Improving Land Use Survey Method using High Resolution ...method through reducing costs and efforts....

Improving Land Use Survey Method using High Resolution Satellite Imagery

M.H.B.P.H.MADANA March 2002

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 Observa-tion in partial fulfilment of the requirements for the degree of Master of Science in Natural Re-sources 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.

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

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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 labori-ous 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 im-age 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 ma-terials 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 pro-cedure. 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.

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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 per-mission, the Dutch Government for providing me financial support through the Netherlands Fellowships Programme (NFP) and International Institute for Geo Infor-mation 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 re-search 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 Man-agement (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.

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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 2001.

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TABLE OF CONTENT

ABSTRACT ........................................................................................................I

ACKNOWLEDGMENTS................................................................................ III

TABLE OF CONTENT..................................................................................... V

LIST OF FIGURES..........................................................................................IX

LIST OF TABLES............................................................................................XI

1 INTRODUCTION ................................................................................... 1

1.1 Background................................................................................................... 1

1.2 Research Problem......................................................................................... 3

1.3 Objectives ..................................................................................................... 5

1.3.1 Main Objective....................................................................................... 5

1.3.2 Secondary Objectives............................................................................. 5

1.4 Research Questions....................................................................................... 6

1.5 Hypothesis .................................................................................................... 6

1.6 Basic Methodology of this Research ............................................................ 7

1.7 Thesis Structure .......................................................................................... 10

2 LITERATURE REVIEW ..................................................................... 13

2.1 An Overview of Agricultural Surveys........................................................ 13

2.2 Agricultural Survey Designs....................................................................... 14

2.2.1 Agricultural Probability Sample Surveys............................................. 14

2.2.1.1 List Frame Sample Surveys........................................................... 14

2.2.1.2 Area Frame Sample Surveys ......................................................... 15

2.2.1.3 Multiple Frame Surveys ................................................................ 17

2.3 General Procedure for Area Frame Construction ....................................... 18

3 STUDY AREA AND MATERIALS.................................................... 23

3.1 Study Area .................................................................................................. 23

VI

3.1.1 Location ............................................................................................... 23

3.1.2 Climate ................................................................................................. 23

3.1.3 Rainfall................................................................................................. 24

3.1.4 Temperature and Relative Humidity.................................................... 24

3.1.5 Topography and Soil ............................................................................ 25

3.1.5.1 Topography ................................................................................... 25

3.1.5.2 Soil ................................................................................................ 25

3.1.6 Mapping Units...................................................................................... 26

3.1.7 Short Soil Descriptions ........................................................................ 27

3.1.8 Agricultural Background in Botswana................................................. 30

3.1.9 Agricultural Lands in Serowe .............................................................. 32

3.2 Materials ..................................................................................................... 33

3.2.1 Remote Sensing Data ........................................................................... 33

3.2.2 Topographic Map................................................................................. 34

3.2.3 Software ............................................................................................... 34

3.2.4 GPS ...................................................................................................... 34

4 PREPARATION OF FRAME CONSTRUCTION............................ 35

4.1 The Potential of Aster Image for Area Frame Construction ...................... 35

4.2 Survey Variables......................................................................................... 35

4.3 Area Frame Construction............................................................................ 35

4.4 Preparation the Frame Materials and Delineation of Frame Limits ........... 36

4.5 Evaluation of Possible Advantages of Aster Image and the Approach to Delineate Fame Limits...................................................................................... 38

5 STRATIFICATION............................................................................... 41

5.1 Necessity of Stratification for Area Frame Sampling ................................ 41

5.2 Advantages of Stratification in Study Area................................................ 41

5.3 Stratification Process .................................................................................. 48

5.4 Sub Stratification (Subdivision of Strata into PSUs ) ................................ 52

VII

5.5 Evaluation of the Potential of Aster Data for Stratification ....................... 56

6 SEGMENTATION ................................................................................ 59

6.1 Size of the Sampling Unit (Segment) ......................................................... 59

6.2 Sampling Frame.......................................................................................... 60

6.3 Sample ........................................................................................................ 64

6.4 Sample Size ................................................................................................ 64

6.5 Sample Allocation to Strata ........................................................................ 66

6.6 Sample Selection ........................................................................................ 67

6.7 Evaluation of using Aster Image for Segmentation the Frame .................. 68

7 PREPARING FOR DATA COLLECTION........................................ 69

7.1 Questionnaire.............................................................................................. 69

7.2 Preparation of Selected Samples for Ground Survey ................................. 70

7.3 Data Collection ........................................................................................... 74

7.4 Evaluation the Potential of Aster Image for Preparation the Materials for Ground Suvey ................................................................................................... 75

8 IDENTIFICATION OF AGRICULTURAL PLOTS ........................ 77

8.1 Identification of Agricultural Areas by Visual Interpretation .................... 77

8.2 Identification of Agricultural Areas by Filtering........................................ 78

8.3 Classification .............................................................................................. 79

8.4 Supervised Classification ........................................................................... 79

8.4.1 Signature Separability of the Classification......................................... 84

8.4.2 Accuracy Assessment of Classification ............................................... 84

8.4.3 Reports of accuracy assessment ........................................................... 85

8.4.3.1 Confusion Matrix .......................................................................... 85

8.4.3.2 Commission and Omission Error .................................................. 85

8.4.3.3 User Accuracy and Producer Accuracy......................................... 85

8.4.3.4 Over all Accuracy.......................................................................... 86

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8.4.3.5 Kappa (K^) Statistics..................................................................... 86

8.4.4 Accuracy of this Classification ............................................................ 86

8.5 Evaluation between Visual Interpretation and Supervised Classification for Identifying Agricultural Plots. .......................................................................... 88

8.5.1 Quantitative Evaluation........................................................................ 88

8.5.2 Qualitative Evaluation.......................................................................... 92

8.6 Comparison between Farmer Reported and Computer Measured Plot Size93

9 CONCLUSIONS .................................................................................... 95

REFERENCES ................................................................................................. 99

Appendix 1: Climatic Data for Mahalapye........................................................ A

Appendix 2: Signature Separability................................................................... D

Appendix 3: Abbreviations.................................................................................E

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LIST OF FIGURES

Figure 1-1: Low-level diagram of area frame sampling process............................ 9

Figure 2-1:Types of agricultural surveys ............................................................. 18

Figure 2-2: Main steps in Area Frame Construction............................................ 21

Figure 3-1: Location of study area ....................................................................... 23

Figure 3-2: Soil map of study area ....................................................................... 29

Figure 3-3: Crop calendar of Botswana ............................................................... 31

Figure 3-4: Main crop zones of Botswana ........................................................... 31

Figure 3-5: Monthly rainfall totals for Mahalapye from year 1996-2001............ 31

Figure 4-1:The process of preparation frame....................................................... 37

Figure 4-2: The steps of frame limits delineation in conventional method.......... 37

Figure 5-1:Differences in agricultural land distribution ...................................... 45

Figure 5-2: No agricultural land use can be found around some ephemerals and main roads ..................................................................................................... 45

Figure 5-3: Figure shows the distribution of agricultural plots on different soil groups ............................................................................................................ 47

Figure 5-4: The steps were followed to create a strata map................................. 52

Figure 5-5: The process of further stratification of strata into sub units.............. 54

Figure 5-6: The strata map with four different strata ........................................... 55

Figure 5-7: Substrata map .................................................................................... 55

Figure 5-8: Figure showing Aster image on 20th October 2000 and LANDSAT image on 1st October 2001............................................................................. 57

Figure 6-1: 1 x 1 km block grid design used for segmentation the survey area... 63

Figure 6-2: Figure showing the dropped and kept pieces of square segments in sample selection............................................................................................. 63

Figure 7-1: Field Observations And Questionnaire Sheet.................................... 70

Figure 7-2: Aster image enlargement of selected sample segment for ground survey............................................................................................................. 73

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Figure 7-3: Aster image in a 1:100, 000 scale showing segment locations for ground survey ................................................................................................ 73

Figure 8-1: Spectral reflectance curves of basic land cover types ....................... 81

Figure 8-2: Image data file values of different plots............................................ 81

Figure 8-3: Appearance of agricultural plots in three stages ............................... 83

Figure 8-4: Part of original image and enhanced image. ..................................... 83

Figure 8-5: The map generated from supervised classification ........................... 83

Figure 8-6: The steps followed for qualitative evaluation of supervised classification and visual interpretation .......................................................... 89

Figure 8-7: The map created by visual interpretation shows agricultural plots ... 91

Figure 8-8:The map subset from classified map based on visually interpreted agric plot layer, showing the different classes within agricultural plots ................ 91

Figure 8-9: The scatter plot of farmer reported and computer measured plot size93

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LIST OF TABLES

Table 3-1: Monthly Open Water Evaporation (OWE), Potential Evapo Transpiration(PET) and Rainfall at Mahalapye (Bahalotra, 1987) ............... 24

Table 3-2: General descriptions of soil units of the study area, summarized from de Wit and Nachtergaele, 1990 ..................................................................... 26

Table 3-3: Spectral Range (µm) of Aster channels (1,2,3) with 15-m resolution (NASA, 2001)................................................................................................ 34

Table 5-1:Land use strata codes and definitions .................................................. 49

Table 5-2: The area frame stratum number and definitions. ................................ 49

Table 5-3: Descriptions and codes strata.............................................................. 50

Table 5-4: Table showing strata descriptions, which are based on proportion of agricultural plots, strata code, total area and agric plot area ......................... 51

Table 5-5: Strata and substrata (PSUs) description, code and area (Ha) ............. 54

Table 6-1: Total no of Segment in each stratum and substratum......................... 64

Table 6-2: Segment allocation by stratum and substratum................................... 67

Table 8-1: Confusion matrix obtained from the classification............................. 87

Table 8-2: Percentages of producer accuracy and user accuracy of each class and overall classification accuracy....................................................................... 87

Table 8-3:Kappa Statistics resulted from classification. ...................................... 87

Table 8-4: Total area in hectare and percentage of area within agric plots and without agric plots of each classified class ................................................... 92

Table 8-5: Quantitative evaluation of supervised classification based on visual interpretation.................................................................................................. 92

Table 8-6: Qualitative evaluation of visual interpretation and supervised classification used in identifying agricultural areas ...................................... 92

Table 8-7: Farmer reported and computer measured plot size ............................. 93

Improving land use survey method using high resolution satellite imagery

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International Institute for Gio Information Sience and Earth Observation 1

1 INTRODUCTION

1.1 Background The need for timely and reliable agricultural information such as crop production esti-mates, livestock inventories and socio-economic data has become more important in de-cision-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 statis-tics 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 re-liable information can only be obtained through an adequate and periodic national agri-cultural surveys based on probability sampling methods (FAO, 1996). Improvement of such survey methods is, therefore, of paramount importance component of the agricul-tural information systems. However, consideration of new options to estimate cultivated crop area in terms of sav-ing 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


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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 sur-vey is manual and paper-based. The construction of an area frame manually based on pa-per-based materials (the paper satellite imagery products, Aerial Photographs mosaics and different type of maps) is costly and very labour intensive. Today many countries ex-perience, 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 meas-urements. 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 ae-rial 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.


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1.2 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 agricul-tural 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 pa-per-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 construc-tion 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 pre-cise 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. Acqui-sition 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. Stratifi-cation, for area frame sample surveys may be according to the proportion of land culti-


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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 agricul-tural lands and their proportion with respect to the total land area. Most area sample sur-veys consider a subdivision of the frame into land use strata to reduce the sampling vari-ability by creating homogeneous groups of sampling units. Stratum boundaries must con-sist 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 suit-able 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.


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1.3 Objectives

1.3.1 Main Objective The specific objective of this case study is to evaluate the potential of using high resolu-tion (15m) Aster image data for improving an area frame sampling survey method in or-der to reduce cost and effort. In area frame agricultural surveys already adopted in many developing countries, as dis-cussed 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 iden-tify current land use in survey areas.

1.3.2 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 sin-gle satellite image. Another objective of this research, therefore, is to run and compare several options, for instance, visual interpretation, supervised classification, edge en-hancement, 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 exam-ine the relationship between the field size reported by the farmers and the figure meas-ured by using the area measurement tool available in the software.


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1.4 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 ne-

cessity 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 us-

ing identifiable physical boundaries.

• Although, in conventional method, requiring scale of aerial photographic enlarge-ment 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.


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• Edge enhancement techniques can be applied to sharpen the Aster image enlarge-ment.

• Reflectance of Aster image (3,2,1 false colour composite) can be used for identi-

fication 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 rela-tionship 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 corre-sponding 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.


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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.


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Figure 1-1: Low-level diagram of area frame sampling process

Ground survey

Agric Plot Map Classified map

Define block size

Topo map Raw satellite data

Geo-correction

Geo-referenced satellite image

Visual interpretation

Strata Map

Grid design Sample selection by sub stratum

Enlargement of segment

Field observation sheet

Data processing

Define criteria for stratification

Soil map

Sub strata map

Topo maps & Aster image (1:100,000)

Define survey variables

Sup. Classification

Comparison

Evaluation


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1.7 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 describ-ing 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 pre-sented. In addition to that the potential of Aster image for preparation the materials for ground survey is included.


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Chapter 8: Identification of agricultural fields Identification of agricultural plots by visual interpretation, filtering and supervised classi-fication is described following by comparison between supervised classification and vis-ual 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.


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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). Ag-ricultural 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 mak-ing and carrying out agricultural policy relating to farm programme legislation, commod-ity 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 dis-tinct “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 pro-vide accurate data, the capacity of staff to organize and conduct the survey programme and prioritisation of the needs of data users.


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2.2 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 classifica-tion 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).

2.2.1 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). 2.2.1.1 List Frame Sample Surveys List frame sample designs are the most commonly used sampling procedure for agricul-tural 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 rap-idly 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, coun-trywide 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 (emails), and by postal mails, if the list frame provides an unambiguous address of the


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holders. However, mail surveys tend to have low response rates demanding repeated fol-low-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 expen-sive 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 enumera-tors also measure the area of the holding since in many countries such basic data is un-known by the holders (FAO, 1996). 2.2.1.2 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 cover-age 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).


