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dc.contributor.advisorRuhiiga, T.M.en_US
dc.contributor.advisorNdou, N.N.en_US
dc.contributor.authorMosepele, E.M.en_US
dc.date.accessioned2020-08-12T11:02:47Z
dc.date.available2020-08-12T11:02:47Z
dc.date.issued2019en_US
dc.identifier.urihttps://orcid.org/0000-0002-3770-7582en_US
dc.identifier.urihttp://hdl.handle.net/10394/35533
dc.descriptionMSc (Environmental Science and Management), North-West University, Mafikeng Campus
dc.description.abstractReducing connectivity, road networks threaten the effectiveness of natural reserves, thus, representing a critical conservation matter. This work aims to: (1) map and classify the road network, (2) characterize road-side natural vegetation condition, and (3) assess the impact of road networks on vegetation cover and condition. First, NDVI image was generated from a multi-temporal image and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface and water. This study applied supervised classification-maximum likelihood algorithm in ArcGIS 10 to detect vegetation cover observed in the study area, using multispectral satellite data obtained from Landsat 8 OLI for the year 2017. The classification results show that total overall accuracy achieved was 0.88.5, and the Kappa coefficient was 0.7992 for the classification of the 2017 image; which is acceptable in both accuracy total and kappa accuracy. Vegetation sampling transect, based quadrats 30m² measurements, were surveyed during field work within the predetermined distances of 50m, 100m and 150m from the road. Road networks were digitized from a high-resolution imagery Google Earth. Vegetation condition was then related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that "there is no variation in vegetation condition as we move away from the road". Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with the critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation as the distance increases away from the road. The results reveal that road network has an obvious impact on roadside natural vegetation, especially vegetation within the road vicinity. The study provides a baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. It is therefore recommended that improvements on unmanaged recreation for instance off-road vehicle use, that result on degradation of roadside vegetation and soils. The conclusion is that the road network plays an important role in the condition of vegetation.en_US
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa)en_US
dc.subjectChi squareden_US
dc.subjectGeographic Information Systemen_US
dc.subjectMultinomial Logistic Regressionen_US
dc.subjectRemote Sensingen_US
dc.subjectRoad Side Vegetationen_US
dc.titleAnalyzing impacts of road network on vegetation using GIS and Remote sensing techniquesen_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID11805994 - Ruhiiga, Tabukeli Musigi (Supervisor)en_US
dc.contributor.researchID17028647 - Ndou, Naledzani Nyahman (Supervisor)en_US


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