REMOTE SENSING DATA FOR LAND-COVER CHANGE DETECTION

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PROBLEM STATEMENT

Anthropogenic changes to natural land cover are being driven by a need to provide water, food and shelter to more than six billion people [1]. Unfortunately, these changes have a major impact on hydrology, biodiversity, climate, socio-economic stability and food security [1,2]. Changes in land-use contribute to human impact on the climate as we are changing the natural rate of exchange of carbon dioxide between the atmosphere and the terrestrial biosphere, for example huge stocks of carbon are released as a result of deforestation [2, 3]. The most pervasive form of land-cover change in South Africa is human settlement expansion [4]. In many cases, new human settlements and settlement expansion are informal and occur in areas that were previously covered by natural vegetation. Informal or unplanned settlements usually evolve as people move closer to employment opportunities [4]. These settlements can occur in various locations and are normally without basic services, which includes electricity, running-water, water-borne sewage and refuse removal. The spatial layout is often not planned but informally developed by the inhabitants of the settlements themselves [5]. Figure 1.1 shows an informal settlement in the Limpopo province of South Africa which developed between 2003 and 2009 in an area that was initially mostly covered by natural vegetation. A report from the nineteenth special session of the general assembly of the United Nations (UN) identified sustainable human settlements as a matter requiring urgent attention and states that local government needs to be empowered to plan, implement, develop and manage human settlements [6]. It further states that local government needs to be enabled to manage existing informal settlements, and prevent the establishment of new ones. The occurrence of new small rural villages and scattered settlements is difficult to monitor by local government as the majority are informal and erected rapidly without the prior consent of the relevant government or municipal authorities. This leads to inadequate water, water-borne sewage and refuse removal provision [7]. Settlements are infrequently mapped on an ad-hoc basis in South Africa. It follows that determining where and when new informal settlements occur is beneficial not only from an environmental, but also from a socio-economic point of view.

OBJECTIVE OF THIS THESIS

As shown in the previous section, there exists a need to perform regular land cover change evaluations to identify change areas of interest. Change detection can be defined as the process of identifying differences in the state of an area by observing it at different times [8]. Human operator-dependent change mapping through visual interpretation of imagery is time consuming and resource intensive. Hence there is a need for automated change detection to reduce operator dependence and to enable large datasets to be processed frequently [9, 10]. Remote sensing is the science of obtaining information on an object or area without being in contact with the object or area under investigation [11]. Using various sensors, data are acquired remotely and analyzed to obtain information on the area that is measured by the sensor. Coarse resolution remotely sensed data provides an effective mechanism to monitor large areas on a frequent basis as the wide swath (2000 – 3000 km) of coarse resolution sensors (250 – 1000 m pixel size) enables the same area to be observed at a very high temporal sampling rate (near daily), thus resulting in a highly sampled (hyper-temporal) coarse spatial resolution time-series. This hyper-temporal time-series could potentially be used as a first step as a change alarm leading to further investigation using higher resolution sensors such as Landsat 7, Ikonos, and QuickBird [12]. Automated land-cover change detection at regional or global scales, using hyper-temporal, coarse resolution satellite data has been a highly desired [13], but elusive goal of environmental remote sensing and has even been described by some as the “holy grail” of remote sensing [9]. Digital change detection encompasses the quantification of temporal phenomena from multi-date imagery that is usually acquired by satellite-based, multi-spectral sensors [14, 15]. Land-cover change can be categorized into two types. The first type is referred to as land-cover modification where subtle changes affect the character of the land-cover without changing its overall classification, such as drought and burned areas within natural vegetation [14]. Land-cover modification is often associated with natural climate variability. The second type of land-cover change is referred to as land-cover conversion where there is a complete replacement of one land-cover type by another such as the transformation of natural vegetation by agriculture. Change detection methods have been extensively reviewed by Lu and Weng [15] as well as Coppin et al. [14]. The majority of the methods that were reviewed by the aforementioned authors are based on image differencing, post-classification comparison and change trajectories of multi-date high resolution data. In most cases, these methods only consider two images for change detection, effectively trying to detect areas of change from one image to the next. Coarse resolution satellite data provide frequent observations (daily or multi-day composites) of land surface conditions at regional to global scales and are thus an attractive option for regional-scale change detection. Many change detection methods based on high-frequency, coarse resolution satellite data do not rely on true time-series analysis. The data are mostly treated as hyper-dimensional or as derived metrics [16–19] but not as hyper-temporal, failing to exploit the valuable temporal components, for example, the phase or frequency modulation of the signal, which is driven by seasonal changes in land surface phenology [20]. In addition, many of these methods consider large-scale ecosystem disturbances, for example, wildfires, insect outbreaks and natural disasters [18, 19] as opposed to the relatively small spatial extent of a new settlement development which involves but a few contiguous MODIS pixels. As stated in the previous section, the most pervasive form of land-cover change in South Africa is human settlement expansion. Consequently, developing a change detection framework for detecting the formation of new settlements using a remote sensing approach will be the point of departure towards the greater objective of developing a global or regional automated land cover change detection method. It follows that the primary objective of this thesis is to develop and test an automated change detection framework that is able to detect the transformation of natural vegetation to human settlement which could then be adapted to consider many other types of land cover change. Two novel change detection methods were formulated to solve the aforementioned problem. Both of these methods utilize the hyper-temporal time-series data that are available from coarse resolution imagery. The novelty of these methods is underpinned by the fact that the temporal dimension of the time-series is considered as a highly sampled (relative to the natural phenological variation) data-stream, and change classification is done by combining standard signal processing based methods for feature extraction with machine learning methods for change classification.

