Agricultural field boundary delineation using earth observation methods and multi-temporal Sentinel-2 imagery

Watkins, Barry (2019-12)

Thesis (MSc)--Stellenbosch University, 2019.

Thesis

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.

AFRIKAANSE OPSOMMING: Akkurate en bygewerkte landboumoniteringstelsels is van kritieke belang vir die voorspelling van oesopbrengs, die beplanning van hulpbronne en die assessering van die impak van bedreigings vir produksie (soos droogtes of vloede). Die ruimtelike omvang en ligging van landboulande beïnvloed hierdie stelsels tot ʼn groot mate. Konvensionele metodes om lande af te baken, soos in situ veldopnames en die visuele interpretasie van beelde, is duur en tydrowend en is dus nie geskik in ʼn operasionele konteks nie. Outomatiese aardwaarnemingstegnieke bied ʼn koste-effektiewe alternatief, aangesien dit gebruik kan word om gereelde en hoogs gedetailleerde ondersoeke van groot gebiede uit te voer. Daar is egter tans geen gevestigde en oordraagbare tegnieke om landbougrondgrense outomaties af te baken nie. Die mees belowende tegnieke wat in die literatuur voorkom, sluit in objek-gebaseerde-beeldanalise (OGBA) en randdeteksie-algoritmes. Hierdie studie het gevolglik verskeie OGBA-benaderings om landbougrondgrense af te baken met multi-temporale Sentinel-2 beelde, vergelyk en geëvalueer. Twee stelle eksperimente is uitgevoer. Die eerste stel eksperimente het ses multi-temporale OGBA-benaderings waarmee aktiewe landbouvelde in ʼn groot besproeiingskema afgebaken en geïdentifiseer is, vergelyk en geëvalueer. Hierdie benaderings kombineer twee randverbeteringsalgoritmes (Canny en Scharr) en drie beeldsegmenteringstegnieke (waterskeiding, multi-drempel en multi-resolusie) om ses scenario’s te skep. Resultate het getoon dat die waterskeidingskenario’s beter presteer as die multi-drempel- en multi-resolusie-segmenteringsalgoritmes. Daarbenewens is bevind dat die Canny-randdeteksie-algoritme, in samewerking met ʼn segmenteringstegniek, hoër grensakkuraathede as sy eweknie, Scharr, produseer. Die tweede stel eksperimente het die beste presterende scenario van die eerste stel eksperimente, naamlik die Canny-randdeteksie-algoritme in samewerking met waterskeidingsegmentasie (CRSW), op vyf streke in Suid-Afrika toegepas. Die doel van hierdie ondersoek was om die robuustheid (oordraagbaarheid) van die metodologie te evalueer. ʼn Standaard per-piksel gerigte klassifikasie is uitgevoer om te dien as ʼn maatstaf waarteen die voorgestelde benadering vergelyk is. Resultate het getoon dat CRSW in alle eksperimente beter as die per-piksel gerigte klassifikasie presteer het. CRSW se robuustheid in verskillende landboulandskappe is verder beklemtoon deur sy skepping van geslote veldgrense, onafhanklikheid van opleidingsdata en oordraagbaarheid. Die kwantitatiewe eksperimente wat in hierdie studie uitgevoer is, het die basis gelê vir die implementering van ʼn operasionele werkvloei vir die afbakening van landbouvelde met behulp van multi-temporale Sentinel-2-beelde. Die onttrekte veldgrense sal waarskynlik landboumoniteringstelsels help om oesopbrengs te beraam en hulpbronbeplanning en voedselsekuriteitsevaluerings te verbeter.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/107064
This item appears in the following collections: