There are few studies on the size and changes in species composition over time for wetlands in South Africa. Techniques such as remote sensing have become popular in assisting the development of management plans due to their spatio-temporal advantages and easily reproducible vegetation and land cover maps. The Wakkerstroom wetland was examined using aerial photography to examine possible changes in the extent and Landsat imaging was used to map its vegetation communities. To assess the distribution of vegetation types on Wakkerstroom wetland, in situ recording of vegetation types and their GPS coordinates was conducted and a Random Forest model was used to predict vegetation types from Landsat pixel spectra across the wetland extent. As calculated from aerial photographs, the Wakkerstroom wetland has increased in extent by 0.483 km2 from 1938 to 2009. The P. australis population density increased significantly over time (r = 0.89), whereas the T. capensis population density had a strong negative correlation over time (r = -0.70). A strong negative relationship between P. australis and T. capensis existed (r = -0.88). A need exists to introduce a management tool that will create a greater mosaic of vegetation communities thus ensuring a greater bird, reptile, and amphibian diversity.
References
[1]
Zedler, J.B. and Kercher, S. (2005) Wetland Resources: Status, Trends, Ecosystem Services, and Restorability. Annual Review of Environmental Resources, 30, 39-74. https://doi.org/10.1146/annurev.energy.30.050504.144248
[2]
Kotze, D.C., Breen, C.M. and Quinn, N. (1995) Wetland Losses in South Africa. In: Gowan, G.I., Ed., Wetlands of South Africa, Department of Environmental Affairs and Tourism, Durban, 263-272.
[3]
Begg, G. (1986) The Wetlands of Natal (Part 1): An Overview of their Extent, Role, and Present Status. Natal Town and Regional Planning Commission Report No. 68, Pietermaritzburg, 114 p.
[4]
Marnewick, M.D., Retief, E.F., Theron, N.T., Wright, D.R. and Anderson, T.A. (2015) Important Bird and Biodiversity Areas of South Africa. BirdLife South Africa, Johannesburg.
[5]
De Steven, D. and Toner, M.M. (2004) Vegetation of Upper Coastal Plain Depression Wetlands: Environmental Templates and Wetland Dynamics within a Landscape Framework. Wetlands, 24, 23-42. https://doi.org/10.1672/0277-5212(2004)024[0023:VOUCPD]2.0.CO;2
[6]
Joubert, R. and Ellery, W.N. (2013) Controls on the Formation of Wakkerstroom Vlei, Mpumalanga Province, South Africa. African Journal of Aquatic Science, 38, 135-151. https://doi.org/10.2989/16085914.2012.762897
[7]
Tooth, S. and McCarthy, T.S. (2007) Wetlands in Drylands: Geomorphological and Sedimentological Characteristics, with Emphasis on Examples from Southern Africa. Progress in Physical Geography, 31, 3-41. https://doi.org/10.1177/0309133307073879
[8]
Macfarlane, D., Kotze, D., Ellery, W., Walters, D., Koopman, V., Goodman, P. and Goge, M. (2009) WET-Health: A Technique for Rapidly Assessing Wetland Health. WRC Report TT 340/09, Accra.
[9]
Joubert, R. (2009) The Origin and Dynamics of Wakkerstroom Vlei, Mpumalanga Province, South Africa. Doctoral Dissertation, University of KwaZulu-Natal, Westville.
[10]
Best, R.G., Wehde, M.E. and Linder, R.L. (1981) Spectral Reflectance of Hydrophytes. Remote Sensing of Environment, 11, 27-35. https://doi.org/10.1016/0034-4257(81)90004-3
[11]
Saltonstall, K. (2002) Cryptic Invasion by a Non-Native Genotype of the Common Reed, Phragmites australis, into North America. Proceedings of the National Academy of Sciences, 99, 2445-2449. https://doi.org/10.1073/pnas.032477999
[12]
Struyf, E., Van Damme, S., Gribsholt, B., Bal, K., Beauchard, O., Middelburg, J.J. and Meire, P. (2007) Phragmites australis and Silica Cycling in Tidal Wetlands. Aquatic Botany, 87, 134-140. https://doi.org/10.1016/j.aquabot.2007.05.002
[13]
Ollis, D.J., Snaddon, C.D. and Job, N.M. (2013) Classification System for Wetlands and Other Aquatic Ecosystems in South Africa. SANBI Biodiversity Series 22, South African National Biodiversity Institute, Pretoria.
[14]
Zhang, Y., Lu, D., Yang, B., Sun, C. and Sun, M. (2011) Coastal Wetland Vegetation Classification with a Landsat Thematic Mapper Image. International Journal of Remote Sensing, 32, 545-561. https://doi.org/10.1080/01431160903475241
[15]
Kotze, D. (2010) WET-Sustainable Use: A System for Assessing the Sustainability of Wetland Use. Water Research Commission, Pretoria.
[16]
Halls, A. (1997) Wetlands, Biodiversity and the Ramsar Convention: The Role of the Convention on Wetlands in the Conservation and Wise Use of Biodiversity. Ramsar Convention Bureau, Gland.
[17]
Bond, W.J. (1997) Fire. Vegetation of Southern Africa. Cambridge University Press, Cambridge, 421-446.
