Improving Land Use Survey Method using High Resolution Satellite Imagery

Transcription

1 Improving Land Use Survey Method using High Resolution Satellite Imagery M.H.B.P.H.MADANA March 2002

2 Improving Land Use Survey Method using High Resolution Satellite Imagery by M.H.B.P.H.MADANA Thesis submitted to the International Institute for Geo Information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Natural Resources Management (Sustainable Agriculture) Degree Assessment Board Prof. Dr. A. K. Skidmore Chairperson of the Board of Examiners and Head of ACE Division, ITC Prof. Pual Driessen External Examiner, Wageningen Agricultural University Dr. M. J. C. Weir Internal Examiner and Programme Director of NRM Division, ITC DR. C.A.J.M. de Bie Primary Thesis Supervisor, ACE Division, ITC DR. H.G.J. Huizing Secondary Thesis Supervisor, ACE Division, ITC INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION (ITC), ENSCHEDE, THE NETHERLANDS.

3 Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo Information Science and Earth Observations. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

4 ABSTRACT Conventional area frame sampling survey, which is a type of agricultural probability sample surveys is a costly and time-consuming activity, requiring an aerial photographs mosaic, paper base satellite image products and current maps, and involving the laborious and meticulous work to delineate strata, primary survey units (PSUs) and segments with identifiable physical boundaries. The aim of this study was to evaluate the potential of using high-resolution (15m) Aster image data instead of aerial photographs improving the area frame sampling survey method through reducing costs and efforts. High-resolution image (3,2,1 false colour composite) was visually analysed in ERDAS Imagine for pre defined survey variables to delineate survey frame limits, strata and PSUs. Last stage sampling units (segments) was formed by means of UTM grids with their coordinates instead of using identifiable physical boundaries. Image enlargements of sample segments were prepared for ground survey. Delineation of survey frame limits, strata and PSU boundaries can most conveniently be done for Aster image by visual interpretation using image-processing software. UTM grid for segmentation the frame reduced time and effort in frame construction. Aster image in a 1:100,000 scale can be used as a substitution for topographic maps for ground survey since it shows the roads and many other necessary details to find the locations of sample segments. The agricultural plots in this study area can easily be identified on an Aster image enlargement at 1:15,000 scales and this leads for reducing costs by making use of often-outdated aerial photos. The Aster image used to develop an area frame saved cost of materials as it was downloaded from the web free of charge. In developing countries, where up-to-date materials are lacking, use of aster images fulfils the need of up-to-date materials for area frame construction. The procedure of using Aster images in image processing software saves more time and cost involved in different steps of area frame sampling survey as it was completely computerized as compared to the time and cost involved in manual procedure. Use of high-resolution Aster images, therefore, constitutes a very important potential improvement and simplification for area frame construction since it avoids the laborious and meticulous work required for conventional methods. I

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6 ACKNOWLEDGMENTS This thesis comes to a successful completion with the assistance and guidance of many people whom I could not mention by names. I wish to express my sincere appreciation and gratitude to all those who in one-way or the other made this research a successful one. My gratitude goes especially to the following: My organization Department of Agriculture, Sri Lanka for giving me the study permission, the Dutch Government for providing me financial support through the Netherlands Fellowships Programme (NFP) and International Institute for Geo Information Science and Earth Observation (ITC) for all arrangements for my study. Dr. Kees de Bie, my primary supervisor whose energetic and indefatigable effort moved me to work hard for providing me valuable books, materials and advices to make this research success. Dr. Herman Huizing, my secondary supervisor, for his proper guidance, suggestions, encouragements and constructive comments to direct me to the right track throughout this research work. His door was always open to me, despite his heavy work, whenever I was in need of his help. I fully say that without his help and guidance, this thesis would not have been achieved. Thank you very much sir! Dr. Michael Weir, our programme director to the division of Natural Resources Management (NRM), for his friendly welcome to me when I was really frustrated and for his encouragements. Dr. Iris Van Duren, my fieldwork supervisor, for her appreciable great service rendered during the fieldwork, in Serowe, Botswana. Dr. Yousif Hussin, for his great support and advices for classifying the image. Mr. Upali Jayalath Witharanage, my fellow Sri Lankan, for giving me lot of courage and great support to end up with successful work during the last eighteen months. Mr. Lalith Chandrapala, my Sri Lankan friend, for helping me whenever I step in his room, in spite of his hard work on his PhD research. III

7 Finally, I would like to dedicate this thesis to my parents who forgo their magnificence in order to ensure my affluence and my beloved husband, Mr. M.H.M.A.Bandara, for his support and understandings. Priyanjani Madana March IV

