Ma Fung-yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 137-141). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Tables --- p.ix / List of Figures --- p.x / Chapter CHAPTER 1 --- INTRODUCTION / Chapter 1.1 --- Introduction and background --- p.1 / Chapter 1.2 --- Objectives --- p.4 / Chapter 1.3 --- Significance --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.5 / Chapter CHAPTER 2 --- LITERATURE REVIEW / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Hyperspectral remote sensing --- p.7 / Chapter 2.2.1 --- Current imaging spectrometers available --- p.8 / Chapter 2.2.2 --- Applications of hyperspectral remote sensing --- p.9 / Chapter 2.2.2.1 --- Biochemistry of vegetation --- p.10 / Chapter 2.2.2.2 --- Spatial and temporal patterns of vegetation --- p.12 / Chapter 2.3 --- Tree species recognition --- p.12 / Chapter 2.3.1 --- Factors affecting spectral reflectance of vegetation --- p.14 / Chapter 2.3.1.1 --- Optical properties of leaf --- p.14 / Chapter 2.3.1.2 --- Canopy structure --- p.15 / Chapter 2.3.1.3 --- Canopy cover --- p.16 / Chapter 2.3.1.4 --- Illumination and viewing geometry --- p.16 / Chapter 2.3.1.5 --- Spatial and temporal dynamics of plants --- p.17 / Chapter 2.3.2 --- Classification algorithms for hyperspectral analysis --- p.17 / Chapter 2.3.2.1 --- Use of derivative spectra for tree species recognition --- p.17 / Chapter 2.3.2.2 --- Linear discriminant analysis --- p.18 / Chapter 2.3.2.3 --- Artificial neural network --- p.19 / Chapter 2.3.3 --- Tree species recognition using hyperspectral data --- p.21 / Chapter 2.4 --- Data compression and feature extraction --- p.22 / Chapter 2.4.1 --- Analytical techniques of data compression --- p.23 / Chapter 2.4.2 --- Analytical techniques of feature extraction --- p.25 / Chapter 2.4.2.1 --- Feature selection by correlation with biochemical and biophysical data --- p.25 / Chapter 2.4.2.2 --- Spatial autocorrelation-based feature selection --- p.27 / Chapter 2.4.2.3 --- Spectral autocorrelation-based feature selection --- p.29 / Chapter 2.4.2.3.1 --- Optimization with distance metrics --- p.29 / Chapter 2.4.2.3.2 --- Stepwise linear discriminant analysis --- p.30 / Chapter 2.5 --- Summary --- p.31 / Chapter CHAPTER 3 --- METHODOLOGY / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Study site --- p.33 / Chapter 3.3 --- Instrumentation --- p.34 / Chapter 3.4 --- Data collection --- p.35 / Chapter 3.4.1 --- Laboratory measurement --- p.36 / Chapter 3.4.2 --- In situ measurement --- p.39 / Chapter 3.5 --- Methods of data analysis --- p.40 / Chapter 3.5.1 --- Preprocessing of data --- p.40 / Chapter 3.5.2 --- Compilation of hyperspectral database --- p.42 / Chapter 3.5.3 --- Tree species recognition --- p.42 / Chapter 3.5.3.1 --- Linear discriminant analysis --- p.44 / Chapter 3.5.3.2 --- Artificial neural network --- p.44 / Chapter 3.5.3.3 --- Accuracy assessment --- p.45 / Chapter 3.5.3.4 --- Comparison of different data processing strategies and classifiers --- p.45 / Chapter 3.5.3.5 --- Comparison of data among different seasons --- p.46 / Chapter 3.5.3.6 --- Comparison of laboratory and in situ data --- p.46 / Chapter 3.5.4 --- Data compression --- p.47 / Chapter 3.5.5 --- Band selection --- p.47 / Chapter 3.6 --- Summary --- p.48 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSIONS OF TREE SPECIES RECOGNITION / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Characteristics of hyperspectral data --- p.50 / Chapter 4.3 --- Tree species recognition --- p.79 / Chapter 4.3.1 --- Comparison of different classifiers --- p.82 / Chapter 4.3.1.1 --- Efficiency of the classifiers --- p.83 / Chapter 4.3.1.2 --- Discussions --- p.83 / Chapter 4.3.2 --- Comparison of different data processing strategies --- p.84 / Chapter 4.3.3 --- Comparison of data among different seasons --- p.86 / Chapter 4.3.4 --- Comparison of laboratory and in situ data --- p.88 / Chapter 4.4 --- Summary --- p.92 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSIONS OF DATA COMPRESSION AND BAND SELECTION / Chapter 5.1 --- Introduction --- p.93 / Chapter 5.2 --- Data compression --- p.93 / Chapter 5.2.1 --- PCA using in situ spectral data --- p.93 / Chapter 5.2.1.1 --- Characteristics of PC loadings --- p.95 / Chapter 5.2.1.2 --- Scatter plots of PC scores --- p.96 / Chapter 5.2.2 --- PCA using laboratory spectral data --- p.99 / Chapter 5.2.2.1 --- Characteristics of PC loadings --- p.102 / Chapter 5.2.2.2 --- Scatter plots of PC scores --- p.103 / Chapter 5.2.2.3 --- Results of tree species recognition using PC scores --- p.107 / Chapter 