Mangroves are salt-tolerable trees
that grow on zones parallel to the coastline along the creeks. They follow the
mud flat accretions which are unvegetated areas consisting of sand or gravel
that are either exposed or flooded by tides. They provide 70% of the wood
requirement along the Kenyan Coast. Currently, there are no harvest plans of
the mangroves and there is selective removal of suitable poles and most of the
quality poles have been wiped out. This not only leaves the inferior species
unsuitable for the market but also affects the quality of the forest. Moreover,
areas that are suitable for mangroves growth have been occupied by human
settlement and infrastructure,hence, there is a need of sustainable use of the mangroves so as to
protect themfrom degradating and eventually extinction. To achieve this, geospatial
techniques need to be employed in order to determine the spatial extent of the
vegetation and devise methods and plans of managing them.The Kilifi Mangrove Forest creek is
home to major six species: Avicennia
marina, Ceriops tagal, Sonneratia alba J., Rhizophora mucronata, Lumnitzera racemosa and Bruguiera gymnorrhiza.This
study showed that the most dominant species in the forest is Avicenna Marina which had a percentage
stand of 25.6%. The less dominant species Lumnitzera
racemosa and Heritiera littoralis had a stand of 0.10% which were restricted for harvesting in the analysis, they
need to be protected so as to prevent its extinction in the forest which will
affect the biodiversity and richness of the forest. Density and heights of the
mangroves were considered so as to decide on which areas to do reforestation in
order to protect the forest and help in preventing soil erosion. The final
suitable area for harvesting after carrying out conditional and majority filter
was 394 acres which are 9% of the total
forest area. The total area
References
[1]
Baral, H. (2004). Applications of GIS in Community-Based Forest Management in Australia (and Nepal). Masters Research Thesis, Melbourne: University of Melbourne.
[2]
Baskent, E. Z., & Jordan, G. A. (1996). Designing Forest Management to Control Spatial Structure of Landscapes. Landscape and Urban Planning, 34, 55-74. https://doi.org/10.1016/0169-2046(95)00200-6
[3]
Bosire, J. O., Dahdouh-Guebas, F., Kairo, J. G., & Koedam, N. (2003). Colonization of Non-Planted Mangrove Species into Restored Mangrove Stands in Gazi Bay, Kenya. Aquatic Botany, 76, 267-279.
[4]
Bosire, J. O., Dahdouh-Guebas, F., Walton, M., Crona, B. I., Lewis, R. R., Field, C., Koedam, N. et al. (2008). Functionality of Restored Mangroves: A Review. Aquatic Botany, 89, 251-259. https://doi.org/10.1016/j.aquabot.2008.03.010
[5]
Gupta, M., Khare, A., & Pathak, S. (2012). Role of Remote Sensing and GIS in Forest. International Journal of Computer Science and Informatics, 29.
[6]
Herrington, L., & Koten, D. E. (1988). A GIS Based Decision Support System for Forest Management. Proceedings of Accessing the World GIS/LIS’88, 2, 825-831.
[7]
Kairo, J. G., Dahdouh-Guebas, F., Gwada, P. O., Ochieng, C., & Koedam, N. (2002). Regeneration Status of Mangrove Forests in Mida Creek, Kenya: A Compromised or Secured Future? AMBIO: A Journal of the Human Environment, 31, 562-568. https://doi.org/10.1579/0044-7447-31.7.562
[8]
Marusák, R., Kaspar, J., & Vopěnka, P. (2015). Decision Support Systems (DSS) Optimal—A Case Study from the Czech Republic. Forests, 6, 163-182. https://doi.org/10.3390/f6010163
[9]
Moore, T. G. E., & Lockwood, C. G. (1990). The HSG wood Supply Model: Description and User’s Manual (No. PI-X-98).
[10]
Pecora, G., Todaro, L., & Moretti, N. (2014). Optimization of Timber Harvesting Using GIS-Based System. Proceeding of Second International Congress of Silviculture, 1, 892-895.
[11]
Sigana, D. O., Mavuti, K. M., & Ruwa, R. K. (2009). Fish Species Composition and Distribution in Kilifi Creek (vol. 20, pp. 15-27). Mombasa: African Studies Centre.
