Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome

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dc.contributor.advisorVisser, Vernon
dc.contributor.authorMasimula, Ntandokazi Sijabulisiwe
dc.date.accessioned2026-09-22T07:49:54Z
dc.date.available2026-09-22T07:49:54Z
dc.date.issued2026
dc.date.updated2026-09-21T07:40:12Z
dc.description.abstractAcacia mearnsii and Pinus pinaster are invasive woody alien plant species threatening the biodiversity of the Fynbos biome, a global biodiversity hotspot in South Africa. Effective monitoring is essential due to their extensive spread and potential for further invasion. This study assessed the use of freely available Sentinel-2 satellite imagery combined with vegetation indices, environmental variables and machine learning for different seasons of peak and non-peak greenness to improve species detection and mapping. Field data from the Langkloof Valley, including GPS invasion points and vegetation cover, were used to train six Random Forest classification models. Predictor variables selected based on a correlation threshold (r ≥ 0.7) included spectral bands, vegetation indices, and environmental factors across peak and non-peak seasonal means. Results highlight the importance of topographic variables, specifically the topographic wetness index, in increasing classification accuracy. The most accurate model, incorporating Sentinel-2 spectral data, vegetation indices, and environmental variables during peak greenness, achieved 94% overall accuracy and a kappa coefficient of 0.79 using 5-fold cross-validation. These findings demonstrate that incorporating spectral data with environmental data offers a cost-effective, repeatable approach for invasive species monitoring in the Fynbos biome. Future research should refine classification models with phenological insights and expand monitoring efforts to improve targeted management interventions.
dc.identifier.apacitationMasimula, N. S. (2026). <i>Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome</i>. (). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/43793en_ZA
dc.identifier.chicagocitationMasimula, Ntandokazi Sijabulisiwe. <i>"Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome."</i> ., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2026. http://hdl.handle.net/11427/43793en_ZA
dc.identifier.citationMasimula, N.S. 2026. Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome. . University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/43793en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Masimula, Ntandokazi Sijabulisiwe AB - Acacia mearnsii and Pinus pinaster are invasive woody alien plant species threatening the biodiversity of the Fynbos biome, a global biodiversity hotspot in South Africa. Effective monitoring is essential due to their extensive spread and potential for further invasion. This study assessed the use of freely available Sentinel-2 satellite imagery combined with vegetation indices, environmental variables and machine learning for different seasons of peak and non-peak greenness to improve species detection and mapping. Field data from the Langkloof Valley, including GPS invasion points and vegetation cover, were used to train six Random Forest classification models. Predictor variables selected based on a correlation threshold (r ≥ 0.7) included spectral bands, vegetation indices, and environmental factors across peak and non-peak seasonal means. Results highlight the importance of topographic variables, specifically the topographic wetness index, in increasing classification accuracy. The most accurate model, incorporating Sentinel-2 spectral data, vegetation indices, and environmental variables during peak greenness, achieved 94% overall accuracy and a kappa coefficient of 0.79 using 5-fold cross-validation. These findings demonstrate that incorporating spectral data with environmental data offers a cost-effective, repeatable approach for invasive species monitoring in the Fynbos biome. Future research should refine classification models with phenological insights and expand monitoring efforts to improve targeted management interventions. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - Sentinel-2 KW - Acacia mearnsii KW - Pinus pinaster KW - Fynbos KW - Biodiversity KW - Random Forest KW - Environmental variables LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome TI - Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome UR - http://hdl.handle.net/11427/43793 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/43793
dc.identifier.vancouvercitationMasimula NS. Exploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome. []. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43793en_ZA
dc.language.isoen
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Statistical Sciences
dc.publisher.facultyFaculty of Science
dc.publisher.institutionUniversity of Cape Town
dc.subjectSentinel-2
dc.subjectAcacia mearnsii
dc.subjectPinus pinaster
dc.subjectFynbos
dc.subjectBiodiversity
dc.subjectRandom Forest
dc.subjectEnvironmental variables
dc.titleExploring the utility of Sentinel-2 imagery in combination with environmental variables for monitoring Acacia mearnsii and Pinus pinaster in the Fynbos biome
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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