Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets
David N. Kuria, Gunter Menz, Salome Misana, Emiliana Mwita, Hans-Peter Thamm, Miguel Alvarez, Neema Mogha, Mathias Becker, Helida Oyieke
Department of Geography, Dar es Salaam University College of Technology, Dar es Salaam, Tanzania.
Department of Geography, Free University Berlin, Malteserstr, Germany.
Department of Geography, University of Bonn, Meckenheimer Allee, Bonn, Germany.
Department of Geography, University of Dar es Salaam, Dar es Salaam, Tanzania.
Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg, Germany.
Institute of Geomatics, GIS & Remote Sensing, Dedan Kimathi University of Technology, Nyeri, Kenya.
National Museums of Kenya, Nairobi, Kenya.
DOI: 10.4236/ars.2014.31004   PDF   HTML   XML   4,679 Downloads   8,183 Views   Citations


Small wetlands in East Africa have grown in prominence driven by the unreliable and diminished rains and the increasing population pressure. Due to their size (less than 500 Ha), these wetlands have not been studied extensively using satellite remote sensing approaches. High spatial resolution remote sensing approaches overcome this limitation allowing detailed inventorying and research on such small wetlands. For understanding the seasonal variations in land cover within the Malinda Wetland in Tanzania (350 Ha), two periods were considered, May 2012 coinciding with the wet period (rainy season) and August 2012 coinciding with a fairly rain depressed period (substantially dry but generally cooler season). The wetland was studied using very high spatial resolution orthophotos derived from Unmanned Aerial Vehicle (UAV) photography fused with TerraSAR-X Spotlight mode dual polarized radar data. Using these fused datasets, five main classes were identified that were used to firstly delineate seasonal changes in land use activities and secondly used in determining phenology changes. Combining fuzzy maximum likelihood classification, knowledge classifier and Change Vector Analysis (CVA), land cover classification was undertaken for both seasons. From the results, manifold anthropogenic activities are taking place between the seasons as evidenced by the high conversion rates (63.01 Ha). The phenological change was also highest within the human influence class due to the growing process of cropped land (26.60 Ha). Much of the changes in both cover and phenology are occurring in the mid upper portion of the wetland, attributed to the presence of springs in this portion of the wetland along the banks of River Mkomazi. There is thus seasonality in the observed anthropogenic influence between the wetland and its periphery.

