more informative than crop areas, at a cost of US$75,000 per state in 2000;
in view of the costs, NASS used IRS as a backup rather than SPOT. Details of
users of CDLs are given by Mueller and Harris (2013).
Van Leeuwen et al. (2011) focus on the FAS in considering the changes
brought about by remote sensing imagery in their decision-support system for
producing assessments of global crop conditions and monthly estimates of
planted areas, yields and production for select commodities such as soybeans,
wheat, corn, rice, cotton and oilseeds. They used NASA-JPL benchmarking to
estimate changes occurring in the Decision Support System due to the
assimilation of the information from the satellites. The benchmarking was
carried out by defining objectives (five main objectives and nine sub-
objectives), the definition of risks and mitigating measures and scores, to derive
a matrix of the rate of achievement of objectives as a function of data supply,
and claim that by adding costs: “This benchmarking approach could also
provide insights to the US Office of Management and Budget about how
investments are used and aid decision making that result in improved and
independent global commodity production intelligence that equally benefits US
and world societies.”
The GMES Copernicus programme examined the potential of Earth observation
and Copernicus downstream services for the agriculture sector (Spacetec,
2012). It considers precision farming under the assumption of guaranteed long-
term availability of free products. It quotes costs of €5/ha and gross benefits of
€10/ha for farmers, which can be compared with production costs of €150 per
ton for cereals. Extrapolating the €5/ha unit cost to the area that could be
managed with Earth observation services, the current agricultural downstream
service value of €35 million is estimated to grow to €400 million with GMES
services.
A few authors discuss the costs of remote sensing applications for agricultural
statistics. With regard to the former PREVS study in Brazil, Bolliger (2014)
notes that the central remote sensing team had 14 technicians and 11
51
statisticians, with four fieldwork coordinators, 20 supervisors and 200
enumerators. Additional costs related to topographical and land-use maps,
Landsat and aerial photograph prints, Intergraph workstations and DB2 and
SAS software.
Latham (2015) refers to two costs in the SUPARCO projects in Pakistan: i) the
decision to start the project arose from the export of 458,000 tons of wheat in
2007 based on a false assessment of the harvest, with subsequent re-importation
at a cost of US$113 million; and ii) comparison of the area frame and Crop
Reporting Service approaches shows that the latter costs US$7 million and
requires 3,500 staff, whereas the SUPARCO survey costs US$300,000 and
needs 18 staff.
Other sources of information are the ESA and European Commission GMFS,
AGRICAB and E-AGRI projects. Limited cost-benefit information is given for
GMFS because it is a pilot, so costs are not final, and because such information
could be useful to competitors. The GMFS project in Malawi is described in
Ceccarelli and Remotti (2012), that in the Sudan by Haub and Brockmann
(2012). In considering the E-AGRI project in China, Dong (2014) notes that the
cost of a survey is €75 per segment and that a SPOT image costs €3,500,
concluding that the achieved relative efficiency of 2.6 corresponded to 135
segments, and use of imagery in that instance was therefore cost-efficient.
The concept of relative efficiency applied to the added value of remotely sensed
information is a way of examining the reduction of the size of field survey
samples. We consider two publications from an extensive literature.
Carfagna (2001; 2013) formulates the concept and explains that for crop area
estimation imagery can be used at the design and estimation levels.
At the stratification level, relative efficiency, expressed as variance without
stratification/variance with stratification, depends on the complexity of
landscape and the diversity of crops. If large non-agricultural zones can be
identified in an image, allocation of a reduced sampling fraction will enable a
drastic reduction of sample size. But for multi-purpose surveys, stratification
will be less efficient: the large corn-, wheat- and soya-growing areas in the
United States, for example, are easier to stratify than farming areas in Europe,
where ten or more crops compose the arable land. In all cases, it is essential to
vary sampling rates between strata to achieve efficiency.
52
The second element in the cost-benefit analysis of stratification is the cost of
the technology. Three opportunities allow for cost reductions:
Whereas Landsat imagery used to be expensive, NASS now has access
to free Landsat (since 2007) and Sentinel 2 (since 2015) imagery for
stratification purposes. Today, images can be handled on personal
computers using open-source software, and a programme that
automatically stratifies manually defined physical boundary segments
eliminates the need for costly human photo-interpretation (Boryan et al.,
2015).
Land-cover maps are now widely available. Teluguntla et al. (2014)
identify four – Thenkabail et al. (2009), Pittman et al. (2010), Friedl et
al. (2010) and Yu et al. (2013); to these we add the ESA Globecover
database (Defourny et al., 2011). In the most recent map, resolution is
down to 30 metres. Such data sets are far from perfect for stratification
because they usually contain only one class of agriculture, but
concentrating the sample on that class is an inexpensive way of
reducing sampling variance.
If a point sampling approach is adopted, stratification can take place on
a grid of points defining a plane square tessellation. Given that the point
is representative of the square, classification into strata can be done by
photo-interpretation of Google Earth imagery (Ceccarelli and Remotti,
2012). Our experience in the LUCAS survey showed that a photo-
interpreter can stratify 800 points per day, which provides an easy and
cost-efficient solution.
With regard to estimation, calibration methods can be used to integrate ground
survey results and image-classification results (Benedetti, 2014). The most
common approach – the regression method – has been applied in US, EU and
international projects for the last 25 years. Relative efficiency is a function of
the crop-specific correlation between ground survey segment crop proportions
and the corresponding classified image segment proportions. Relative-
efficiency values between 1.5 and 3.0 are commonly reported: their cost
efficiency depends on the cost of field surveys and the cost of acquiring and
interpreting imagery. Giovacchini (2015) notes that in Africa crops are usually
mixed, and a crop in a given area can be at any stage of development from
sowing to harvest. This drastically reduces the potential cost efficiency of
remotely sensed data – considering that the accuracy of classification in such
projects is only 40 percent. In the AGRICAB project in Kenya and Senegal the
cost of acquiring a RapidEye 5-metre resolution image was equivalent to 150
53
days’ wages for a field surveyor. It is difficult to achieve cost efficiency in such
circumstances, even if Landsat-8 imagery can be downloaded free; as their
spatial resolution of 30 metres is in any case far too large for the field sizes
encountered in African regions.
Gallego (2014) provides an exhaustive bibliography on relative efficiency
analysis in estimation, and focuses on the Ukraine to quantify the relative
efficiency – the extent to which satellite imagery can reduce the error of area
estimates – and cost efficiency – the extent to which satellite imagery can
reduce the cost of ground surveys for crop area estimation – of the MODIS,
Landsat-5/TM, AWiFS, LISS-III and RapidEye systems. With Er defined as the
ratio of the variance of the ground survey estimator divided by the variance of
the regression estimator, cost efficiency is Er multiplied by the ratio of the cost
of the ground survey divided by the sum of the ground survey and imagery
costs. On an area of 78,000 km2 and with a sample rate of 2 percent – 90 square
segments of 16 km2 – stratification was derived from the ESA GlobCover
(Arino et al., 2008). CVs of about 25 percent were obtained for crops covering
approximately 200,000 ha. With classification accuracy between 45 percent and
65 percent using decadal MODIS imagery or three dates for the other image
types, all types of imagery provided comparable relative efficiency between 1.5
and 1.6. In terms of cost efficiency, if the survey cost is €167 per segment only
the free-access MODIS and Landsat images enable cost efficiency of about 1.5,
and all the other sources become inefficient.
Studies from other sectors also consider the cost efficiency of remote sensing
for environmental monitoring. Mumby et al. (1999) review the price structure
of remote sensing, and even though today’s prices are lower than they were 15
years ago, it is useful to quote from the abstract of their paper: “Satellite
imagery is suitable for coarse detail habitat mapping where overall accuracies
of c. 70 percent can be achieved but is inadequate for fine detail mapping,
achieving c. 40 percent accuracy. Four types of cost are encountered when
undertaking remote sensing: i) set-up costs, ii) field survey costs, iii) image
acquisition costs and iv) the time spent on analysis of field data and processing
imagery. The largest of these are set-up costs such as the acquisition of
hardware and software which may comprise 48–78 percent of the total cost of
the project depending on specific objectives. For coarse-detail habitat mapping
with satellite imagery, the second highest cost is field survey which can account
for c. 20 percent of total costs and >80 percent of total costs if a remote sensing
facility already exists.”
54
Lewis et al. (2013) discuss the cost-effectiveness of remote sensing approaches
for mapping a tropical savannah environment. They believe that decision-
making should be based on the accuracy of results and the costs involved, and
identify cost components as: i) acquisition and preparation of field data; ii)
acquisition and preparation of image data; and iii) image classification and
accuracy assessment. Accuracy is quantified as the overall accuracy derived
from a confusion matrix comparing ground measurements with image
classification. In their study, results ranged from 28 percent to 67 percent, and
costs – 90 percent of which pertained to fieldwork – varied by only 15 percent.
Hence accuracy remained the dominant factor in the selection of approach.
Satellite image classification costs were never more than 1 percent of total
project costs.
De Bruin and Hunter (2003) study the cost efficiency of remote sensing for
checking European subsidies. The abstract states: “A growing problem
confronting users of remotely sensed imagery is whether the use of additional
or different imagery to improve decision quality is actually justified by its cost.
This paper discusses how to compare these competing factors so that an
acceptable trade-off may be made between them. The proposed method is based
on probabilistic cost-benefit analysis. The concept of “value of information” is
introduced in a practical case study using remote sensing to verify farmers’
declarations for a crop subsidy program in the European Union. Application of
the method requires that: i) the problem at hand can be represented by a
decision tree; ii) the desirability of each decision outcome can be expressed
numerically; iii) the imagery reveals information about the occurrence of events
not under the decision maker’s control; and iv) the probabilities of these events
and the extent to which they are detectable in remotely sensed imagery can be
assessed.” An average value is fixed for all outcome possibilities by creating a
multi-level decision tree in which each node has an associated probability of
movement up or down. The method consists of computing the average decision
value and comparing it with the cost of the technology used. As with area
statistics, part of the problem is choosing between fieldwork and remote
sensing: but because the issue is not covered in the paper its interest for us is
limited.
In conclusion, the literature provides two promising approaches for evaluating
the cost efficiency of remote sensing:
The most attractive is the USGS approach using a cost-valuation
method based on a survey of thousands of users, which enables reliable
55
estimation of the value that they attribute to imagery. The population of
users of remote sensing for agricultural statistics, however, is so limited
that no survey can be envisaged.
The other approach consists of evaluating cost efficiency by comparing
the costs of alternative technical solutions. If the classical relative-
efficiency model is adopted, conclusions can be drawn as to the
suitability of different options. This will be the model used in Chapter 5
on case studies.
56
5
Cost Case Studies
5.1. Haiti: Point Area Frame Sampling
5.1.1. Description of the point sampling frame
Haiti tested its first area frame survey in 1997 through a USAID project, using
the classic area frame with physical segments of 100 ha. Because of the nature
of the landscape and field sizes, the experience was inconclusive and did not
result in any practical implementation.
From 2008 to 2014, the Centre National d’Information Geo-Spatiale (CNIGS)
and the ITA and I-Mage companies implemented an EC-FED project entitled
Programme d’Informations Territoriales pour le Développement Durable
(PITDD) with the support of a €1.85 million FED grant (Giovacchini, 2014).
The six components were:
an agriculture and food security information system;
a land-use observation system;
management of national parks;
communications infrastructure;
land-use planning; and
watershed management.
We consider the first component. One of its initial outcomes was the Référentiel
National d’Observations Ponctuelles (RENOP) database, essentially the
equivalent of a land-use map. A set of 1,728,000 points on a regular square grid
of 125x125 metres was photo-interpreted on 2001 IGN imagery of 50 cm
resolution. Fifteen local operators spent 3,400 days between June 2008 and
February 2009 on photo-interpretation using a two-level system of 24
categories. Accuracy was verified through quality controls of 3 percent, giving
an average accuracy of 95 percent.
The classification system and the land cover statistics obtained at the national
level are shown in Table 2.
57
Table 2. Haiti, Point Area Frame Land Covers Percentages
Level 1 Level 2 %
1. Artificial
1.1. Populated urban zones 0.56
1.2. Populated rural zones 1.30
1.3. Industrial and commercial zones 0.02
1.4. Urban green, park and garden zones 0.01
1.5. Sport equipment (playgrounds) 0.01
1.6. Road network 0.28
1.7. Ports and airports 0.02
1.8. Zones under construction, waste sites 0.04
1.9. Mines and quarries 0.01
2. Agricultural 2.1. Alimentary multi-cropping 33.95
2.2. Agroforestry 16.56
3. Natural / semi-natural
3.1. Meadow or grassland 22.91
3.2. Shrub vegetation 16.60
3.3. Forest and wooded land 3.24
3.4. Mangrove areas 0.53
3.5. Trees not in Forest 0.76
4. Open space with no or
little vegetation
4.1. Sparsely vegetated areas 0.52
4.2. Bareland and rocks 0.38
4.3. River beds or recent alluvial soils 0.56
4.4. Dunes and beaches 0.03
4.5. Burned areas 0.12
5. Water area
5.1. Humid zones 0.65
5.2. Internal waters (lake, ponds, rivers) 0.79
5.3. Costal maritime waters 0.21
58
Land-use maps could also be produced from RENOP (Giovacchini, 2014):
Figure 1. Haiti – RENOP Land-Use Map, 1998
Table 3: Haiti, Stratification plan
Stratum Label Land-use codes Area (km2)
0 Non-sampled 14-19, 34, 42-45, 51-53 1 012
1 Urban 11, 12, 13 529
2 Arable 21 9 409
3 Orchards 22 4 590
4 Pastures 31, 35, 41 6 704
5 Forests 32, 33 5 499
59
The stratification used for the agriculture survey was based on the land-use map
and comprised six strata. It should be noted that the non-sampled stratum
accounts for only 4 percent of the total area, probably because of the
complexity of terrain. This limited from the outset the gain generally provided
by the non-sampled part of the territory because a small part of the sample was
redirected on the agricultural zones.
Sample allocation followed the method described in Bethel (1985): this
involved refining the sampling plan according to survey costs per stratum – the
forest stratum was twice as costly as the agriculture strata, which were in turn
twice as costly as the urban stratum – and targeting a 5 percent to 7 percent CV
for maize, bananas and sugarcane at the district level. Sample allocation is one
of the functionalities of the STAT-AGRI software developed by ITA for Haiti;
the Bethel method was provided by ISTAT. Initial variances were derived from
the first 2009 survey of two departments. To monitor the representativeness of
the sample, 20 percent was changed each year.
For the first 2013 growing season, 84 percent of the planned sampling (see
Table 4) was actually carried out.
60
Table 4. Haiti – Sampling Plan for 2013 Growing Season
Department Sample
size (ha)
RENOP size
strata 1-5
Area
visited (ha)
%
visited
Ouest 2,847 262,282 2,230 78
Sud Est 2,111 112,289 1,699 80
Nord 2,055 129,027 1,731 84
Nord Est 1,882 100,028 1,743 93
Artibonite 4,331 288,555 3,636 84
Centre 2,868 212,244 2,148 75
Sud 2,448 159,007 2,147 88
Grande Anse 1,640 116,911 1,371 84
Nord Ouest 2,088 131,250 1,986 95
Nippes 1,511 75,772 1,320 87
Total 23,781 1,587,365 20,011 84
5.1.2. Description of survey components
The survey planned in the initial project comprised the components described in
the following paragraphs.
Crop area phase 1 – first growing season, April–May
Once the 150 surveyors and their supervisors had been trained, the sampled
survey points were visited. Land use was observed in a 3-metre to 15-metre
circle round the point and classified at one of 178 levels. Where several crops
were grown together, each crop percentage was estimated and recorded. When
crops were grown under tree cover the forest was assigned a value of 1 and the
crop(s) were assigned area percentages, giving a total value between 1 and 2.
Crop area estimation was carried out by stratum at the department level. As
always in point sampling, the computations were simple because the crop area
at the stratum level is simply the observed average percentage (p) for the crop
multiplied by the stratum area. Aggregation of total areas at the department and
national level was obtained by summing the estimated areas at the stratum and
then the department level. Variances at the stratum level were computed on the
basis that point-level values follow a Bernouilli distribution whose variance is
p(1-p). Visits were not made to all sample points because: i) some were
61
inaccessible to the surveyors; and ii) imagery showed that some were located on
houses, so observation was irrelevant. This second case illustrates one element
of the cost-efficiency of using remote sensing.
Another contribution of remote sensing to the survey efficiency was the use of
image printouts during the field survey. They made it easy to reach the sample
locations, and requiring reference to images rather than GPS coordinates
limited location errors in later phases of the survey.
There were no surveys in 2010 because of the earthquake in January that year.
Surveys were completed in 2009 in two departments, in 2011 in six
departments, and in 2012, 2013 and 2014 in ten departments; it is not known if
the phase 2 survey was completed in in 2014.
Crop area, phase 2 – second growing season, November–December
Because Haiti has two crop-growing seasons, the survey was repeated to obtain
total annual crop areas.
Yield and production survey
The plan was to estimate the yields of maize, rice, bananas, yams and beans, but
lack of available staff at the Ministry of Agriculture limited the work to writing
survey manuals, training in plot selection and crop cutting and weighing, and
defining the 10 percent sample of points containing maize and rice in Artibonite
and Sud. No field survey was carried out. For maize, double-phase sampling by
regressing a limited number of fields (k) with precise yield measurements on a
larger sample (n) with stalks counts was envisaged.
Survey of holdings
The holding frame was based on the RENOP grid. Points were aggregated in
groups of four to define a square segment of 1.56 ha. Stratification involved
characterizing the segment according to land use at the four corner points. If no
points fell into stratum 2, 3 or 4 of the crop-area frame, the square was classed
as part of a non-agriculture stratum; if the segment had 1, 2, 3 or 4 agricultural
corners, it was allocated to stratum 1, 2, 3 or 4 according to the intensity of
agriculture.
To achieve a sample size of 700 holdings, and considering that the average size
according to RGA-MARNDR 2009 was approximately 0.74 ha, the sample size
62
was fixed at 350. Again, staffing limitations prevented the completion of this
activity.
5.1.3. Phase 1 crop area results, 2013
Table 5 is extracted from the EC-FED report (Giovacchini, 2014): it shows the
estimation for the main crop areas in hectares in the first growing season of
2013 and the 2009 RGA total areas for both growing seasons (MARNDR,
2009).
63
Table 5. Estimated crop areas, 2013 first growing season
Maize
Bananas
Beans
Rice
Department Area CV Area CV Area CV Area CV
Ouest 30,732 7 11,263 10 5,147 17
Sud Est 23,910 6 5,042 12 1,090 28 6 313
Nord 11,317 10 20,464 7 5,645 13 931 34
Nord Est 6,088 11 1,941 19 2,954 17 1,840 22
Artibonite 28,120 6 11,453 9 9,897 11 8,893 9
Centre 51,371 5 17,019 9 4,713 19 888 42
Sud 29,078 6 6,105 13 2,664 19 2,914 18
Grande Anse 9,702 11 7,493 12 1,521 28 789 42
Nord Ouest 12,658 8 10,110 9 516 40 37 140
Nippes 11,559 8 4,399 13 2,432 18 62 99
Total 214,540 2 95,293 3 36,582 6 16,364 7
RGA 2009 393,095 NA 97,537 NA 247,067 NA 75,861 NA
Sorghum
Gungo peas Sugarcane Unused land
Department Area CV Area CV Area CV Area CV
Ouest 7,044 15 18,974 9 6,652 13 70,324 4
Sud Est 1 11 5.6 12 779 33 25,204 6
Nord 321 56 5,088 14 12,399 9 26,945 7
Nord Est 240 61 3,657 16 2,278 19 15,587 7
Artibonite 10,367 12 12,421 10 18,012 7 64,415 4
Centre 8,931 13 18,247 9 13,659 11 24,976 8
Sud 4,144 18 7,622 13 1,418 29 34,499 6
Grande Anse 3,399 20 3,471 20 32,660 6
Nord Ouest 840 32 7,366 12 946 30 16,058 8
Nippes 624 39 4,346 14 1,967 20 8,133 10
Total 39,190 6 86,725 4 61,586 4 318,807 2
RGA 126,775 NA 108,633 NA NA NA NA NA
64
These results show that with the selected sample size the CV of the major crops
is below 5 percent when a crop area is greater than 50,000 ha, and below
10 percent when a crop area is greater than 10,000 ha.