CONTENTS :

• CHAPTER 1 – INTRODUCTION
  • 1.1 Problem statement
  • 1.2 Objective of this thesis
  • 1.3 Proposed solution
  • 1.4 Outline of this thesis
• CHAPTER 2 – REMOTE SENSING DATA FOR LAND-COVER CHANGE DETECTION
  • 2.1 Early history of remote sensing
  • 2.1.1 Military reconnaissance satellites
  • 2.1.2 Manned space flight
  • 2.1.3 Meteorological satellites
  • 2.1.4 Earth resources satellites
  • 2.2 Electromagnetic radiation
  • 2.2.1 Interaction of electromagnetic radiation with the atmosphere
  • 2.2.2 Interaction of electromagnetic radiation with a surface
  • 2.3 Resolution
  • 2.3.1 Spatial
  • 2.3.2 Spectral
  • 2.3.3 Temporal
  • 2.3.4 Radiometric
  • 2.4 Choosing a remote sensing system
  • 2.5 MODerate-resolution imaging spectroradiometer
  • 2.6 Vegetation indices
  • 2.6.1 Normalized difference vegetation index
  • 2.6.2 Enhanced vegetation index
  • 2.6.3 Using vegetation indices for land cover change detection
  • 2.7 Change detection methods
  • 2.7.1 Hyper-temporal time-series analysis
  • 2.7.2 MODIS land cover change products
  • 2.8 Summary
• CHAPTER 3 – THE EXTENDED KALMAN FILTER
  • 3.1 Introduction
  • 3.2 Conceptual state-space filtering solution
  • 3.3 Kalman filter
  • 3.4 Extended Kalman filter
  • 3.5 Example of an EKF tracking application
• CHAPTER 4 – IMPROVING LAND-COVER SEPARABILITY USING AN EXTENDED KALMAN
  • FILTER
  • 4.1 Introduction
  • 4.2 Land-cover class separation using the FFT
  • 4.3 Triply modulated cosine model
  • 4.4 New class similarity metric
  • 4.5 Sliding window FFT approach
  • 4.6 Summary
• CHAPTER 5 – DETECTING LAND-COVER CHANGE USING MODIS TIME-SERIES DATA
  • 5.1 Introduction
  • 5.2 EKF change detection method
  • 5.2.1 Change metric formulation
  • 5.2.2 Off-line optimization phase
  • 5.2.3 Operational phase
  • 5.3 Temporal ACF method
  • 5.3.1 Change metric formulation
  • 5.3.2 Off-line optimization phase
  • 5.3.3 Operational phase
  • 5.4 Annual NDVI differencing method
  • 5.5 Summary
• CHAPTER 6 – RESULTS
  • 6.1 Identifying examples of settlement development
  • 6.1.1 Identification of change pixels
  • 6.1.2 Identification of no-change pixels
  • 6.1.3 Validation of MODIS pixels using Google Earth
  • 6.2 Improving class separability using an extended Kalman filter
  • 6.2.1 Study area used for testing class separability
  • 6.2.2 Separability results and discussion
  • 6.3 Detecting land-cover change in the Limpopo province of South Africa
  • 6.3.1 Evaluation of the EKF change detection method in Limpopo
  • 6.3.2 Evaluation of the temporal ACF change detection method in Limpopo
  • 6.3.3 Evaluation of the NDVI differencing method in Limpopo
  • 6.4 Detecting land-cover change in the Gauteng province of South Africa
  • 6.4.1 Evaluation of the EKF change detection method in Gauteng
  • 6.4.2 Evaluation of the temporal ACF change detection method in Gauteng
  • 6.4.3 Evaluation of the NDVI differencing method in Gauteng
  • 6.5 Discussion of the change detection methods
  • 6.5.1 Discussion of the EKF change detection method results
  • 6.5.2 Discussion of the temporal ACF change detection method results
  • 6.5.3 Discussion of the NDVI differencing method
  • 6.6 Conclusion
• CHAPTER 7 – CONCLUSION AND FUTURE RESEARCH
  • 7.1 Concluding remarks
  • 7.2 Future research
  • REFERENCES
  • APPENDIX A – PUBLICATIONS EMANATING FROM THIS THESIS AND RELATED WORK
  • A.1 Papers that appeared in Thomson Institute for Scientific Information (ISI) journals
  • A.2 Papers submitted to Thomson ISI journals
  • A.3 Papers published in refereed accredited conference proceedings
  • A.4 Invited conference papers in refereed accredited conference proceedings

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