5.2.3 --- Implications --- p.107 / Chapter 5.3 --- Band selection --- p.108 / Chapter 5.3.1 --- Preliminary band selection using stepwise discriminant analysis --- p.108 / Chapter 5.3.1.1 --- Selection of spectral bands --- p.109 / Chapter 5.3.1.2 --- Classification results of the selected bands --- p.109 / Chapter 5.3.1.3 --- Seasonal comparison using stepwise linear discriminant analysis --- p.114 / Chapter 5.3.1.4 --- Implications --- p.116 / Chapter 5.3.2 --- Band selection using hierarchical clustering technique --- p.116 / Chapter 5.3.2.1 --- Hierarchical clustering procedure --- p.116 / Chapter 5.3.2.2 --- Selection of spectral band sets --- p.119 / Chapter 5.3.2.3 --- Classification results of the selected band sets --- p.124 / Chapter 5.4 --- Summary --- p.127 / Chapter CHAPTER 6 --- SUMMARY AND CONCLUSION / Chapter 6.1 --- Introduction --- p.129 / Chapter 6.2 --- Summary --- p.129 / Chapter 6.2.1 --- Tree species recognition --- p.129 / Chapter 6.2.2 --- Data compression --- p.130 / Chapter 6.2.3 --- Band selection --- p.131 / Chapter 6.3 --- Limitations of this study --- p.132 / Chapter 6.4 --- Recommendations for further studies --- p.133 / Chapter 6.5 --- Conclusion --- p.136 / BIBLIOGRAPHY --- p.137 / APPENDICES / Appendix 1 Reflectance of the 25 tree species in four seasons with three levels of leaf density --- p.142-166 / "Appendix 2 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by linear discriminant analysis for each season" --- p.167-178 / "Appendix 3 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by neural networks for each season" --- p.179-190 / Appendix 4 Confusion matrices of tree species recognition using 21 tree species with original spectra classified by linear discriminant analysis for seasonal comparison --- p.191-193 / Appendix 5 Confusion matrices of tree species recognition using the first eight PC scores classified by linear discriminant analysis for each season --- p.194-197 / "Appendix 6 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 2) for each season" --- p.198-209 / "Appendix 7 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 3) for each season" --- p.210-220 / "Appendix 8 Confusion matrices of tree species recognition using 21 tree species with original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis for seasonal comparison" --- p.221-229 / Appendix 9 Confusion matrices of tree species recognition using the spectral bands selected by hierarchical clustering procedures and classified by linear discriminant analysis for each season --- p.230-257

Motivation behind this master dissertation is to introduce a novel study called " A fuzzy logic micro-controller enabled system for the monitoring of micro-climatic parameters of a greenhouse" which is capable of intelligently monitoring and controlling the greenhouse climate conditions in a preprogrammed manner. The proposed system consists of three stations: Sensor Station, Coordinator Station, and Central Station. To allow for better monitoring of the climate condition in the greenhouse, fuzzy logic controller is embedded in the system as the system becomes more intelligent with fuzzy decision making. The sensor station is equipped with several sensor elements such as MQ-7 (Carbon monoxide sensor), DHT11 (Temperature and humidity sensor), LDR (light sensor), grove moisture sensor (soil moisture sensor). The communication between the sensor station and the coordinator station is achieved through XBee wireless modules connected to the Arduino Mega and the communication between coordinator station and the central station is also achieved via XBee wireless modules connected to the Arduino Mega. The experiments and tests of the system were carried out at one of IKHALA TVET COLLEGE’s greenhouses that is used for learning purposes by students studying agriculture at the college. The purpose of conducting the experiments at the college’s green house was to determine the functionality and reliability of the designed wireless sensor network using ZigBee wireless technology. The experiment result indicated that XBee modules could be used as one solution to lower the installation cost, increase flexibility and reliability and create a greenhouse management system that is only based on wireless nodes. The experiment result also showed that the system became more intelligent if fuzzy logic was used by the system for decision making. The overall system design showed advantages in cost, size, power, flexibility and intelligence. It is trusted that the results of the project will give the chance for further research and development of a low cost greenhouse monitoring system for commercial use. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Electrical Engineering at the Cape Peninsula University of Technology, 2012 / This research project addresses the unique challenges of extensive farming in terms of monitoring and controlling remote equipment or events. Poorly maintained roads and escalating fuel costs increase difficulty of farming and the time spent on physically monitoring remote sites further reduces financial yields. The research showed that there are very few solutions that implement wireless or electronic technology to overcome the challenges associated with these isolated and arid areas and that a low-cost, long range wireless telemetry solution that is easy to use would be beneficial for the extensive farming industry. It was therefore the aim of this project to develop a remote monitoring and controlling solution that implements wireless technology to convey information of activities around the farm utilising electronic means. To be able to successfully develop a wireless telemetry solution that will accurately meet the needs of this specific sector of industry, market research was conducted. To guide the research, the QFD (quality function deployment) process for product development has been implemented. The research consisted out of various aspects including a survey, financial considerations and international comparisons. The research also aided in the understanding of the day-to-day activities and also the physical parameters of extensive farms. Also, currently available technologies and products were evaluated to establish whether similarities exist that will aid in the development of a new product. The development process was based on the results obtained in the market research and resulted in a wireless telemetry solution that overcame all the design challenges and proved to be technically feasible, successfully addressing the application requirements. Zigbee technology was utilized for wireless communication because it provided an off-the-shelf solution with a number of readily available development platforms from various technology providers. A communication range of up to 6 kilometres with a transmitted power of 11dBm was achieved for point-to-point communication and a mesh network topology has been implemented for even longer range and complete coverage on farms. Various types of measurements have been catered for, with custom-designed instrumentation which enabled measurements such as water levels, movement and analogue signals. Also, a basic user interface was developed to enable the user to monitor or control the equipment or events remotely from a personal computer, locally or even over the internet. The results of this research project showed that by carefully selecting available technologies and understanding the application, it is possible to develop a solution that addresses the monitoring and controlling needs associated with extensive farming. The wireless telemetry system that was developed resulted in a saving equal to 10% of the total expenses of the farms per year. The telemetry system is therefore a financially feasible solution with a payback period of less than 1 year and far below the initial estimated budget. Without the need to physically monitoring equipment and events, an increase in productivity and the expansion of the overall enterprise is a further benefit added unto the monetary savings. In addition to the financial benefits of implementing new wireless technology, this is an opportunity to contribute to a cleaner and more sustained future as a legacy for the next generation by reducing the carbon footprint of the farm.

Knowledge of the geospatial distribution of vegetation is fundamental for resource management. The objective of this study is to investigate the possible use of airborne LIDAR (light detection and ranging) data to improve classification accuracy of high spatial resolution optical imagery and compare the ability of two classification algorithms to accurately identify and map wetland vegetation communities. In this study, high resolution imagery integrated with LIDAR data was compared jointly and alone; and the nearest neighbor (NN) and machine learning random forest (RF) classifiers were assessed in semi-automated geographic object-based image analysis (GEOBIA) approaches for classification accuracy of heterogeneous vegetation assemblages at Everglades National Park, FL, USA. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection