Spatio-temporal assessment of mangrove cover in the Gambia using combined mangrove recognition index

Main Article Content

Abstract

This study is an effort to assess mangrove cover in The Gambia utilizing Landsat Data. The gradual land use and land cover variations along the coastal estuarine area of The Gambia provoked this paper to assess variation on the mangrove forest cover. The aim of this research is to do spatiotemporal monitoring and mapping to detect changes in mangrove forest cover of the Gambia from 2000 to 2020. For analyzing change, the technique of supervised classification with CMRI was applied on three multi-temporal Landsat images. Combined Mangrove Recognition Index (CMRI) approach is used in this research which is an index for distinction of mangrove forest from non-mangrove area. In this study, mangrove forest cover decline has been found to be approximately from 1811km2 to 853km2 in the last two decades. The main vegetation being deforested in Greater Banjul Area is mangroves. This impact has significantly increased the vulnerability of residents to flash floods after heavy rainfalls especially in towns like Serrekunda, Tallinding, Bundung, New Jeshwang and even the Capital City, Banjul. These findings can be significant for conservation authorities and research groups. The maps presented in this analysis will be a valuable guide and provide soft grounds for coastline regulatory body to formulate better sustainable development strategies for this region. Alongside, it a matter of global concern because its deforestation has not only threatened the mangrove ecosystem but also increased vulnerability of the nearby communities and fish breeding grounds. The results of the study will help environmental protection agency, agriculture and forest departments to design location specific strategies to reduce mangrove cover degradation.

Article Details

How to Cite
Bayo, B., Habib, W., & Mahmood, S. (2022). Spatio-temporal assessment of mangrove cover in the Gambia using combined mangrove recognition index. Advanced Remote Sensing, 2(2), 74–84. Retrieved from https://publish.mersin.edu.tr/index.php/arsej/article/view/685
Section
Articles

References

Elmahdy, S. I., Ali, T. A., Mohamed, M. M., Howari, F. M., Abouleish, M., & Simonet, D. (2020). Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the Northern Emirates, UAE using random forest, Kernel logistic regression and Naive Bayes Tree models. Frontiers in Environmental Science, 8, 102.

Bryan-Brown, D. N., Connolly, R. M., Richards, D. R., Adame, F., Friess, D. A., & Brown, C. J. (2020). Global trends in mangrove forest fragmentation. Scientific reports, 10(1), 1-8.

Gao, J., Lundquist, C. J., & Schwendenmann, L. (2018). Characterizing landscape patterns in changing mangrove ecosystems at high latitudes using spatial metrics. Estuarine, Coastal and Shelf Science, 215, 1-10.

Dan, T. T., Chen, C. F., Chiang, S. H., & Ogawa, S. (2016). Mapping and change analysis in Mangrove Forest by using Landsat imagery. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3(8), 109-116

Basheer, M. A., El Kafrawy, S. B., & Mekawy, A. A. (2019). Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt. Egyptian Journal of Aquatic Biology and Fisheries, 23(1), 27-36.

Sari, S. P., & Rosalina, D. (2016). Mapping and monitoring of mangrove density changes on tin mining area. Procedia Environmental Sciences, 33, 436-442.

Lucas, R., Otero, V., Van De Kerchove, R., Lagomasino, D., Satyanarayana, B., Fatoyinbo, T., & Dahdouh‐Guebas, F. (2021). Monitoring Matang's Mangroves in Peninsular Malaysia through Earth observations: A globally relevant approach. Land Degradation & Development, 32(1), 354-373.

Jia, M., Wang, Z., Zhang, Y., Mao, D., & Wang, C. (2018). Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China. International journal of applied earth observation and geoinformation, 73, 535-545.

Hamilton, S. E., Castellanos-Galindo, G. A., Millones-Mayer, M., & Chen, M. (2018). Remote sensing of mangrove forests: Current techniques and existing databases. In Threats to Mangrove Forests (pp. 497-520). Springer, Cham.

Jayakumar, K. (2019). Managing mangrove forests using open source-based webgis. In Coastal management (pp. 301-321). Academic Press.

Andrieu, J., Cormier-Salem, M. C., Descroix, L., Sané, T., & Ndour, N. (2019). Correctly assessing forest change in a priority West African mangrove ecosystem: 1986–2010 An answer to Carney et al. (2014) paper “Assessing forest change in a priority West African mangrove ecosystem: 1986–2010”. Remote Sensing Applications: Society and Environment, 13, 337-347.

Wang, L., Jia, M., Yin, D., & Tian, J. (2019). A review of remote sensing for mangrove forests: 1956–2018. Remote Sensing of Environment, 231, 111223.

Saravanan, S., Jegankumar, R., Selvaraj, A., Jennifer, J. J., & Parthasarathy, K. S. S. (2019). Utility of landsat data for assessing mangrove degradation in Muthupet Lagoon, South India. In Coastal zone management (pp. 471-484). Elsevier.

Nguyen, L. D., Nguyen, C. T., Le, H. S., & Tran, B. Q. (2019). Mangrove mapping and above-ground biomass change detection using satellite images in coastal areas of Thai Binh Province, Vietnam. Forest and Society, 3(2), 248-261.

Zhang, X., Treitz, P. M., Chen, D., Quan, C., Shi, L., & Li, X. (2017). Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International journal of applied earth observation and geoinformation, 62, 201-214.

Navarro, J. A., Algeet, N., Fernández-Landa, A., Esteban, J., Rodríguez-Noriega, P., & Guillén-Climent, M. L. (2019). Integration of UAV, Sentinel-1, and Sentinel-2 data for mangrove plantation aboveground biomass monitoring in Senegal. Remote Sensing, 11(1), 77.

Pham, T. D., Yokoya, N., Bui, D. T., Yoshino, K., & Friess, D. A. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sensing, 11(3), 230.

West African Bird Study Association (2020). A Report on survey on the distribution, identification and suitable sites for mangrove enrichment and restoration in LRR and CRR. Mangrove survey report, Banjul, The Gambia.

Satyanarayana, B., Bhanderi, P., Debry, M., Maniatis, D., Foré, F., Badgie, D., ... & Dahdouh-Guebas, F. (2012). A socio-ecological assessment aiming at improved forest resource management and sustainable ecotourism development in the mangroves of Tanbi Wetland National Park, The Gambia, West Africa. Ambio, 41(5), 513-526.

Fent, A., Bardou, R., Carney, J., & Cavanaugh, K. (2019). Transborder political ecology of mangroves in Senegal and The Gambia. Global Environmental Change, 54, 214-226.

Rivera, J., Ceesay, A. A., & Sillah, A. (2020). Challenges to disaster risk management in The Gambia: A preliminary investigation of the disaster management system's structure. Progress in Disaster Science, 6, 100075.

Dampha, N. K. (2021). Change detection (1985-2020): Projections on land-use land cover, carbon storage, sequestration, and valuation in Southwestern Gambia. Sustainable Environment, 7(1), 1875556.

Solly, B., Jarju, A. M., Sonko, E., Yaffa, S., & Sawaneh, M. (2021). Detection of recent changes in Gambia vegetation cover using time series MODIS NDVI. Belgeo. Revue belge de géographie, (1).

https://www.glovis.usgs.gov

Estoque, R. C., Myint, S. W., Wang, C., Ishtiaque, A., Aung, T. T., Emerton, L., ... & Fan, C. (2018). Assessing environmental impacts and change in Myanmar's mangrove ecosystem service value due to deforestation (2000–2014). Global change biology, 24(11), 5391-5410.