[18]
Van Wilgen, B.W. (2009) The Evolution of Fire and Invasive Alien Plant Management Practices in Fynbos. South African Journal of Science, 105, 335-342. https://doi.org/10.4102/sajs.v105i9/10.106
[19]
Ailstock, M.S., Norman, C.M. and Bushmann, P.J. (2001) Common Reed Phragmites australis: Control and Effects upon Biodiversity in Freshwater Nontidal Wetlands. Restoration Ecology, 9, 49-59. https://doi.org/10.1046/j.1526-100x.2001.009001049.x
[20]
Kotze, D.C. (2013) The Effects of Fire on Wetland Structure and Functioning. African Journal of Aquatic Science, 38, 237-247. https://doi.org/10.2989/16085914.2013.828008
[21]
Lotter, M.C., Mucina, L. and Witkowski, E.T.F. (2014) Classification of the Indigenous Forests of Mpumalanga Province, South Africa. South African Journal of Botany, 90, 37-51. https://doi.org/10.1016/j.sajb.2013.09.010
[22]
Tooth, S., McCarthy, T., Rodnight, H., Keen-Zebert, A., Rowberry, M. and Brandt, D. (2014) Late Holocene Development of a Major Fluvial Discontinuity in Floodplain Wetlands of the Blood River, Eastern South Africa. Geomorphology, 205, 128-141. https://doi.org/10.1016/j.geomorph.2011.12.045
[23]
R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
[24]
Liaw, A. and Wiener, M. (2002) Classification and Regression by Random Forest. R news, 2, 18-22.
[25]
Tuominen, S. and Pekkarinen, A. (2004) Local Radiometric Correction of Digital Aerial Photographs for Multi-Source Forest Inventory. Remote Sensing of Environment, 89, 72-82. https://doi.org/10.1016/j.rse.2003.10.005
[26]
Harvey, K.R. and Hill, G.J.E. (2001) Vegetation Mapping of a Tropical Freshwater Swamp in the Northern Territory, Australia: A Comparison of Aerial Photography, Landsat TM and SPOT Satellite Imagery. International Journal of Remote Sensing, 22, 2911-2925. https://doi.org/10.1080/01431160119174
[27]
National Research Council (1995) Wetlands: Characteristics and Boundaries. National Academies Press, Washington DC.
[28]
Dronova, I. (2015) Object-Based Image Analysis in Wetland Research: A Review. Remote Sensing, 7, 6380-6413. https://doi.org/10.3390/rs70506380
[29]
Civco, D.L., Kennard, W.C. and Lefor, M.W. (1978) A Technique for Evaluating Inland Wetland Photointerpretation: The Cell Analytical Method (CAM). Photogrammetric Engineering and Remote Sensing, 44, 1045-1052.
[30]
Davis, C.H. and Wang, X. (2003) Planimetric Accuracy of Ikonos 1 M Panchromatic Orthoimage Products and Their Utility for Local Government GIS Base Map Applications. International Journal of Remote Sensing, 24, 4267-4288. https://doi.org/10.1080/0143116031000070328
[31]
Pal, M. (2005) Random Forest Classifier for Remote Sensing Classification. International Journal of Remote Sensing, 26, 217-222. https://doi.org/10.1080/01431160412331269698
[32]
Whiteside, T. and Bartolo, R. (2015) Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery. Remote Sensing, 7, 11664-11694. https://doi.org/10.3390/rs70911664
[33]
Kirimi, F., Kuria, D. N., Thonfeld, F., Amler, E., Mubea, K., Misana, S., Menz, G. (2016) Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malina Wetland, Tanzania. Advances in Remote Sensing, 5, 28-42. https://doi.org/10.4236/ars.2016.51003
[34]
Fang, C., Tao, Z., Gao, D. and Wu, H. (2016) Wetland Mapping and Wetland Temporal Dynamic Analysis in the Nanjishan Wetland Using Gaofen One Data. Annals of GIS, 22, 259-271. https://doi.org/10.1080/19475683.2016.1231719
[35]
Wulder, M.A., White, J.C., Coops, N.C., Ortlepp, S., Warner, T.A., Nellis, M.D. and Foody, G.M. (2009) Remote Sensing for Studies of Vegetation Condition: Theory and Application. In: Warner, T.A., Nellis, M.D. and Foody, G.M., Eds., The SAGE Handbook of Remote Sensing, Sage Publishing, Thousand Oaks, 357-367. https://doi.org/10.4135/9780857021052.n25
[36]
Mahdavi, S., Salehi, B., Granger, J., Amani, M., Brisco, B. and Huang, W. (2018) Remote Sensing for Wetland Classification: A Comprehensive Review. GIScience and Remote Sensing, 55, 623-658. https://doi.org/10.1080/15481603.2017.1419602
[37]
Wang, C., Liu, H.Y., Zhang, Y. and Li, Y.F. (2014) Classification of land-Cover Types in Muddy Tidal Flat Wetlands Using Remote Sensing Data. Journal of Applied Remote Sensing, 7, Article ID: 073457. https://doi.org/10.1117/1.JRS.7.073457