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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 in-formation. 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 col-lection (FAO, 1996). Field data collection is carried out by enumerators that complete a questionnaire by per-sonal 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 enumera-tor 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 identifi-cation 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 bounda-ries of the segment. This is called the segment photo. For each tract within a given sam-ple 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


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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 (House-man, 1975). 2.2.1.3 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 de-scribed 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 com-pletely 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 con-tained 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 pro-vide adequate estimates for important agricultural variables that have a highly skewed frequency distribution. As it is known, a number of important agricultural variables con-centrate 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 multi-ple 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 hard-ware 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 mul-tiple frame designs, although the most efficient, is not feasible in most developing coun-


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ties. On the other hand, the most practical multiple frame methods for developing coun-tries are those with a relatively short list of holdings, to be completely enumerated, used as a complement to the are sample (FAO, 1996).

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 carto-graphic materials. The resolution must be sufficient to allow stratification and the sub-sequent 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 pho-tography on which the boundary of the corresponding PSU have been transferred. Fi-

Agricultural surveys

Census Sample surveys

Probability Sample Non- Probability (Subjective)

List Frame

Area Frame

Multiple frames


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nally, the selected sample segments are located on aerial photo enlargements used to con-trol field data collection (FAO, 1996). Area frame construction process is shown in Fig-ure 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 orthopho-tomaps, 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 den-sity 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, predomi-nance 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.


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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 or-dinary 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 ap-proximated to the nearest integer. The measure of size of the PSU should be an integer.


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Figure 2-2: Main steps in Area Frame Construction

Maps Aerial photo-

graphs Satellite Image

STRATA

PSU’SLarge scale air photographs

Identify segments boundaries construct segments (the last-stage sampling units)

SEGMENTS

Construct PSU Transfer PSU to map and sequentially order PSU’s measure area of PSU, assign measures of size to PSU”s, Strata and to total frame

Prepare the frame materials, Delineate frame limits, measure total frame area Delineate and meas-ure non-agricultural lands except urban areas Out line and measure the urban and agro-urban strata Delineate strata Review of stratification Transfer Strata boundaries to map, measure and field verify the strata


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3 STUDY AREA AND MATERIALS

3.1 Study Area

3.1.1 Location The study area is situated in Central District of Botswana, about 275km North East of Capital City (Gaborone), and in the surroundings of Serowe settlement. The study area is bounded by UTM coordinates: 470753E to 509014E and 7494991N to 7563487N. Figure 3-1: Location of study area

3.1.2 Climate The climate of the study area is characterised as semi-arid, with cool dry winters and hot moist summers, which is influenced in its variability by the movement of the Inter Tropi-cal Convergence Zone (ITCZ). Mean annual rainfall is 450 mm (SGS, 1988 and Vossen, 1989). Rainfall is seasonal, with most rainfall occurring in summer followed by a dry


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winter period. The summer season stretches from October to April whereas the winter begins in May and ends in August (Tyson, 1986 and Bhalotra, 1987).

3.1.3 Rainfall In general, most of the rainfall occurs between October and May with a peak occurring in January. However, this situation may change drastically during drought periods. It should be borne in mind that these data are not sufficient to fully describe the rainfall pattern in the study area, since the rainfall is generally erratic in time and space (refer to Tyson, 1986). Despite its limitations the data confirms the findings of previous investigators (Bhalotra, 1987; SGS, 1988 and Vossen, 1989) about the seasonality of the rainfall in the area. Rainfall stations are located at Serowe, Paje, Mabeleapudi and Tlhabala, with a more comprehensive meteorological station situated at Mahalapye. Long-term mean monthly rainfall, Potential Evapo Transpiration (PET) and open water evaporation (OWE) data are shown in Table3-1.

Table 3-1: Monthly Open Water Evaporation (OWE), Potential Evapo Transpiration(PET) and Rainfall at

Mahalapye (Bahalotra, 1987) Previous 18 years rainfall data are given in Appendix 1. The graph (Figure 3-5) shows last six years monthly rainfall totals for Mahalapye. Comparatively, low rainfall occurred between October 2000 and May 2001.

3.1.4 Temperature and Relative Humidity In general, the mean maximum temperature ranges between 22°C and 31°C while the mean minimum temperature lies between 5°C and 19°C. The higher temperatures are ob-served during the month of November, December and January, and the lowest tempera-tures are experienced in June, July and August.

Station Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Annual (mm)

OWE 187 191 216 205 175 166 130 104 81 91 121 158 1825

PET 161 163 183 173 147 138 109 88 69 78 104 137 1550

Rainfall 30 67 80 92 78 68 25 10 3 3 3 8 467


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The relative humidity, in general, is higher during the morning hours and lies between the range 53% and 69%. RH in the afternoon is generally between 26% and 42%. All the climatic data for last 18 years is given in Appendix 1.

3.1.5 Topography and Soil 3.1.5.1 Topography The study area has in general, a gentle topography, which varies approximately from 1060 to 1240 meters above mean sea level (m.s.l) near Mokongweng cattle post. The ele-vation is lower in the east and south east of the region while the highest elevation is found in the vicinity of the escarpment edge, which is a prominent topographic feature in the project area. From the escarpment, the average slope is 5% and it gradually decreases to less than 1% towards the east and southeast. Rock outcrops are found mainly at the escarpment and along river valleys below the es-carpment. Elsewhere Kalahari sands and superficial deposits overlie rocks. 3.1.5.2 Soil Soils in the Serowe area were mapped by the soil survey section, Land Utilization Divi-sion, Ministry of Agriculture, Republic of Botswana, in co-operation with Food and Ag-riculture Organization of the United Nations, with financial support from the United Na-tions Development programme and the government of the Netherlands, at a scale of 1:1 000 000 (de Wit and Nachtergaele, 1990). The soil units found in the study area are Arenosols, Regosols, Lixisols, Luvisols, Vertisols, Acrisols and Calcisols . A generalised description of each soil unit is summarised in Table 3-2.


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Table 3-2: General descriptions of soil units of the study area, summarized from de Wit and Nachtergaele,

1990

3.1.6 Mapping Units. Map unit symbols explain the soil association as follows. The first three letters (two capi-tal letters for the soil group, and a simple letter for the sub group) indicate the type of soil. The next figure denotes the association. The descriptive legend on the map gives the associated soils, the inclusions, the soil phases and the third level classification of each of these, if applicable. Example: - RGe31- Eutric Regosols with petric phase calcisols, petrocalcic- eutric lepto-sols occur as inclusions (FAO, 1990).

Soil class

General description

Arenosols (AR)

- Soil is excessively well drained, and have low water retention capacity- Predominantly fine sands (> 75%) with minor clay or silt (<5%).

Regosols (RG)

-Well drained to moderately well drained, but less drained than Arenosols - Sandy loam to clay loam, with minor coarse sand.

Lixisolls (LX)

-Soils are well drained -Sand content is about 20%, and a high clay content (usually>20%)

Luvisols (LV)

-Soils are well drained -Consist of nearly equal proportions of coarse fine sand. The sand con-tent is about 20% to 30% with a high clay content (usually>20% as in lixisols)

Vertisols (VR)

-The soil unit is poor to imperfectly drained -Soils are predominantly clays and fine silt. The clay content is gener-ally in excess of 50%.

Acrisols (AC) Soils having weakly developed structure

Calcisols (CL) Soils are predominantly occupied by calcium.


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3.1.7 Short Soil Descriptions ARo – Ferralic Arenosols Deep to very deep, well to excessively drained, yellowish brown to red, coarse sand to loamy fine sand. ARl – Luvic Arenosols Deep to very deep, well to somewhat excessively drained, yellowish brown to red, fine and medium sand to lomy fine sand. RGe – Eutric Regosols Shallow to moderately deep, moderately well to excessively drained, greyish brown to yellowish red, coarse sand to clay loam. LPq – Lithic Leptosols Very shallow, well-drained, dark brown, sandy loam. LVk – Calcic Luvisols Deep to very deep, moderately well to well drained, dark brown-to-brown, calcareous, sandy clay loam. LXf – Ferric Lixisols Very deep, well-drained, dark red, sandy clay loam, with mottles in the subsoil. VRe – Eutric Vertisols Deep, imperfectly drained, dark brown, heavy clay. LPe – Eutric Leptosols Soils, which are limited in depth by continuous hard rock or a continuous, cemented layer within 10cm of the surface. CLh – Haplic Calcisols Soil having petrocalcic horizon. The upper part of which occurs within 100cm. ACh – Haplic Acrisols Soils having plinthite (NH4oAc) within 125 cm from the surface.

LVx – Chromic Luvisols Soils showing gleyic properties and occurs within 100 cm from the surface. Strong brown to red and a chroma of more than four.


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Soil map of study area (Figure 3-2) were created by scanning the above-mentioned exist-ing soil map of Botswana.

Figure 3-2: Soil map of study area

RGe31

RGe31

LVk47

LVk20

LVk50

ARo35

ARo35

ARo35

ARo35

ARo21

ARl29

ARl33

LPe15

LPq7

LPq16

LPq1

CLh21

CLh37

LXf12

LVx16

LVx15 LVx24 ACh2

VRe11

Soil Map


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3.1.8 Agricultural Background in Botswana Through direct and indirect linkages, agriculture contributes to both the rural and na-tional economy of Botswana. It is therefore in the long-term interest of the country for the government to promote productive investment in this sector. In the eastern section especially, where conditions are more favourable, the aims are to grow appropriate crops for both domestic and export market. Import some foodstuffs, which are expensive to produce within the country. The agriculture sector’s contribution to the gross domestic product has declined from 40 percent at the time of independence to 4 percent during the country’s seventh national development plan. Although agriculture and livestock production accounts for only 4% of the country's GDP, it employs about 25,7% of the country's labour force. The major part of the country has semi-desert and partly savannah conditions with erratic rainfall and poor soil conditions making it more suitable to grazing than crop production the live-stock production is therefore the main agricultural sector. Potential for investment in the agricultural sector lies in adding value to primary agricul-tural products as well as supplying the growing demand for farm machinery, irrigation and water pumps, and water transportation equipment. Specific opportunities exist in the following areas: processing of hides and skins into finished products; meat processing; manufacturing of chicken and cattle feed; agricultural chemicals; manufacture of agricul-tural machinery; construction of grain storage facilities. While the distribution of income in the agricultural sector favours relatively few large commercial cattle farmers, subsistence farming by peasant farmers is the predominant form of agricultural activity providing food, income, employment and capital for two thirds of the country's population. Yet Botswana is essentially arid and subject to fre-quent and extensive droughts. Irrigated crop farming has proved difficult to promote and the country has to import up to 80% of its food requirements. The principal food crops are sorghum, maize, millet, pulses, groundnuts (peanuts), beans, and sunflower seed. Marketing of agricultural products is handled by the Botswana Agricultural Marketing Board, which guarantees a minimum price to producers (Mendip, 2000). Crop calendar data and main crop zones of Botswana can be found respectively in Figure 3-3 and 3-4.


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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 1996-2001

JAN

FEBMAR

APRMAY

JUN

JUL

AUGSEP

OCTNOV

DEC

2001

1999

19970

50

100

150

200

250

300

Rainfall (mm)

MonthYear

Monthly Rainfall Totals For Mahalapye

200120001999199819971996

2001 8.5 127.3 36.5 15.9 6.6 5.2 0 0 0

2000 122.5 262.6 100.6 24.6 8.4 8.7 0 0 0 11 24.1 35.8

1999 18.9 5.7 14.8 0.3 7.8 0 0 0 0 1.3 51 146.6

1998 87 4.2 66.9 0 0 0 0 0 0 35.7 92.2 147.3

1997 245.8 7.2 70.7 5.6 31.6 0 0 0 8.1 17.8 99.9 108.4

1996 84 102.7 0.6 2 9.2 0 0 0 1.2 14.6 126.8 44.9

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC


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3.1.9 Agricultural Lands in Serowe Definitions of field, plot, parcel “A field is a piece of land in a parcel separated from the rest of the parcel by easily rec-ognisable demarcation lines, such as paths, fences, cadastral boundaries and/or hedges, and on which a specific variety of crop and planting date, or specific crop mixture is cul-tivated” (FAO, 1996). A field as defined here corresponds to a plot in (FAO, 1995). “A plot is defined as a piece of land, considered homogeneous in terms of land resources and assigned to one specific land use” (de Bie, 2000). “ A parcel is defined as a contiguous piece of land with uniform tenure and physical characteristics. It is adjacent to land with other tenure and/or physical characteristics, or infrastructure, e.g. roads or water. A parcel may consist of one or more plots adjacent to each other” (De Bie, 2000). The agricultural land pieces those are owned by one farmer (one tenure) in Serowe area can easily be recognised by ground survey with the fences around them. Mostly, within this piece of land they cultivate mixture of crops (one specific land use). Some times one or two crops are grown separately and all other crops are mixed together within the rest of the area in the same piece of land. Sometimes two major crops (sorghum and maize) are grown separately but all other crops (beans, melons, water melons, pumpkins, sweets reeds, millets, courgettes, ground nuts, etc.) are mixed with both or one of them. On the image, these land pieces can easily be identified with their demarcated edges and uniform pattern. Identifiable land pieces on the image should be agricultural plots. Be-cause we can’t identify the tenure-ship on the image but uniformed land use can be rec-ognised. Therefore, according to the definitions and the characteristics of these pieces of agricultural lands in Serowe area on the image, as mentioned above, can be named as ag-ricultural plots.


