Geospatial modelling approach of water surface area fluctuations and sustainable strategies of Lake Chad Central Africa

Alhaji Hussaini, Kelvin Kang Wee Tang, Sulaiman Ibrahim Musa, Kabiru Shehu, Mohammed Adamu Dogon-Yoro

Abstract


Most of the Lake Chad areas have been affected by climatic and environmental changes. This has been worsened by land cover variations, thereby increasing the rate of their shrinkage. The Chad basin had supported business, economics and bourgeoning monetary task for around 30 million people in the area and it has shrunk to more than 20% of its original size and have devastated local economies, destroying livelihoods in farming, fishing and herding, leading to widespread poverty, food insecurity and displacement, as mentioned earlier millions of people depends on its resources. This research aims to model and investigate the effect of environmental variables which is very important that influences the surface and ground water, the variable include geological features of water surface area fluctuations using geospatial modelling approach in Lake Chad, Central Africa. In this study therefore, numerous software bundles were used at various processing stages. These include IDRISI selva, ENVI 5.1 and ESRI ArcGIS. Accordingly, ESRI ArcGIS, ENVI and IDRISI software were utilized for production of suitability maps/modelling, image classification and accuracy assessment, image correction and assessment of the outcomes. Meanwhile, ArcGIS also was utilized for geographic database, map production and presentation, on the other hand IDRISI software was used for CA-Markov chain analysis and modelling as well as forecasting of water surface area fluctuations. The changes to Lake Chad were analysed between the years 1985, 2000 and 2015 which uncovered a rapid shrinkage of water level in the study area. The expected outcomes revealed that the main accuracies of image classification of Landsat-TM was 93.80, Landsat-ETM+ was 90.80 and Landsat-OLI was 86.20 respectively. Furthermore, different information from different data sources with regards to geological variables of the study area were considered so as in having an updated map and the map layers were obtained to revealed diverse levels of geographical features details at various scales to facilitate uses of the map. Th Cellular Automata model was used to predict the year 2030 land change of the area using some designed constraints populations and geological features. Therefore, on validation, the overall model accuracies of 99.15% was achieved. Moreover, to analyse the gains and lost, land cover conversion results were also analysed within the year 2015-2030 and was attained by the aid of cross-tabulation analysis; with barren land having net loss of 695.57km2 (6.69%), farmland has net gained of 586.63km2 (1.1%), gallery forest has net gained of 365.71km2 (3.61%), shrub has net loss of 146.41km2 (2.6%) and water body has net loss of 110.36km2 (3.93%). These findings highlight the urgent need for sustainable water resource management, as Lake Chad’s decline threatens ecosystems, agriculture and water supply for surrounding communities. This study provides a geospatial framework for monitoring lake dynamics and informing policy interventions.

 

Keywords: Geospatial modelling, sustainable strategies, water surface fluctuations


Keywords


Geospatial modelling, sustainable strategies, water surface fluctuations

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References


Achmad, A., Irwansyah, M., & Ramli, I. (2018). Prediction of future urban growth using CA-Markov for urban sustainability planning of Banda Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 012166.

Afrakhteh, R., Asgarian, A., Sakieh, Y., & Soffianian, A. (2016). Evaluating the strategy of integrated urban-rural planning system and analyzing its effects on land surface temperature in a rapidly developing region. Habitat International, 56, 147-156.

Al-sharif, A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291-4301.

Al Rawashdeh, S., Ruzouq, R., Pradhan, B., Ziad, S. A.-H., & Ghayda, A. R. (2013). Monitoring of Dead Sea water surface variation using multi-temporal satellite data and GIS. Arabian Journal of Geosciences, 6(9), 3241-3248.

Alimi, T. O., Fuller, D. O., Herrera, S. V., Arevalo-Herrera, M., Quinones, M. L., Stoler, J. B., & Beier, J. C. (2016). A multi-criteria decision analysis approach to assessing malaria risk in northern South America. BMC Public Health, 16(1), 221.

Altunkaynak, A. (2014). Predicting water level fluctuations in Lake Michigan-Huron using wavelet-expert system methods. Water Resources Management, 28(8), 2293-2314.

Ariffin, N. A., Ibrahim, W. M. M. W., Rainis, R., Samat, N., Nasir, M. I. M., Rashid, S. M. R. A., & Zakaria, Y. S. (2024). Identification of trends, direction of distribution and spatial pattern of tuberculosis disease (2015-2017) in Penang. Geografia-Malaysian Journal of Society and Space, 20(1), 68-84.

Arsanjani, T. J., Javidan, R., Nazemosadat, M. J., Arsanjani, J. J., & Vaz, E. (2015). Spatiotemporal monitoring of Bakhtegan Lake's areal fluctuations and an exploration of its future status by applying a cellular automata model. Computers & Geosciences, 78, 37-43.

Avila-Aceves, E., Plata-Rocha, W., Monjardin-Armenta, S. A., & Rangel-Peraza, J. G. (2023). Geospatial modelling of floods: A literature review. Stochastic Environmental Research and Risk Assessment, 37(11), 4109-4128.

Babamaaji, R. A., & Lee, J. (2014). Land use/land cover classification of the vicinity of Lake Chad using NigeriaSat-1 and Landsat data. Environmental Earth Sciences, 71(10), 4309-4317.

Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16.

Bruno, D., Barca, E., Goncalves, R., Lay-Ekuakille, A., Maggi, S. and Passarella, G. (2016, January 6-11). Evolutionary polynomial regression model for the prediction of coastal dynamics. Proceedings of the 6th IMEKO TC19 Symposium on Environmental Instrumentation and Measurements.

Islam, M. S., & Crawford, T. W. (2022). Assessment of spatio-temporal empirical forecasting performance of future shoreline positions. Remote Sensing, 14(24), 6364.

Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.

Eastman, J. (2012). IDRISI Selva: Guide to GIS and Image Processing. Clark Labratories, Clark University.

Edwin, I. E., Chukwuka, O., Ochege, F. U., Ling, Q., Chen, B., Nzabarinda, V., & Luo, G. (2024). Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia. Journal of Environmental Management, 361, 121218.

El-Hallaq, M. A., & Habboub, M. O. (2015). Using cellular automata-Markov analysis and multi criteria evaluation for predicting the shape of the Dead Sea. Advances in Remote Sensing, 4(01), 83.

Elhaja, M. E., Csaplovics, E., Abdelkareem, O. E., Adam, H. E., Awad El Karim, S., Ibrahim, K. A., & Eltahir, M. E. (2017). Land use land cover changes detection in White Nile State, Sudan using remote sensing and GIS techniques. International Journal of Environmental Monitoring and Protection, 4(3), 14-19.

Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.

Gao, H., Bohn, T., Podest, E., McDonald, K., & Lettenmaier, D. (2011). On the causes of the shrinking of Lake Chad. Environmental Research Letters, 6(3), 034021.

Gong, W., Yuan, L., Fan, W., & Stott, P. (2015). Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata—Markov modelling. International Journal of Applied Earth Observation and Geoinformation, 34, 207-216.

Hyandye, C., & Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International Journal of Remote Sensing. 38(1), 64-81.

Hussaini, A., Mahmud, M. R., & Tang, K. K. W. (2020). Change detection for the past three decades using geospatial approach in Lake Chad, Central Africa. IOP Conference Series: Earth and Environmental Science, 540(1), 012001.

Jayabaskaran, M., & Das, B. (2023). Land Use Land Cover (LULC) dynamics by CA-ANN and CA-Markov model approaches: A case study of Ranipet Town, India. Nature Environment & Pollution Technology, 22(3).

Kamta, F. N., Schilling, J., & Scheffran, J. (2021). Water Resources, Forced Migration and Tensions with Host Communities in the Nigerian Part of the Lake Chad Basin. Resources, 10(4), 27.

Li, B., Yang, G., Wan, R., Dai, X., & Zhang, Y. (2016). Comparison of random forests and other statistical methods for the prediction of lake water level: A case study of the Poyang Lake in China. Hydrology Research. nh2016264.

Mahamat, A. A. A., Al-Hurban, A., & Saied, N. (2021). Change detection of Lake Chad water surface area using remote sensing and satellite imagery. Journal of Geographic Information System, 13(5), 561-577.

Mishra, V. N., Rai, P. K., Prasad, R., Punia, M., & Nistor, M.-M. (2018). Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models. Applied Geomatics, 10(3), 257-276.

Modibbo, U. M., Heman, E. D., & Hafisu, R. (2019). Multi-criteria decision analysis for malaria control strategies using analytic hierarchy process: A case of Yola North Local Government Area, Adamawa State Nigeria. Amity Journal of Computational Sciences, 3(2), 43-50.

Okpara, U. T., Stringer, L. C., Dougill, A. J., & Bila, M. D. (2015). Conflicts about water in Lake Chad: Are environmental, vulnerability and security issues linked? Progress in Development Studies, 15(4), 308-325.

Ovie, S., Emma, B., De Young, C., Sheridan, S., Davies, S., Hjort, A., Fisheries, F., Aquaculture Department, R. P., & Division, E. (2012). Identification and reduction of climate change vulnerability in the fisheries of the Lake Chad Basin. FAO, Rome (Italy).

Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.

Said, M., Hyandye, C., Komakech, H. C., Mjemah, I. C., & Munishi, L. K. (2021). Predicting land use/cover changes and its association to agricultural production on the slopes of Mount Kilimanjaro, Tanzania. Annals of GIS, 27(2), 189-209.

Sangrat, W., Thanapongtharm, W., & Poolkhet, C. (2020). Identification of risk areas for foot and mouth disease in Thailand using a geographic information system-based multi-criteria decision analysis. Preventive Veterinary Medicine, 105183.

Sohl, T., Dornbierer, J., Wika, S., & Robison, C. (2019). Remote sensing as the foundation for high-resolution United States landscape projections–The Land Change Monitoring, assessment, and projection (LCMAP) initiative. Environmental Modelling & Software, 120, 104495.

Su, G., Tedesco, P. A., Toussaint, A., Villéger, S., & Brosse, S. (2022). Contemporary environment and historical legacy explain functional diversity of freshwater fishes in the world rivers. Global Ecology and Biogeography, 31(4), 700-713.

Tang, K. K. W., & Mahmud, M. R. (2021). The Accuracy of Satellite Derived Bathymetry in Coastal and Shallow Water Zone. International Journal of Built Environment and Sustainability, 8(3), 1-8.

Thodi, M. F. C., Gopinath, G., Surendran, U., Prem, P., Al‐Ansari, N., & Mattar, M. A. (2023). Using RS and GIS Techniques to Assess and Monitor Coastal Changes of Coastal Islands in the Marine Environment of a Humid Tropical Region. Water, 15(21), 3819.

Wang, R., & Murayama, Y. (2017). Change of Land Use/cover in Tianjin city based on the Markov and cellular automata models. ISPRS International Journal of Geo-Information, 6(5), 150.

Yuan, Y., Zeng, G., Liang, J., Huang, L., Hua, S., Li, F., Zhu, Y., Wu, H., Liu, J., & He, X. (2015). Variation of water level in Dongting Lake over a 50-year period: Implications for the impacts of anthropogenic and climatic factors. Journal of Hydrology, 525, 450-456.


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