8 TABLE OF CONTENT ABSTRACT...I ACKNOWLEDGMENTS... III TABLE OF CONTENT... V LIST OF FIGURES...IX LIST OF TABLES...XI 1 INTRODUCTION Background Research Problem Objectives Main Objective Secondary Objectives Research Questions Hypothesis Basic Methodology of this Research Thesis Structure LITERATURE REVIEW An Overview of Agricultural Surveys Agricultural Survey Designs Agricultural Probability Sample Surveys List Frame Sample Surveys Area Frame Sample Surveys Multiple Frame Surveys General Procedure for Area Frame Construction STUDY AREA AND MATERIALS Study Area V

9 3.1.1 Location Climate Rainfall Temperature and Relative Humidity Topography and Soil Topography Soil Mapping Units Short Soil Descriptions Agricultural Background in Botswana Agricultural Lands in Serowe Materials Remote Sensing Data Topographic Map Software GPS PREPARATION OF FRAME CONSTRUCTION The Potential of Aster Image for Area Frame Construction Survey Variables Area Frame Construction Preparation the Frame Materials and Delineation of Frame Limits Evaluation of Possible Advantages of Aster Image and the Approach to Delineate Fame Limits STRATIFICATION Necessity of Stratification for Area Frame Sampling Advantages of Stratification in Study Area Stratification Process Sub Stratification (Subdivision of Strata into PSUs ) VI

10 5.5 Evaluation of the Potential of Aster Data for Stratification SEGMENTATION Size of the Sampling Unit (Segment) Sampling Frame Sample Sample Size Sample Allocation to Strata Sample Selection Evaluation of using Aster Image for Segmentation the Frame PREPARING FOR DATA COLLECTION Questionnaire Preparation of Selected Samples for Ground Survey Data Collection Evaluation the Potential of Aster Image for Preparation the Materials for Ground Suvey IDENTIFICATION OF AGRICULTURAL PLOTS Identification of Agricultural Areas by Visual Interpretation Identification of Agricultural Areas by Filtering Classification Supervised Classification Signature Separability of the Classification Accuracy Assessment of Classification Reports of accuracy assessment Confusion Matrix Commission and Omission Error User Accuracy and Producer Accuracy Over all Accuracy VII

11 Kappa (K^) Statistics Accuracy of this Classification Evaluation between Visual Interpretation and Supervised Classification for Identifying Agricultural Plots Quantitative Evaluation Qualitative Evaluation Comparison between Farmer Reported and Computer Measured Plot Size93 9 CONCLUSIONS REFERENCES Appendix 1: Climatic Data for Mahalapye... A Appendix 2: Signature Separability... D Appendix 3: Abbreviations...E VIII

12 LIST OF FIGURES Figure 1-1: Low-level diagram of area frame sampling process... 9 Figure 2-1:Types of agricultural surveys Figure 2-2: Main steps in Area Frame Construction Figure 3-1: Location of study area Figure 3-2: Soil map of study area Figure 3-3: Crop calendar of Botswana Figure 3-4: Main crop zones of Botswana Figure 3-5: Monthly rainfall totals for Mahalapye from year Figure 4-1:The process of preparation frame Figure 4-2: The steps of frame limits delineation in conventional method Figure 5-1:Differences in agricultural land distribution Figure 5-2: No agricultural land use can be found around some ephemerals and main roads Figure 5-3: Figure shows the distribution of agricultural plots on different soil groups Figure 5-4: The steps were followed to create a strata map Figure 5-5: The process of further stratification of strata into sub units Figure 5-6: The strata map with four different strata Figure 5-7: Substrata map Figure 5-8: Figure showing Aster image on 20 th October 2000 and LANDSAT image on 1 st October Figure 6-1: 1 x 1 km block grid design used for segmentation the survey area Figure 6-2: Figure showing the dropped and kept pieces of square segments in sample selection Figure 7-1: Field Observations And Questionnaire Sheet Figure 7-2: Aster image enlargement of selected sample segment for ground survey IX

13 Figure 7-3: Aster image in a 1:100, 000 scale showing segment locations for ground survey Figure 8-1: Spectral reflectance curves of basic land cover types Figure 8-2: Image data file values of different plots Figure 8-3: Appearance of agricultural plots in three stages Figure 8-4: Part of original image and enhanced image Figure 8-5: The map generated from supervised classification Figure 8-6: The steps followed for qualitative evaluation of supervised classification and visual interpretation Figure 8-7: The map created by visual interpretation shows agricultural plots Figure 8-8:The map subset from classified map based on visually interpreted agric plot layer, showing the different classes within agricultural plots Figure 8-9: The scatter plot of farmer reported and computer measured plot size93 X