The estimated crop areas do not match the RGA 2009 results. This is because
they refer to different years and different growing seasons; the large
discrepancies reflect methodological differences in the two approaches.
The graphs in Figure 2 show the variance of crop areas rescaled according to
the density of points per km2, which enables comparison of the efficiency of the
sampling plan with similar point surveys. The survey in Haiti appears to be
twice as accurate as TerUti 2014 – the slope was of 0.10 as opposed to 0.20 –,
possibly as a result of stratification and the fact that the points in TerUti are
clustered. Because details of crops and pastures are not publicly available, the
TerUti computations rely on the urban, bare-soil and wooded land classes.
Figure 2. Comparison of the standardized sampling variances:
a) Haiti survey
b) French TerUti survey
5.1.4. Stratification Efficiency
Stratification is carried out at the stratum level through photo-interpretation and
at the department level by administrative splitting, but to evaluate the cost
efficiency of imagery for stratification we must work at the department level,
and subsequently aggregate efficiency at the national level.
Following Cochran (1977), the variance of the simple random sampling plan
can be derived from a stratified sample by splitting the total variance into two;
65
the in-stratum variance and between-strata variance. At the department level,
this gives us:
_ s s _ _
V(Ysrs) =((1-f)/n)*(1/(N-1))*
{
Σ (Nh-1)* S
2h + Σ Nh (Yh – Y)
2}
h=1 h=1
where: N is the total population size;
n is the total sample size;
f is the total sampling fraction n/N;
s is the number of strata;
Nh is the population size for stratum h;
S2h is the variance within stratum h;
_
Yh is the average of stratum h; and
_
Y is the department average.
As usual, relative efficiency is expressed as the ratio of variances:
Er= Vsrs/Vstr
It should be noted that relative efficiency is the same whether computed on the
average or on the total.
An initial step is to look at the sample allocation by stratum and by department,
shown in Table 6.
66
Table 6. Sample Allocation by Stratum
Stratum Label Sample size
(2013 phase 1)
Sampling fraction
0 Non-sampled 0 0
1 Urban 303 0.008
2 Arable 10 655 0.017
3 Orchards 3 942 0.018
4 Pastures 3 107 0.008
5 Forests 2 058 0.006
20 065 0.012
Stratification efficiency evidently comes from (i) the splitting of total variance
into in-stratum and between-strata, and (ii) the varying sampling fraction
among strata: “non-sampled” is zero, and the values for “arable” and “orchards”
are double those of the other strata.
The variance gains at the department and national levels for the selected crops
and land uses are shown in Table 7.
Table 7. Variance Gains at the Department and National Levels
Dept 1 Dept 2 Dept 3 Dept 4 Dept 5 Dept 6 Dept 7 Dept 8 Dept 9 Dept 10 Haiti
Dry forest 0.86 0.72 0.30 0.79 0.53 0.77 0.67 0.76 0.74 0.78 0.67
Savannah 0.87 0.79 0.56 0.80 0.62 0.76 0.69 0.95 0.79 0.80 0.73
Maize 1.31 1.21 0.91 1.07 1.05 0.97 1.04 1.09 1.15 1.11 1.09
Rice NaN 1.75 1.06 0.95 1.77 1.00 1.22 0.91 1.33 1.43 1.38
Sorghum 1.10 1.28 1.14 0.97 0.81 0.92 0.84 NaN 1.15 0.92 0.97
Bananas 1.59 1.34 1.01 1.23 1.22 1.00 1.13 1.21 1.29 1.11 1.18
Manioc 1.15 1.22 0.98 1.05 0.98 0.96 0.86 0.86 1.00 1.00 0.99
Potatoes 1.54 1.08 1.09 0.97 1.45 0.92 0.92 0.86 0.99 1.07 1.09
Beans 1.25 1.17 1.13 1.00 1.02 0.92 1.20 1.07 1.24 1.04 1.08
Gungo peas 1.19 1.14 1.11 0.96 0.97 0.89 0.81 0.98 1.00 1.04 1.02
Citrus NaN 1.32 1.08 NaN 1.22 NaN NaN 1.01 0.64 1.26 1.02
Coffee 1.64 1.24 1.18 1.25 1.21 1.11 1.18 1.39 1.59 1.04 1.24
Sugarcane 1.70 1.25 1.11 1.09 1.35 0.93 0.98 0.96 1.19 1.06 1.17
Fallow 1.09 1.07 0.75 1.04 0.87 0.94 0.89 0.97 1.00 0.95 0.97
67
Various conclusions can be drawn:
Departments 3, 6 and 8 seem to have less stratification efficiency, and
department 6 has only one land use with an efficiency above 1, which
suggests that photo-interpretation was unreliable or that land uses
changed drastically between 2001 and 2013.
Of the land uses studied, bananas, beans, coffee, maize, rice and
sugarcane show efficiency gains between 8 percent and 38 percent at
the national level.
As expected, non-agricultural land use suffers from the stratification
and shows efficiency less than 1.
At the department level, stratification efficiencies are between 1.5 and
1.7.
5.1.5. Cost aspects
The cost structure of the survey has two elements: i) the costs met locally by
CNIGS; and ii) the costs met by the EC contractor. The crop-area survey cost
components were offices, staff, computer hardware and software, photo-
interpretation, sample selection, implementation and interpretation of results.
Offices were provided at no cost by CNIGS, which also provided local project-
management staff. The costs of expert staff were covered by the EC grant. Over
the five years, RENOP and survey activities cost about US$400,000 of the EC
grant.
Hardware – 15 personal computers and 100 GPS sets – was made available at
no cost, as they were available from earlier cooperation projects. The only
software needed were the DET database, which was available from an earlier
GMFS project and adapted for the Haiti survey by ITA, and the STAT-AGRI
software for sample extraction and estimation, the costs of which were included
in the US$400,000.
Fifteen local photo-interpreters were needed for eight months, at a total cost of
US$24,000. The cost of the 3 percent quality control by ITA experts was
included in the project cost. It should be noted that the survey did not require
the photo-interpretation of such a dense set of points: a 500-metre grid would
have been sufficient and would have required 16 times fewer staff.
Local management staff received specific training, but sample selection and
results interpretation were carried out locally each year by ITA experts; the
68
costs were thus covered by the EC grant. Field visits were carried out by a
private contractor paid by CNIGS: in the two phases, visits to the 20,000
sample points cost US$150,000 annually (approximately US$3-4 per point and
visit).
The annual cost of the survey from 2011 to 2014 was therefore US$256,000 –
ITA inputs were US$100,000, LU stratification cost US$6,000 and the field
survey cost US$150,000.
Setting aside the fact that land-use photo-interpretation was used for
additional purposes beyond the crop-area survey – costs could otherwise have
been divided by 16 – it is evident that imagery-based stratification incurred a
cost increase of 2 percent. But it provided a decrease in variance of between
8 percent and 38 percent for major crops, which illustrates the cost efficiency of
remote sensing for stratification in Haiti.
The USDA financed new aerial coverage in 2014, so that the imagery could be
updated and orthorectified at a cost of US$5/km2 – US$135,000 for the whole
country – and photo-interpretation could be repeated on a 500-metre grid at a
cost of US$1,500. The efficiency of the stratification would certainly be
enhanced.
5.2. Morocco: area frame sampling
5.2.1. Description of the area sampling frame
Morocco has used a multiple-frame sampling approach since 1978 to collect
agricultural statistical information. Surveys cover crop areas and yields on
10 million hectares, 30 million heads of livestock, market prices for 40 products
at central markets and souks, and agricultural holdings. The area frame covers
the 40 western provinces; its 3,000 segments contain 95 percent of the national
sample of holdings. The list frame covers the eastern and southern areas, where
agriculture is more marginal, and is largely based on administrative registers.
For crops under contract such as sugar-beet, administrative data replace the
area-frame survey results because they are considered to be much more
accurate.
In 2008 the Ministry of Agriculture started to renew the area frame, which was
originally based on a stratification extracted from existing 1/50,000
topographical maps. The main change is the creation of 1/25,000 land-use maps
69
derived from SPOT 2.5 metre resolution imagery covering ten classes: rain-fed
arable land, irrigated arable land, orchards, forest, grazing land, large urban and
small urban areas, villages, bare land and water bodies.
From 2011 to 2014 an automated GIS stratification tool supported the updating
of cereal survey samples over 24 of the 40 area-frame provinces (thus covering
an area of 66,900 km2)
. Samples are selected independently for each crop
survey. Sample sizes are initially computed at the province and stratum levels
with the NASS ALLOCATE software, which uses Neyman optimal allocation
based on constant costs and a priori variances taken from the preceding annual
surveys. Hitherto, 1,035 segments have been allocated in the three main cereal
strata: 10 arable rain-fed, covering 52 percent of the area, 20 arable irrigated
covering 8 percent of the area and 80 large douars covering 1 percent of the
area, which are important for counting livestock.
The GIS tool enables the extraction of segments at the stratum level. First,
rectangular 600 ha primary sampling units are defined for strata 10 and 20.
Second, PSUs are subdivided in segments of 30 ha for stratum 10 and 20 and
4 ha for stratum 80. A single segment is selected from each PSU; this stage will
soon disappear because it is a technical constraint inherited from the past. The
sample per strata (nh) is then split into artificial strata (kh, function of slope) and
systematic replicates (rh) with nh = kh rh.
5.2.2. Main results of the 2013 cereal survey
Micro-data were accessed for the Chaouia–Ouardiga, Doukala–Abda, Gharb–
Chrarda–Beni Hssen, Grand Casablanca and Rabat–Sale–Zemmour–Zaer
regions, which have 18 provinces and a total administrative area of 49,000 km2.
The data consisted at the tract level of the crop code and the tract area, which
extended beyond the segment. We decided to work at the level of the 810
segments on the percentages of the extended segment area computed as the
ratio of the extended crop areas and the extended segment area.
