The University of Edinburgh School of GeoSciences Spatial Pattern Recognition for Crop-Livestock Systems using Multispectral Data by Adri´an Gonz´alez Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy January 2008 Contents Contents ........................................ i List of Figures ..................................... v List of Tables ...................................... viii Chapter 1. General Introduction ........................... 12 1.1 Objectives................................. 15 1.2 Studyarea................................. 16 1.3 Learning from instances in agriculture . 28 1.3.1 Generalproblem ......................... 28 1.3.2 Particular cases . 30 1.4 Thesisoutline............................... 32 Chapter 2. Spatial Geovisualisation and Unsupervised Classification ....... 33 2.1 Abstract.................................. 33 2.2 Introduction................................ 34 2.3 Visualization of indicators in crop-livestock systems .......... 36 2.3.1 Representation of spatial pattern . 37 2.3.2 Spatial point pattern interpolation . 42 2.4 Featureexraction ............................. 45 2.5 Datapreprocessingandmethods . 47 i 2.5.1 Censusdata............................ 47 2.5.2 Samplesizeandsampling . 47 2.5.3 Spatial activity density estimation . 50 2.5.4 Statistical analysis . 51 2.6 Resultsanddiscussion .......................... 52 2.6.1 Aragua-Guarico set . 53 2.6.1.1 Multivariate density visualisation . 53 2.6.1.2 Exploratory cluster analysis . 59 2.6.1.3 Summary of farm’s classes . 65 2.6.2 Guaricoset ............................ 70 2.6.2.1 Multivariate density visualisation . 70 2.6.2.2 Exploratory cluster analysis . 75 2.6.2.3 Summary of farms’ classes . 80 2.6.3 Discriminant analysis . 86 2.7 Conclusion................................. 88 Chapter 3. Kernel Based Unsupervised Classification ............... 90 3.1 Abstract.................................. 90 3.2 Introduction................................ 91 3.3 Principalcomponentsanalysis. 92 3.3.1 Kernel principal components analysis . 97 3.4 Datapreprocessingandmethods . 102 3.5 Resultsanddiscussion . .. .. .. .. .. .. .. 104 ii 3.5.1 Aragua-Guarico set . 104 3.5.1.1 Feature extraction . 104 3.5.1.2 Clustering performance . 105 3.5.1.3 Summary of farm’s classes . 109 3.5.2 Guaricoset ............................ 110 3.5.2.1 Feature extraction . 110 3.5.2.2 Clustering performance . 112 3.5.2.3 Summary of farm classes . 116 3.6 Conclusion................................. 122 Chapter 4. Kernel Based Supervised Classification ................. 123 4.1 Abstract.................................. 123 4.2 Introduction................................ 124 4.3 Thekernel-adatronmethod . 125 4.4 TheLandsatdata............................. 127 4.5 Datapreprocessingandmethods . 129 4.5.1 Informationalclasses . 129 4.5.2 Datapreprocessing . 131 4.5.3 Dataanalysis ........................... 131 4.5.4 Coping with nonlinearities . 135 4.5.5 Experiments............................ 136 4.6 Resultanddiscussion........................... 137 4.6.1 Dimensionality reduction . 137 iii 4.6.1.1 Aragua-Guarico set . 137 4.6.1.2 Guaricoset ....................... 138 4.6.2 Trainingsetimpact. 143 4.6.2.1 Aragua-Guarico set . 144 4.6.2.2 Guaricoset ....................... 145 4.6.3 Classification ........................... 148 4.6.3.1 Aragua-Guarico set . 149 4.6.3.2 Guaricoset ....................... 152 4.7 Unsupervised classification of forest cover . 157 4.8 Conclusion................................. 164 Chapter 5. General Discussion ............................ 165 Chapter 6. General Conclusion ............................ 169 References ........................................ 172 iv List of Figures 1.1 Studyarealocation.............................. 17 1.2 Vegetationcover ............................... 18 1.3 Study area land cover classification . 19 1.4 Demography variation of livestock . 20 1.5 Variation map of meat production in Venezuela . ..... 21 1.6 Variation map of milk production in Venezuela . ..... 22 1.7 Most used cattle breeds within the study area . ..... 25 2.1 Spatial analysis classification scheme . ...... 43 2.2 Experimentalgroupslocation . 50 2.3 Kernel density estimates of intensity for productive attributes . 54 2.4 Kernel density estimates of intensity for land cover attributes . 56 2.5 Lengthofgrowingperiod .......................... 58 2.6 PCA loadings for productive and land cover attributes data set. 62 2.7 Scatterplot of variables loadings Aragua-Guarico group.......... 63 2.8 PCA scores plot of farm’s classes for Aragua-Guarico group....... 64 2.9 Boxplots of productive and land cover attributes variations. ....... 67 2.10 Kernel density estimates of productive attributes for Guarico group . 