Abdel-Rahman, 2014, Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers, ISPRS J. Photogramm. Remote Sens., 88, 48, 10.1016/j.isprsjprs.2013.11.013
Adam, 2017, Mapping Prosopis glandulosa (mesquite) in the semi-arid environment of South Africa using high-resolution WorldView-2 imagery and machine learning classifiers, J. Arid Environ., 145, 43, 10.1016/j.jaridenv.2017.05.001
Adam, 2014, Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers, Int. J. Remote Sens., 35, 3440, 10.1080/01431161.2014.903435
AngloGold, A., 2004. Case studies. Woodlands Project–good progress being made with phytoremediation project.
Ardila, 2012, Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images, Int. J. Appl. Earth Observ. Geoinf., 15, 57, 10.1016/j.jag.2011.06.005
Astrium, 2015. SP0T 6/ SPOT 7 Technical sheet. Available at: https://www.intelligence-airbusds.com/files/pmedia/public/r12317_9_spot6-7_technical_sheet.pdf. [Accessed 10 Oct 2018].
Atkinson, 2017, Mapping Bugweed (Solanum mauritianum) Infestations in Pinus patula plantations using Hyperspectral imagery and Support Vector Machines, J. Selected Topic Appl. Earth Observ. Remote Sens., 7, 17, 10.1109/JSTARS.2013.2257988
Balcika, F.B., Kuzucua, A.K., 2016. Determination of land cover/land use using spot 7 data with supervised classification methods. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W1, 2016 3rd International GeoAdvances Workshop, 16–17 October 2016, Istanbul, Turkey.
Boggs, 2010, Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas, Int. J. Appl. Earth Observ. Geoinf., 12, 217, 10.1016/j.jag.2009.11.001
Breiman, 2001, Random forests, Machine Learn., 45, 5, 10.1023/A:1010933404324
Buff, A., 2012. A quick history of Joburg's trees. Johannesburg City Parks and Zoo Tree Management Strategy. Available at: http://www.jhbcityparks.com/index.php/tree-planting/tree-planting-updates/1288-a-quick-history-of-joburgs-trees [Accessed 22 Oct 2017].
Carbonnier, L., Marques, C., Coutinho, J., Madeira, M., Tomé, M., 2004. The future of Eucalyptus plantations. Borralho NMG PJS, IUFRO on silviculture and improvement of Eucalypts: ‘Eucalyptus in a changing world’. Portugal: Aveiro.
Chen, 2011
Congalton, 1991, A review of assessing the accuracy of classifications of remotely sensed data, Remote Sens. Environ., 37, 35, 10.1016/0034-4257(91)90048-B
Cortes, 1995, Support-vector networks, Machine Learn., 20, 273, 10.1007/BF00994018
Demir, B., Erturk, S., 2008. Spectral magnitude and spectral derivative feature fusion for improved classification of hyperspectral images. In: Geoscience and Remote Sensing Symposium. IGARSS 2008. IEEE International, 1020(3).
Dennis, 2020, Investigate the possible reduction of mine water ingress by introducing tree plantations: case study of Cooke 4 mine (South Africa), J. Afr. Earth Sc., 161
DigitalGlobe, 2012. The benefits of the 8 spectral bands of WorldView-2. Available at: https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/35/DG-8SPECTRAL-WP_0.pdf. (Accessed 22 Oct 2017).
Foody, 2002, Status of land cover classification accuracy assessment, Remote Sens. Environ., 80, 185, 10.1016/S0034-4257(01)00295-4
Gaertner, 2016, Managing invasive species in cities: a framework from Cape Town, South Africa, Landsc Urban Plan, 151, 1, 10.1016/j.landurbplan.2016.03.010
Ghosh, 2014, A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery, Int. J. Appl. Earth Observ. Geoinf., 26, 298, 10.1016/j.jag.2013.08.011
Gislason, 2006, Random Forests for land cover classification, Pattern Recogn. Lett., 27, 294, 10.1016/j.patrec.2005.08.011
Gong, 1997, Conifer species recognition: an exploratory analysis of in situ hyperspectral data, Remote Sens. Environ., 62, 189, 10.1016/S0034-4257(97)00094-1
Gudex-Cross, 2017, Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery, Remote Sens. Environ., 196, 193, 10.1016/j.rse.2017.05.006
Guenther, 1995, A global model of natural volatile organic compound emissions, J. Geophys. Res.: Atmos., 100, 8873, 10.1029/94JD02950
Holgate, 2007, Factors and actors in climate change mitigation: a tale of two South African cities, Local Environ., 12, 471, 10.1080/13549830701656994
Huang, 2002, An assessment of support vector machines for land cover classification, Int. J. Remote Sens., 23, 725, 10.1080/01431160110040323
Immitzer, 2012, Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data, Remote Sens., 4, 2661, 10.3390/rs4092661
Jensen, J.R., Lulla, K., 1987. Introductory digital image processing: a remote sensing perspective.
Jombo, 2020, Evaluating the capability of Worldview-2 imagery for mapping alien tree species in a heterogeneous urban environment, Cogent Soc. Sci., 6, 10
Kavzoglu, 2017, Object-oriented random forest for high resolution land cover mapping using quickbird-2 imagery
Le Maitre, 2020, Impacts of plant invasions on terrestrial water flows in South Africa, 429
Li, C., Yin, J., Zhao, J., 2010. Extraction of urban vegetation from high resolution remote sensing image. In: Computer Design and Applications (ICCDA), International Conference, 403(4).
Mashao, D.J., 2003. Comparing SVM and GMM on parametric feature-sets. In: Proceedings of the 14th Annual Symposium of the Pattern Recognition Association of South Africa. 27–28 November, 2003, Langebaan, South Africa. IAPR, 15–20.
Mutanga, 2012, High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm, Int. J. Appl. Earth Observ. Geoinf., 18, 399, 10.1016/j.jag.2012.03.012
Newete, 2011, The host range of the Eucalyptus Weevil, Gonipterus “scutellatus” Gyllenhal (Coleoptera: Curculionidae), in South Africa, Ann. For. Sci., 68, 1005, 10.1007/s13595-011-0108-9
Newete, 2016, The capacity of aquatic macrophytes for phytoremediation and their disposal with specific reference to water hyacinth, Environ. Sci. Pollut. Res., 23, 10630, 10.1007/s11356-016-6329-6
Odindi, 2014, Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm, J. Appl. Remote Sens., 8, 1, 10.1117/1.JRS.8.083527
Omar, H., 2010. Commercial timber tree species identification using multispectral Worldview2 data. Digital Globe® 8Bands Research Challenge. pp. 2–13.
Oommen, 2008, An objective analysis of support vector machine based classification for remote sensing, Math Geosci., 40, 409, 10.1007/s11004-008-9156-6
Ouma, 2008, Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification, ISPRS J. Photogramm. Remote Sens., 63, 333, 10.1016/j.isprsjprs.2007.10.006
Pal, 2005, Random forest classifier for remote sensing classification, Int. J. Remote Sens., 26, 217, 10.1080/01431160412331269698
Pal, 2004, Assessment of the effectiveness of support vector machine for hyperspectral data, Future Gen. Comput. Syst., 20, 1215, 10.1016/j.future.2003.11.011
Pejchar, 2009, Invasive species, ecosystem services and human wellbeing, Trends Ecol. Evol., 24, 497, 10.1016/j.tree.2009.03.016
Pu, 2009, Broadleaf species recognition with in situ hyperspectral data, Int. J. Remote Sens., 30, 2759, 10.1080/01431160802555820
Pu, 2012, A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species, Remote Sens. Environ., 124, 516, 10.1016/j.rse.2012.06.011
Reynolds, 2020, Mapping the socio-ecological impacts of invasive plants in South Africa: Are poorer households with high ecosystem service use most at risk?, Ecosyst. Serv., 42, 10.1016/j.ecoser.2020.101075
Richardson, 2004, Invasive alien plants in South Africa: how well do we understand the ecological impacts? Working for Water, S. Afr. J. Sci., 100, 45
Richardson, 2020, The biogeography of South African terrestrial plant invasions, 67
Rodriguez-Galiano, 2012, An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens., 67, 93, 10.1016/j.isprsjprs.2011.11.002
Schäffler, 2013, Valuing green infrastructure in an urban environment under pressure—The Johannesburg case, Ecol. Econ., 86, 246, 10.1016/j.ecolecon.2012.05.008
Shafri, 2012, Hyperspectral remote sensing of urban areas: an overview of techniques and applications, Res. J. Appl. Sci., Eng. Technol., 4, 1557
Shojanoori, 2016, Review on the use of remote sensing for urban forest monitoring, Arboric Urban For, 42, 400
Sothe, 2019, Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data, Remote Sens., 11, 1338, 10.3390/rs11111338
Tsoeleng, 2020, A comparison of two morphological techniques in the classification of urban land cover, Remote Sens., 12, 1089, 10.3390/rs12071089
Zengeya, 2017, Managing conflict-generating invasive species in South Africa: challenges and trade-offs, Bothalia-African Biodiv. Conserv., 47, 1, 10.4102/abc.v47i2.2160
Zhang, 2012, Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery, Photogramm. Eng. Remote Sens., 78, 1079, 10.14358/PERS.78.10.1079