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Nkinda, et al.

                mixed–use  landscapes.  Classification  accuracy  was   land use policies are essential. Developing scenario-
                affected  by  cloud  cover,  atmospheric  conditions,  and   based spatial models that account for multiple variables
                seasonal  variability,  despite  efforts  to  minimize  these   will enhance
                through  image  preprocessing  and  ground–truthing.
                Misclassifications  were  particularly  common  between   Acknowledgments
                spectrally  similar  classes, such as sparse vegetation
                and shrubland, cropland and barren land, or plantation   We are grateful to Joshua  Saitoti, Lusekelo Silabi,
                forests  and  orchards.  Ground-truthing  was also   Geofrey Mgini, and local communities living adjacent
                constrained by limited spatial coverage and access to   to  the  Ngerengere  River  whose  efforts  made  this
                remote areas, which may have introduced sampling bias.   research successful.  We thank the Mbeya University
                In addition,  the  study focused mainly  on biophysical   of  Science  and  Technology  in  Tanzania  of  providing
                drivers of change and did not fully incorporate complex   in-kind support, including working space and internet
                socio-economic,  institutional,  or political  factors that   access to facilitate this research. Moreover, we would
                influence land use decisions. The 2034 projections were   like to thank all of our friends who helped us during the
                based on historical  trends and assumptions, without   design and writing of this article by offering insightful
                accounting  for future uncertainties  such as policy   and practical  suggestions.  We also thank them  for
                shifts, technological developments, or climate change   taking the time to polish the manuscript and provide an
                impacts – which, although recognized, were treated as   incisive critique that greatly improved the work.
                a constant rather than modeled directly. Finally, the use
                of secondary data for historical  validation  may have   Funding
                introduced inconsistencies or reduced the precision of
                results.                                            None.

                6. Conclusion                                       Conflict of interest

                Overall, the study revealed that between 2004 and 2024,   The authors declare they have no competing interests.
                extensive areas of forested land within the Ngerengere
                River catchment were cleared  and converted into    Author contributions
                croplands for cultivating crops such as bananas, cassava,
                and  maize.  This  land  conversion  reflects  growing   Conceptualization: Rose Nkinda, Fredrick Ojija,
                pressure from subsistence  farming  and population      Gisandu Malunguja
                expansion.  The  observed  increase  in  shrubland  and   Formal analysis: All authors
                sparse vegetation,  alongside  changes in bare land   Investigation: Rose Nkinda
                and built-up areas, indicates ongoing ecological    Methodology: All authors
                degradation,  habitat  fragmentation,  and shifting  land   Supervision: Fredrick Ojija, Giovanni Bacaro, Gisandu
                use patterns.  The application  of GEE enabled high-    Malunguja
                resolution spatial and temporal analysis, making it an   Writing – original draft: Rose Nkinda, Fredrick Ojija
                effective tool for monitoring and projecting land cover   Writing – review & editing: All authors
                changes. These findings highlight the urgent need for
                sustainable land management, reforestation efforts, and   Availability of data
                active  community engagement to restore ecological
                integrity  and support long-term environmental  and   Data are available from the corresponding author upon
                socioeconomic  resilience.  Despite  these  valuable   reasonable request.
                insights, the  study had several  shortcomings,  such as
                limited detection of fine-scale changes due to the use   References
                of  medium-resolution  imagery  and  under-exploration
                of socioeconomic and institutional drivers. Therefore,   1.  Abera W, Tamene L, Kassawmar T, et al. Impacts of land
                future research should utilize  high-resolution satellite   use and land cover dynamics on ecosystem services in
                data, integrate socioeconomic and policy analysis, and   the Yayo coffee forest biosphere reserve, southwestern
                incorporate climate change modeling. In addition, long-  Ethiopia. Ecosyst Serv. 2021;50:101338.
                term hydrological monitoring and evaluation of current      doi: 10.1016/j.ecoser.2021.101338



                Volume 22 Issue 5 (2025)                       124                           doi: 10.36922/AJWEP025180137
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