71 2.11 Kernel density estimates of land cover attributes for Guarico group . 73 2.12 Loadings plot of productive and land cover attributes for Guarico group 78 v 2.13 Scatterplot of variables loadings for Guarico group . .......... 79 2.14 PCA scores plot of farms’ classes for Guarico group . ........ 80 2.15 Boxplots of productive and land cover attributes variations. 83 3.1 Methodologyscheme.............................. 102 3.2 Mahalanobis distance by feature extraction method (Aragua-Guarico) . 106 3.3 Projection onto PCs by feature extration method (Aragua-Guarico) . 109 3.4 Mahalanobis distance by feature extraction method (Guarico) . 113 3.5 Projection onto PCs by feature extraction method (Guarico) . 116 3.6 Boxplots of productive and land cover attributes (Guarico) . 120 4.1 Toy example of decision boundaries between classes . ........ 126 4.2 Landsat image segmentation procedure . 132 4.3 Nonlinearmapping.............................. 136 4.4 Histograms of farm’s class relative frequency (Aragua-Guarico) . 139 4.5 Impact of feature extraction method on class separation ......... 140 4.6 Histograms of farm’s class relative frequency (Guarico).......... 141 4.7 Scatterplot of first three PCs by feature extraction method ....... 142 4.8 Impact of training set size (Aragua-Guarico) . 144 4.9 Impact of training set size (Guarico) . 147 4.10 Separating hyperplanes for Aragua-Guarico group . ........ 150 4.11 Separating hyperplanes for Guarico group . 153 4.12 Original images used for unsupervised classification . ........... 158 4.13 Classification map of land cover data . 159 vi 4.14 Vegetationmap................................ 160 4.15 Classification plot for observed group and predicted probabilities . 162 4.16 Prediction surface for different farm typologies . ......... 163 vii List of Tables 1.1 Cattle population and grassland areas . 23 1.2 Performance parameters of crop-livestock systems . ......... 26 2.1 List of variables examined in this research . ...... 48 2.2 Correlation matrix for Aragua-Guarico dataset . ....... 60 2.3 Histogram of components eigenvalues. 61 2.4 Summary statistics by farm’s class for Aragua-Guarico group ...... 69 2.5 Correlation matrix for Guarico dataset . ..... 76 2.6 Histogram of components eigenvalues. 77 2.7 Summary statistics by farm class for Guarico group . ....... 85 2.8 Discriminant analysis summary statistics by experimental group. 86 3.1 Impact of selected features on clustering performance (Aragua-Guarico) 104 3.2 Impact of kernel function on clustering performance (Aragua-Guarico) . 105 3.3 Aragua-Guarico paired samples statistics. ....... 107 3.4 Aragua-Guarico paired sample test . 108 3.5 Impact of selected features on clustering performance (Guarico) . 111 3.6 Impact of kernel function on clustering performance (Guarico) . 112 3.7 Guarico paired samples statistics. 114 3.8 Guaricopairedsampletest . 115 3.9 Summary statistics of attributes by farm’s class (Guarico) . 121 viii 4.1 Informational classes descrition . 130 4.2 Confusion matrix for the accuracy on segmentation (Aragua-Guarico) . 149 4.3 LDA Confusion matrix for the accuracy on the segmentation (Aragua- Guarico).................................... 151 4.4 Confusion matrix for the segmentation (Guarico) . ....... 154 4.5 LDA Confusion matrix for the segmentation (Guarico) . ....... 155 4.6 Coefficients of the variables in the equation . 161 ix Acknowledgements First and foremost, I am greatly indebted to the Venezuelan Government, specially to the Ministry of Science and Technology, where Marta Rodr´ıguez (FONACIT) and Nuris Orihuela (Venezuelan Spatial Centre) rescued this study from its very early stages, and granted me economical means to complete this research. On the data side, I wish to thank The Venezuelan Geographical Institute (IGVSB), and The National Land Institute (INTI) which gave me much-needed satelite images and farms attributive information respectively. Such support have given me not only a indispensable material assistance to complete the job, but also the opportunities to be open to a completely new application within the agricultural field, to work on a project I am interested in, and to prepare myself to research in a very exiting field of study. There are three people whom I would like to acknowledge individually. First, I am deeply grateful to Dr. Graham Russell, for his encouragement and careful guidance of my project. Secondly, I owe a great deal to Dr. Jos´eAli Moreno (Universidad Central de Venezuela), my second supervisor, whose supervision and advise have contributed substantially to my study. Thirdly, I am enormously grateful to Dr. James Wright for his faith in me and his ability to provide the right support at difficult times. Finally, I would like to thank my friends