Technical Report on Developing More Efficient and Accurate Methods for the Use of Remote Sensing in Agricultural Statistics

Technical Report on Developing More Efficient and Accurate Methods for the Use of Remote Sensing in Agricultural Statistics Publication prepared in the framework of the Global Strategy to improve Agricultural and Rural Statistics September 2014 Technical Report on Developing More Efficient and Accurate Methods for the Use of Remote Sensing in Agricultural Statistics Table of contents Preface 6 Acknowledgments 8 1. Introduction 9 2. New technologies of remote sensing 17 2.1 A note on the use of high resolution images 33 3. Methods for using remote sensing data at the design level 35 3.1 Introduction 35 3.2 Multivariate auxiliaries in π ps sampling 38 3.3 Optimal stratification 48 3.4 Spatially balanced samples 54 3.5 Auxiliary and survey variables 65 4. Extension of calibration estimator 70 4.1 Introduction 70 4.2 The calibration approach to estimation 72 4.3 Extension of calibration estimators 78 4.4 Model calibration 79 4.4.1 Nonparametric model assisted estimator 86 4.4.2 Model assisted estimator of spatially 88 autocorrelated data 4.4.3 Model assisted estimator for geographical 91 local – stationary data 4.4.4 Model – assisted estimator for 95 zero inflated – data 4.4.5 Endogenous post – stratification 97 4.5 Calibration on complex auxiliary information 99 4.6 Calibration for non- response adjustment 100 4.7 Missing values in auxiliary spatial variables 102 4.8 Computational issues 105 5. Benchmarking the estimators adopted for producing 107 agricultural and rural statistics 6. Comparison of regression and calibration estimators with 113 small area estimators 6.1 Introduction 113 6.2 Models for small area estimation 115 6.3 Area level models 116 6.4 Unit level models 119 6.5 Extension of Area – level model for the analysis of 121 spatially autocorrelated data 6.6 Small area estimation under a zero – inflated model 123 6.7 Local stationarity in small area estimation models 124 7. Statistical methods for quality assessment of land use/land 127 cover databases 7.1 Land cover change analysis 137 8. Assessment of the applicability in developing countries of developed methods 143 9. Concluding remarks 145 References 151 Preface This Technical Paper on Developing More Efficient and Accurate Methods for the use of Remote Sensing 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)1 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. This technical report on Developing More Efficient and Accurate Methods for the Use of Remote Sensing is the result of a comprehensive literature review on the subject, followed by a gap analysis and, finally, the development of innovative methodological proposals for addressing the various issues arising. 1 The SAC is composed of 10 well-known Senior Experts in various fields relevant to the Global Strategy’s Research Programme. These Experts are appointed for a 2-year term. The current membership is composed of Vijay Bhatia, Seghir Bouzaffour, Ray Chambers, Jacques Delincé, Cristiano Ferraz, Miguel Galmes, Ben Kiregyera, Sarah Nusser, Fred Vogel, and Anders Walgreen. 6 The main purpose of this report is to summarize certain topics concerning the use of remotely sensed data for agricultural statistics. In particular, the report seeks to describe the new technologies of remote sensing, and their influence on any improvements that can be made to the methods proposed. The Report first provides a comprehensive review of the literature on this subject, dividing it into six main topics: New technologies of remote sensing, Methods for using remote sensing data at the design level, Extension of the regression or calibration estimators, Robustness of the estimators adopted for producing agricultural and rural statistics, Comparison of regression and calibration estimators with small area estimators, Statistical methods for quality assessment of land use/land cover databases. The Report also contains a critical analysis of the papers in these six topics, and proposes methodological and operational solutions to fill the gaps and to analyse certain methodological issues identified and examined subsequently to the literature review. The work will constitute an input for several Guidelines, including the Guidelines on developing and maintaining a Master Sampling Frame for integrated agricultural surveys, which are currently under preparation. The technical papers are updated as required with the results of in-country field tests and with country feedback and experiences. 7 Acknowledgments The Technical Paper on Developing More Efficient and Accurate Methods for the Use of Remote Sensing was prepared by Roberto Benedetti (Professor), University “G. d’Annunzio” of Chieti-Pescara, with the assistance of Paolo Postiglione (Professor), University “G. d’Annunzio” of Chieti-Pescara, Domenica Panzera (PhD), University “G. d’Annunzio” of Chieti-Pescara, and Danila Filipponi (PhD, researcher), Italian National Statistical Institute. The Report was prepared with the guidance and supervision of Elisabetta Carfagna, FAO, Javier Galego, JRC, and Naman Keita, FAO. Valuable inputs and comments were provided at various stages by the SAC members and by Loredana Di Consiglio, ISTAT. This publication was prepared with the support of the Trust Fund of the Global Strategy, funded by the UK’s Department for International Development (DFID) and the Bill and Melinda Gates Foundation (BMGF). 8 1 Introduction Remote sensing is an important tool in the study of natural resources and environment. The possible applications of remotely sensed data are manifold: the identification of potential archaeological sites, the assessment of drought and flood damage, the monitoring and management of land use, the compilation of crop inventories and forecasts, etc. Today, remotely sensed images constitute a basic instrument for monitoring natural resources, the environment, and agriculture. Remote sensing has also become crucial for protecting the global environment, reducing disaster losses, and achieving sustainable development. Furthermore, they provide invaluable information on the state of art of the agricultural sector for both developed and developing countries. Remote sensing is defined as the technique of deriving information on the features of the earth’s surface, and estimating their geo-bio-physical properties using electromagnetic radiation as a medium of interaction (Canada Centre for Remote Sensing, 2003). This acquisition occurs without physical contact with the earth. The process involves registering observations using sensors (i.e. cameras, scanners, radiometer, radar, etc.) mounted on platforms (i.e. aircraft and satellites), which are at a considerable height from the earth’s surface, and recording the observations on a suitable medium (i.e. images on photographic films, and videotapes or digital data on magnetic tapes). Then, the data obtained is usually stored and manipulated using computers. Remote sensing data, also combined with in situ observations, are widely used in agriculture and agronomy (Dorigo et al., 2007). They ensure a good spatial coverage of the field under investigation. This is particularly evident for many developing countries, where satellite data represent the only source of information on the weather, the earth’s surface, and, of course, agriculture. Remote sensing has been increasingly considered for its capacity to provide a standardized, faster, and possibly cheaper methodology for agricultural statistics. Several developing and developed countries have remote sensing projects to support the official agricultural statistics programs (see Becker-Reshef et al., 2010). The wavelengths used in most agricultural

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