Masters Degrees (Geography and Environmental Studies)

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Browsing Masters Degrees (Geography and Environmental Studies) by Subject "Agricultural field boundary delineation"

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    Agricultural field boundary delineation using earth observation methods and multi-temporal Sentinel-2 imagery
    (Stellenbosch : Stellenbosch University, 2019-12) Watkins, Barry; Van Niekerk, Adriaan; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Accurate and up-to-date agricultural monitoring systems are critical for forecasting crop yield, planning resources and assessing the impact of threats to production (such as droughts or floods). The spatial extent and location of agricultural fields greatly influence these systems. Conventional methods of delineating agricultural fields, such as in situ field surveys and manual interpretation of imagery, are costly and time-consuming and are thus not suitable in an operational context. Automated earth observation techniques offer a cost-effective alternative as they can be used to execute frequent and highly detailed investigations of large areas. However, there are currently no well-established and transferable techniques to automatically delineate agricultural field boundaries. The most promising techniques found in literature include object-based image analysis (OBIA) and edge detection algorithms. This study consequently compared and evaluated multiple OBIA approaches for delineating agricultural field boundaries with multi-temporal Sentinel-2 imagery. Two sets of experiments were carried out. The first set of experiments compared and evaluated six multi-temporal OBIA approaches with which active agricultural fields in a large irrigation scheme were delineated and identified. These approaches combined two edge enhancement algorithms (Canny and Scharr) and three image segmentation techniques (watershed, multi-threshold and multi-resolution) to create six scenarios. Results showed that the watershed segmentation scenarios outperformed the multi-threshold and multi-resolution segmentation algorithms. In addition, the Canny edge detection algorithm, in conjunction with a segmentation technique, was found to produce higher boundary accuracies than its counterpart, Scharr. In the second set of experiments the best performing scenario from the first set of experiments, namely Canny edge detection in conjunction with watershed segmentation (CEWS), was modified slightly and applied to five regions in South Africa. The purpose of this investigation was to assess the robustness (transferability) of the methodology. A standard per-pixel supervised classification was performed to serve as a benchmark against which the CEWS approach was compared. Results showed that CEWS outperformed the supervised per-pixel classification in all experiments. CEWS’ robustness in different agricultural landscapes was furthermore highlighted by its creation of closed field boundaries, independence from training data and transferability. The quantitative experiments carried out in this study lay the foundation for the implementation of an operational workflow for delineating agricultural fields with the use of multi-temporal Sentinel-2 imagery. The extracted field boundaries will likely aid agricultural monitoring systems in estimating crop yield and improve resource planning and food security assessments.