The main results – estimated areas in hectares and CVs – are shown in Table 8
at the regional level for wheat, durum wheat and barley, and at the global level
for the ten most important crops.
70
Table 8. Morocco Estimated Crop Areas (ha)
Regions Three cereals (ha) CV
Chaouia–Ouardiga 798,190 2.1
Doukala–Abda 590,860 4.0
Gharb– Chrarda–Beni Hassan 298,970 3.3
Grand Casablanca 30,260 9.8
Rabat–Sale–Zemmour–Zaer 231,360 6.3
Total 1,949,640 1.8
Ten most important crops
Area (ha) CV
Total three cereals area 1,949,640 1.8
Wheat 917,971 3.5
Fallow land 649,352 4.1
Barley 647,315 4.1
Durum wheat 384,337 6.3
Pasture 122,668 16.3
Corn 108,831 12.1
Waste land 86,342 10.4
Beans 68,771 11.5
Berseem clover 36,903 12.6
Chickpeas 35,207 13.7
Lentils 31,173 15.7
5.2.3. Stratification efficiency
This section looks at the efficiency of the stratification based on the land-cover
maps derived from SPOT imagery. Relative efficiency is derived through the
method set out in Cochran (1977).
For the total cereals area –wheat, durum wheat and barley – it can be shown
that relative efficiency varies from province to province between 1.4 and 14. As
in Figure 5, an important explanatory variable of the reduction in variance is the
percentage of province area excluded from the sampled area. This arises from
the land-use maps and the identification of areas where there is a low
probability of cereals being grown. This gain results from the concentration of
the limited sample available in the areas where cropping is intense.
71
Figure 3. Stratification Efficiency versus proportion of Non-Sampled Area in a Province
For the most important crops, it is evident that optimum relative efficiency is
obtained for the total cereals area (see Table 9). This is positive because these
are the main crops of interest for the survey. For pasture and corn, no gain of
efficiency is observed.
Table 9. Morocco, stratification relative efficiencies at national level
As shown in Table 10, the estimation of crop areas at the provincial level
benefits more from stratification, which is of interest because local estimates
Crop Relative efficiency
Total three cereals 3.0
Wheat 2.1
Fallow land 1.9
Barley 1.6
Durum wheat 1.3
Pasture 1.0
Corn 1.0
Waste land 1.8
Beans 1.4
Berseem clover 1.3
y = 11.546x + 0.6378 R² = 0.5742
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
0.00 0.20 0.40 0.60 0.80 1.00
Re
lati
ve e
ffic
ien
cy
% of non sampled area in the province
72
are always needed and usually suffer from greater variance because sample
sizes are smaller. Cases of relative efficiency above 3 are not exceptional. The
case of waste land in Kouribga – Er = 40 – appears abnormal: it is caused by
the fact that the 8 segments of 4 ha of stratum 80 (large douars) show a
percentage of waste land of 80 percent, whereas stratum 10 (rain-fed arable
land) shows a percentage of 1 percent.
73
Table 10. Morocco, stratification relative efficiencies at province level
Region Province Total Soft Fallow Barley Durum Pasture Corn Waste Beans Clover
3 Cereals Wheat Land Wheat Land Land
Chaouia –
Ouardigha
Benslimane 4.4 2.5 2.4 2 1.8 1.6 1.6 6.7 1.8 NaN
Berrechid 2 1.2 1.2 1 1.1 1 NaN 2.4 1 NaN
Khourbiga 6.3 1.8 2.1 3.4 1.7 1.5 NaN 40.6 NaN NaN
Settat 2.9 1.7 1.2 1.3 1.7 0.8 NaN 1.2 1.4 NaN
Doukkala –
Abda
El Jadida 1.7 1.3 1.7 1.3 1.2 0.9 1.1 5.6 1.3 2.1
SAFI 1.4 1 1 1 1 0.9 0.9 2.6 0.9 8.6
Sidi Bennour 1.6 1.1 1.2 1 1.1 NaN 0.9 3.2 1.6 3.1
Youssoufia 1.4 0.9 0.9 1 0.9 0.84 NaN 1.5 NaN NaN
Ghrab -
Chrarda-
Beni Hssen
Kenitra 3.2 4 1.5 0.9 1.9 0.8 1.7 1.2 1.9 1.5
Sidi Kacem 4.5 4.6 1.6 0.8 1.4 0.9 NaN 1.6 1.4 0.9
Sidi Silmane 13 10.1 2 2.2 2.3 2 NaN 2.8 2 2.1
Grand
Casablanca
Mediouna 2.9 1.5 4.8 3 1.5 NaN 1.5 1.9 1.5 NaN
Mohammedia 4.2 2.9 2.4 3 1.6 1.6 1.7 1.6 1.5 1.5
Nouacer 2.9 1.5 1.6 1.7 1.4 1.3 1.5 1.8 1.3 NaN
Rabat –
Sale-
Zemmour
Zaer
Khemiset 4.5 3.2 3.1 2.5 2.1 2.1 2.1 2.2 2.1 NaN
Rabat 10.2 10.2 10.2 10.2 NaN 10.2 NaN NaN 10.2 NaN
Sale 6.6 4.1 8.5 6 2.8 2.9 3.5 2.7 2.8 NaN
Skhirate Temara 7.1 4.6 3.8 3.8 2.4 2.5 2.7 2.4 NaN NaN
74
5.2.4. Cost aspects
The first cost component of the survey was the 1/25,000 land-use maps, each of
which covered 16,000 ha and cost US$1,100; of this, 50 percent was imagery
costs, 35 percent field validation and 15 percent photo-interpretation. The total
for the 24 provinces covering 66,900 km2 was US$460,000.
The second cost component was sample extraction with the free NASS
ALLOCATE software and the GIS mapping tool, which cost US$20,000 to
buy. These helped to reduce the time required for each region from ten months
in the first survey to about one week.
The third component was field visits to the selected segments to complete the
first survey and identify the holdings linked to each segment. This work was
contracted to the private sector and cost an average of US$225 per segment, to
which the US$140 borne by the Ministry of Agriculture must be added. In
subsequent years survey costs amounted to US$125 per segment.
The estimated cost of the cereals survey in the first year was therefore
US$378,000 for the 24 provinces. The annual cost in the subsequent nine years
amounted to US$130,000. The average cost for the ten-year survey was hence
US$155,000. Stratification therefore increased annual costs by 30 percent.
Considering the reduction in variance at the global level described in Section
5.2.3, it is evident that except for pasture land and corn the relative efficiency of
stratification was greater than in 1.3, and reached a value of 3 for the three
major cereals. At the provincial level, Er values above 3 were not exceptional.
We can therefore conclude that the stratification of the cereal survey in
Morocco was cost-efficient.
5.3. China: Area Frame Sampling and Regression
Analysis
Several entities produce agricultural statistics in China (Zhang, 2015). Some are
official: the National Bureau of Statistics (NBS), the Ministry of Agriculture,
the State Administration of Grain and the China Meteorological Office. Other
non-official sources include the Institute of Remote Sensing and Digital Earth
Imagery and the Chinese Academy of Sciences. Of these, the NBS is the only
one delivering official statistics and also using remote sensing for area frames,
stratification and image classification for regression.
75
Figure 4. Administrative Divisions of the People’s Republic of China
Source: http://en.wikipedia.org/wiki/Administrative_divisions_of_China
5.3.1. Description of the area sampling frame
Increasing economic migration from rural areas to towns was the main reason
for replacing the former list frame with an area frame. When rural residents
leave, their land is redistributed among the other rural workers, which leads to
land fragmentation and rapid depreciation of the list frame. Using an area frame
is thus a way to stabilize the sampling frame to be used over a ten-year period.
The NBS uses area frames in seven of the 33 provinces of China (Yu, 2013);
area frames for three additional provinces planned for 2015 are not yet
operational. The administrative area covered by the ten provinces is
1,652,083 km2, 17 percent of the national land area. Table 11 shows the year of
area frame creation, the year of the imagery used and the year of the first area
frame survey in these provinces.
76
Table 11. Area Frame Surveys in China
Province Km2 Location Image year for
census update
First survey
year
Counties
with area frames
Anhui 139 400 East-central 2013 2014 63
Hebei 188 434 East-central 2014 2016
Henan 167 000 East-central 2012 2013 43
Hubei 185 900 East-central 2012 2013 68
Hunan 211 855 South-east 2014 2016
Jiangsu 102 600 East-central 2012 2013 75
Jiangxi 166 894 South-east 2014 2016
Jilin 187 400 North-east 2013 2014 44
Liaoning 145 900 North-east 2012 2013 43
Shandong 156 700 East-central 2014 2015 110
Creation of the area frames takes advantage of existing lists of small-scale
farmers and detailed land-use maps at the individual plot level. Using ArcGIS
and ENVI, village administrative limits and land use maps are merged within
the GIS tool, after which remotely sensed imagery is used to update the land-
use maps by photo-interpretation and automatic classification.
On the basis of previous surveys, counties of marginal agricultural importance
are excluded from the frame, but the objective of surveying at least 90 percent
of the total crop area is maintained. Stratification is then carried out at the
village level that defines PSUs. The number of villages in each county ranges
from 100 to 500, each with an area of between 1 km2 and 5 km
2.
Using the total crop areas at the village level classified by remote sensing,
between two and seven strata are defined and the PSUs are classified into them.
The sample size for villages per county is kept to between 15 and 20; sample
size per stratum follows rules akin to the Neyman allocation method involving
the function of strata size and variability. The stratum with the least agriculture
is excluded from the surveyed population. The PSUs are selected with
probability proportional to size, the auxiliary information being cropping
intensity.
Finally, five secondary sampling units, equivalent to segments, are selected
from each sampled village. A 2 ha or 5 ha grid is overlaid on the village limits,
77
and five grid elements are randomly selected. These segments are adjusted in
two ways: the non-arable area obtained from remote-sensing classification is
excluded from the segment, and field plots intersected by the segment are
entirely included to give irregular physical segments of random size.
This process results in a stratified two-stage sampling design with PPS selection
at the first stage and random selection at the second stage. The sampling
fraction is of the order of 0.2 percent.
Table 12 shows the official 2013 statistics for the area frame provinces.