14 LIST OF TABLES Table 3-1: Monthly Open Water Evaporation (OWE), Potential Evapo Transpiration(PET) and Rainfall at Mahalapye (Bahalotra, 1987) Table 3-2: General descriptions of soil units of the study area, summarized from de Wit and Nachtergaele, Table 3-3: Spectral Range (µm) of Aster channels (1,2,3) with 15-m resolution (NASA, 2001) Table 5-1:Land use strata codes and definitions Table 5-2: The area frame stratum number and definitions Table 5-3: Descriptions and codes strata Table 5-4: Table showing strata descriptions, which are based on proportion of agricultural plots, strata code, total area and agric plot area Table 5-5: Strata and substrata (PSUs) description, code and area (Ha) Table 6-1: Total no of Segment in each stratum and substratum Table 6-2: Segment allocation by stratum and substratum Table 8-1: Confusion matrix obtained from the classification Table 8-2: Percentages of producer accuracy and user accuracy of each class and overall classification accuracy Table 8-3:Kappa Statistics resulted from classification Table 8-4: Total area in hectare and percentage of area within agric plots and without agric plots of each classified class Table 8-5: Quantitative evaluation of supervised classification based on visual interpretation Table 8-6: Qualitative evaluation of visual interpretation and supervised classification used in identifying agricultural areas Table 8-7: Farmer reported and computer measured plot size XI

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16 Improving land use survey method using high resolution satellite imagery chapter-1 1 INTRODUCTION 1.1 Background The need for timely and reliable agricultural information such as crop production estimates, livestock inventories and socio-economic data has become more important in decision-making process at international and national level in almost all countries with the global shift towards market economies. Increasing population pressure may result in food scarcity especially in developing countries. Therefore, reliable crop area estimations are very important factors in food security. In many countries, agricultural production statistics are very poor, inadequate or low qualitative due to many reasons, for instance, lack of political support for data collection, the high cost of agricultural surveys, the shortage of requisite skills and the failure to identify the most appropriate method. Timely and reliable information can only be obtained through an adequate and periodic national agricultural surveys based on probability sampling methods (FAO, 1996). Improvement of such survey methods is, therefore, of paramount importance component of the agricultural information systems. However, consideration of new options to estimate cultivated crop area in terms of saving time and costs, and appropriateness of the methods is essential to acquire accurate statistics. Agricultural probability sample surveys in terms of the last stage sampling unit and the rules to assign their probabilities of selection can be divided into two basic types namely, area frame sample surveys and list frame sample surveys. An area frame sample survey method is one of the statistical methods that can be used to estimate the cultivated crop area and production of certain crops (Gallego, 1995). The concept of area frame sampling is dividing the total area to be surveyed into N small blocks (Segments) without any overlap or omission, furthermore select a random sample of n small blocks and get the desired data for reporting units of the population that is in the sample blocks. Stratification of survey area into number of strata and, again strata International Institute for Gio Information Sience and Earth Observation 1

17 Improving land use survey method using high resolution satellite imagery chapter-1 into PSUs, for reducing sampling variance, regarding the homogeneity within a stratum is very important to gather more accurate estimates. In this method, crop areas can be sampled on the basis of remote sensing (Groten, 2001). Maps of different types and scales, satellite images and aerial photography are used for identifying and measuring areas for area frame construction and sample selection. The conventional method that will be discussed in chapter two for area frame sampling survey is manual and paper-based. The construction of an area frame manually based on paper-based materials (the paper satellite imagery products, Aerial Photographs mosaics and different type of maps) is costly and very labour intensive. Today many countries experience, developing the area frames using digital inputs (Digital-based area frames), which result in a tremendous savings in labour costs (Cotter and Tomczak, 1994). Satellite images can be most conveniently used to delineate strata and primary survey units (PSUs). The use of satellite images constitutes a very important improvement and simplification for area frame construction. It avoids the laborious and meticulous work involved in the construction of photo-mosaics, and allows for more precise area measurements. Also, the acquisition of current satellite images of a given large area is much cheaper than obtaining current aerial photos. Furthermore, satellite images provide more updated cartographic base for the frame than the information provided by available aerial photography (FAO, 1996). Satellite images are not usually suitable for subdividing PSUs into segments since they provide imprecise boundaries. Some satellite images cannot efficiently substitute the aerial photographic enlargement of sample segments used for data collection (FAO, 1996). However, Terra satellite, having higher resolution on the ground, provides Aster images at high level of resolution (15m) that they may be a better solution to subdivide PSUs into segments and even to substitute segment enlargements for enumeration. Therefore, use of very recent high resolution Aster images with three spectral bands (15m resolution), as a replacement of air photographs (AP s), which can be obtained free of cost presently, for estimation of crop area using area frame sampling survey method, may offer huge savings of time and costs. This research mainly focuses on the potentials of high resolution Aster image for improving area frame sample survey method with the aid of computer in different aspects. International Institute for Gio Information Sience and Earth Observation 2

