Current status and strategic way forward for long-term management of Lake Kivu (East Africa)

Amisi, Muvundja Fabrice; Mulungula, Masilya Pascal; Kisse, Kisekelwa Tchalondawa; Muhigirwa, Balagizi Charles; Pasche, Natacha; Lwikitcha, Hyangya Béni; Eric, Mudakikwa Ruhanamirindi; Désiré, Akonkwa Balagizi; Déo, Nahayo; Migeni, Ajode Z.; Smith, Stephanie M.; Wüest, Alfred; Lawrence, T.S.

doi:10.1016/j.jglr.2022.04.004

Cited by 4 publications

(1 citation statement)

References 109 publications

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“…This trend coincides with the observed urban sprawl. Factors contributing to this expansion include rapid population growth, infrastructure development, and overall urbanization processes in these areas (Amisi et al, 2022). Additionally, there is a consistent decrease in the area covered by river, which can be attributed to measures taken for riverbank protection such as the planting of trees.…”

Section: Discussionmentioning

confidence: 99%

Land Use Land Cover Change in the African Great Lakes Region: A Spatial-Temporal Analysis and Future Predictions for the Lake Kivu catchment, Rwanda.

Kayitesi,

Guzha,

Tonini

et al. 2024

Preprint

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The African Great Lakes Region has experienced substantial Land Use Land Cover Change (LULCC) over the last decades. The main drivers of LULCC include an interplay of political, demographic, and socio-economic factors. This study focused on the Lake Kivu catchment in Rwanda, a critical ecosystem in the African Great Lakes Regions, exploring historical LULCC, their major drivers, and predicting future LULC for different development scenarios. The methodology involved image classification using seasonal composites and integrating spectral indices with topographic features to enhance the discrimination and capturing seasonal variations. The classification results demonstrated an overall accuracy and kappa exceeding 83%. Historical LULCC analysis showed significant changes, particularly the 1990–2000 decade, marked by forest loss ranging from 26.6–18.7% and an increase in agricultural land (from 27.7–43%). These changes were attributed to political conflicts in the region and population movements. Subsequent decades (2000–2010 and 2010–2020) witnessed forest recovery (24.8% by 2020). Artificial neural networks were used to predict future LULC scenarios, considering natural and socio-economic explanatory variables and historical LULC transitions. The analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain, in LULCC. River proximity drove agricultural and grassland expansion. The predicted future LULC for 2030 and 2050 indicate distinct trajectories likely to be influenced by demographic and socio-economic development trends. The findings of this study contribute to identifying opportunities for land restoration and conservation efforts, thereby ensuring the preservation of Lake Kivu catchment’s ecological integrity.

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Section: Discussionmentioning

confidence: 99%

Land Use Land Cover Change in the African Great Lakes Region: A Spatial-Temporal Analysis and Future Predictions for the Lake Kivu catchment, Rwanda.

Kayitesi,

Guzha,

Tonini

et al. 2024

Preprint

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Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions

Kayitesi,

Guzha,

Tonini

et al. 2024

Environ Monit Assess

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The African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990–2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda’s sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment’s ecological integrity, in alignment with national and global goals. Graphical Abstract

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Future success and ways forward for scientific approaches on the African Great Lakes

Lawrence,

Achieng,

Chavula

et al. 2023

Journal of Great Lakes Research

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Current status and strategic way forward for long-term management of Lake Kivu (East Africa)

Cited by 4 publications

References 109 publications

Land Use Land Cover Change in the African Great Lakes Region: A Spatial-Temporal Analysis and Future Predictions for the Lake Kivu catchment, Rwanda.

Land Use Land Cover Change in the African Great Lakes Region: A Spatial-Temporal Analysis and Future Predictions for the Lake Kivu catchment, Rwanda.

Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions

Future success and ways forward for scientific approaches on the African Great Lakes

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