Table 12. China: Official Statistics for 2013 in Area Frame Provinces
Province Wheat
1000ha
Total/rice
1000ha
Corn
1000ha
Rapeseed
1000ha
Anhui 2432.9 2214.1 845.1 568.1
Henan 5366.7 641.3 3203.3 371.3
Hubei 1094.8 2101.2 573.5 1226.3
Jiangsu 2146.9 2265.7 426.6 413.9
Jilin 0 726.7 3499.1 0
Liaoning 5.6 649.2 2245.6 0.6
Shandong 3673.3 123.1 3060.7 9.5
Source: China rural statistical yearbook (2014).
5.3.2. Main results in the province of Anhui
Confidentiality restrictions prevent access to survey results, even when
aggregated at the stratum level per county. But access to the survey design data
was granted, which made it possible to work on the expected relative efficiency
of the design and to estimate sampling variance. The estimator of variance in
two-stage sampling with PPS in the first stage is usually obtained by
approximation, using the Taylor series development or re-sampling methods
such as bootstrapping or jack-knifing. Working at the design level enabled 100
sampling runs, which made it possible to obtain a variance component at the
PSU level. The second-stage variance component is usually marginal at
5 percent to 10 percent of total variance, so the relative efficiencies computed
later can be taken as representative of the true values.
78
The crop areas obtained at the design level are in line with the 2013 survey
results shown above, and thus give confidence in the derived CVs (see Table
13.). They show that because of the stratification and PPS, CVs below 5 percent
are obtained at the province level for crop extents above 100,000.
Table 13. – China, design level crop areas in Anhui province
Crop Area (ha) CV
Wheat 2.231.460 1.3
Early Rice 102.790 5.4
Middle Rice 1.895.270 0.9
Late Rice 870 116.7
Corn 1.021.100 3.0
Rapeseed 205.050 4.6
Expected estimated crop areas at the county level and their CVs are given in
Table 15 on page 80. At the county level, with 20 PSUs and 100 SSUs, CVs
below 10 percent are often obtained, but for a fixed crop extent a large range of
CVs values is observed.
Figures 5 and 6 show the relationship between the logarithm of the crop area
and the expected CV for wheat and middle rice.
Figure 5. – China, relation between wheat CVs and areas at county level in Anhui province
79
Figure 6. – China, relation between middle rice CVs and areas at county level in Anhui province
5.3.3. Stratification efficiency
Using the Cochran (1977) formula mentioned earlier, relative efficiency was
computed by adding the “between strata” variance to the “within strata”
variance observed for the stratified sample, and calculating the ratio between
the non-stratified and stratified variances at the county and province levels. We
computed the efficiencies in two ways: i) including the non-sampled stratum;
and ii) excluding the non-sampled stratum. And given that the PPS sampling
could invalidate the use of the Cochran Er formulae, we created a set of 100
sampling simulations, keeping the PPS selection of PSUs but without
stratification.
Table 14 compares the efficiencies obtained by both methods for the six crops
in Anhui province.
80
Table 14. Comparison of Er Calculation Methods
Comparison of Er calculation methods
Wheat Early Rice
Middle Rice Late Rice
Corn Rapeseed
Cochran with non- surveyed stratum
3.6 1.4 7.7 0.3 1.6 1.1
Cochran without surveyed stratum
1.3 0.8 2.2 0.3 0.9 0.7
Monte Carlo 1.4 1.0 2.8 0.9 1.1 1.0
Following Cochran’s approach, it is clear that at the province level efficiency
reaches a value of 7.7 for middle-season rice, 3.6 for winter wheat and 1.6 for
corn. Rapeseed, also a winter crop, and early and late rice show poor
efficiencies. As with the preceding case studies, a large part of the efficiency
results from not sampling the low intensity stratum of the villages. The
efficiency computed for the sampled strata only reduces efficiency to 1.2 for
winter wheat and 2.2 for middle-season rice, and to less than 1 for the other
crops. In Monte Carlo simulations, efficiency is slightly less.
In what follows we will rely on the efficiency obtained through the Monte
Carlo simulations. Efficiencies at the county level are shown in Table 15 for
wheat and middle-season rice only because the Er is nearly equal to 1 for the
other crops. At the county level, it is evident efficiency varies considerably,
mainly as a function of the extent of the non-sampled strata and of the
difference in average and variances among strata.
81
Table 15. China: Efficiencies at the County Level
82
5.3.4. Costs: Area Frame and Stratification with Remote Sensing
The costs associated with the area frame survey can be subdivided into the cost
of constructing the area frame, including stratification, the cost of carrying out
the survey and the cost of analysing the results. One part of the costs is the fees
for the work sub-contracted to commercial companies and to the counties,
provinces and headquarters; another is the NBS charges.
The NBS considers that the area frame approach is more expensive than the list
frame in terms of both frame construction and the field survey. This is because
of the technology used – satellite images, digital land-use maps, workstations,
GIS software and PDAs – and because the sample was more compact in the list
frame, which was based on the hamlet level, than in the area frame, which was
based on the village level. The NBS prefers the area frame approach because of
its robustness.
For each province, the two main cost components can be summarized as:
i) RMB500,000 for sub-contracted work on creating the area frame, which is
expected to last for ten years; and ii) RMB50,000 per county for the annual
field survey; for Anhui province, the total is therefore RMB 3 million.
The sub-contracted costs cover image acquisition on three dates at 200 GB per
province, and classification. Using land-use maps limits updating, frame setting
and the sample extraction, which requires up to 20 persons full time for two
months.
The NBS costs relate to: i) central organization: two staff for two months,
hardware and software costing RMB500,000 for 20 stations with ArcGis and
ENVI centrally, plus provincial stations; ii) province-level work: printing maps
for 150 counties and interpreting survey results; and iii) county-level work: 3
field visits by two enumerators per village with field data entry into electronic
systems.
In summary, area frame creation and stratification cost an estimated
RMB1 million, compared with the field survey annual cost of RMB3 million.
Assuming that the area frame will be used for ten years, the additional cost of
the area frame and stratification are 3 percent.
83
Considering the relative efficiency of stratification and the cost efficiency of
remote sensing for area frame creation and stratification, the approach is clearly
cost-efficient.
5.3.5. Using Remote Sensing to improve crop-area estimation
The use of satellite imagery to improve agricultural statistics through the
regression method is still experimental at NBS. In 2014, two provinces were
fully covered: Liaoning – 145,900 km2 – and Xinjiang – 1,664,897 km2; most
of Zhejiang province was also surveyed. The province of Anhui – 139,400 km2
– is being covered in 2015; the survey of winter wheat is already complete.
The following discussion of methods is based on Xinjiang province, where
cotton is the main crop of interest. The 2013 crop areas were: i) wheat –
1.1 million ha; ii) rice – 67,000 ha; iii) corn – 900,000 ha; iv) rapeseed –
44,000 ha; and v) cotton – 1.7 million ha.
The first step was to overlay a 3x3 km grid on the province. The grid cells
would form the PSUs. Because much of the province is mountain and desert, a
large part of the administrative area was excluded from the frame, reducing the
population target area to 200,000 km2. This resulted in a population of 22,000
PSUs, from which a sample of 500 was to be selected. The PSUs were then
stratified into 14 classes such as dryland, wetland and cotton cropping on the
basis of land-use maps and image coverage from 2013. PSUs were selected at
the county level, with PPS as a function of total arable land.
The second stage of the sampling consisted of random selection of three
segments as SSUs of 300x300 metres, totalling 9 ha. Total province sampling
size amounted to 1,500 segments.
Using the current year’s ground survey, satellite coverage from three dates was
classified into the main crop classes, aiming at 75 percent accuracy on training
set. Some 1,000 free-access GF-1 or ZY-3 scenes enabled coverage of
70 percent of the area; the missing part required the purchase of expensive
commercial imagery such as Ikonos and Worldview2 covering 1,000 km2 each.
Some of the target area could not be imaged, but there was imagery for all 500
sampled grids.
This exercise enabled three types of estimate: i) direct expansion of the results
of the 1,500 segments of the ground survey; ii) direct expansion of the
classification results for the 500 sampled grid cells, the PSUs; and
84
iii) regression estimation merging the ground survey and classification results at
the segment level; 840 segments contained cotton; this was done on 40
classified GF-1 images and 20 Landsat 8 images.
For reasons of confidentiality, access to detailed results was denied. The
difference between the first two approaches was understood to be of the order
of 5 percent, which would have been an excellent result. Regression analysis
was applied at the province level without subdivision based on image
acquisition dates or strata. The coefficient of determination – R2 – for cotton
was announced as about 0.66, which again appeared excellent for such an
imprecise approach.
5.3.6. Cost aspects of Using Remote Sensing to improve crop-area
estimation
As with the area frame exercise, the work was shared between NBS and a
contracted commercial company. All the subcontracted technical work was
carried out at NBS premises at a cost of RMB700,000 for the province.
Internal NBS costs were mainly the ground survey, on which 200 surveyors
with their drivers worked for two weeks in each survey period, covering an
aggregate of 20,000 km. Salaries were RMB150 per day for each of the
surveyors; drivers with private car were paid RMB250 per day, and fuel costs
amounted to RMB100 per day for each team. Data were entered on hand-held
computers. Each segment had averagely 15 to 20 fields. The overall cost of the
ground survey was approximately RMB1 million for the province.
5.4. India: Area Frame Sampling and Pixel Counting
The Directorate of Economics and Statistics of the Ministry of Agriculture
produces agricultural statistics in India based on information from sources such
as the -Forecasting Agricultural output using Space, Agro-meteorology and
Land-based observations initiative (FASAL), of which the Mahalanobis
National Crop Forecast Centre (MNCFC, created in 2012), is the operational
arm. MNCFC’s mandate covers crop forecasts, monitoring of extreme weather,
horticulture assessment and crop insurance. But its outputs in terms of crop
areas and production cannot be distributed because they are unofficial.
