Technical Report on Cost – Effectiveness of Remote Sensing for Agricultural Statistics in Developing and Emerging Economies Publication prepared in the framework of the Global Strategy to improve Agricultural and Rural Statistics 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 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 4 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)……………………………………………. 90 Gujarat State Landsat Data (31 Dec 2014)…………………………………………… 91 5 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 6 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 7 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