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3.2 Materials

3.2.1 Remote Sensing Data ASTER NASA's Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) image data are now available free of charge to the public. ASTER is a general-purpose instrument and it has capabilities for a variety of users and applications. It can map the planet's surface and how it changes with time, and can determine the characteristics of land and water surfaces (Sullivant, 2000). ASTER provides high spatial resolution (15- to 90-m) multi-spectral images of the Earth's surface and clouds in order to better understand the physical processes that affect climate change. While the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectro-Radiometer (MISR) will monitor many of the same variables globally and on a daily basis, ASTER will provide data at a scale that can be directly related to detailed physical processes. These data will bridge the gap between field observations and data acquired by MODIS and MISR, and between process models and climate and/or forecast models. ASTER data will also be used for long-term moni-toring of local and regional changes on the Earth surface, which either lead to or are in response to global climate change (e.g., land use, deforestation, desertification, lake and playa water level changes, and other changes in vegetation communities, glacial move-ment, and volcanic processes) (NASA, 2001). ASTER operate in 14 spectral bands, extremely high spatial resolution and stereo imag-ing capabilities, three visible and near-infrared (VNIR) channels between 0.5 and 0.9 m, with 15-m resolution; six short-wave infrared (SWIR) channels between 1.6 and 2.5 m, with 30-m resolution; and five thermal infrared (TIR) channels between 8 and 12 m, with 90-m resolution. The instrument will acquire data over a 60-km swath whose centre is point-able cross-track +/-8.5deg. In the SWIR and TIR, with the VNIR point-able out to +/-24deg. An additional telescope (aft pointing) covers the wavelength range of channel 3. By combining these data with those for channel 3, stereo views can be treated, with a base-to-height ratio of 0.6. Aster’s pointing capabilities will be such that any point on the globe will be accessible at least once every 16 days in all 14 bands and once every 5 days in the three VNIR channels (Michael, 1999).


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Table 3-3: Spectral Range (µm) of Aster channels (1,2,3) with 15-m resolution (NASA, 2001). For this research, 15m resolution Aster images are used. This image may help the pur-pose of area frame construction in area frame sampling survey. Its high resolution can be used for identification of different features on the image. It can be used for area frame construction as a replacement of aerial photograph. In this study, Aster image is used for stratification, identification of area of interest, area frame construction and sample selec-tion. Usually, for ground survey, aerial photo enlargement of selected sample (segment) is used. Use of Aster image enlargement of segment with known coordinates as a re-placement of photo enlargement is possible for fieldwork purposes. This may save cost and time of agricultural surveys.

• 2 Aster images – Serowe Area, Botswana, 23rd October 2000,RGB-321 (15 m pixels), are used for constructing the survey frame and survey data classification and processing in this case study.

• Satellite (Aster) image in the scale of 1: 100,000 will be used for ground survey when the topographic maps could not be found.

3.2.2 Topographic Map This is used for geo-correcting the satellite image and for verifying some features on the image before going to the field observations. During fieldwork, topographic map is used to identify the locations of the sample segments and to recognize some features on the ground for example, roads, veterinary fences, rivers (ephemerals) rocks etc.

3.2.3 Software ERDAS IMAGINE, Arc View, Ilwis, Microsoft Excel, Microsoft Word 2000 and End-note …etc.

3.2.4 GPS GPS (Global Positioning System) was used to find the geo-position of segment bounda-ries. Here, identifiable physical boundaries were not checked.

VNIR (15 Meters)

Spectral Range (µm)

Band 1 (0.52 - 0.60)

Band 2 (0.63 - 0.69)

Band 3N (0.78 - 0.86)

Band 3B (0.78 - 0.86)

Improving Land Use Survey Method Using High Resolution Satellite Imagery

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4 PREPARATION OF FRAME CONSTRUCTION

4.1 The Potential of Aster Image for Area Frame Construction

Main objective of this research is searching, finding ways, testing, and validating meth-ods regarding reducing cost and effort in frame construction for an area frame sampling survey. The conventional procedure of area frame construction using paper base carto-graphic materials, as already described, is time consuming and, need more cost due to more labour requirement and up-to-date material requirement. High resolution (15m) As-ter image, which can obtain free of charge recently, is used for constructing area frame. For reducing cost, it can be used as a replacement of aerial photographs. Mainly, the po-tential of Aster image for stratification, identification of the location of crop areas, seg-mentation with less effort is explored.

4.2 Survey Variables Defined survey variables for this study, are the total cultivated area of each crop in a given year and the crop yield. The criteria used for area frame construction is very impor-tant factor to obtain more accurate estimates. Different variables need drastically differ-ent methods and criteria. For example, one might prefer to estimate the crop yields; the frame construction should be based on the factors really affecting on the crop yields in particular area of interest. If the survey variable is planted and harvested crop area; the criteria for frame construction are defined by proportion of cultivated land.

4.3 Area Frame Construction The concept of area frame sampling is very simple: Divide 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 popu-lation that is in the sample blocks.


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A segment is a piece of land with boundaries delineated on a map. In area frame sam-pling, the total area for the population to be sampled is divided into segments. In addition to meaning a piece of land “segment” is used in sampling terminology instead of “area sampling unit”(Gallego, 1995). The frame construction is involved four main steps: Delineation of frame limits, stratifi-cation of area sample design, identification of the locations of area of interest and seg-mentation. Maps of different types and scales, satellite images and aerial photographs are used for identifying and measuring areas for area frame construction and sample selection. Digital format of Aster image, in image processing software (Erdas Imagine), with 15m resolu-tions and at a scale 1:50,000, is used for area frame construction, in this case study.

4.4 Preparation the Frame Materials and Delineation of Frame Limits

Preparation of frame materials will be provided the “view” of large areas for stratification purposes. Figure 4-1 shows the processes of preparation the frame materials and delinea-tion of frame limits. The steps followed, in conventional method are shown in Figure 4-2. First, the band 3 (infrared), band 2 (green) and band 1(blue) of raw Aster images were geo-referenced to the coordinate system and were combined them to get colour compos-ite. To delineate the frame limits, two Aster colour composites were mosaiced using mo-saic image tools in Data Preparation menu in image processing software. Finally, the study area, which was actually observed, was selected by subseting (breaking out a por-tion of a larger image file into a smaller file) a window, from the mosaicked images for testing the possibilities of aster data for area frame sampling.


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Figure 4-1:The process of preparation frame materials and delineation of frame limits Figure 4-2: The steps of frame limits delineation in conven-

tional method.

Geo-reference

Raw satellite data

Geo-corrected images

Mosaic

Large image file

Subset

Study area im-age

Construct photo mosaic or

Use orthophotomaps or

Use satellite images or

Use maps or

Combination of any or all

Draw the boundaries of the frame manually on one page at a time

Transfer the boundaries manu-ally one to the other

Transfer reviewed boundaries manually from satellite images or photo mosaics to

maps


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4.5 Evaluation of Possible Advantages of Aster Image and the Approach to Delineate Fame Limits

Aster images that were used to develop an efficient area frame sampling method in this case study saved cost of materials as it was downloaded free of charge. Other possible satellite images and aerial photographs are much expensive than obtaining Aster images. Different kind of maps, aerial photographs, satellite images, orthophotomaps, or combi-nation of any or all can be used for area frame construction. The most important things are the appropriateness of the materials for identification of necessary information for stratification related to the survey variables and the validity. The validity of materials rapidly decreases due to the land use changes. In developing countries, as up-to-date ma-terials are lack, use of Aster image fulfils the necessity of up-to-date materials. Even though the aerial photographs are more appropriate for visual interpretation, it is very expensive to upgrade them and laborious in frame construction. High-resolution (15m) Aster data also appropriate for visual interpretation and it helps to reduce effort and cost involved in frame construction. The procedure used in this study saves much time in different steps of frame delineation as it was completely computerised. It avoids the laborious and meticulous work involved in the construction of aerial photo-mosaics. Aster images cover an area of 60km x 60km. If the area needs to be surveyed is larger than the area covered by single image, several images can easily be put together in one frame by mosaicking them in image processing software without much effort. If the image contains areas much lager than a particular study area, in this case, subsetting is helpful to reduce the size of the image to include only the area of interest. For frame limit delineation, if needed to use regional or country boundaries on existing maps, it can easily be done using vector tools to copy selection to area of interest layer (AOI) and using subset within that selected AOI. Use of LANDSAT images also possible to delineate frame limits. It covers an area of 180km x 180km. Because of its large coverage it is easier to handle for frame limits de-lineation in a large study area than handle the Aster images. When considering the resolution, Aster image is more suitable than LANDSAT image.


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Drawing the boundaries manually on map pages, in conventional method, as discussed earlier, consumes lot of time and effort. Using computer tools it can be done easily. For, transferring the boundaries from one map page to other, points and features that area common to both must be found. Much effort and time, therefore, are needed to spend for transferring boundaries manually. Vector tools for onscreen digitising can be used, in this approach, to draw the boundaries and frame limits without much effort and time. This reduces, therefore, the labour cost of frame construction.


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Chapter-5


5 STRATIFICATION

The process of dividing a population into subpopulations, or strata, which are each one by them assumed to be more homogeneous than the population as a whole, is called stratification.

5.1 Necessity of Stratification for Area Frame Sampling Area frame sample survey is a statistical method for calculating area estimates. Accuracy of the estimates depends on the sampling procedure. The sampling error of the survey design depends largely on the standard error. Generally, the sampling error can be re-duced by increasing the sample size (Cochran, 1977). The option of increasing the sam-ple size may not always be an acceptable one. For example, available funds and/or time may prevent us from such extended fieldwork. Thus the question arises whether there are other ways to reduce the sampling error. However, the sampling error also depends on the variance of the sample (Cochran, 1977). Consequently, a lower variance would also affect the sampling error. A large vari-ance in the sample will reflect a large variance in the population. In other words, when there is a large variance, we can say that the population is heterogeneous; conversely, when the variance is small, the population is more homogeneous. When the population is divided into sub-populations regarding the homogeneity within the sub-population the variance of the representative samples will be reduced. Therefore, stratification in an area frame sample survey should reduce the sampling vari-ance thereby increase the precision of the most important estimates. In grouping similar units into a stratum the aim is to have the lowest possible variation between units within a stratum and the highest possible variation between strata. The stratification makes it possible to limit the number of samples necessary for the extrapolation of the results to the whole area without reducing the reliability of the results (Houseman, 1975).

5.2 Advantages of Stratification in Study Area Percentage of agricultural area from the total area is about 6% in Serowe. These agricul-tural lands are scattered over the study area. Some parts of the study area is not included


Chapter-5


any agricultural land use (Figure 5-1). Using stratification, non-agricultural areas can be excluded from the survey samples. The distribution of agricultural lands over the study area varies from place to place and is non- homogeneous. More agricultural lands are clustered in some places whereas in some areas they are scattered (Figure 5-1). One of the survey variables is the estimated total crop area per each crop. This is done by statistically extrapolating the estimated crop area of sample segments over the total survey population. When the distribution of agricul-tural lands over the survey population is non-homogeneous this can lead to inaccurate estimations. When the total study area is segmented into sampling units without a proper stratification, the proportion of agricultural lands may vary within each sampling units, thus leading to inaccurate total crop area estimations. Therefore, to take into account this fact and to reduce inaccuracies in total crop area estimations, it is necessary to group seg-ments with similar proportions of agricultural lands before estimations are carried out. A stratification based on the proportion of agricultural lands per segment is, therefore, a requirement for accurate estimations in areas such as this where agricultural land distri-bution in non-homogenous. The locations of agricultural lands, in some countries, are related with road systems or drainage systems. According to the image characteristics, in this area, the agricultural plots are always not clustered related to the roads or drainage system (Ephemeral). Around some ephemerals and main roads, in Serowe area, agricultural plots can’t be seen (Figure 5-2). Most probably, agricultural plots are clustered based on the soils, on which good yields can be expected. Therefore, in areas with high percentage of agricultural plots, higher crop yield can be expected than in areas with low percentage of agricultural plots. The proportion of agricultural plots as a criterion for stratification is also needed with respect to the second survey variable, which is crop yield. Crop yield may be varying according to the soil group. In Serowe area, agricultural land use can be found on various soil groups such as Arenosols, Luvisols, Acrisols and Lix-isols. Arenosols are sandy and poor in fertility. The crop yields can be expected to be low on Arenosols as generally the fertilizer usage is also very low in Serowe area. On Luvi-sols, which are mainly formed on riverbanks with high fertility levels, the crop yield can be expected to be high. Lixisols (calcareous soils), on which different crop types and yield levels can be expected, is more alkaline and contain more carbonates. On Acrisols, intermediate levels can be expected.


Chapter-5


When comparing the soil map with the distribution of agricultural areas as shown in Fig-ure 5-3, the areas where agric plots are mostly clustered can be found on Luvisols on which, higher crop yields can be expected than the yields on the other soil groups. In this area most common soil group is Arenosols, on which, low crop yield levels can be ex-pected. On this soil group, agric plots are scattered. Proportionally, the distribution of agric plots is more on Luvisols than the distribution on Arenosols. Although very small part of the study area is covered by Acrisols, on which the expected yields are intermedi-ate, better agricultural plot cluster than the clusters on Arenosols and Lixisols can be seen. On Luxisols, proportionally, few agric plots are clustered. On some soil groups, such as Regosols, Leptisols, Calcisols no agricultural land use are found. Therefore, a variation can be identified on the distributions of agricultural lands according to different soil groups. Considering these variations to create more homogenous groups of sampling unit further stratification is needed based on soil groups.


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Chapter-5


Figure 5-1:Differences in agricultural land distribution

Figure 5-2: No agricultural land use can be found around some ephemerals and main roads

Study area (15m resolution - Aster)

High % of Ag: lands clustered in these areas

Low % of Agric lands scattered in these areas

No Agric lands

Ephemerals

Roads


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Chapter-5


Figure 5-3: Figure shows the distribution of agricultural plots on different soil groups


Chapter-5


5.3 Stratification Process The process of dividing the survey population into homogeneous subgroups is called stratification. Stratification in relation to target variables enables a concentration of resources. Land is not likely to have much or any agriculture, such as deserts, virgin forest, national parks, military reservations, and central urban areas, can be separated from the areas of primary interest and sampled at a low rate, or not at all. Stratification can also be used in cases where different segments sizes need to be used (Cotter and Tomaczak, 1994). When the objective of using permanent boundaries conflicts in actual practice with the objective of obtaining homogeneous sampling units, permanent boundaries take prece-dence. For example, cultivated fields can be located at the base of a mountain but a good boundary does not exist to include these fields in the appropriate strata, and, therefore, they my be placed in a <15 percent cultivated strata with better boundaries. Roads and rivers make good strata boundaries, while intermittent streams and field edges do not and rarely be used. The geographic features most frequently used for strata boundaries ranked from highest to lowest quality area

• Paved highways, • Secondary all weather roads, • Local farm to marked roads, • Railroads and, • Permanent rivers and streams (Cheng et al., 1989).