Crop forecasts are based on conventional surveys and remote sensing. Pre-
season and early-season forecasts are based on econometric and agro-
meteorology models; mid-season, state-level and district-level pre-harvest
85
forecasts rely on remotely sensed information. The MNCFC issues 18 annual
forecasts based on remote sensing (see Table 16) for crop areas and productions
for the eight main crops – Kharif rice, wheat, cotton, sugarcane, jute, rapeseed
and mustard, Rabi rice and Rabi sorghum – on 102 million ha of the
120 million ha of cropland in India.
Table 16. India Annual Forecasts based on Remote Sensing
Crop States Forecasts Date Level Area Yield Area Mha Off. Stats
Jute
3 F1 July 18 2014 District SAR Agromet 0.78
Kharif Rice 13 F1 Sep. 05 2014 State SAR Agromet 33.9
13 F2 Oct. 01 2014 District SAR Agromet
14 F3 Feb. 03 2015 District SAR
RS + Agromet
+ crop Cutting
Sugarcane 5 F1 Aug. 08 2014 State Awifs Agromet
3.86 5 F2 Dec. 17 2014 District Liss3
+L8 Agromet
Cotton 8 F1 Nov. 07 2014 State Awifs Agromet 12.2
8 F2 Dec 17 2014 District Liss3+L8
Agromet
Rapeseed &
Mustard
5 F1 Dec. 31 2014 State Awifs Agromet
5 F2 Feb. 03 2015 State Awifs Agromet 5.48
6 F3 Mar. 05 2015 District Liss3+L8
RS spectral mode + Agromet
Rabi Sorghum
2 F1 Feb. 03. 2015 District Liss3+L8
Agromet 3.67
Wheat 6 F1 Feb. 09 2015 State Awifs Agromet 36.91
6 F2 Mar. 05. 2015 State Awifs Agromet
8 F3 Apr. 10 2015 District Liss3+L8
RS spectral mode + Agromet
Rabi Rice 5 F1 Apr. 01 2015 District Risat Agromet 3.41
Potato 5 F1 Feb 06 2015 State
District
Awifs Agromet 1.37
5 F2 Mar. 05 2015 District Liss3+L8
Agromet
86
MNCFC forecasts up to the district level aim to cover at least 90 percent of
national production of the eight major crops, so only 19 of the 29 states are
included in the analysis. MNCFC collaborates with 90 national and state
entities:
Space Application Centre-Indian Space Research Organization (SAC-
ISRO) for research and development, with objectives fixed at six-
monthly meetings; new approaches are tested by SAC for four years,
after which MNCFC staff run it for two years under SAC supervision.
Estimation of crop areas is based on:
ground truth collected by 19 state agricultural departments;
MNCFC state-level area estimations based on remote sensing; and
district-level area estimations from state remote-sensing centres; image
analysis is carried out at MNCFC by state experts.
Operational estimation of yield and production is based on:
Agromet and simulation models run by the Indian Meteorological
Department in collaboration with 46 universities; and
econometric models run by the Institute of Economic Growth.
5.4.1. Crop area estimation: methods and results
The 29 states of India cover 3.29 million ha. The 2012 MNCFC sampling plan
based on 19 states was revised in 2014 and now consists of 5x5 km segments
following tests by SAC-ISRO. Initially, classified remotely sensed images
provided crop maps that provided the percentage of the eight major crops in all
segments, which in turn enabled the creation of between two and four strata
with crop-specific class limits at the state level.
A crop-specific sample of segments is then randomly selected at the district
level to ensure representativeness, with a 15 percent to 20 percent sampling
fraction per stratum except for strata with less than 5 percent for important
crops and 2 percent for minor crops, which are not sampled. The national
sample size is of the order of 25,000.
For classification, ground truth is collected for 30 percent of the sample –
8,000 points, one field per segment. Of these fields, 6,000 are surveyed using
87
smartphones for photography, GPS recording and digital data entry; real-time
results can be monitored at the ISRO Bhuvan website:
http://bhuvan.nrsc.gov.in/bhuvan_links.php Information on planting and
harvest dates, areas, crop development and diseases is also acquired.
For rice and jute, RISAT 1 images – 18-metre ground sampling distance (GSD)
C band, like Radarsat – from three dates are selected to detect planted areas in
the four cycles: 13–25 July, 15 July–15 August, 2 August–15 September and 27
August–26 September. On the basis of the ground-truth data, back-scattering
model limits are established at the district level by computing means and
standard deviation for fields containing at least 7 pixels. Rice and jute estimates
are derived from the 25,000 classified segments of the sample.
For the eight states with archived results – there are no district-level archives –
the CV and relative efficiencies of rice estimates are given in Table 17. For rice
areas between 1 and 3 million ha, the coefficients of variation are between
1 percent and 3 percent; there is, as expected, a clear relationship between CV
and crop extent at a fixed sampling rate.
The efficiency of stratification based on radar imagery ranges from 1.3 to 3.2 –
an excellent result in view of the “noise” encountered.
88
Table 17. Rice Sampling Plan, CV and Stratification Efficiency
Rice Sampling Plan, Coefficient of Variation and Stratification Efficiency
State CV (%)
Effective Degrees of Freedom
Efficiency/ Gain
Population Samples Population
Total Sample
Total Sampling Fraction
A B C D a b c d N n %
Andhra Pradesh
2.30 267 3.22 316 581 736 1201 49 91 114 182 2834 436 15%
Bihar 1.41 644 1.30 649 989 1091 784 134 183 206 159 3510 682 19%
Jharkhand 1.92 350 1.30 233 552 747 872 47 93 130 144 2404 414 17%
Karnataka 3.34 269 1.66 195 521 741 1350 41 91 123 204 2807 459 16%
Odisha 1.53 709 1.38 774 1192 1346 1486 126 194 222 246 4798 788 16%
Chhattisgarh 1.61 529 1.46 711 979 1042 1347 114 157 163 208 4079 642 16%
UP 1.23 1230 1.31 1332 2218 2154 1826 226 364 365 312 7530 1267 17%
West Bengal 1.15 469 2.17 603 907 900 676 98 139 143 115 3086 495 16%
89
Figure 7. CV as a Function of Rice Crop Area, by State
For all other crops, AWIFS and LISS-3 images are used and area estimates are
based on the classification results at the population level. For technical progress
allowing, no sampling has been carried out since 2014.
At the start of the crop season, the 10-day NDVI temporal series from the two
AWIFS instruments – these cover a 700 km swath, repeated every five days –
are clustered; pixels with the crop pattern observed in the ground-truth data set
are counted in the relevant crop area. For the last pre-harvest estimate, one
LISS-3 image is classified by maximum likelihood: this is done crop-by-crop,
but where wheat is involved, pixels already classified as mustard or potatoes are
masked.
Because there are no archives, information on the CVs and stratification
efficiencies could not be obtained. The MNCFC, however, re-ran classification
procedures so that matrices could be provided for two states – an easy one and a
difficult one. Even though the matrices are over-optimistic because they are
based on the training sets, it is evident that in the Punjab example excellent
(Kha)
90
96 percent overall accuracy is obtained, whereas in Gujarat the early image date
for wheat and mustard development limited accuracy to 72 percent.
To quantify possible bias implied by the classification errors, we looked at
errors of omission and commission: in Punjab, the bias is 1.2 percent for cotton
and minus 1.2 percent for paddy rice; in Gujarat; the bias is minus 10.9 percent
for wheat and 2.9 percent for mustard (see Table 18).
Table 18.
Punjabe State
LISS III Data (24 Sept 2014)
Classes Cluster Bean
Plantation Cotton Paddy Fallow Mixed Crop
Row Total
User Accuracy
Cluster Bean
13 0 0 0 5 0 18 72.22
Plantation 0 15 0 0 0 0 15 100.00
Cotton 0 0 78 2 0 0 80 97.50
Paddy 0 0 3 81 0 0 84 96.43
Fallow 1 0 0 0 49 0 50 98.00
Mixed Crop
0 0 0 0 0 26 26 100.00
Column Total
14 15 81 83 54 26 273
Producer Accuracy
92.86 100.00 96.30 97.59 100.00
Overall Accuracy 95.97
Punjab is an agriculturally dominant area, During 24 Sept, Rice will be senescence phase and
cotton will have very good vegetative growth.
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Gujarat State
Landsat Data (31 Dec 2014)
Classes Potato Other Crop Wheat Mustard Row Total
User Accuracy
(%)
Potato 88 3 14 8 113 77.88
Other Crop 0 138 2 3 143 96.50
Wheat 9 11 46 15 81 56.79
Mustard 12 5 6 40 63 63.49
Column Total 109 193 68 66 436
User’s Accuracy
80.73 71.50 67.65 60.61
Overall Accuracy 71.56
The Lower Accuracy for Wheat is due to the fact that, the crop is at very early stage in
December and hence difficult to classify. Similary the optimum date for Mustard is
Mid – January.
5.4.2. Method for crop yield estimation
Agromet models
After adjustment at the district level of the trend for each crop yield in a 25-year
time series, annual deviations are computed. Next, correlations with annual
yield deviations are obtained for meteorological variables for 14-day periods
such as Tmax, Tmean, sunshine hours, soil humidity and rainfall. For each
meteorological variable, a weighted variable is defined as the sum of the
preceding fortnight’s value weighted by the observed correlations, and a
stepwise regression is adjusted to predict crop yield at that date.
Crop simulation models (rice and wheat)
In the last three years, the DSSAT model has been calibrated for several
varieties. The model gives crop yields based on soil, meteorological and remote
sensing information from systems such as AWIFS NDVI and RISAT: sowing
date for wheat or transplantation date for rice, variety, phenology, leaf-area
index, biomass and yield. This model is used for final forecasting; it is adjusted
at the district level.
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Spectral models based on remote sensing
For rice, a physical model (Choudhury et al., 2007) is adjusted in relation to
biomass from the back-scattering in RISAT imagery, with crop age taken into
consideration. Reference tables established by ISRO on the basis of the crop
harvest index are used to convert biomass into yield.
For wheat and mustard, a Monteith empirical model is used to relate crop
biomass to received light. Using the AWIFS crop masks and working at 1 km
per pixel, the model is fed with EPAR and land surface temperature from
MODIS, received sunlight from Insat and crop mask/sowing mask from
AWIFS. Current year harvest indexes relating yield to biomass are derived from
ground values.