18 Improving land use survey method using high resolution satellite imagery chapter Research Problem The demographic structure of the world also changes quickly, mainly due to migration and high birth rates. The utilization of land is always changing. As a result, an agricultural survey frame ages and survey statistics begin to decrease in reliability. Therefore, there is a necessity for constructing new survey frames or finding another options that can clearly be reflected these periodic changes. The current manual procedure with paper-based materials as mentioned above used to develop area frames or list frames for multiple frame agricultural surveys are expensive and time consuming. This procedure needs current maps with different types and scales, paper-based image products, recent aerial photographs, multi-temporal and ground truth data for field verification of the strata with identifiable physical boundaries, and labour source for area frame construction and sample selection (Cotter and Tomczak, 1994). The global tendency of the evolution of area frame construction and sample selection changed from manual paper-based procedures to computerised digital procedures. As a result, the National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture (USDA) have developed a Geographic Information System called CASS (Computer Aided Stratification and sampling) for area frame construction and sample selection. This procedure is highly computerized and requires materials, mainly mapping. These requirements are difficult to meet in many countries (FAO, 1996). Therefore, especially in developing countries, there is a necessity for improving methods for constructing an area frame and selection of field survey sample to collect highly precise agricultural information through agricultural surveys. Still there is a gap to fill up in the process of analysing agricultural survey data collected through probability sample surveys. For agricultural surveys to prepare an area frame, the first requirement is the availability of up-to-date cartographic materials covering the land to be included in the frame. Due to demographic and land use changes, aerial photographs become rapidly out dated. Acquisition of recent aerial photographs of a given large area is more expensive than obtaining current satellite images. Construction of photo mosaic for delineation frame boundaries, stratification the frame and formation of PSUs needs more effort and is time consuming. The resolution or detail of the materials must be adequate to allow stratification. Stratification, for area frame sample surveys may be according to the proportion of land culti- International Institute for Gio Information Sience and Earth Observation 3

19 Improving land use survey method using high resolution satellite imagery chapter-1 vated, special agricultural practices, average size of cultivated fields, predominance of certain crops or other uses of land, etc. Therefore, it is very important to identify agricultural lands and their proportion with respect to the total land area. Most area sample surveys consider a subdivision of the frame into land use strata to reduce the sampling variability by creating homogeneous groups of sampling units. Stratum boundaries must consist of physical terrain features (roads, paths, rivers, etc.) that can be located on the ground. These land-use strata should be subdivided into areas called primary survey units (PSU s) having recognizable permanent physical boundaries. This provides a further stratification, which is applied in order to improve the efficiency of the design (FAO, 1996). Although the aerial photographs are expensive they makes it possible to see the terrain features to establish good boundaries and thereby to classify most of the land into strata and construct PSUs with the least fieldwork. Use of satellite images makes area frame construction simple and low cost. But they are not usually suitable for subdividing PSUs into segments. Satellite images with enough resolution can be utilized to delineate strata and PSUs. Anyway they are not usually suitable for segmentation of PSUs since they provide imprecise boundaries. As discussed earlier, some satellite images cannot efficiently substitute the aerial photographic enlargements of sample segments used for data collection due to the low resolution. The data collection for area frame sample agricultural surveys, in addition to completion of a questionnaire, often involves identification and measurement of agricultural areas. Such identified agricultural areas in each sample segment can later be measured using a computerized measurement instrument or a planimeter. The checking of area estimates made by holders and /or enumerators provide a very important feature concerning data reliability. But in some cases the areas reported by farmers are not reliable (FAO, 1996). However, in some cases when the checking of crop area estimates by enumerator is not possible, there is no other option other than consider farmers reports. International Institute for Gio Information Sience and Earth Observation 4

20 Improving land use survey method using high resolution satellite imagery chapter Objectives Main Objective The specific objective of this case study is to evaluate the potential of using high resolution (15m) Aster image data for improving an area frame sampling survey method in order to reduce cost and effort. In area frame agricultural surveys already adopted in many developing countries, as discussed above, aerial photographs (AP s), satellite images or current maps are used as a guide to delineate strata, primary survey units (PSU s) and segments with identifiable physical boundaries. The procedure of forming a frame of the area to be surveyed using recent and good quality AP s is costly and laborious. Another bottleneck of this system is rapid reduction of the survey frame age due to land use changes in the world. Therefore, there is a necessity to use recent aerial photographs to update the land use changes and physical boundaries within the survey frame. As a replacement of aerial photographs, the use of recent high resolution remotely sensed images such as ASTER images (Advanced Space born Thermal Emission and Reflection Radiometer) from Terra satellite might provide better options to reduce cost and time of survey frame construction and to identify current land use in survey areas Secondary Objectives As discussed above, the identification of agricultural lands is very important to build an area frame. Although using a series of images for the identification of agricultural fields and for the calculation of their proportion is easy, it is difficult to do the same with a single satellite image. Another objective of this research, therefore, is to run and compare several options, for instance, visual interpretation, supervised classification, edge enhancement, for identifying agricultural lands using a single high resolution Aster image. As mentioned above in case of impossibility for checking of area by enumerator the areas reported by farmers should be considered. The other objective of this research is to examine the relationship between the field size reported by the farmers and the figure measured by using the area measurement tool available in the software. International Institute for Gio Information Sience and Earth Observation 5

21 Improving land use survey method using high resolution satellite imagery chapter Research Questions The following research questions need to be addressed in achieving the above-mentioned objectives. How to construct an area frame for agricultural probability sample surveys with less cost and effort than conventional method? Which are the suitable options considering time, cost, reliability, quality and necessity of expert knowledge to identify agricultural fields in high-resolution aster image? Does the field size reported by farmers tally with the area estimated by using the image processing software? 1.5 Hypothesis The use of high resolution Aster image constitutes a very important improvement and simplification for area frame construction and it avoids the laborious and meticulous work involved in conventional methods. The following issues will be tested in this research. Aster image in a 1: 50, 000 scale can be most conveniently used to delineate frame limits, strata and PSUs. Segmentation can be done by means of grids with their coordinates instead of using identifiable physical boundaries. Although, in conventional method, requiring scale of aerial photographic enlargement of sample segment for ground survey purposes is 1:5, 000, for this situation, Aster image enlargement of sample segments in a scale 1: 15, 000 can be used for ground survey. Aster image in a 1:100,000 scale can be used for ground survey as a substitution for updated topographic maps and road maps when they are not available. International Institute for Gio Information Sience and Earth Observation 6

22 Improving land use survey method using high resolution satellite imagery chapter-1 Edge enhancement techniques can be applied to sharpen the Aster image enlargement. Reflectance of Aster image (3,2,1 false colour composite) can be used for identification and calculation the proportion of agricultural plots. The field size reported by farmers tally with the area estimated by using the image processing software. 1.6 Basic Methodology of this Research High resolution Aster image was used to test the possibilities of frame construction and sample selection, and as a material for fieldwork in an area frame sampling survey. The image was visually interpreted for identifying agricultural and non-agricultural areas, and stratifying survey area. The criteria for stratification were defined based on the relationship between survey variables and image characteristics. 1 x 1 km UTM grids were used for dividing the total area to be surveyed into small blocks (segments). Sample selection by stratum was made randomly from the corresponding set of segments to the particular set of substrata. Aster image enlargement of selected sample segment at 1:15,000 scale was prepared for field work. Aster image in a scale 1:100,000 were prepared for ground survey in the areas where the topographic maps were not available. Ground survey was carried out to collect data through field observation sheet and questionnaire. Area of each crop was not estimated since collected data was not associated with all of the land inside the sample area. Supervised classification was run with the help of ground observation, farmer recordings and image characteristics. Visual interpretation and supervised classification were treated as means of identifying agricultural classes and was compared to each other considering time, cost, reliability, quality, necessity of expert knowledge. The relationship between the field size reported by farmers and measured using the area measurement tool available in the image processing software was tested. International Institute for Gio Information Sience and Earth Observation 7

23 Improving land use survey method using high resolution satellite imagery chapter-1 Finally the method used for constructing area frame sample survey in this research was evaluated with conventional methodology in different aspects such as time, cost, effort etc. The steps followed are shown in Figure 1-1. International Institute for Gio Information Sience and Earth Observation 8

24 Improving land use survey method using high resolution satellite imagery chapter-1 Topo map Raw satellite data Geo-correction Define survey variables Sup. Classification Geo-referenced satellite image Visual interpretation Define criteria for stratification Classified map Agric Plot Map Strata Map Define block size Comparison Soil map Sub strata map Grid design Sample selection by sub stratum Enlargement of segment Field observation sheet Ground survey Topo maps & Aster image (1:100,000) Data processing Evaluation Figure 1-1: Low-level diagram of area frame sampling process International Institute for Gio Information Sience and Earth Observation 9