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Kuria, D. , Menz, G. , Misana, S. , Mwita, E. , Thamm, H. , Alvarez, M. , Mogha, N. , Becker, M. and Oyieke, H. (2014) Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets. Advances in Remote Sensing, 3, 33-48. doi: 10.4236/ars.2014.31004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Bradley, B.A. and Mustard, J.F. (2008) Comparison of Phenology Trends by Land Cover Class: A Case Study in the Great Basin, USA. Global Change Biology, 14, 334-346.
[2] Franke, J., Becker, M., Menz, G., Misana, S., Mwita, E. and Nienkemper, P. (2009) Aerial Imagery for Monitoring Land Use in East African Wetland Ecosystems. IEEE International Geoscience and Remote Sensing Symposium, Cape Town, 12-17 July 2009, 288-291.
[3] Dwivedi, R.S., Rao, B.R.M. and Bhattacharya, S. (1999) Mapping Wetlands of the Sundaban Delta and Its Environs Using ERS-1 Data. International Journal of Remote Sensing, 20, 2235-2247.
[4] Li, J. and Chen, W. (2005) A Rule-Based Method for Mapping Canada’s Wetlands Using Optical, Radar and DEM Data. International Journal of Remote Sensing, 26, 5051-5069.
[5] Ye, Y., Liu, G. and Ning, J. (2010) Wetland Mappping Using Classification Trees to Combine TM Imagery and Climato-Topological Index in Zoige Plateau. 3rd International Congress on Image and Signal Processing (CISP2010), Yantai, 16-18 October 2010, 1989-1993.
[6] Jiao C.C., Zhou, D.M., Li, N. and Li, S.H. (2011) Uncertainty in Mapping of Typical Wetland Plants Using Remote Sensing Images. International Symposium on Water Resource and Environmental Protection, Xi’an, 20-22 May 2011, 3035-3038.
[7] Mwita, E. (2010) Remote Sensing Based Assessment of Small Wetlands in East Africa. PhD Thesis, The University of Bonn, Bonn.
[8] Sakané, N., Alvarez, M., Becker, M., Böhme, B., Handa, C., Kamiri, H.W., Langensiepen, M., Menz, G., Misana, S., Mogha, N.G., Möseler, B.M., Mwita, E.J., Oyieke, H.A. and Van Wijk, M.T. (2011) Classification, Characterisation and Use of Small Wetlands in East Africa. Wetlands, 31, 1103-1116.
[9] Hunt Jr., E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughty, C.S. and McCarty, G.W. (2010) Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sensing, 2, 290-305.
[10] Baghdadi, N., Aubert, M. and Zribi, M. (2012) Use of TerraSAR-X Data to Retrieve Soil Moisture over Bare Soil Agricultural Fields. IEEE Geoscience and Remote Sensing Letters, 9, 512-516.
[11] Kseneman, M., Gleich, D. and Potocnik, B. (2012) Soil Moisture Estimation from TerraSAR-X Data Using Neural Networks. Machine Vision and Applications, 23, 937-952.
[12] Engelhart, S., Keuck, V. and Siegert, F. (2011) Aboveground Biomass Retrieval in Ttropical Forests—The Potential of Combined Xand L-Band SAR Data Use. Remote Sensing of Environment, 115, 1260-1271.
[13] Pohl, C. and Van Genderen, L. (1998) Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications. International Journal of Remote Sensing, 19, 823-854.
[14] Klonus, S. and Ehlers, M. (2009) Performance of Evaluation Methods in Image Fusion. 12th International Conference on Information Fusion, Seattle, 6-9 July 2009, 1409-1416.
[15] Cao, M., Liu, G. and Zhang, X. (2007) An Object-Oriented Approach to Map Wetland Vegetation: A Case Study of Yellow River Delta. IEEE International Geoscience and Remote Sensing Symposium, Barcelona, 23-28 July 2007, 4585-4587.
[16] Friedl, M., Henebry, G., Reed, B., Huete, A., White, M., Morisette, J., Nemani, R., Zhang, X. and Myneni, R. (2006) Land Surface Phenology. Community White Paper.
[17] Piekielek, N.B. (2012) Remote Sensing Grassland Phenology in the Greater Yellowstone Ecosystem: Biophysical Correlates, Land Use Effects and Patch Dynamics. PhD Thesis, Montana State University, Montana.
[18] Buyantuyev, A. and Wu, J. (2012) Urbanization Diversifies Land Surface Phenology in Arid Environments: Interactions Among Vegetation, Climatic Variation, and Land Use Pattern in the Phoenix Metropolitan Region, USA. Landscape and Urban Planning, 105, 149-159.
[19] Chen, J., Gong, P., He, C., Pu, R. and Shi, P. (2003) Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis. Photogrammetric Engineering & Remote Sensing, 69, 369-379.
[20] Mwita, E., Menz, G., Misana, S., Becker, M., Kisanga, D. and Boehme, B. (2013) Mapping Small Wetlands of Kenya and Tanzania Using Remote Sensing Techniques. International Journal of Applied Earth Observation and Geoinformation, 21, 173-183.
[21] Lillesand, T.M., Kiefer, R.W. and Chipman, J.W. (2008) Remote Sensing and Image Interpretation. John Wiley & Sons, Hoboken.
[22] Agusteijn, M.F. and Warrender, C.E. (1998) Wetland Classification Using Optical and Radar Data and Neural Network Classification. International Journal of Remote Sensing, 19, 1545-1560.
[23] Bourgeau-Chavez, L.L., Kasischke, E.S., Brunell, S.M., Mudd, J.P., Smith, K.B. and Frick, A.L. (2001) Analysis of Space-borne SAR Data for Wetland Mapping in Virginia Riparian Ecosystems. International Journal of Remote Sensing, 22, 3665-3687.
[24] Lopes, A., Nezry, E., Touzi, R. and Laur, H. (1990) Maximum A Posteriori Speckle Filtering and First Order Texture Models in SAR Images. International Geoscience and Remote Sensing Symposium, College Park, 20-24 May 1990, 2409-2412.
[25] Dong, Y., Milne, A.K. and Forster, B.C. (2000) A Review of SAR Speckle Filters: Texture Restoration and Preservation,” International Geoscience and Remote Sensing Symposium, Honolulu, 24-28 July 2000, 633-635.
[26] Xiao, J., Li, J. and Moody, A. (2003) A Detail Preserving and Flexible Adaptive Filter for Speckle Suppression in SAR Imagery. International Journal of Remote Sensing, 24, 2451-2465.
[27] Kushwaha, S.P.S., Dwivedi, R.S. and Rao, B.R.M. (2010) Evaluation of Various Digital Image Processing Techniques for Detection of Coastal Wetlands Using ERS-1 SAR Data. International Journal of Remote Sensing, 21, 565-579.
[28] Klonus, S. and Ehlers, M. (2007) Image Fusion Using the Ehlers Spectral Characteristics Preserving Algorithm. GIScience and Remote Sensing, 44, 93-116.
[29] Liu, K., Shi, W. and Zhang, H. (2011) A Fuzzy Topology-Based Maximum Likelihood Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 103-114.
[30] Ganesh, B.P., Rajendran, S., Thirunavukkarasu, A. and Maharani, K. (2009) Visualizing Uncertainty—How Fuzzy Logic Approach Can Help to Explore Iron Ore Deposits. Journal of Indian Society of Remote Sensing, 37, 1-8.
[31] Si, S.O., Thi, L.P. and Van, C.P. (2009) Land Cover Change Analysis Using Change Vector Analysis Method in Duy Tien District, Ha Nam Province in Vietnam. 7th FIG Regional Conference, Hanoi, 19-22 October 2009, 1-9.

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