Crop cutting experiments
For rice and wheat, the sample of 2,000 crop cutting experiments is optimized
on the basis of remote sensing to derive state-level yield estimates. After the
final classification – in September for rice and in March for wheat – the current
year crop masks are used and subdivided in four classes of max seasonal NDVI,
with a random selection of points taken per stratum. In 2015, ten samples will
be taken per district.
Current research targets the use of Agromet models for more crops, and the use
of radar for maize and cotton areas with a view to obtain earlier forecasts in
rainy seasons, when optical data are scarce.
5.4.3. Costs and resources
The MNCFC has an annual budget of US$1.7 million for offices, salaries, field
surveys, imagery, hardware and software. Its 31 staff members come from
ISRO, ISS, DAC and contractors. There are 19 workstations under four
ARCGIS licences, 13 Erdas Imagine licences, 5 Geometica licences and two
Statistica licenses. They work with a server providing 16 TB of disk space.
Ground truth is collected every year by DAC in 8,000 field visits costing
US$15 each and 2,000 crop-cutting visits costing US$25, giving a total cost of
US$170,000, 10 percent of the budget. Salary costs are supported by the DAC.
Collection requires two staff days per district for ten field visits, with one
hour’s travel between visits on average.
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Satellite imagery is provided by ISRO at preferential prices below those applied
to clients outside India; the annual budget is US$275,000. The products are:
LISS-3 ortho-rectified images at US$233 each, or US$111 for a basic product;
AWIFS ortho-rectified images at US$500 each, or US$250 for a basic product;
weekly country-wide NDVI AWIFS mosaics costing US$1,000; and
RISAT radar images at US$190 per scene, well below the US$2,600 charged
for RADARSAT until 2012.
The time needed to issue a crop area bulletin, including image downloading,
registration and analysis, is estimated as eight to nine working days per state. A
final bulletin for rice requires 120 staff days. It is impossible to estimate the
staff needed for a crop-yield bulletin because 46 state remote sensing institutes
are involved.
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6
What’s next
6.1. Imagery access: the 2015 situation
In theory, access to satellite imagery is no longer a problem. Although some
sensor series have been modified and renamed – AVHRR was replaced by
METOP, VEGETATION by PROBA V and ENVISAT/ERS by SENTINEL-1
– numerous sensors are currently available at various levels of spatial and
spectral resolution. According to a report by the United Nation Office for Outer
Space Affairs in January 2014, there were 3,921 satellites in orbit; of the 1,167
that are active, 192 are Earth observation satellites. Belward and Skoien (2015)
give a full review of the situation at the end of 2013, and more recent
information can be obtained at:
https://eoportal.org/web/eoportal/satellite-missions
http://www.wmo-sat.info/oscar/satellites
http://database.eohandbook.com/database/missiontable.aspx
Current problems largely relate to acquisition capacity. The oldest sensors are
limited by: i) their recording and downloading capacities – Cosmoskynet is an
example; ii) service prices even for non-priority products; very high resolution
satellites often charge more than US$20 per km2; and iii) non-availability for
non-national projects: examples include Chinese satellites such as GF-1
launched in 2013 covering a 46 km swath with a 2/8 metre P/MS sensor and a
16 metre WFV sensor and GF-2 launched in 2014 with sub-metric resolution
only.
For low-resolution imagery – above 100 metre ground sampling distance –
various free-access services are provided by geo-stationary and geo-
synchronized satellites:
The geo-stationary Meteosat-10 second-generation satellite
EUMETSAT, launched in 2012, provides images every 15 minutes of
all of Europe and Africa; Asia is covered by MTSAT and America by
GOES; 11 channels provide a 3 km GSD in visible and infrared light,
and one channel provides a high-resolution 1 km signal in visible light.
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The NASA soil moisture active/passive spacecraft launched in January
2015 started delivering 9 km resolution soil moisture information to a
depth of 5 cm through a passive radiometer merged with an SAR L-
band. Due to radar failure in July 2015, the current resolution is limited
to 40 km.
The geo-synchronized EUMETSAT METOP A launched in 2006 and B
launched in 2012 continue the service of the former NOAA-AVHRR-3
sensor, providing six channels daily in the visible and infrared bands at
a minimum 1.1 km resolution.
Since 2000 and 2002, MODIS imagery is provided by the Terra and Aqua
satellites. Of 36 channels, the best for agriculture are the two bands imaged at
250 metres in visible light and the five bands imaged at 500 metres in the infra-
red; it covers a 2,330 km swath every one or two days.
MERIS is a non-operational sensor on the ESA ENVISAT platform launched in
2002. It delivered a 300 metre resolution signal in 15 spectral bands in visible
and infrared light covering a swath of 1.15 km every three days; it enabled the
development of the ESA GLOBECOVER in 2009. When launched in early
2016, Sentinel 3 will provide MERIS-like imagery with a 1 km GSD over water
and 300 metres over land.
Data from the ESA PROBA-V launched in 2013 continue the vegetation
instrument data set, and should be seen as a gap-filler for Sentinel-3. Since
March 2015, imagery at 100 metre GSD – free if older than a month – has been
available at VITO in the visible and infrared range covering a 2.15 km swath
daily. The Earth’s land is covered daily at 350 metres resolution.
For high-resolution imagery above 5 metres GSD, some imagery is free – but
most is still very costly.
Landsat 8, launched in 2013, was the first free worldwide provider of high-
resolution optical data. Geocoded products with a resolution of 15 metres in
panchromatic and 30 metres in eight MS bands are available within 24 hours.
Its 170 km swath makes it an optimum data source for agricultural statistics.
Sentinel 1 radar imagery in the C band has been available free since 2014.
Operating in four modes and with variable polarimetry, its main purpose is
disaster monitoring; it should therefore be seen as an auxiliary source of
information for agriculture, particularly in regions with persistent cloud
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coverage. Imagery can be resampled at 20 metre resolution; its swath is
250 km.
Resourcesat-2 (ISRO, IRS-P6), launched in 2011, has three instruments:
LISS-4 with 5 metre resolution from three bands in the visible and
near-infrared range; a 70x70 km image costs US$2,750;
LISS-3 with 23 metre resolution from four bands in the visible, near-
infrared and short-wave infrared; a 170x170 km image costs US$2,750;
and
AWIFS with 56 metre resolution from four bands in the visible, near-
infrared and short-wave infrared; a 350x350 km image costs US$850.
The four CBERS-2B satellites launched in 2010 and 2014 under a
Brazilian/Chinese cooperation deliver imagery with various characteristics:
panchromatic at 5 metres GSD in a 60 km swath, and MS 4 bands in the visible
and near-infrared at 10.2 metre and 64 metre GSD in swaths of 60 km, 120 km
and 866 km. Imagery is free in the INPE reception area; beyond Central and
South America other reception stations cover most of Africa, Russia and China.
It is not easy to order the images.
Spot 7, launched in 2014, has the same characteristics as its predecessor
SPOT6, which is still operational. It offers a panchromatic band at 2.2 metre
resolution and four MS bands at 8.8 metres. A 60x60 km image costs about
US$5,000, giving a price of US$1.5 per km2 for non-processed archived
products. Geocoded programmed XS products cost aboutUS$3.50 per km2.
The five Rapideye satellites have served the agriculture and agricultural
insurance sectors since 2008. Archived images from four multispectral bands in
the visible and near-infrared range covering a swath of 77 km can be ordered
for about US$1 per km2.
The second-generation DMC series launched in 2009 – UK-DMC 2 and
DEIMOS 1 – provides three bands in the visible and near-infrared range at
22 metre GSD in a 650 km swath that can be up to 1,800 km long. The system
has been used operationally by the European Commission and the United
States. Archive imagery costs 9 US cents per km2. In 2011, NigeriaSat 2 and
NigeriaSat X also started to provide panchromatic imagery at 2.5 metre
resolution and MS imagery at 5 metres resolution in a 22 km swath.
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The Airbus DS FORMOSAT-2 has been delivering 2 metre panchromatic and
8 metre multispectral imagery in a swath of 24 km since 2004. Products cost
US$4.5 per km2.
Thailand financed the 2008 launch of THEOS, which has a 2 metre
panchromatic band and three multispectral bands at 15 metre resolution. Data
are not generally accessible outside the Geo-Informatics and Space Technology
Development Agency.
Aster is an old US/Japanese instrument on the Terra satellite launched in 1995.
With a 15 metre GSD and a swath of 60 km, it delivers four bands in the visible
and near-infrared range at 90 US$ per scene; new acquisitions cost US$350.
Canada’s Radarsat, launched in 2007, is a taskable C-band SAR providing
ascending and descending imagery. The costs per image is about US$3,600 for
resolutions from 8 metres to 100 metres and swaths from 50 km to 500 km.
RISAT 1 and 2 launched in 2012 and 2014 provide C-band multi-polarimetric
and multi-resolution imagery in a swath of 115 km in 25x8 MRS resolution at a
cost of US$190 for Indian users. Worldwide imagery is available from Tromsö
station.
ALOS-2 launched in 2014 is unlike ALOS-1 in that it does not provide optical
images – only SAR L-band imaging at a GSD of between 3 metres and
100 metres. One of its objectives is the monitoring of rice growing in Asia.
Other radar imagery is available, but it has limited applicability to agricultural
statistics because of the image bands, re-sampling and prices. The Cosmoskynet
system of four satellites is dedicated to interferometry; the same holds for
Terrasar X.
For very high resolution of less than 5 metres GSD, most of the available
imagery is so costly that it is unlikely to be accessible for agricultural statistics.
The available systems are listed below.
Google Earth provides free access to very high resolution aerial and satellite
imagery for most of the world. Its use in agricultural statistics has its limitations
due to changes in acquisition dates, geometry or radiometry, but Google Earth
material can be useful for printing field survey documents or stratifying land
cover. All printed images must show a visible Google copyright logo.
98
Bing provides Microsoft aerial photographs on the Bing Maps application.
Limited use can be made of it without a licence, so usage for agricultural
statistics may be allowed.
Digital Globe launched the Quickbird-2 satellite in 2001 and the WorldView 1,
2 and 3 satellites in 2007, 2009 and 2014. Exceptional spatial resolution is
offered with 31 cm GSD in eight bands in the visible and near infra-red range.