The stratification is performed on a country-by-country basis for administrative purposes. Each stratification analyst works a country until its completion. Stratification generally begins with determining the urban and agric-urban strata for the country. The agriculture areas are then stratified. The criteria for stratification should be directly associated with the information required. The main stratification criteria for the cultivated land and pastures of an area frame is based on a single characteristic, namely proportion of land cultivated. Additional strata, usually substrata, are formed, using special site or crop specific information. Therefore 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 agriculture activities (FAO, 1996).


Chapter-5


Table 5-1 displays the complete set of land-use categories which were used in the devel-opment of Missouri’s are frame in 1987. The strata for general cropland can vary slightly depending on the amount of cultivation and agriculture activity in the state (USDA, 1995).

Table 5-1:Land use strata codes and definitions

The area frame stratum definitions used in Honduras (FAO, 1998) are shown in the fol-lowing table.

Table 5-2: The area frame stratum number and definitions.

Another example of stratification used in Sanmatenga province, Burkina Faso (Leeuwen et al., 2000) is as follows.

Stratum Code

Definition

11 General cropland, 75% or more cultivated. 12 General cropland, 50 - 75% cultivated. 20 General cropland, 15 - 49% cultivated. 31 Ag-Urban, less than 15% cultivated, more than 100 dwellings per

square mile, residential mixed with agriculture. 32 Residential/Commercial, no cultivation more than 100 dwellings per

square mile. 40 Range and pasture, less than 15% cultivated. 50 Non-agricultural, variable size. 62 Water

Stratum Number Stratum Definition 1 61% to 100% Cultivated 2 31% to 60% cultivated 3 0% to 30% cultivated 4 Mostly forest land 5 Non-agricultural land

6.1 Urban areas 6.2 Agro-urban areas 7 Permanent water 8 Projected water


Chapter-5


Table 5-3: Descriptions and codes strata

Landsat TM satellite images are considered most suitable for a visual stratification due to their pixel resolution (30m) and the large coverage of 180 x 180 km, which makes it pos-sible to cover a whole province of large parts of it with one image. Nine SPOT images for comparison would be necessary to cover the same area and which would result in higher costs per km2 (Leeuwen, 2000). Aster image has a potential for a visual stratification due to its resolution (15m) and for reducing cost due to free availability. In this case, before going to collect the ground truth data, stratification was done by broad visual interpretation with the knowledge in remote sensing regarding image char-acteristics of the satellite image (Aster) in image processing software, Erdas Imagine. Some image characteristics were confirmed with referred to the updated topographical maps. Therefore, different land use land cover characteristics were identified. Non-agricultural areas such as villages, water, escarpment, hills, desert, Zones without agriculture etc were identified by visual interpretation of the image. For stratification, at first, these non-agricultural areas were separated from the areas where agricultural plots are exist. Agricultural plots were clearly identified on the Aster image, which provides better op-tion for a visual interpretation due to their high pixel resolution (15m). The interpretation elements used for identifying agricultural plots and the methodology followed will be discussed in chapter 8. In this case, the main stratification criteria, for the agricultural

Stratum Description code Lowland 1 Slopes and rock outcrops 2 Upland plain > 50% fields 3.1 Upland plain and slopes 2 to 50% fields 3.2 Upland plain, < 2% fields 3.3 Excluded zones (villages, water, nature reserves, irri-gated plots, Zones without agriculture)

4

Total


Chapter-5


land of an area frame is based on proportion of agricultural plots. According to this crite-rion, the study area was divided into four different strata as follows.

Table 5-4: Table showing strata descriptions, which are based on proportion of agricultural plots, strata

code, total area and agric plot area To build a strata map, first, the image was saved in a vector layer. Then, using vector tools, the polygons and lines were drawn. Next, the acquired data set were checked using clean-up operations for consistency and completeness. Finally, the topological structure of the vector layer was build. The steps of creating a strata map are shown in Figure 5-4. The stratified map with five different stratums created in image processing software fol-lowing the procedure, as described above, is shown in Figure 5-6.

Stratum Description Code

Total Area (Ha)

Ag: Plot area (Ha)

% Ag: Plots

Ag: Plots > 40% 1 6635 3221 49 Ag: Plots 20%- 40% 2 10281 3080 30 Ag: Plots 1%- 20% 3 37428 6521 17 Excluded zones (villages, water, escarpment, hills, desert, Zones without agriculture)

4 149812 0 0

Total 204155 12822 6


Chapter-5


Figure 5-4: The steps were followed to create a strata map

5.4 Sub Stratification (Subdivision of Strata into PSUs ) Most area sample designs consider sampling selection methods within substrata (Zones) of the land use strata. This provides a future stratification, which is applied in order to improve the efficiency of the design by means of reducing the variance of survey popula-tion. Therefore, the variance within the substrata is assumed to be smaller than the vari-ance of the strata. Finally, dividing the strata into homogeneous substrata (PSUs/CU’s/Zones) further reduce the sampling variance. Each stratum should be completely partitioned into non-overlapping areas with physical boundaries called primary sampling units (PSUs) or counting units (CU’s). Additional strata, usually substrata (PSUs/CU’s/Zones), are formed by using special site or crop specific information. The PSUs are subdivisions of the strata and preferably also of the administrative subdivisions of the province. The PSUs may even be subdivisions of agri-cultural or population census Enumeration Areas. This will allow for additional agricul-tural or demographic information to be used (FAO, 1996).

On screen digitising

Save in Vector layer

Polygons

Clean-up vector layer

Build Topology Strata map

Aster image


Chapter-5


As discussed earlier one of the survey variable is in this case study is crop yield. Further stratification was preformed considering the soil differences to form more homogeneous groups of sampling units within the strata as the crop yields may differ based on soil characteristics. Only main soil classes were considered instead of considering more de-tails of soil differences. Soil map of Serowe area was scanned from the existing soil map of Botswana. For generating the soil map of study area, soil classes’ boundaries covering the study area were digitised in a vector layer from the scanned map. Identification of boundaries for sub stratification was done by overlaying the soil map on strata map. For verification, both vector layers open on the Aster image. Substrata map was drawn by onscreen digitising using digitising tools in a vector layer. The process is shown in Figure 5-5. The resulting map and description of substrata (PSUs) are shown in Figure: 5-7and Table 5-5 respectively.


Chapter-5


Figure 5-5: The process of further stratification of strata into sub units

Table 5-5: Strata and substrata (PSUs) description, code and area (Ha)

Strata Substrata (PSUs) Description Code Description Code Area (Ha)

Luvisols 1.1 4585 Ag: Plots > 40% 1 Acrisols 1.2 2049 Arenosols 2.1 3030 Ag: Plots 20%- 40% 2 Luvisols 2.2 7251 Luvisols 3.1 8520 Arenosols 3.2 21989

Ag: Plots 1% - 20% 3

Lixisols 3.3 6918

Strata map

Existing soil map

On screen digitising

Scanned soil map

Soil map of study area

Delineation boundaries of substrata

Substrata map


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Figure 5-6: The strata map with four different strata

Figure 5-7: Substrata map

Legend Excluded

Ag: Plots > 40%

Ag: Plots 20%-40%

Ag: Plots 1%-20%

Legend


Chapter-5


5.5 Evaluation of the Potential of Aster Data for Stratification

Strata and PSUs, in conventional methodology, are usually identified and delineated on satellite images or a photo mosaic of aerial photography and then manually transferred to map and measured. This manual procedure consumes more time and labour. Using Aster image, in this approach, much effort for stratification can be saved with low cost, as it constitutes a very important improvement and simplification. In this approach for stratifi-cation, which is stratifying the Aster image by visual interpretation in image processing software, transferring the boundaries of strata and PSUs to map and measure the size of them is easier than doing the same thing manually, as image processing software vector tools can be used for creating map and measurements are shown in vector attributes. Usually, LANDSAT images in a 1:100,000 scales is used to delineate strata and PSUs. But Aster images (15m resolutions) in a 1:100,000 scales show more details than details in LANDSAT images. Aster image in a 1: 50,000 scale can be most conveniently used for visual interpretation to delineate strata and PSUs as it clearly shows more details such as agricultural areas, roads, rivers, urban areas, water bodies, paddocks, hilly areas, non-agricultural areas than details in LANDSAT image. Figure 5-8 shows aster image on Oc-tober 20th in year 2000 in a 1: 50,000 and in a 1:100,000 scale, and LANDSAT image on October 1st in year 2001in a 1: 50,000 and in a 1:100,000 scales scale.


Chapter-5


Figure 5-8: Figure showing Aster image on 20th October 2000 and LANDSAT image on 1st October 2001


Chapter-5


As discussed earlier, obtaining current aerial photographs is much expensive than acqui-sition of recent satellite images. Using Aster images are much cheaper because of its possibility to obtain fee of charge. Furthermore, satellite images that used are generally more recent than available aerial photography providing a more updated cartographic base for the frame. Even though aerial photographs provide more details area frame construction is difficult. Photo mosaic must be assembled with particular care matching terrain features with no overlap or no omission. Satellite images, however, cannot be utilized in areas with frequently cloud cover, which is less of a problem for aerial pho-tography.


Chapter-6


6 SEGMENTATION

6.1 Size of the Sampling Unit (Segment) Compact pieces of a stratum outlined on a photograph, map or image are referred to as stratum blocks. An absolute minimum size for a stratum block can be set at the equiva-lent of one segment. However, for practical reasons, stratum blocks should be made as large as possible (Houseman, 1975). In area frame sampling, segment size can refer to the measure of land area of a segment (its total area), to its area of cultivated land or, in special strata, to an area of specific use, such as area irrigated or its area under vineyards (FAO, 1996). In other words, segments size can refer to its measure of size given by a variable closely related to the survey vari-ables of interest. The determination of the optimum target segment size in a given stratum involves the study of a number of factors, for example: the comparison of sampling variance for dif-ferent cluster sizes and the percentage of holdings reporting non-zero values for the dif-ferent survey variables (Houseman, 1975). In this study area, some agricultural plots are clustered within very small isolated areas. Size of stratum blocks, which is compact pieces of a stratum outlined on the image, is vary from 250 ha to 78373 ha. According to Houseman, 1975 segment size should be equal or less than the size of stratum block. Therefore, suitable segment size for this situation is less than 250ha. Other factors for determination of the optimum target segment size are cost considera-tions, the enumerator daily workload, coverage problems associated with segment boundaries, the availability of physical boundaries on maps, data from previous surveys (Houseman, 1975). There are no coverage problems associated with segment boundaries because these segments with known coordinates can be covered using GPS, as this sam-pling frame is computer based.


Chapter-6


In order to minimize the sampling variance, since sampling variance depends on the av-erage segment size, it is in principle convenient to define the segments as small as possi-ble. Because segments with one reporting unit per segment cannot be defined in practice. Segment size may also refer to the size of the cluster (number of tracts in the segment, or the number of associated holdings – reporting units -). Initial surveys in some countries have been seriously delayed by selection of segments containing too many reporting units (FAO, 1996). Knowledge of the average size of holdings is therefore impotent to obtain an approximation of the number of holdings per segment. In Serowe area, holding size differ from about 25 ha to 5 ha. Therefore, the number of associated holdings with in 100 ha segment varies between about minimum possible 4 holdings to maximum possible 20 holdings. This amount is reasonable for the enumerator daily workload. When all these factors are considered, suitable sampling unit (segment) size for this case is decided as 100ha (1 km2).

6.2 Sampling Frame In this case study, main objective is reducing cost and time of constructing a frame. With this in mind, the area (sampling) frame carry out in this case makes use of the Universal Transverse Mercator (UTM) projection of 1 x 1 km squires that cover the study area. A pilot survey in Navarra, Spain compared the results of an area sample whose segments (sample units) had physical boundaries with an area sample where the segments were squares. In this case, the precision was found to be about equal but the cost of construct-ing a frame with physical boundaries was significantly higher (FAO 1998). An area frame covering 17 administrative regions in Spain was formed laying UTM grids on standard topographic map drawn at the scale of 1: 50,000. In this case, frame construc-tion and sample selection was done manually following the same procedure that was mentioned earlier. Since sampling variance depend on the variation of segment size, it follows that seg-ments should be of approximate equal size (FAO 1996). An area frame where the seg-ments are squares instead of segments with physical boundaries can easily be managed with equal size segments. In this research, for the purpose of sample selection, the survey area was divided into blocks using the 1 x 1 km UTM grid squares over the survey area. 1 x 1 km UTM grid square layer was overlaid on the map with strata and substrata boundaries (Figure 6-1).


Chapter-6


Then the segments (1 x1 km blocks) within the stratum boundaries were identified. Each square within substrata was given a unique number. Some problems arise when consider-ing the squares that straddle the border of the target area. To avoid this problem, an ap-proximation of the region is made by dropping the small pieces and keeping the large pieces giving them the same weight as if they were full squares (Figure 6-2). The total population of segments then determined by adding the assigned number of segments for all the substrata (PSUs), which, in tern, cover the entire frame area. As-signed total no of segments is given in Table 6-1.


Chapter-6



Chapter-6


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

Dropped the small pieces

Kept the large pieces


Chapter-6


Table 6-1: Total no of Segment in each stratum and substratum :

6.3 Sample The entire group of individuals that we want information about is called the population. A sample is a part of the population that we actually examine in order to gather informa-tion (Moore and Macabe, 1999). In this case, total population (total number of segments with in the frame) is 543 seg-ments. To estimate the area of each crop and the yields the representative sample of seg-ment from the population need to be drawn. Substantial savings in resources, time, money and qualified personnel can be made through sampling. Much valuable information could not have been obtained without sampling. (Thompson, 1992).

