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Decision tree development for land cover classification in the Eastern Cape
Thesis (MSc)--Stellenbosch University, 2017.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2017
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| _version_ | 1867613832689483776 |
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| access_status_str | Open Access |
| author | Verhulp, Julie Katherine |
| author2 | Van Niekerk, Adriaan |
| author_browse | Van Niekerk, Adriaan Verhulp, Julie Katherine |
| author_facet | Van Niekerk, Adriaan Verhulp, Julie Katherine |
| author_sort | Verhulp, Julie Katherine |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2017. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/101188 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:42:24.259Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/101188 Decision tree development for land cover classification in the Eastern Cape Verhulp, Julie Katherine Van Niekerk, Adriaan Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. Land cover Supervised classification Decision tree learning Classification and Regression Tree (CART) Classifier extension Remote sensing Jeffries-Matusita Distance Landsat-8 Learning Classifier Systems UCTD Thesis (MSc)--Stellenbosch University, 2017. ENGLISH ABSTRACT: The purpose of this study was to develop a cost-effective and practical method for producing land cover maps by assessing the efficacy of classifier extension in a highly heterogeneous area. Effective classifier extension would reduce the amount of training data required. The high costs and excessive time taken for the collection of such data would therefore also be reduced. The highly heterogeneous Eastern Cape Province in South Africa was selected as the area of interest. Landsat-8 imagery from both the spring and summer season was acquired, and two experiments were carried out. The first experiment analysed the spectral separability of four Landsat-8 scenes in the study area. By using training data for eight land cover classes, the spectral separability for each individual scene, and that of a two-, three- and four-scene mosaic, was calculated. Tests were successfully repeated for each season and for a two-season composite. The results indicated that, while the separability of certain land cover classes decreased with the addition of more scenes, the overall separability remained constant. Further results revealed a better spectral separability from the two seasons composite compared to that of each individual season. Most classes were sufficiently separable in all scenes. The aim of the second experiment was to develop a transferable decision tree (DT) ruleset. A randomised sampling allowed for the selection of various points from the polygon training samples. Information on the pixel values of the bands, various indices, textures and elevation data was extracted for each point. A DT was developed from the dataset using the classification and regression trees (CART) algorithm. The DT was pruned and the rules applied to the four Landsat-8 and the two adjacent scenes. The four Landsat-8 scenes achieved an accuracy of 80.6%, and the two adjacent scenes 83.7% and 64.1%. The poor results of the second adjacent scene were attributed to large discrepancies in vegetation between the wet and dry seasons, causing confusion for certain classes. The inclusion of a vegetation mask elevated the accuracy of the classification to 70.4%. This research has shown that it is possible to develop a DT to accurately classify land cover in a large heterogeneous area, but that the complexity of the area can have a detrimental effect on accuracy. Additionally, it is evident that despite sufficient spectral separability, classifier AFRIKAANS OPSOMMING: Die doel van hierdie studie was om 'n koste-effektiewe en praktiese metode vir die vervaardiging van grondbedekking kaarte te ontwikkel deur die effektiwiteit van klassifiseerder-uitbreiding in 'n hoogs heterogene area te bepaal. Effektiewe klassifiseerderuitbreiding sal die hoeveelheid opleidingdata wat benodig word verminder. Die hoë koste en oormatige tyd wat dit neem om sulke inligting in te samel sal dus ook verminder word. Die hoogs heterogene Oos-Kaap Provinsie in Suid-Afrika is gekies as die area van belang. Landsat-8 beelde van beide die lente en somer is verkry en twee eksperimente is uitgevoer. Die eerste eksperiment het die spektrale skeibaarheid van vier Landsat-8 beelde in die studiearea ontleed. Deur gebruik te maak van die opleidingsdata van agt grondbedekkingsklasse, is die spektrale skeibaarheid vir elke individuele beeld asook 'n twee-, drie-, en vier-toneel mosaïek bereken. Toetse is suksesvol herhaal vir elke seisoen, en vir 'n twee-seisoensamestelling. Die resultate dui daarop dat, alhoewel die skeibaarheid van sekere grondbedekkingsklasse met die toevoeging van meer tonele afgeneem het, die algehele skeibaarheid konstant gebly het. Verdere resultate het gedui op 'n beter spektrale skeibaarheid van die twee-seisoen-samestelling as vir elk van die individuele seisoene. Die meeste klasse het voldoende skeibaarheid in alle tonele getoon. Die doel van die tweede eksperiment was om 'n stel oordraagbare besluitnemingskemareëls (“decision tree rulesets”) te ontwikkel. 'n Ewekansige steekproefneming het die keuse van verskeie punte op die veelhoekige opleidingsmonsters toegelaat. Inligting oor die beeldelementwaardes van die bande, verskeie indekse, teksture en hoogte-data van elke punt is bekom. 'n Besluitnemingskema is ontwikkel deur 'n klassifikasie-en-regressieskema- (CART)-algoritme toe te pas. Die besluitnemingskema is gesnoei en die reëls is op die vier Landsat-8 tonele en die twee aangrensende tonele toegepas. Die vier Landsat-8 tonele het 'n akkuraatheid van 80.6% bekom, terwyl die twee aangrensende tonele onderskeidelik 83.7% en 64.1% behaal het. Die swak resultate van die tweede aangrensende toneel is toegeskryf aan groot kontraste tussen die plantegroei van die nat en droë seisoene, wat verwarring vir sekere klasse veroorsaak het. Die toepassing van 'n plantegroeimasker het die akkuraatheid van die klassifikasie na 70.4% verhoog. Hierdie navorsing toon dat dit moontlik is om 'n besluitnemingskema te ontwikkel om grondbedekking in 'n groot heterogene omgewing akkuraat te klassifiseer, maar dat die Stellenbosch University https://scholar.sun.ac.za vi kompleksiteit van die area die akkuraatheid nadelig kan beïnvloed. Verder is dit duidelik dat, ten spyte van voldoende spektrale skeibaarheid, klassifiseerder-uitbreiding via besluitnemingskemas onbetroubaar is en dat deskundige reëls of addisionele GIS data benodig mag word om oordraagbaarheid te verbeter. Masters 2017-02-22T07:18:44Z 2017-03-29T12:18:26Z 2017-02-22T07:18:44Z 2017-03-29T12:18:26Z 2017-03 Thesis http://hdl.handle.net/10019.1/101188 en_ZA Stellenbosch University xviii, 122 pages ; illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Land cover Supervised classification Decision tree learning Classification and Regression Tree (CART) Classifier extension Remote sensing Jeffries-Matusita Distance Landsat-8 Learning Classifier Systems UCTD Verhulp, Julie Katherine Decision tree development for land cover classification in the Eastern Cape |
| title | Decision tree development for land cover classification in the Eastern Cape |
| title_full | Decision tree development for land cover classification in the Eastern Cape |
| title_fullStr | Decision tree development for land cover classification in the Eastern Cape |
| title_full_unstemmed | Decision tree development for land cover classification in the Eastern Cape |
| title_short | Decision tree development for land cover classification in the Eastern Cape |
| title_sort | decision tree development for land cover classification in the eastern cape |
| topic | Land cover Supervised classification Decision tree learning Classification and Regression Tree (CART) Classifier extension Remote sensing Jeffries-Matusita Distance Landsat-8 Learning Classifier Systems UCTD |
| url | http://hdl.handle.net/10019.1/101188 |
| work_keys_str_mv | AT verhulpjuliekatherine decisiontreedevelopmentforlandcoverclassificationintheeasterncape |