25 Improving land use survey method using high resolution satellite imagery chapter Thesis Structure The thesis comprises of nine chapters as outlined below. Chapter 1: Introduction Chapter 1 of the thesis gives a brief general introduction to background of this research. It, then, explains the research problem, objectives of the research, research questions and hypothesis. A brief explanation to the methodology is finally included. Chapter 2: Literature Review This chapter discusses an overview of agricultural surveys. In addition, it aims at describing some related topics on agriculture probability sample surveys, with a literature review on conventional method for area frame sample surveys. Chapter 3: Study area and materials This chapter focuses on a description of study area covering the climate, topography, soil and agricultural background, and material used. Chapter 4: Preparation of frame construction This chapter covers the potential of Aster image for area frame construction, preparation the frame materials and delineation of frame limits. Evaluation of possible advantages of Aster image and the approach to delineate frame limits is also included. Chapter 5: Stratification This chapter presents necessity and advantages of stratification followed by stratification process. An evaluation of the potential of Aster data for stratification, too, is included in this chapter. Chapter 6: Segmentation This Chapter is about the sampling frame and the sample selection for area frame survey. It is ended with the evaluation of using Aster image for segmentation the frame. Chapter 7: Preparing for data collection In this chapter questionnaire, selected sample preparation and data collection are presented. In addition to that the potential of Aster image for preparation the materials for ground survey is included. International Institute for Gio Information Sience and Earth Observation 10

26 Improving land use survey method using high resolution satellite imagery chapter-1 Chapter 8: Identification of agricultural fields Identification of agricultural plots by visual interpretation, filtering and supervised classification is described following by comparison between supervised classification and visual interpretation. Apart from that a comparison between farmer reported and computer measured agric plot size, is presented too. Chapter nine: Conclusions Chapter 9 concludes the whole effort of this study. It gives a summary of conclusions highlighting prominent outcomes of the research. International Institute for Gio Information Sience and Earth Observation 11

27 Improving land use survey method using high resolution satellite imagery chapter-1 International Institute for Gio Information Sience and Earth Observation 12

28 Improving land use survey method using high resolution satellite imagery chapter-2 2 LITERATURE REVIEW 2.1 An Overview of Agricultural Surveys Current Agricultural surveys, which are periodic (annual or seasonal) national (or large scale) or multipurpose agricultural data collection programs are established to obtain many different kinds of data as a required information source in the decision making process for development in agricultural sector in almost every country (FAO, 1996). Agricultural statistics obtained through surveys are essential for the orderly development of production and marketing decisions by farmers, ranchers, and other agribusinesses. These data series are also used for monitoring the ever-changing agriculture sector and for making and carrying out agricultural policy relating to farm programme legislation, commodity programmes, agricultural research, agricultural chemical usage, rural development and related activities (Cotter and Tomczak, 1994). Agricultural surveys are usually most difficult and complex because that single word covers a tremendous variety of activities and purposes in four ways (FAO, 1987). First, they are multi-subject because agriculture covers a great variety of distinct industries. Second, often they must also be multi-method because different variables and subjects need drastically different methods of measurement. Third, both natural conditions and cultural norms impose even greater variety than required by the several subject. Fourth, repeated or periodic surveys are often needed for collecting agricultural data. The quality and usefulness of information and estimates collected through surveys, in decision making process and for other purposes, is based on the holders capacity to provide accurate data, the capacity of staff to organize and conduct the survey programme and prioritisation of the needs of data users. International Institute for Gio Information Sience and Earth Observation 13

29 Improving land use survey method using high resolution satellite imagery chapter Agricultural Survey Designs The agricultural surveys can be classified basically into two types such as Censuses and Sample Surveys. An Agricultural Census is a survey that provides a detailed classification of the agricultural structure of the country whereas Agricultural Sample Surveys are conducted to measure the performance of the agricultural structure. Again, Agricultural Sample Surveys are divided as Probability Sample Surveys, which is applied to estimate the survey variables based on probability sampling and methods, and Non-probability (Subjective) Sample Survey, which provides estimation of the variables, not based on probability sampling and estimation methods. Although Non-probability (Subjective) Sample Surveys are used in cases when statistically accurate data is not required or when there are no resources for its production, Probability Sample Surveys allow calculation of the statistical precision of the estimates (FAO, 1996) Agricultural Probability Sample Surveys Agricultural probability sample surveys in terms of the last stage sampling unit and the rules to assign their probabilities of selection can be divided into two basic types, namely area frame sample surveys and list frame sample surveys (Figure 2-1) List Frame Sample Surveys List frame sample designs are the most commonly used sampling procedure for agricultural surveys (FAO, 1987). The last stage-sampling units of a list frame are usually the holdings or holders address. A list frame might be very good but cover only a part of the population (Houseman, 1975). Its incompleteness and inaccuracies that degenerates rapidly over time are the main weakness of this method. If the list is a few years old, many of the names will no longer represent due to sales, deaths, and abandonment and new holdings will not be represented. Some European countries are able to utilise large, countrywide list frames effectively because of administrative procedures whereas in most countries, a reliable countrywide list frame if holdings or holders address does not exist and so a rough approximates is used. Furthermore, constructing list frames by merging lists from different sources is costly and can include undetected duplicates (FAO, 1996 and 1998; Kish, 1989). Data collection is possible through personal interview with the holder physically in the field or by some other means of communication like telephones, Electronic mails ( s), and by postal mails, if the list frame provides an unambiguous address of the International Institute for Gio Information Sience and Earth Observation 14

30 Improving land use survey method using high resolution satellite imagery chapter-2 holders. However, mail surveys tend to have low response rates demanding repeated follow-ups. Usually data from mail surveys need to be edited more carefully than collected through personal interviews, thus raising costs. Telephone surveys, which are less expensive than personal interviews tend to have better response rates and allow the data to be edited while the respondent is on the telephone, despite their higher cost compared with mail surveys. During data collection of personal interviews in fields, often the enumerators also measure the area of the holding since in many countries such basic data is unknown by the holders (FAO, 1996) Area Frame Sample Surveys An area sample survey is a probability sample survey, which is introduced as a vehicle for conducting surveys to gather information regarding crop acreage, cost of production, farm expenditures, yield and production, livestock inventories and other agricultural items (Cotter and Nealson, 1987). The most common survey variables are the followings. Planted and harvested area, area intended for harvest, potential and actual crop yield of each crop or variety of crop, crop production and number of trees. Livestock and poultry inventories. Production of milk, eggs, honey and seeds. Number and types of farming methods and agricultural inputs including labour, type and quantity of seeds, fertilizers and pesticides, source of irrigation water, drainage, extent of shifting cultivation. Number and types of holdings. Cost of production and value of sales. The final stage-sampling units of an area frame are land areas called segments, which should not be overlap and must cover the entire survey area. These land parcels can be determined based on factors such as ownership or based simply on easily identifiable boundaries or square segments defined by straight lines forming squares whose end points are established by map coordinates (FAO, 1996). Area frames are critical to producing quality estimates, as they provide complete coverage withal land areas being represented in a probability survey with a known chance of selection (Cotter and Tomczak, 1994). This frame does not become out dated rapidly over time unless the population extends into areas not covered by the frame (FAO, 1996). International Institute for Gio Information Sience and Earth Observation 15

31 Improving land use survey method using high resolution satellite imagery chapter-2 An area frame is generally derived by dividing the total area to be surveyed into land use strata. The land use strata are defined by proportion of cultivated land, predominance of certain crops, special agricultural practices, average size of cultivated stated etc. The purpose of stratification is to reduce the sampling variability by creating homogeneous groups of sampling units. Rather than dividing an entire frame into final sampling units called segments, these strata are divided into non-overlapping areas with physical boundaries called primary survey units (PSU s) or counting units (CU s). A random sample of PSU s is further divided into segments. Segments will eventually be visited by an interviewer to gather agricultural information (Houseman, 1975). A survey is a very expensive undertaking that involves an important logistic effort; it is vital that the enumerators are in the field at the proper time for gathering the desired information. Being there at the time of an event or immediately thereafter avoids memory bias which can be significant in countries where the farmers do not keep records. Avoid, if possible, period of heavy rains, in order to facilitate the logistics and survey data collection (FAO, 1996). Field data collection is carried out by enumerators that complete a questionnaire by personal interviews with the holder or other respondent who can provide information on the tract included in each selected sample segment. The enumerator uses the topographic map or a road map showing segment location to identify routes of access and arrive at the segment. The enumerator should go around each segment on foot or in a vehicle. After the enumerator is satisfied that the boundaries are well defined, enumeration commences at the nearest occupied dwelling from where he is, or by approaching the nearest visible worker in a field inside a segment. The next step is to identify the holder of a tract within the segment. The holder helps the enumerator delineate the tract on the transparent overlay of the segment photo. A questionnaire should be complete for the tract (Houseman, 1975). The data collection, in addition to completion of a questionnaire, often involves identification and measurement of cultivated areas. For each sample segment, the enumerator uses an aerial photo enlargement (or a map or scale drawing), which includes the boundaries of the segment. This is called the segment photo. For each tract within a given sample segment the enumerator delineates on the segment photo the boundaries of the tract and the boundaries of all fields included in the tract. The enumerator verifies the crops International Institute for Gio Information Sience and Earth Observation 16

32 Improving land use survey method using high resolution satellite imagery chapter-2 planted and other uses of land for each field, information provided also by the holder. During the interview, the enumerator may also use a transparent grid on the segment photo to verify, approximately, the reported area of fields. Such identified agricultural areas in each sample segment can later be measured in the office using a computerized measurement instrument or a planimeter. The checking of area estimates made by holders and /or enumerators provide a very important feature concerning data reliability (Houseman, 1975) Multiple Frame Surveys Multiple frames are a combination of both an area frame and list frame, an area sample component with a list sample component. The multiple frame sampling methods described combine a probability sample of land areas called segments, selected from an area frame, with a complementary short list of special agricultural holdings to be completely enumerated during the survey field data collection. The multiple frame estimates combine estimates from the area sample with estimates obtained from the list of special agricultural holdings. In general, a multiple frame design consists of a set of frames that together cover all the units in the population. It is essential that every unit in the population of interest be contained in at least one of the frames; list frame or area frame. But all holdings in the list frame must be removed from the area frame. In other words, all tracts in the selected sample segments that correspond to holdings in the list frame should not be considered to obtain the area sample estimates. A list frame of special holdings is a necessary addition to an area sample in order to provide adequate estimates for important agricultural variables that have a highly skewed frequency distribution. As it is known, a number of important agricultural variables concentrate in a small proportion of the holdings. For each of these variables, the list sample should account for the skew ness of its distribution. As a result, the corresponding multiple frame estimates will be more precise than the area frame sample estimates. The list frame of a multiple frame survey can be a large, nationwide list of holdings. The preparation and updating of such frame requires a heavy investment in computer hardware and software and a very controlled field operation for its use in combination with area sample. In the USA and Canada, for example, the multiple frame survey designs combine a large, nationwide list sample with an area sample. However, such type of multiple frame designs, although the most efficient, is not feasible in most developing coun- International Institute for Gio Information Sience and Earth Observation 17

33 Improving land use survey method using high resolution satellite imagery chapter-2 ties. On the other hand, the most practical multiple frame methods for developing countries are those with a relatively short list of holdings, to be completely enumerated, used as a complement to the are sample (FAO, 1996). Agricultural surveys Census Sample surveys Probability Sample Non- Probability (Subjective) List Frame Area Frame Multiple frames Figure 2-1:Types of agricultural surveys 2.3 General Procedure for Area Frame Construction To prepare an area frame, the first requirement is the availability of up-to-date cartographic materials. The resolution must be sufficient to allow stratification and the subsequent subdivision of these strata into PSUs, which must have recognizable permanent physical boundaries. PSUs are usually constructed on photography or satellite images that show the boundaries of the strata. They are transferred to maps for measurements. Each PSU must be measured and assigned a target number of segments. The number of segments in each stratum and summed again to provide the total in the frame. Then a two-stage probability sample of segments is selected from each stratum using a replicated selection procedure. Each sample segment is constructed on small mosaics of aerial photography on which the boundary of the corresponding PSU have been transferred. Fi- International Institute for Gio Information Sience and Earth Observation 18

34 Improving land use survey method using high resolution satellite imagery chapter-2 nally, the selected sample segments are located on aerial photo enlargements used to control field data collection (FAO, 1996). Area frame construction process is shown in Figure 2-2. Step One: Prepare the frame materials, Delineate frame limits, and measure total frame area. This step will provide the view of large areas for stratification purposes. At this point the decision may have been made to construct simple photo-mosaics, use available orthophotomaps, use satellite images, use only maps, or to use a combination of any or all of these. Once the material is ready, the necessary boundaries are drawn using grease pencil or special lead pencils on photographs or images to delineate frame limits. Scaling rulers are an important asset for locating boundaries. Step Two: Delineate and measure areas covered with water (Lakes, Large rivers, etc.), heavy forest, high mountains, national parks, military reserves, and other non-agricultural land, except urban areas. This step follows step one using the same procedure as described above. Step Three: Outline and measure the urban and agro-urban strata. Central portion of the city is defined as non-agricultural. Area with high population density that also includes patches of agriculture is delineated as agro-urban agriculture. Step Four: Delineate strata The strata, in agriculture areas, are defined by proportion of cultivated land, predominance of certain crops, average size of cultivated fields and special sites of agricultural activities. Step Five: Review of stratification. The stratified map page will be passed on to another team member for review. International Institute for Gio Information Sience and Earth Observation 19

35 Improving land use survey method using high resolution satellite imagery chapter-2 Step Six: Transfer strata boundaries to map. Measure and field verify the strata. The review strata boundaries are transferred from satellite images or photo mosaics to maps. At this step strata should be measured and the total area compared with available data. Step Seven: Construct PSUs PSU should reflect on a small scale what are seen to be the stratum characteristics. Step Eight: Transfer PSUs to maps and order PSUs In this step, the PSUs are transferred to the maps. The initial transfer is done with an ordinary pencil. After review of the transfer is complete, they are outlined on the map in the appropriate stratum colour. Step Nine: Measure area of PSUs Measurement of PSUs can be done with planimeter and conveniently can be done with a computer graphic system or a digitising table. Step Ten: Assign measures of size to PSUs, strata and to total Frame As PSU areas are determined and approved, they are entered on another listing sheet. Next, segments are assigned to the PSUs as determined by the area of PSU and the target size of segments in that stratum. The number of assigned segments is equal to the area of the PSU divided by the target size of the segments in the stratum, and the results approximated to the nearest integer. The measure of size of the PSU should be an integer. International Institute for Gio Information Sience and Earth Observation 20