6.4 Sample Size Since selected sample represent the total population in survey area, the size of the sample should be drawn considering many factors to gather more precise estimates. The sample in each stratum can be established based upon consideration of estimated coefficients of variation (CV) of the main variables, non-sampling errors cost and time available for the survey. Usually taking into accounts the required precision of the estimates data and vari-ance from pilot survey was used to calculate the initial sample and allocation. The calcu-lations are straightforward if variances are known. That, usually, is not the case. With a new area sample survey, the expected variances can only be estimated (FAO, 1996).

Stratum Substratum Code Total segments Code Total segments

1.1 46 1 66 1.2 20 2.1 30 2 103 2.2 73 3.1 85 3.2 220

3 374

3.3 69 Total 543 543


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The formula used to estimate sample size is as follows, n req = t2 * CV% AE% Where, n req = number of sample required t2 = tabulated t- value CV% = coefficient of variance AE% = allowable error Coefficient of variation is the standard deviation, expressed as a percentage of the mean. It is defined as: CV = S / Χ Where, CV = coefficient of variance. S = standard deviation. Χ = mean. When reliable estimates of these parameters are lacking, a good starting point is referred to the design of agricultural sample surveys in other countries. Sample size of crop pro-duction agricultural survey based on area frame sample survey (1988 – 1997) in Italy, For some crops, a coefficient of variance less than or equal to 10% was established. Sample size of general purpose agricultural survey based on area frame sample survey (1905 – 1997) in Pakistan were determined at the district level taking into consideration the area of the major crops grown in the district and a 10% CV (FAO, 1998). Proportion of agricultural plots and soil types of Serowe area, which can affect on the survey variable in this study are varying in place to place. That means the expected vari-ance of survey population should be high. As we know, if the variance of the population is high standard error is also high. When the standard error of the population is high CV is high too. According to above formula, when the coefficient of variance of the popula-tion is high required sample size is also high. On the other hand, if the variance of the


Chapter-6


population is unknown it is reasonable to expect the high coefficient of variance instead of expecting a low CV when calculating the sample size. Since the coefficient of vari-ance was not determined by previous similar survey in this area the assumption was made to achieve a coefficient of variance less than or equal to 11% based on the exam-ples from other countries and considering above discussed factors. It is essential to include the probability level, since that determine the value of t. In agri-culture or forestry one often work with a probability level of 5 % or 10%; in the medicine profession 0.1% may be still too high (Freese, 1976). Therefore in this case probability level is considered as 5%. To determine the value of t, we encounter a problem, because to determine t, we need to know the df (degree of freedom), which are based on the sample size. But this is what we do not know. The solution is to assume that the sample size will be large (de Gier, 2000). Assume n = 30, we find df (df = n – 1) 29, at p = 0.05 t = 2.045. When considering the sampling error is large, it is possible to calculate the sample size for a pre-selected sampling error (de Gier, 2000). Assume that we want the sampling er-ror not to exceed 6%, we call this error the allowable error (AE%). The sample size of this case study is, n req = t2 * CV% AE% Sample size = 2.0452 * 112 / 62 = 14.06 = 14 According to the above selected sample size, sampling rate of this study can be calcu-lated as 2.5%.

6.5 Sample Allocation to Strata Sampling unit should be distributed over the strata, in proportion to the size of the strata. The stratum sizes should thus be known, and should (again ideally) be expressed in the same units as the sampling unit (Cochran, 1977).


Chapter-6


The sampling units here are segments. Therefore, the size of stratum should be expressed in number of segments. The sampling interval was defined by dividing the accumulated segment numbers for each stratum by the total number of segments. The sample then al-locates among substrata again considering the proportion of their size. The size of sub-strata should also be expressed in number of segments. The size of the strata and sub-strata, and allocated number of segments is shown in Table 6-2 bellow.

Table 6-2: Segment allocation by stratum and substratum

6.6 Sample Selection In area sample survey, the selection of sample of segments within each stratum can be done using a random or systematic sample selection procedure (FAO, 1996). Area frame sample survey is base on a probability sampling methods. A probability sam-ple gives each member of the population a known chance (grater than zero) to be se-lected. Some probability sampling designs like simple sample random sampling design gives each member of the population an equal chance to be selected. But here the selec-tion is based on stratified random sampling design for, which first divided the population into groups of similar individuals, called strata. Then choose a separate random sample in each stratum and combine these random samples to form the full sample (Moore and Mccabe, 1999). As described above each square segment within substrata was given a unique number. Selection of allocated segments was made randomly from the corresponding set of seg-

Stratum Substratum Code Size (no of total

segments) Allocation (segments)

Code Size (no of total segments)

Allocation (segments)

1.1 46 1 1 66 2 1.2 20 1 2.1 30 1 2 102 3 2.2 73 2 3.1 85 2 3.2 220 5

3 374 9

3.3 69 2 Total 542 14 542 14


Chapter-6


ments to the particular set of substrata. It can be seen that, from the Table 10, each sub-stratum is allocated with different sample size (No: of segments). In the situation, where one substratum is allocated with more than one segment, the sub stratum is divided into the respective number of allocated sample segments to develop the efficiency of prob-ability stratified random selection. For example, if the substratum is allocated with two samples, the substratum is divided into two units containing equal number of segments. Then the selection of one segment can randomly be drawn from each unit.

6.7 Evaluation of using Aster Image for Segmentation the Frame

Sample segments are usually identified and delineated on mosaics of aerial photos since this requires many details. Low-resolution satellite images are not usually suitable for subdividing PSUs into segments since they provide imprecise boundaries. In this re-search, segmentation was done by means of UTM grid with low effort and time. Some early investigations in Navarra, Spain, as discussed earlier, showed instead of using iden-tifiable physical boundaries for identification and delineation of segments, square seg-ments could be used without loosing the precision but keeping the cost of constructing a frame at a low level. Anyway that procedure is manual. But use of grid squires for seg-mentation and sample selection in software is much easier than doing it manually.


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7 PREPARING FOR DATA COLLECTION

7.1 Questionnaire The design of a new questionnaire should not begin until the end product of the survey is defined. Assuming the end products of this study are crop area and yields estimation the following field observation sheet and questionnaire was designed. Field Observation Sheet And Questionnaire.

1.Date: 2.Farmer Name:

3.Reference: Sub Stratum No: Sample NO: Plot N0:

4.Terrain: Slope % in area:

General landform:

5. Plot size:

6. Cropping calendar:

Operational 1999 2000 Crop J F M A M J J A S O N D J F M A M Ploughing Planting

Harvesting Ploughing Planting




Harvesting 2001 J J A S O N D J F M A M J J A S O


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Ploughing Planting





Harvesting 6.crop yield (kg/ha):

8. Remarks:

Figure 7-1: Field Observations And Questionnaire Sheet

7.2 Preparation of Selected Samples for Ground Survey Low-resolution satellite images cannot be efficiently enlarged to a scale larger than 1:50,000 far from the 1:5,000 scales required for such purposes. But high-resolution im-ages like Aster 15m can easily be enlarged with the scale of 1:15,000 with good quality. In this situation, decided scale of the image enlargement of the segment (sampling unit) is of 1:15,000. Instead of using identifiable physical boundaries the geo-position of the segments can be used to locate the segment boundaries on the ground. In Serowe area, the land plots are relatively big (varying from 5ha to 25ha). They are not compacted and are located with the clear boundaries around them on the ground. On the image the plot

Crop 1999 2000 2001


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boundaries are clearly identifiable. Therefore, this scale is enough for identifying agricul-tural land plots during ground survey. Spatial enhancement techniques can be applied to sharpen the image. More details will be discussed in chapter 8. Before enlarge the aster image 3 x 3 high pass filter (high-frequency kernel) was used. High pass filters used to sharpen images are, in essence, edge enhancers. The Edge Enhancement function is ideal for many applications where edge and line definition is important (Jensen, 1996). Using high pass kernels agricultural plots on the image can easily be interpreted visually. When they are not clear on the im-age this technique will help to sharpen. With the scale of 1:15, 000 enlarged image of selected 1 x 1 km blocks as shown bellow can also prepared for ground survey purposes (Figure 7-2). Grid lines (segment bounda-ries) are shown in purple colour. Geo-coordinates of the selected segment also included for identifying the boundaries on the ground. Maps showing the location of sample segments must be prepared for fieldwork. Topog-raphic maps in the scale of 1:50,000 are ideal for this as they also show roads and other means of access. Segments are out lined in red permanent colour pencil. The segment number is shown inside or alongside the segments also in red. Master segment location maps for regions or larger areas can be prepared on small-scale maps (1:250, 000 or 1:500, 000). Up to date road maps will be needed (FAO, 1996) The Aster image (1:100,000) with 1 x 1 km UTM grid lines can be used during the field-work for identifying the location of the strata and segments with identifiable physical features. On Aster image with the scale 1:100,000 Highways, main roads and ephemerals can easily be seen for identifying the location of the segments. Segment numbers can also be included on the satellite image as shown in Figure 7-3. In this case, as the correct geo-position of the segment is known GPS can be used to find the exact point on the ground. Topographic maps in the scale of 1:50, 000 also show these features. In case of lacking topographic maps, this image can be utilised.


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Figure 7-2: Aster image enlargement of selected sample segment for ground survey

Figure 7-3: Aster image in a 1:100, 000 scale showing segment locations for ground survey

Sample No: 501000 502000

7499000

7498000

Scale - 1: 15, 000

Scale 1:100,000

Selected segment

1x1km blockEphemeral

Road


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7.3 Data Collection The data Collection is carried out for two weeks period at the end of September in year 2001. In this study area, crops are grown in one season, which is starting in November and ending around May. There was a drought at Serowe area in year 2000/2001 (see the rainfall figures given in chapter 3). Therefore, in some places, the crops are not grown in that year as the agriculture in Serowe area mainly depends on the rainfall. Since the fieldwork was carried out in September 2001 identification and verification of crops planted was not possible. Crop residues of plots on which crops have been cultivated also cannot be seen because, usually, the cattle are release to the agricultural fields at the end of the growing season. Only possibility for the data collection is to completion of a ques-tionnaire with the owner of the land. In this area the main problem is farmers have two houses, one is within the village or city and the other is around the agricultural plots. They didn’t live near the agricultural plots during the fieldwork period, as it was not a growing period. It was very difficult to find farmers around the agricultural plots. Therefore, randomly selected fields were surveyed according to the farmer availability. During ground survey, mainly two different types of fallow agricultural plots were ob-served. Some agricultural plots are covered mainly by Acacia bushes (height 1- 1.5 ft) whereas some of them are covered by grasses. Very few of them are already cleaned for preparation of lands prior to next cultivation. The owners of the cleaned plots except one of them could not be interviewed. Taking into account the climate in Serowe in year 2000/2001, field observations and farmers recordings, crops have not been grown on the plots in year 2000/2001, which were observed as the plots dense with Acacia bushes dur-ing the fieldwork. Similarly, crops have been grown in that year on the plots, which were covered by grasses. However, collected data do not cover complete segment. If an interviewer cannot accu-rately locate the sampled area, or does not collect data associated with all of the land in-side the sampled area or collects data for an area out side of that selected, then non-sampling error will occur (Houseman, 1975). Therefore, the area estimation could not be calculated.


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7.4 Evaluation the Potential of Aster Image for Preparation the Materials for Ground Suvey

In existing methodology aerial photo enlargement of the segments is used for ground sur-vey. Satellite images are not usually suitable for this purpose. Enlargement scales ranging from 1: 3,000 to 1: 10,000 have been used to control segment enumeration; the most common, and possibly the most practical, being the scale of 1: 5,000. There is no abso-lute rule about the scale to be used; the basic need is for the enumerator to be able to de-lineate the fields. If the fields are small (one half hectare), a 1:3,00 or 1:5,000 scale will be best, but if fields are consistently large, 1:10,000 scale enlargements can be used. The agricultural plots in Serowe area are relatively large and they can easily be identified in a 1:15,000 scale. Therefore, in this case, Aster image enlargement of segments can ef-ficiently substitute the aerial photo enlargement with respect to reducing cost. But in the case where the agricultural plots are relatively small, Aster image may not be able to use. Since the area frame sampling method used in this study was completely done in image processing software spatial enhancement techniques (high-frequency kernel) were used to sharpen the aster image enlargement. Using these techniques agricultural plots and roads became more visible than the features on original image. LANDSAT image can’t be used as a replacement of aerial photograph for this purpose since its resolution is lower than the resolution of Aster image. The maps showing the location of sample segments are used for ground survey. Usually, the topographic maps in the scale of 1:50,000 are used. Sometimes mostly in developing countries, updated topographic maps or road maps are not available. In such cases, Aster image in a 1:100,000 scale can be used as a substitution for topographic maps since it shows the roads and many other details of physical features due to its high resolution.


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Chapter-8


8 IDENTIFICATION OF AGRICULTURAL PLOTS

8.1 Identification of Agricultural Areas by Visual Interpretation

For sampling frame construction, agricultural land plots were identified by visual inter-pretation. When dealing with image data, visualised as pictures, a set of terms is required to express and define characteristics present in a pictures. These characteristics are called interpretation elements such as tone/hue, texture, shape, size, pattern, site and associa-tion, and are used, for example, to define interpretation keys, which provide guidelines on how to recognise certain objects (Bakker et al., 2000). In this study agricultural plots were identified using a combination of such elements as follows.

• Tone/Hue: Tone is defined as the relative brightness of black and white image. Hue refers to the colour on the image as defined in the intensity-hue-saturation (IHS) system. Most agricultural plots give relatively lighter colours such as white, light green, pink, light blue etc.

• Texture: Texture relates to the frequency of tonal change. Most cases, the texture

of the parcels is even.

• Shape: The shape of object often helps to determine the characteristics of the ob-ject. Shapes of the plots are regular and their edges are straight.

• Size: Size of the objects can be considered in relative or absolute sense. In this

image, size of the plots is relatively small even though they are in similar shape.

• Pattern: Pattern can be described by terms such as concentric, radial, checker-board.


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• Site: It relates to the topographic or geographic location. These plots can be found in flat lands not on the top of the hills or escarpment. Hilly area and flat lands were distinguished with the help of topographic maps.

Agricultural plot map was obtained by on-screen digitising the image in image process-ing software in a vector layer to identify and delineate scattered agricultural clusters and individual agricultural plots with known geo-codes instead of using recent aerial photo-graphs or reliable maps to delineate areas with physical boundaries. Then the vector layer was cleaned up for consistency and completeness. After that, the vector layer’s topology was build. Figure 8-7 shows the Agriculture plot map.

8.2 Identification of Agricultural Areas by Filtering Filter operations are usually carried out on an image for spatial image enhancement, for example to reduce noise or to sharpen the images. Spatial enhancement modifies pixel values based on the values of surrounding pixels. Spatial enhancement deals largely with spatial frequency, which is the difference between the highest and lowest values of a con-tiguous set of pixels. Jensen (1986) defines spatial frequency as “ the number of changes in brightness value per unit distance for any particular part of an image”. In this study, high pass filter (high frequency kernel), which has the effect of increasing spatial frequency (Jensen, 1986), was used since it brings out the edges between homo-geneous groups of pixels. Using these technique agricultural plots can be brought out for interpretation. This technique is useful for some areas of the image where the agricultural plots and some line features such as roads are not clearly visible. Edge enhanced image enlargements are more suitable for sample enlargement, as discussed earlier, than the original image enlargements. The Figure 8-4 below shows the difference between the edge enhanced image and original image.


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8.3 Classification Multispectral classification is the process of shorting pixels into a finite number of indi-vidual classes, or categories of data, based on their data file values. First, the computer system must be trained to recognise patterns in the data. Training process is the process of defining the criteria by which these patterns are recognised (Hord, 1982). Training can be performed with either a supervised or an unsupervised method. Supervised training is closely controlled by the analyst. In this process, user select pixels that represent patterns or land cover features that you recognize, or that user can recog-nise, or that user can identify with the help from other sources, such as aerial photos, ground truth data, or maps. Knowledge of the data, and of the classes desired, is required before classification (star and Estes, 1990). Unsupervised training is more computer-automated. It enables user to specify some pa-rameters that the computer uses to uncover statistical patterns that are inherent in the data. These patterns are simply clusters of pixels with similar spectral characteristics. In some cases, it may be mere important to identify group of pixels with similar spectral characteristics than it is to sort pixels into recognisable categories (Diday, 1994).

8.4 Supervised Classification The aster image was classified in Erdas Imagine to evaluate the possibilities for identify-ing agricultural lands and area estimation. Erdas Imagine enables user to identify training samples using one or more of the methods such as:

• Using a vector layer • Defining a polygon in the image • Identifying a training sample of contiguous pixels with similar spectral character-

istics • Identifying a training sample of contiguous pixels within certain area, with or

without similar spectral characteristics • Using a class from a thematic raster layer from an image file of the same area i.e.

the results of an unsupervised classification. In this classification, user defined polygons [AOI (Area of Interest) training sample] were used to create the parametric signatures of each class. These AOIs for each class


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were created based on the field observations, farmer recordings and image data file val-ues. Classes for classification were defined based on image data file values, field observations and farmer recordings. Data file values referred to different “classes” were clarified based on idealized spectral reflectance curves (Hoffner and Lindenlaub, 1976, cited in Gils et al., 1982) (see Figure 8-1) As discussed earlier in previous chapter under the heading data collection, two main different types of agricultural areas were observed based on the land cover characteristics and farmers recordings. They are the plots on which, crops have been grown (the plots covered by grasses during the field observation in September 2001) and crops have not been grown (the plots covered by Acacia bushes at the field observation) in year 2000/2001 (Figure 8-3). The image data file values of the plots, which are identified as the cultivated lands in year 2000/2001 show high reflectance values in green and red band, and low reflectance in infrared band when comparing with non cultivated (fallow) lands (Figure 8-2). Based on these values of each band, these plots are likely to be cleaned plots at the time of the im-age acquisition i.e. in 2000 October 20th. This could be due to the preparation of land prior to the crop cultivation in November 2000. Likewise, the plots, which, were identify, as non-cultivated lands in year 2000/2001 are likely to be fallow fields (vegetative) according to the data file values, which show high reflectance in Infrared band, and low reflectance in green and red band as compared to the cultivated plots (Figure 8-2). The reason behind this, is those lands are not expected to be cultivated since the year 2000/2001 is very dry year. Therefore, in this classifica-tion, the classes were defined as Rock out crops, Cultivated plots, Fallow plots, Bare land, Savannah and Water based on field observations and image data file values. Figure 8-5 shows the classified map.


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Figure 8-1: Spectral reflectance curves of basic land cover types (Hoffner and Lindenlaub, 1976, cited in Gils et al., 1982)

Figure 8-2: Image data file values of different plots

Data File Values (reflectance)

Band Cleaned Plots

Fallow Plots

Green

High

Low

Red

High

Low

Infrared

Low

High


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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

2000 November – 2001 April Growing Period

2000 October Image Acquisition

2001 September Field observations

Cultivated Plots

Cleaned

Fallow

Crops

Fallow

Fallow with Grasses

Fallow with Acacia

Fallow plots


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8.4.1 Signature Separability of the Classification Signature separability, which is a statistical measure of distance between two signatures, was calculated using transformed divergence to evaluate the above-mentioned classes before the classification performed. According to Jensen, the transformed divergence “gives an exponentially decreasing weight to increasing distance between the classes.” The scale of the divergence values can range from 0 to 2,000. Interpreting the results af-ter applying transformed divergence requires user to analyse those numerical divergence values. As a general rule, if the result is greater than 1900 then the classes can be sepa-rated. Between 1700 and 1900, the separation is fairly good. Below 1700, the separation is poor (Jensen, 1996). According to the resulting seperability values, except between the class Savannah and Fallow plots, among all other classes good separation can be seen (Appendix 2). Between Savannah and Fallow plots the resulting value is 1847, that means the separation is fairly good.

8.4.2 Accuracy Assessment of Classification Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Usually the assumed- true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. There-fore, a set of reference pixels is usually used. Reference pixels are points on classified image for which actual data are known. The reference pixels are randomly selected (Congalton, 1991). In this case, Accuracy assessment was done in Erdas Imagine. For selecting reference pixels, three different types of distribution such as random, stratified random or equalized random are offered, in image processing software (ERDAS Field Guide). In this accuracy assessment process stratified random method was used to select refer-ence pixels. In this process, user has to assign the class values of each pixel based on true data. Therefore, the class values for reference points were assigned according to the field experiences and ground truth data gained during the fieldwork.


Chapter-8


8.4.3 Reports of accuracy assessment Three kinds of reports can be derived, in image processing software such as confusion matrix, percentage of accuracy and Kappa statistics. 8.4.3.1 Confusion Matrix A confusion matrix, also known as error matrix or contingency matrix is a square array of numbers laid in rows and columns. These numbers represents number of sample units assigned to a particular class in one classification with respective to the number of sam-ple units assigned to a particular class in another classification for example, clusters, polygons area, pixels etc. One of these two classifications is considered to be the most accurate and called reference data. In the confusion matrix, reference data is usually rep-resented by columns, the rows being the classified data. Generally, reference data can be generated by different means such as aerial photographs and ground observations and measurements (Congalton and Green, 1999). In this work, the reference data was generated by ground observations. 8.4.3.2 Commission and Omission Error A commission error, also called as error of inclusion, is simply defined as inclusion of an area into a class to which that area actually does not belong. Similarly, an omission error or error of exclusion can be defined as exclusion of an area truly belongs to that category (Weir, 2001). Another way of expressing the accuracy of classification is user and producer accuracy. The next section will briefly explain these two accuracy measures. 8.4.3.3 User Accuracy and Producer Accuracy User accuracy and producer accuracy are direct products of commission error and omis-sion error, respectively. The user accuracy is the probability that a certain reference class is classified to that particular class, whilst the producer accuracy is the probability that a sample point in the map is that particular class. In addition to these two ways, another measure of assessing accuracy in RS data called over all accuracy has been introduced recently.


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8.4.3.4 Over all Accuracy The over all accuracy is the most common and popular mean of indicating the accuracy. This measure indicates the correctly classified sample units in the entire matrix, and sim-ply given by the division of the sum of diagonal elements by the total number of sample units. However, it has to be borne in mind that, though the over all accuracy is the com-monly used measure, it does not explicitly indicate the accuracy of individual class clas-sification. 8.4.3.5 Kappa (K^) Statistics “ Kappa analysis is used to describe the degree of agreement between two data sets, while allowing for agreement (i.e. correct classification) due to chance. It varies between +1.0 for perfect agreement down to 0.0” (Weir, 2001). Same as the Confusion matrix, Kappa also shows low classification accuracy for non-cultivated agricultural plots in year 2000/2001.

8.4.4 Accuracy of this Classification Confusion matrix, percentage of accuracy and Kappa statistics obtained from this accu-racy assessment are shown respectively in Tables 8-1, 8-2 and 8-3. Although the overall accuracy of this classification is 81% the user accuracy of one agricultural class (Fallow plots) is very low i.e. 30%. This agricultural class is mixed with the class Savannah. It was very difficult to separate both Savannah and Fallow agricultural plots in year 2000/2001 by classification. Because when this Aster image acquired some agricultural plots, which are named, as Cultivated plots in map legend are cleaned prior to the next cultivation whereas some of them, which is named as Fallow plots, are dense with natu-ral vegetation. Therefore, this image is not good for identifying agricultural areas and es-timation of crop area in Serowe by classification based on reflectance values, as it was not acquired in the correct time when the agricultural crops are grown on the plots.


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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.

Conditional Kappa for Each Category

Class Name Kappa Water 1 Rock out crops 0.9 Savannah 0.9 Cultivated plots 0.6 Fallow plots 0.2 Bare land 0.7 Overall Kappa Statistics = 0.74

Table 8-3:Kappa Statistics resulted from classification.

Reference Data

Water Rock out

crops SavannahCultivated

plots Fallow plots Bare land Row Total

Water 5 0 0 0 0 0 5 Rock out crops 0 47 0 0 0 2 50 Savannah 0 0 116 1 2 0 120 Cultivated plots 0 5 0 31 0 9 45 Fallow plots 0 1 31 0 13 0 45 Bare land 0 0 1 6 2 41 50

Cla

ssifi

ed D

ata

Column Total 5 53 148 38 17 52 315

Class Name ProducersAccuracy

Users Accuracy

Water 100% 100% Rock out crops 89% 94% Savannah 79% 96% Cultivated plots 82% 69% Fallow plots 77% 30% Bare Land 79% 82%

Overall Classification Accuracy = 81%


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8.5 Evaluation between Visual Interpretation and Supervised Classification for Identifying Agricultural Plots.

8.5.1 Quantitative Evaluation For quantitative evaluation between visual interpretation and supervised classification, classified map were compared with visually interpreted agricultural plot vector layer. The portion of visually interpreted agricultural plots vector layer was broken out from classi-fied map using subsetting operations for calculating the percentage of area of each classi-fied class within agricultural plots. In this process (Figure 8-7), first the visually inter-preted vector layer was copied to an AOI layer. Then, from the classified map the AOI layer was subsetted. Finally, using this subset map (see Figure 8-8), the area within agri-cultural plots of each classified class were estimated. Using raster attributes of the classi-fied map total area of each class were calculated. When the total area and the area within agricultural plots of each class are known area without plots can be calculated. The re-sults are shown as percentages in the Table 8-4. These results show how much do the agriculture and non-agriculture areas mix with each other among the classified classes. As shown above the user accuracy of the class “Fal-low plots” is very low, the accuracy of this class is low too when comparing the classi-fied map with visually interpreted agric plot vector layer. Only 7% of area of this class is included within agricultural areas of visually interterpreted map. The user accuracy of the class “Cultivated plots” is about 69% according to the accuracy assessment of the classi-fication. Here also, the accuracy of this class is found to be higher than the accuracy of the class “Fallow plots” since cultivated plots show 52% of area within agric plots. If this classification is accurate, visually interpreted agricultural plot layer should contain only two agricultural classes but not the non-agricultural classes. However, according the figures shown in Table 8-5, 62% of the visually interpreted agric plot area has been clas-sified as non-agriculture. Furthermore, it can be seen that only 5% of the area is classified as fallow plots while 33% of area is classified as cultivated plots. These results further confirm that fallow plots gains a very low accuracy.


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Figure 8-6: The steps followed for qualitative evaluation of supervised classification and visual interpreta-

tion

Aster Image

Visual interpretation Classification

Agric plot vec-tor layer

Classified Map

AIO layer

Raster attributes

Total area of each class

Subset

Subset Map

Area of each class within agric plots

Area of each class without agric

% Without% Within

Quantitative Evaluation


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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


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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

8.5.2 Qualitative Evaluation

Table 8-6: Qualitative evaluation of visual interpretation and supervised classification used in identifying

agricultural areas

Land cover classes Total area

(Ha)

%Area within agric

plots

%Area without agric

plots Water 187 0 100

Rock out crops 23686 1 99

Savannah 128305 4 96

Cultivated plots 8830 52 48

Fallow plots 9920 7 93

Bare land 22651 14 86

Visually interpreted Agric Plot map

(Ha) (%) Cultivated

plots 4607 33

Fallow plots 676 5

Su

perv

ise

Cla

ssi-

ficat

ion

Non- Agricul-ture 8794 62

Visual Interpretation Supervised Classification Time Time consuming

(One week time) Less time consuming (5 hours)

Cost Need more cost due to high labour consuming.

Less cost needed compared with visual Interpretation

Expert knowledge requirement

Same Same

Accuracy In this case, Visual Interpretation is more accurate

Less accurate due to difficulties in separation of agricultural classes from others

Quality Different classes can’t be identified among agricul-tural fields

Can be identified


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8.6 Comparison between Farmer Reported and Computer Measured Plot Size

During the fieldwork phase, data about the plot size, which was surveyed, were collected from farmers. Farmers reported the plot size that mentioned on their landowner ship document. During the interview, a transparent sheet on the enlarged block of satellite photograph was used to verify, the reported field. Such identified agricultural areas in each sample block later were measured using computerized measurement tool in Erdas Imagine.

Figure 8-9: The scatter plot of farmer reported and computer measured plot size

Table 8-7: Farmer reported and computer measured plot size According to the above scatter plot drawn between farmer reported plot size and com-puter measured, there is positive linear correlation can be seen. As discussed earlier in some cases the areas reported by farmers are not reliable. However, in this case farmer reports can be consider as reliable information.

Field no Farmer reported

Computer measured

1 10 9.2 2 20 18.9 3 25 25.2 4 9 9.8 5 10 10 6 24 24.8 7 10 10.8 8 8 7.6 9 15 15.6 10 7 6.4 11 8 8.5 12 7 6.7 13 5 4.8 14 5 5.44 15 5 5.9 16 5 4.9 17 5 4.2 18 6 6.8 19 17 16.4 20 14 14.8 21 3.5 3.8 22 3 2.96 23 10 10.4 24 5 4.88 25 16 16 26 20 20.8 27 20 20.4 28 6 6.8 29 15 15.5 30 20 21 31 10 10 32 9 9.2 33 10 10.4 34 25 26.2 35 15 14.8 36 15 15.7 37 9 9.6 38 8 8.4 39 3 3.8 40 10 10.2

The Relation Ship Between Farmer Reported And Computer Measured Ag:Plot Size

y = 1.0156x + 0.0779R2 = 0.9927

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Farmer Reported Plot Size

Com

pute

r M

easu

red

Plot

Siz

e


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Chapter-9


9 CONCLUSIONS

In this case study, the Aster image used to develop an efficient area frame sampling sur-vey method saved cost of materials as it was downloaded from the web free of charge. Conventional method which, is manual and requiring up-to-date aerial photographs, sat-ellite images, current maps or combination of all is costly and involved laborious and meticulous work. Obtaining Aster images are much cheaper than obtaining other possible satellite images and updated aerial photographs. Although the aerial photographs provide more details for frame construction, high-resolution (15m) Aster data also appropriate for visual interpretation. The method using Aster image in image processing software can most conveniently used to delineate frame limits, strata and Primary Survey Units (PSUs) since this procedure is completely com-puterised. It reduces much effort involved in different steps of frame construction. Strata and PSUs boundaries, and frame limits can easily be delineate by visual interpretation using vector tools. If the area needs to be surveyed is much larger than the area covered by single Aster image (60km x 60km), several images can be put together in one frame by using mosaic tools in image processing software with less effort than the effort in-volved in aerial photo mosaic. Subset techniques also can easily be used if needed to re-duce the size of survey area from large frame. In conventional method, strata and PSUs boundaries, and frame limits are manually transferred to map and measured. It needs much time and labour cost. Use Aster image consumes less time and labour cost because creating map in an image processing software like Erdas Imagine is much easier than do-ing the same thing manually. Even though the LANDSAT images also good for strata and PSUs delineation due to its large coverage (180km x 180km), Aster are more appro-priate for area frame construction since it provides more details than LANDSAT data due to its high resolution (15m). Important features for frame construction such as roads, riv-ers, water bodies, agricultural fields, urban areas, rocks etc can easily be recognised on Aster image. Instead of using physical boundaries for identification and delineation of segments as used in conventional methods, use of square segments keeps the cost and effort of frame


Chapter-9


construction at a low level without loosing the precision. Doing segmentation by means of UTM grids in image processing software is much easier than doing it manually. Aster image enlargement of segment for ground survey purposes can efficiently substi-tute the aerial photo enlargement with respect to reducing cost and effort. Although in conventional method, requiring scale of aerial photo enlargement is usually 1:5,000 Aster image enlargements in a 1:15,000 scale can be utilised for this situation as the agricul-tural plots are relatively large in Serowe area. Edge enhancement techniques are useful to sharpen the image enlargement. Agricultural plots and other important physical features like roads become more clear and visible than the clearness and visibility of the features on original image. LANDSAT image (30m) is not suitable for replacing the photo enlargement. In the case where the agricultural plots are relatively small, Aster image may not be able to be used. Aster image at the scale of 1:100,000 is suitable for ground survey as a substitution for topographic maps when they are not available. Aster image provides more updated ground information than the information included in available topographic maps and road maps, in many developing countries. This helps to reduce material cost involved in area frame sampling surveys. LANDSAT image can’t be used this purpose since its low resolution. When comparing the visual interpretation and supervised classification for identifying agricultural plots in Serowe area using single image, visual interpretation is more accu-rate than the classification. Since the time of image acquisition did not coincide with the crop-growing period, the reflectance in the image did not address the actual crop areas. Visual interpretation is more time consuming whereas supervised classification is less time consuming. Due to the high labour requirement visual interpretation is more costly than supervised classification. The necessity of expert knowledge is same for both tech-niques. When considering the quality of both techniques, supervised classification is more qualitative in terms of the quality of the output. Because by visual interpretation the agricultural plots are not easy to differentiate between each other. By supervised classifi-cation different classes can be identified among agricultural plots. Even though farmer reports are not reliable in some cases, the field size reported by farm-ers tally with the area estimated by using the image software measuring tools in this case.


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Considering above discussed factors, it can be concluded that high-resolution Aster im-ages have good potential for improving an area frame sampling survey method with re-spect to reduce cost and effort.


Chapter-9



References


REFERENCES Andrews, H. C. (1972). Introduction to mathematical techniques in pattern recognition. New York: Wiley & Sons. Baker, W. H., Janssen, L. L. F., Weir, M. J. C., Gorte, B. G. H., Phol, C., Woldai, T., Horn, J. A., & Reeves, C. V. (2000). Principles of Remote Sensing.: International Institute for Geo Information Science and Earth Observation (ITC), Enschede, The Neth-erlands. Bhalotra, Y. P. (1987). Climate of Botswana.: Department of meteorological services, Ministry of Works, Transport and communications, Gaborone, Botswana. Cheng, T., Angelic, G., Slye, R., & Ma, M. (1989). Computer Aided Boundary Delineation of Agricultural Lands. Cochran, W. G. (1997). Sampling Techniques. New Yoke: John Wiley and Sons. Congalton, R. G. (1991). A review of assessing the accuracy of classification of re-motely sensed data. Remote Sensingvof the environment (37), 35-46. Congalton, R. G., & Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Washington: LEWIS PUBLISHERS. Cotter, J. J. & Nealson, J. (1987). Area Frame design for agricultural surveys. Cotter, J. J., & Tomaczak, C. M. (1994). An Image Analysis system to develop Area sampling Frames for Agricultural Surveys. Photogrammetric Engineering and Remote Sensing, 60 (N 3, March 1994), 299 - 306. de Bie, C. A. J. M. (2000). Comparative Performance Analysis of Agro-Ecosystems. PhD Thesis, Wageningen University, The Netherlands. de Gier, A. (2000). Sampling Basics (Lecture Handout Note for Statistics): International Institute for Geo Information Science and Earth Observation (ITC), Enschede, The Neth-erlands. Diday, E., Lechevallier, Y., Schader, M., Bertrand, P., & Burtshcy, B. (1994). New Approaches in Classification and Data Analysis. Berlin: Springer- Verlag.


References


ERDAS. ERDAS Field Guide.: ERDAS, Inc. Atlanta, Georgia. ERDAS. ERDAS IMAGINE Tour Guide.: ERDAS, Inc. Atlanta, Georgia. FAO. (1996). Multiple Frame Agricultural Surveys - Current surveys based on area and list sampling method ( Vol. 1). Rome. FAO. (1998). Multiple Frame Agricultural Surveys - Agricultural Survey Programmes Based on Area Frame or Dual Frame (area and List) sampling design (Vol. 2). Rome. FAO, UNESCO, ISRIC. (1994). Soil Map of World.: ISRIC. FAO, UNDP, Republic Of Botswana. (1990). Explanatory note on the soil map of the Republic of Botswana.: Soil Mapping and Advisory Services, Botswana. FAO/GIEWS. (2001). Agriculture in Botswana. SADC/FSTAU. Available: http://www.fao.org/giews/english/basedocs/bot/bottoc2e.stm [2001, 7/3]. Freese, F. (1976). Elemantary Forest Sampling. Agric. Handbook 23,USDA. Gallegos, F. J. (1995). Sampling Frame Of Square Segments. (Vol. Report EUR 16317 EN, JRC-EC.). Gils, H. V., Zonneveld, I. S., & Westinga, E. (1982). Vegetation And Rangeland Sur-vey (Lecture note N-7 Rural Survey Course 1982/1983): Department of Vegetation and Agricultural Land Use Survey, International Institute for Geo Information Science and earth Observation (ITC), Enschede, The Netherlands. Groten, S. M. E. (2001). Monitoring Food Security in Burkina Faso 1995-2000.: NRSP-2. 00-31, BCRS, The Netherlands. Hord, R. M. (1982). Digital Image Processing of Remotely Sensed Data. New York: Academic Press. Houseman, E. E. (1975). Area Frame Sampling in Agriculture (Vol. SRS No.20). Wash-ington: Statistical Reporting Service.


References


Jensen, J. R. (1986). "Urban/Suburban Land Use Analysis" Chapter 30. In R. N. Colwell (Ed.), Manual of Remote Sensing.: Falls Church, Verginia: American Society of Photo-grammetry. Jensen, J. R. (1996). Introductory Digital Image Processing. Englewood Clffs, New Jer-sey: Prentice Hall. Kish, L. (1989). Sampling methods for agricultural surveys. FAO, Italy,ROME. Landgrege, D. A., Min, P. H., & Swain, K. S. (1988). The Application Of Pattern Rec-ognition Techniques To A Remote Sensing Problem (LARS Information Note 080568). West Lafayette: Purdue University, Laboratory for Applications of Remote Sensing (LARS). Leeuwen, L. V., Zoungrana, B., & Groten, S. M. E. (2000). Crop Area Assessment Using Area Frame Sampling (Vol. NRSP-2. 00-31, BCRS, The Netherlands). MBendi. (2000). Botswana: Agriculture, Forestry, Fishing - Overview. MBendi informa-tion for Africa. Available: http://www.mbendi.co.za/indy/agff/af/bo/p0005.htm [2001, 7/3]. Michael, D. K., & Reynold, G. (1999). 1999 EOS Reference Handbook. NASA/Gobbard Space Flight Center. Available: http:eos.nasa.gov/. Moore, D. S., & Macabe, G. P. (1999). The Inroduction to the Practice of Statistics. Prude University, New Yoke: W.H.Freeman and Company. NASA. (2001a). ADVANCED SPACEBORNE THERMAL EMISSION AND RE-FLECTION RADIOMETER. CIESIN. Available: http://www.ciesin.org/docs/005-089/005-089art10.html [2001, 6/22]. NASA. (2001b). Aster Images Research. Google's cashe. Available: http://www.msu.edu/~wallaceo/research.htm. [2001, 2001.6.22.]. NASA. (2001c). ASTER Data Products and Applications. USGS EROS DATA CEN-TER. Available: http://edcdaac.usgs.gov/aster/ast_l1a.html [2001, 6.22].


References


Rosemary, S. (2000). TERRA SATELLITE'S ASTER DATA NOW AVAILABLE TO PUBLIC. MEDIA RELATIONS OFFICE CALIFORNIA INSTITUTE OF TECHNOL-OGY. Available: http://www.jpl.nasa.gov/releases/2000/terra.html [2001, 6.22.]. Star, J., & Estes, J. (1990). Geographic Information Systems: An Introduction. Engle-wood Cliffs, New Jersey: Prentice-Hall. Swedish Goelogical Survey (SGR). (1998). Serowe Groundwater Resources Evaluation Project, Final report. Lobatse, Botswana. Thompson, K. M. (1992). Sampling. New York. Tyson, P. D. (1989). Climatic Change And Variability in Southern Africa. Cape Town, South Africa.: Oxford University Press. USDA. (1995). AreaFrame Design Information.: National Agricultural Statistics Service, Washington. Vossen, P. (1989). An agrometerological contribution to quantitative rainy season quality monitoring in Botswana. Unpublished Unpublised PhD, State University of Gent, Bel-gium. Weir, M. J. C. (2001). Quality of Image Classification (Lecture Notes For Advance Re-mote Sensing and GIS): International Institute for Geo Information Science and Earth Observation (ITC), Enschede, The Netherlands Wellfield Consulting Services (WCS). (2000). Serowe Wellfield 2, Extention Project, Final report. Republic of Botswana, Ministry of Minerals & Water Affairs, Department of Water Affairs, Gaborone, Botswana., 1.

APPEENDIX-1

A

Appendix 1: Climatic Data for Mahalapye MONTHLY MAXIMUM TEMPERATURE FOR MAHALAPYE. FILE ANDREAS11

MONTHLY MINIMUM TEMPERATURE FOR MAHALAPYE. FILE ANDREAS.

