Assessment of coastal vegetation degradation using remote sensing in False Bay, South Africa
| dc.contributor.advisor | Luck-Vogel, Melanie | en_ZA |
| dc.contributor.author | Mbolambi, Cikizwa | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. | en_ZA |
| dc.date.accessioned | 2016-12-22T13:31:58Z | |
| dc.date.available | 2016-12-22T13:31:58Z | |
| dc.date.issued | 2016-12 | |
| dc.description | Thesis (MSc)--Stellenbosch University, 2016. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT: The coastal zone, the interface between land and sea, faces much pressure from human activities. These coastal pressures make it difficult for the coastal zones to fulfil their natural functions, so threatening the state of coastal environments and making them vulnerable to coastal disasters and degradation. This study aimed to test whether remote sensing techniques can be implemented to assess the intactness of terrestrial coastal vegetation at the high spatial resolution required for coastal management. The study focused on the northern False Bay coast, Western Cape, South Africa. The research used is a modification of the method developed by Lück-Vogel, O’Farrell & Roberts (2013) which involved image segmentation and a habitat intactness index using image derivatives. The procedure used Worldview-2 (WV-2) images of high spatial, spectral and temporal resolution acquired on 25 February 2014 and 11 October 2014. Both images were pre-processed and segmented into meaningful objects using object-based image analysis (OBIA). Five image derivatives and the eight spectral bands were stacked into a single image to extract field-informed training points. Regression analysis was performed on eight spectral bands and five image derivatives to evaluate the most suitable bands to produce a habitat intactness index in a subsequent decision tree classification. Decision tree classification was generated using two spectral bands, namely the RED and NIR1 bands. These bands were chosen because they gave the best regression results and they are available in most sensors. The bands were also chosen because the study deals with vegetation assessment. The overall accuracy of the results was 80.5% which was a satisfactory result with a kappa value of 0.75 (75%) that indicates a substantial agreement between the remotely sensed result and the reference data. A key finding is the importance of seasonality to delineate natural and alien vegetation which is better achieved in the dry season. Validation of the results was done using the field-validation points of a field visit conducted in June 2016. The output maps generated for habitat intactness consisted of five habitat intactness classes namely highly, moderately and lightly degraded, intact vegetation and alien vegetation. The output maps can be used to inform coastal managers about conservation at a local scale. It is recommended that validation of remote sensing results be done in the same season that satellite images were taken. | en_ZA |
| dc.description.abstract | AFRIKAANS OPSOMMING: Die koppelvlak tussen die land en die see, ook bekend as die kussone, verkeer onder druk weens antropologiese invloede. Menslike bedrywighede belemmer die kus se natuurlike funksie en stel die kus en sy nabygeleë omgewing bloot aan kusrampe en degradasie. Hierdie studie probeer bepaal of afstandswaarnemingstegnieke toegepas kan word om habitatsongeskondenheid langs die kus, teen ‘n geskikte resolusie vir kusbestuurdoeleindes, te modelleer. Ons fokus spesifiek op die noordelike deel van Valsbaai in die Wes-Kaap Provinsie van Suid-Afrika. Ons bou voort op metodes wat oorspronklik deur Lück-Vogel, O’Farell & Roberts (2013) gedoen is. Die voorgenoemde studie gebruik beeldsegmentasie en beeldafgeleides ten einde ‘n habitatsongeskondenheidsindeks op te stel. Die metode wat in hierdie studie gebruik word maak gebruik van twee WorldView-2 (WV-2) beelde wat teen ‘n hoë ruimtelike-, spektrale- en tydsresolusie, onderskeidelik, op 25 Februarie en 11 Oktober geneem is. Voorwerpgebasseerde beeldverwerking is toegepas om die voorverwerking en segmentasie op hierdie beelde te doen om sodoende sinvolle beeldvoorwerpe te verkry. Vyf beeldafgeleides en agt spektrale bande is gestapel om ‘n enkele beeld te vorm ten einde die toetspunte te isoleer. Regressie-analise is gedoen om die mees toepaslike bande te bepaal om ‘n habitatsongeskondenheidsindeks daar te stel deur van ‘n klassifikasie-beslissingsboom gebruik te maak. Die RED en NIR1 spektraalbande is gebruik om beslissingsboomklassifikasie te doen. Hierdie bande is gekies omdat hulle die beste regressie resultate gelewer het, beskikbaar is op die meeste sensors en omdat hierdie studie plantegroei-assesering behels. Die algehele akkuraatheid van ons bevindinge is 80.5% en word beskou as ‘n bevredigende resultaat met ‘n kappa waarde van 0.75 (75%) wat aandui dat daar ‘n wesenlike ooreenkoms tussen die afstandswaargenome resultaat en die verwysingsdata is. Een van die sleutelbevindinge is die belangrike rol wat seisonaliteit speel in die beskrywing van inheemse en uitheemse plantegroei. Sulkse beskrywings is meer wesenlik in die droë seisoen. Bevestiging van die resultate is gedoen deur van veld-validasie punte, wat tydens ‘n veldbesoek in 2016 geneem is, gebruik te maak. Die gegenereerde habitatsongeskondenheidskaarte bestaan uit vyf habitatsongeskondenheidsklasse naamlik hoog, matig- en ligtelik gedegradeer, ongeskonde plantegroei en uitheemse plantegroei. Hierdie afvoerkaarte kan gebruik word om kusbestuurders in te lig oor bewaring op 'n lokale skaal. Dit word aanbeveel dat die validasie van die afstandswaarnemingsresultate gedoen word in dieselfde seisoen waarin die satelliet beelde geneem is. | af_ZA |
| dc.format.extent | viii, 110 pages | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/100251 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Alien plants -- Coast ranges | en_ZA |
| dc.subject | Coastal zone management -- Remote sensing | en_ZA |
| dc.subject | False Bay (Western Cape South Africa) --Environmental conditions | en_ZA |
| dc.subject | Habitat (ecology) -- South Africa | en_ZA |
| dc.subject | UCTD | |
| dc.title | Assessment of coastal vegetation degradation using remote sensing in False Bay, South Africa | en_ZA |
| dc.type | Thesis | en_ZA |