Browsing by Author "Twisa, Sekela"

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Analysis of land use and land-cover pattern to monitor dynamics of Ngorongoro world heritage site (Tanzania) using hybrid cellular automata-Markov model
(Elsevier, 2022) Mwabumba, Mohamed; Yadav, Brijesh; Rwiza, Mwemezi; Larbi, Isaac; Twisa, Sekela
Assessment of land-use and land-cover (LULC) change of any region is one of the prominent features used in environmental resource management and its overall sustainable development. This study analyzed the LULC changes of Ngorongoro Conservation Area (NCA) and its surroundings using Remote Sensing and Geographical Information System integrated with Cellular Automata-Markov model. The LULC maps for the years 1995, 2005, and 2016 were classified using unsupervised and supervised classification procedure, and projected for 2025 and 2035 under business-as-usual scenario using the CA–Markov model. The results indicated maximum gains and losses in cultivated land and woodland in the study duration, respectively. The projected LULC for the period 2025 to 2035 showed a reduction in bushland, forest, water, and woodland, but an intensification in cultivated land, grassland, bare land, and the built-up area. The natural forests with high environmental values were found to be continuously declining under the current land management trend, causing the loss in the NCA’s ecological values. For sustainable management, the authorities must reach conciliation between the existing LULC patterns change and ecosystem services monitoring. A rational land use plan must be made to control the increase of cultivated land and built-up area counting a rational land use plan and ecosystem services protection guidelines. Decision makers should involve stakeholder to support improved land use management practices for balanced and sustainable ecosystem services strategies.
Assessing Land Use/Cover dynamics of the Ngorongoro world heritage site in Tanzania using a hybrid CA–Markov model
(Authorea, 2020-10) Mwabumba, Mohamed; Yadav, BK; Rwiza, Mwemezi; Twisa, Sekela; Larbi, Isaac
In this study, land-use/cover pattern of the UNESCO world heritage site, Ngorongoro Conservation Area; is analyzed using the CA–Markov model with the help of RS and GIS. Hybrid classification techniques ware used to monitor land use/cover changes, using Landsat images for 1995, 2005 and 2016. The CA-Markov model is then used to predict the land use /cover maps for 2025 and 2035. The highest net gain from 1995-2016 observed in cultivated land (6.55%), grassland (2.68%), bare land (1.82%), bushland (0.48%) and built-up area (0.01%), and the net loss found in woodland (8.38%), forest (1.52%), wetland (1.41%), and water cover area (0.24%). However, reduction is expected in bushland (4.88%), forest (0.82%), water (0.77%) and woodland (0.07%) during 2025-2035 with increase in cultivated land (2.73%), grassland (1.19%), bare land (1.79%) and built-up area (0.14%). As per the current trend in land use management, forest cover is significantly declining; leading to the loss in the ecological values of the Ngorongoro Conservation Area and its surroundings. The results of this study can be used directly by the policymakers to plan appropriate conservation schemes to endorse improved land use management practices for ecological protection of the heritage site.
Future Trade-Off for Water Resource Allocation: The Role of Land Cover/Land Use Change
(MDPI, 2024-02-02) Sigalla, Onesmo; Twisa, Sekela; Chilagane, Nyemo; Mwabumba, Mohamed; Selemani, Juma; Valimba, Patrick
Global croplands, pastures, and human settlements Have expanded in recent decades. This is accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. In sub-Saharan Africa, policies are implemented without critical consideration; e.g., agricultural expansions impair ecosystem services. We studied land use/cover and the associated rate of change for four time epochs, i.e., 1991, 2001, 2011, and 2021. This employed remote sensing and GIS techniques for analysis, while future projections were modeled using cellular automata and the Markov chain. The kappa coefficient statistics were used to assess the accuracy of the final classified image, while reference images for accuracy assessment were developed based on ground truthing. Overall change between 1991 and 2021 showed that major percentage losses were experienced by water, forest, woodland, and wetland, which decreased by 8222 Ha (44.11%), 426,161 Ha (35.72%), 399,584 Ha (35.01%), and 105,186 Ha (34.82%), respectively. On the other Hand, a percentage increase during the same period was experienced in cultivated land, built-up areas, and grasslands, which increased by 659,346 Ha (205.28%), 11,894 Ha (159.93%), and 33,547 Ha (98.47%), respectively. However, this expansion of thirsty sectors Has not reversed the increasing amount of water discharged out of the Kilombero River catchment. We recommend the promotion of agroforests along with participatory law enforcement and capacity building of local communities’ institutions.