MONTHS YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1984 20.3 20.2 18.3 13.3 8.3 4.5 5.5 8.6 13.7 17.9 18.1 19.61985 20.0 19.6 18.6 13.2 9.4 6.4 5.6 9.2 13.8 17.4 18.3 19.71986 21.2 19.7 18.8 16.1 10.5 6.0 N/A 9.7 13.0 16.2 17.4 19.51987 19.7 21.5 19.6 16.7 10.8 5.9 4.8 8.6 14.2 16.6 20.7 20.21988 19.9 20.0 18.5 14.3 8.4 4.7 3.9 7.7 11.9 16.4 16.7 18.31989 18.7 19.1 16.7 13.2 10.3 7.9 5.4 9.8 11.7 N/A 17.2 18.51990 20.2 18.1 18.2 15.2 8.9 6.6 7.1 7.7 12.0 17.1 18.7 20.01991 19.8 19.4 17.3 11.3 8.4 6.7 5.0 8.1 15.1 17.1 19.2 19.41992 21.0 21.5 19.5 16.3 10.3 6.9 6.7 8.2 16.1 18.4 18.0 19.41993 19.2 19.5 17.7 15.3 11.6 6.6 9.2 9.2 13.9 18.7 18.7 19.31994 18.7 18.9 18.4 14.9 9.3 6.3 3.7 7.9 12.9 16.0 20.0 19.61995 20.5 19.9 18.3 14.3 10.9 5.3 6.7 9.8 15.2 18.6 19.5 17.61996 19.4 18.3 14.8 12.3 9.8 5.4 4.9 9.2 12.8 17.8 18.3 18.41997 19.3 17.9 17.2 11.7 8.0 6.1 8.7 8.3 13.7 13.8 18.0 19.61998 19.6 19.5 20.0 15.5 9.1 5.8 6.9 8.3 14.0 16.9 18.3 18.81999 19.1 19.3 18.2 15.3 12.0 7.5 7.7 9.6 12.6 15.6 19.5 19.32000 17.9 18.8 17.7 13.2 7.1 7.6 4.1 7.4 12.6 16.0 19.1 18.0Mean 19.7 19.5 18.1 14.2 9.6 6.2 5.6 8.7 13.5 15.9 18.6 19.1

MONTHS YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

1984 33.7 33.1 29.6 26.7 25.7 21.5 21.6 25 29.9 31.8 29.5 32.41985 31.1 30.9 30.9 28.3 24.9 22.6 22.6 25.9 27.8 30.8 31.8 31.51986 32.6 31.7 31.3 26.4 25.8 22.5 N/A 27 27.9 28.4 29.7 31.71987 33.7 34 32.6 31 27.9 21.8 21.7 24.8 27.1 30.3 32.9 30.61988 33.1 28.8 27.9 26.2 23.9 22.4 23.1 25.6 27.9 30 30.5 29.61989 30.6 29.7 30.2 25.8 25.7 22.5 23.1 27.6 29.9 N/A 30.6 32.51990 32 30.3 30.5 28.4 25 23.9 25 25.1 28.7 31.2 33.1 32.31991 30.6 30.9 28.3 27.3 26.2 22.3 22.9 26.1 30.1 32.2 31.6 31.31992 34.6 35.0 32.5 30.8 26.6 23.6 22.8 23.7 31.4 31.7 29.9 31.51993 31.4 N/A 30.6 28.6 28.5 23.2 23 24.7 N/A 30.9 28.9 30.71994 29.6 29.6 32 29.1 26.2 21.9 20.8 24.7 29.7 29.2 32.1 32.11995 33.1 32 28.8 27 22.6 22 23 25.6 30.2 31.3 32.2 29.41996 29 25.1 28.9 26.7 24.5 23.4 22.3 25.4 30 32.7 31.2 31.21997 30.5 31.1 29 27.5 24.7 24.7 22.9 27.4 28.4 30.9 32.1 33.51998 31.3 33.4 33.8 31.2 27.4 25.5 24 24.8 29.6 29.8 30.7 29.51999 31.4 33 30.9 29.3 27 24.3 22.8 25.9 27.8 30.4 31.7 30.42000 28.1 27.5 27.9 25.6 23.9 21.6 21.9 25.8 29.3 31 31.2 31.8Mean 31.5 31.0 30.3 27.9 25.6 22.9 22.7 25.5 29.1 30.7 31.1 31.2

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MONTHLY RAINFALL TOTALS (mm) FOR MAHALAPYE. FILE MAHA DATA.


1984 71.0 24.2 257.2 1.4 1.4 0.2 11.5 0 0.3 21.3 47.6 29.5

1985 79.8 32.6 36.3 0.6 0 0 0.1 1.9 0 46.6 15 31.3

1986 3.1 44.3 22.6 86.1 0 5.5 0 0 12.5 60.8 148.7 41.1

1987 32.4 11.4 17.6 1.6 0 0 0 0 21.6 1.0 57.3 83.3

1988 57.1 447 119.7 46.7 0 0 0 0 12.1 25.1 32.7 75.9

1989 51.3 149.1 37.9 40.9 0 28.5 0 0.6 0 24.3 63.4 74.2

1990 62.9 102.6 129.5 21.8 7.5 0 0 0 2.1 32.3 25.8 83.5

1991 173.4 112.1 107.2 0 2.5 22.6 0 0 4.7 23.8 26.0 26.5

1992 90.4 40.1 27.5 2.7 0 0 0 0 6.4 85.0 60.1 124.7

1993 96.8 56.0 38.4 8.3 0.2 0 7.2 0 19.8 32.4 67.5 72.0

1994 50.9 51.8 28.2 2.6 0 0 0 0 0 11.1 136.5 30.5

1995 49.0 161.3 67.4 30.3 16.8 0 0 0 1.3 7.9 52.6 327.4

1996 84.0 102.7 0.6 2.0 9.2 0 0 0 1.2 14.6 126.8 44.9

1997 245.8 7.2 70.7 5.6 31.6 0 0 0 8.1 17.8 99.9 108.4

1998 87.0 4.2 66.9 0 0 0 0 0 0 35.7 92.2 147.3

1999 18.9 5.7 14.8 0.3 7.8 0 0 0 0 1.3 51.0 146.6

2000 122.5 262.6 100.6 24.6 8.4 8.7 0 0 0 11 24.1 35.8

2001 8.5 127.3 36.5 15.9 6.6 5.2 NIL NIL NIL

Mean 76.9 96.8 65.5 16.2 5.1 3.9 1.1 0.1 5.3 26.6 66.3 87.2 MEAN MONTHLY RELATIVE HUMIDITY AT 1400hrs(%) FOR MAHALAPYE. FILE MAHA DATA.


1984 32.3 34.4 46.4 44.6 31.7 34.6 36.5 31.0 29.1 30.2 43.1 34.7

1985 42.6 39.7 36.1 27.9 30.2 28.0 26.8 25.7 31.9 29.2 34.1 38.3

1986 33.5 37.2 30.5 45.7 31.0 28.1 26.4 19.1 27.7 38.0 38.5 41.3

1987 35.6 33.0 31.5 28.8 22.7 27.3 25.1 28.6 36.5 27.3 34.1 49.6

1988 36.7 57.0 56.2 47.4 37.9 28.7 23.3 24.5 26.8 38.7 35.3 45.3

1989 44.4 49.6 38.7 46.0 39.4 40.8 25.8 27.1 23.7 29.5 36.0 35.0

1991 48.9 46.1 55.7 31.2 28.9 34.8 24.4 22.0 29.9 26.3 34.8 37.4

1992 31.5 28.6 30.3 26.6 23.5 29.0 29.3 28.0 N/A 26.8 41.3 41.3

1993 40.6 45.3 38.3 34.3 24.6 31.0 36.1 29.2 26.3 36.7 46.2 45.0

1994 47.9 45.8 33.1 30.8 27.1 28.0 28.8 26.4 20.9 32.1 34.4 36.4

1995 38.2 40.4 50.5 43.5 51.4 35.0 32.0 27.6 23.1 24.1 36.2 48.9

1996 59.0 59.8 45.4 40.1 40.1 30.2 30.7 28.8 23.4 25.6 38.1 41.0

1997 52.6 43.3 48.3 40.2 33.8 27.6 39.2 24.4 38.8 33.0 36.8 39.0

1998 50.1 39.3 35.3 34.8 28.9 28.7 33.0 30.4 29.8 42.9 43.0 53.0

1999 45.1 38.8 40.7 21.2 34.6 31.8 42.0 27.0 30.0 29.8 39.0 47.0

Mean 42.6 42.6 41.1 36.2 32.4 30.9 30.6 26.7 28.4 31.3 38.1 42.2

APPEENDIX-1

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MEAN MONTHLY RELATIVE HUMIDITY AT '0800hrs(%) FOR MAHALAPYE. FILE MAHA DATA.


1984 55.4 59.4 71.0 73.3 63.4 69.4 67.8 62 56.7 54.0 63.8 59.2

1985 67.7 67.8 65.5 55.7 64.5 61.1 56.1 55.8 57.6 49.0 54.8 63.5

1986 56.5 60.5 56.8 72.3 64.2 59.3 59.5 44.5 51.2 56.3 58.4 62.3

1987 62.7 55.4 62.6 56.5 50.2 55.8 58.2 56.5 55.9 48.3 52.4 71.2

1988 61.3 75.4 79.8 75.7 76.1 65.5 60.4 56.4 54.5 60.4 56.8 68

1989 69.7 75.3 70.1 71.2 76.3 79.4 64.4 57.9 49.0 48.5 60.3 61.7

1991 73.1 74.9 79.3 65.8 64.9 76.5 60.6 53.4 59.4 51.1 53.5 59.5

1992 56.7 50.9 56.4 53.4 50.0 59.3 60.8 53.9 N/A 50.2 63.2 60.1

1993 63.6 73.0 70.3 63.6 55.3 58.0 69.7 57.6 47.6 59.6 65.9 66.3

1994 69.1 68.9 60.8 57.7 54.8 55.1 59.0 58.3 48.0 53.2 55.2 58

1995 61.5 63.2 71.0 71.5 86.0 73.4 68.1 56.7 47.3 44.3 57.3 69

1996 76.5 81.9 75.8 74.1 74.1 67.9 61.8 60.9 50.9 48.4 58.9 67.1

1997 76.1 72.0 80.4 73.1 70.1 64.8 76.7 52.2 64.7 56.9 56.1 64

1998 75.5 67.3 63.9 65.2 57.4 61.0 63.2 60.9 53.1 64.4 63.7 72.3

1999 70.6 70.3 73.7 46.6 69.1 60.5 68.5 54.5 52.7 56.2 58.3 71.1

Mean 66.4 67.7 69.2 65.0 65.1 64.5 63.7 56.1 53.5 53.4 58.6 64.9 MEAN MONTHLY SUNSHINE DURATION (hours) FOR MAHALAPYE. FILE MAHA DATA.


1984 10.0 10.0 7.8 8.9 9.9 8.8 8.1 9.8 9.0 8.3 8.0 9.1

1985 8.1 10.3 9.2 10.0 8.7 7.1 9.1 9.5 8.5 9.9 10.6 6.3

1986 8.9 9.2 8.9 6.0 9.3 9.5 N/A 9.5 8.7 7.7 8.7 7.4

1987 8.9 9.6 8.2 8.9 9.4 9.1 N/A N/A 7.4 9.2 7.2 5.0

1988 9.2 6.9 7.7 7.5 8.6 8.6 9.4 N/A 8.9 7.1 9.1 4.5

1990 N/A 8.9 6.7 8.5 8.6 N/A 9.3 9.8 9.3 9.0 9.2 6.7

1991 7.8 8.7 N/A 9.9 9.5 8.7 9.9 9.7 8.0 9.7 8.6 7.4

1992 9.5 10.5 9.0 8.9 9.9 9.3 9.3 9.9 8.6 8.7 8.0 8.0

1993 8.9 7.6 8.3 8.5 9.7 9.3 8.1 9.4 9.3 7.9 7.8 8.0

1994 7.4 8.2 9.6 9.6 10.0 9.1 10.2 10.3 10.3 9.5 8.3 8.9

1995 9.2 9.4 8.3 8.5 7.7 9.4 9.3 9.7 9.3 9.4 8.6 8.9

1996 6.9 7.6 9.3 9.0 7.8 9.4 8.7 9.2 9.0 10.0 7.4 9.1

1997 8.5 9.0 7.1 9.3 9.7 10.2 9.0 10.4 7.0 8.2 9.1 8.6

1998 7.7 8.8 8.7 10.2 10.6 10.2 9.4 10.2 8.8 6.5 7.6 7.3

1999 9.8 10.3 9.0 9.6 9.5 9.6 8.5 10.3 9.2 10.0 6.9 7.7

2000 7.0 4.9 7.2 7.9 9.9 6.9 9.6 10.6 9.2 8.6 9.5 8.5

2001 11.4 5.3 7.3 6.6 9.2 9.6 9.6 9.5 8.3

Mean 8.7 8.5 8.3 8.7 9.3 9.1 9.2 9.9 8.8 8.7 8.4 7.6

APPEENDIX-2

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Appendix 2: Signature Separability Distance measure: Transformed Divergence Using bands: 1 2 3 Taken 3 at a time Classes 1 Rock out crops 2 Cultivated plots 3 Fallow plots 4 Bare land 5 Savannah 6 Water Best Minimum Separability Bands AVE MIN Class Pairs: 1: 2 1: 3 1: 4 1: 5 1: 6 2: 3 2: 4 2: 5 2: 6 3: 4 3: 5 3: 6 4: 5 4: 6 5: 6 1 2 3 1976 1847 1982 2000 1937 1942 2000 2000 2000 1998 2000 1977 1847 2000 1952 2000 2000 Best Average Separability Bands AVE MIN Class Pairs: 1: 2 1: 3 1: 4 1: 5 1: 6 2: 3 2: 4 2: 5 2: 6 3: 4 3: 5 3: 6 4: 5 4: 6 5: 6 1 2 3 1976 1847 1982 2000 1937 1942 2000 2000 2000 1998 2000 1977 1847 2000 1952 2000 2000

APPEENDIX-3

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Appendix 3: Abbreviations FAO – Food and Agricultural Organization. NASS – National Agricultural Statistical Service. USDA – United State Department of Agriculture. CASS - Computer Aided Stratification and Sampling PSUs – Primary Survey Units. CUs – Counting Units. PET – Potential Evapo Transpiration. OWE – Open Water Evaporation. m.s.l. – mean sea level. RH – Relative Humidity. AR – Arenosols. RG – Regosols. LX – Lixisolls. LV – Luvisols. VR – Vertisols. AC – Acrisols. CL – Calsisols.

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Aro – Ferralic Arenosols ARl – Luvic Arenosols RGe – Eutric Regosols LPq – Lithic Leptosols LVk – Calcic Luvisols LXf – Ferric Lixisols VRe – Eutric Vertisols LPe – Eutric Leptosols CLh – Haplic Calcisols ACh – Haplic Acrisols LVx – Chromic Luvisols AOI – Area of Interest. UTM – Universal Transverse Mercator. GPS – Global Positioning System. CV – Coefficient of variance. df - degree of freedom. AE – Allowable Error. IHS – Intensity- Hue- Saturation.

APPEENDIX-3

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RS - Remote Sensing

Improving Land Use Survey Method using High Resolution ...method through reducing costs and efforts....

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