Prices reflect the quality of the service; archive imagery costs US$20 per km2.
The Korean KOMPSAT-3 launched in 2012 delivers panchromatic and MS 4
bands in the visible and near infra-red range at GSDs of 0.7 metres and
2.8 metres. For standard tasks, the cost is US$16 per km2 from GISTDA;
archive products cost US$8 per km2
from Apollomapping. KOMPSAT-5
launched in 2013 is an X-band SAR satellite with 1 metre to 20 metre re-
sampling.
The Airbus DS Pleiade 1A and 1B launched in 2013 provide panchromatic
0.7 metre and MS 2.8 metre imagery in the red/green/blue and near-infrared
ranges. They can be tasked to cover strips of up to 150x40 km. The costs of
archive products is about US$13 per km2.
Images from Ikonos 2, launched in 1999, in the red/green/blue and near-
infrared ranges at 82 cm and from Geoeye-1, launched in 2008, in the same
ranges at 41 cm can be ordered from e-GEOS or Apollomapping. Prices vary
according to the product; archive imagery ranges from US$15 to US$40 per
km2.
EROS B, launched in 2006, was an ImageSat satellite delivering 70 cm
panchromatic imagery in a 7 km swath at a cost of US$7.5 per km2, available
from Apollomapping.
The SkySat 1 and 2 satellites, now owned by Google, were launched in 2013
and 2014 as the first components of a 24-satellite system for daily passes with
panchromatic 90 cm and MS 2.0 metre imagery in the red/green/blue and near-
infrared ranges in an 8 km swath. They can be tasked to provide videos. New
and archive imagery costs US$10 per km2; videos cost US$5,000 for 30
seconds.
Planet Labs USA launched its first three satellites in 2013. In February 2014
and July 2015, the Flock1 and 2 constellation (41 doves, 3-5m MS resolution,
no infrared band. See https://www.planet.com/flock1/) was deployed from the
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International Space Station. The plan is to increase the number of doves to 131
in 2015. In September 2015, Planet Labs announced a US$60 million grant of
imagery for the benefit of the UN agencies.
6.2. The immediate future of imagery and software
The main development in the use of remote sensing for agriculture applications
will be the services of the Copernicus programme provided by the Sentinel
system. Sentinel-2, launched in June 2015 to monitor vegetation, provides
images in 13 bands in the visible, near-infrared and shortwave infrared ranges
with a GSD of 10 metres in four bands with a ten-day repeat cycle at equator;
the swath is 290 km. Its systematic land-acquisition mode will guarantee image
acquisition except where there is cloud cover.
In addition to real-time delivery of five standard products, an open-source
portable toolbox documented, tested and validated on the 12 JECAM sites will
provide cloud-free surface reflectance composites, dynamic cropland masks,
cultivated crop-type maps and area estimates, and vegetation status.
In the context of its climate change initiative, ESA has identified five essential
climate variables for mapping: land cover, fires, ice sheets, glaciers and soil
moisture. For Africa, the intention is to deliver monthly 10–20 metre land-cover
maps to monitor vegetation phenology. Future services will integrate Sentinel-2
daily 10 metre 5 gigabytes images, Landsat-8 30 metre images, PROBA-V
100 metre images and Sentinel-3 300 metre imagery. Archive land cover
imagery and a toolbox are freely downloadable (see:
http://maps.elie.ucl.ac.be/CCI/viewer). Project progress can be followed at
http://www.esa-landcover-cci.org/?q=node/126
The other major change will come from Google. Six additional SkySat satellites
will be launched in 2016, providing vastly enhanced image acquisition
capability, though less than that of the planned constellation of 23 satellites.
Another interesting development is the Earth Engine application, which enables
real-time data access to the Landsat 8 and Sentinel 1 and 2 satellites. A user can
work on a personal computer to access pre-processed data on radiometry and
geometry, apply the analysis routines and integrate the outcome with images or
GIS data sets. It is currently limited to the scientific community, but
professional licences can be expected in the near future; prices will reflect the
Google business model.
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It is also worth mentioning that in 2015, NOAA launched its Big Data Alliance
initiative, selecting five partners (Amazon, IBM, Google, Microsoft and the
Open Cloud Consortium) for archiving, distribution, and enhancement of its
20TB daily acquisition; opening access to these would give rise to a business
growth of US$3 trillion (https://data-alliance.noaa.gov)
Additional commercial optical satellite launches have been announced.
Three additional DMC-3 satellites will service users of remote sensing from
2015. The Twenty First Century Aerospace Technology Company of China has
bought all the imagery for a seven-year period. With daily revisits, DMC-3 will
provide panchromatic 0.50 GSD and MS 3 metre imagery, which will be a
major improvement over the Beijing-1 satellite imagery available since 2005.
The United States/Argentina Satellogic group launched three MS satellites in
2013 and 2014: CubeBug1 and 2 and BugSat-1 provide 2 metre resolution in
current tests, and the company plans to launch 15 more 1 metre MS satellites in
2015 with a two-hour revisiting period anywhere on the globe. The idea is to
sell more than raw imagery, and the company envisages increasing the number
of satellites to a possible 300.
An ALOS-3 optical satellite to be launched for JAXA in 2016 will have a
panchromatic band of 0.8 metre GSD and a swath of 50 km, and four MS bands
of 5 metre GSD and a swath of 90 km.
6.3. The use of drones for field surveys:
ITA, NBS and VITO
6.3.1. ITA in Malawi
In a World Bank project, in 2015 Malawi has been testing a point area frame for
its 118,500 km2 area. This context created, for the contractor ITA, an
opportunity to examine the use of drones for agricultural surveys in an African
country.
The Ministry of Agriculture needs crop estimates in February, April and June.
An early area survey of maize in March covered 300 clusters of 16 points on a
local 250 metre grid defining a 1 km2 segment in which fields were measured
with GPS.
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A full crop area survey consisted of 1,500 clusters and 25,000 points targeting a
CV for maize of approximately 5 percent. Points were clustered because of the
time and cost involved for accessing 25,000 individual points and the low
spatial correlation between points distant 250 metres apart.
A final production estimate was based on 900 farm interviews; it covered maize
and some other crops. The fieldwork was done by the Ministry of Agriculture.
ITA tested the feasibility and accuracy of using drones to acquire images
because the survey involved hiring and running 25 vehicles for 50 days at a cost
of US$250,000. A FLYGEO drone was used by the Brindisi-based STF
company on 20 of the 1 km2 clusters of 16 points. It was equipped with a Sony
Alpha 6000 camera providing 2 cm GSD colour images and cost US$20,000; it
had a wingspan of 2 metres, length of 75 cm and weight of 1.8 kg and could fly
for three hours at a distance of 3 km guided by GPS with pre-set waypoints;
altitude was selected on the basis of the Google 3D digital elevation model.
Customs restrictions meant that the drone had to be imported unofficially, and
no flight authorization could be obtained. Six days and 500 km of road travel
were necessary to cover the 20 segments compared with 15 minutes of flight
per cluster. Photographs covering areas of 117x78 metres required 200
gigabytes of post-processed data using APS and Photoscan software.
The main conclusions are:
Even though the imagery is impressive, the mixture of crops may have
been too great for the imagery used: 30 percent of maize fields are
mixed with pigeon peas, vegetables, cow peas, sorghum, ground peas,
soya, cassava, tobacco and sweet potatoes; even with 2 cm resolution,
the secondary crops could not be identified.
The drone had to be imported under cover in separate pieces and flight
authorization could not be given, so it could not be used for official
surveys.
The time advantage of using the drone to cover three clusters per day
was not great in that the field survey covered 1.5 clusters per day.
6.3.2. NBS in China
In Inhai province in 2014, NBS obtained 390 images with a drone equipped
with a Sony Nex-7 camera with a frame of 6,000x4,000 pixels. The TH-D
Technology Company provided the service. The drone flew for 40 minutes to
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cover a segment of 2x2 km and provide a mosaic of 300x200 metre images.
GSD was 5 cm. The August 2014 image below shows corn and peanuts.
6.3.3. Drones at VITO Belgium
In recent years, VITO has begun research on and to examine applications of
drone technology.
In compliance with the legal limitations restricting the use of drones in
the European airspace, VITO developed the first drone management
platform that allows commercial drone operators and air traffic
controllers to make use of this airspace.
Initially dedicated to the monitoring of pipelines, their use of drones
expanded to more diversified applications: monitoring of pest spreading
in orchards for administrative bodies, monitoring of field trials
(breeding and phytosanitary experiments) for the private sector.
In light of the constraints arising cloud coverage on satellite images
acquisition, VITO experimented with large-scale mapping and crop
conditions monitoring in South America (specifically, Peru).
The main types of drones experimented with by VITO are listed in the Table
below. It should be noted that at least the “SenseFly eBee” fits into an ordinary
commercial airplane’s overhead locker, which facilitates its international
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transport. VITO’s experience appears to confirm the robustness of the drones
and payloads tested.
Name Manufacturer Payload
Draganfly X6
Draganfly Innovations Inc., 2108 St. George Avenue, Saskatoon, SK S7M0K7, Canada
Panasonic LX3
Aerialtronics AT8
Aerialtronics, Dr. Lelykade 20, 2583CM Den Haag, Netherlands
Sony Nex 6
SenseFly eBee
Sensefly, Route de Genève 38, (Z.I. Châtelard Sud)
1033 Cheseaux-Lausanne, Switzerland
Canon IXUS
RE camera MultiSPEC
Aerialtronics ATX8
Aerialtronics, Dr. Lelykade 20, 2583CM Den Haag, Netherlands
Sony Nex 6
Panasonic GH4 + gimbal
AV200 mount
Canon 5D ET-Air Cruiser
ET-Air, Stangelandsv. 190b, N-4354 Voll, Norway
Multiplex Mentor & Cularis
Aerobertics, Maalse Steenweg 367, 8310 St-Kruis, Brugge
UAV Navigation Oculus
UAV Navigation, Avda Pirineos 7, B11, 28703, San Sebastian de los
Reyes, Spain
OTUS 170 Gimbal
Explorer
Aerobertics, Maalse Steenweg 367, 8310 St-Kruis, Brugge
104
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