Land Use and Land Cover Change Detection in Rural Areas of River Lwiro Micro-catchment, Lake Kivu, Democratic Republic of Congo

Bagalwa, Mashimango; Majaliwa, Jackson-Gilbert Mwanjalolo; Kansiime, Frank; Bashwira, S; Tenywa, Moses; Karume, Katcho; Adipala, E.

doi:10.9734/jsrr/2016/15850

Cited by 7 publications

(7 citation statements)

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“…Additionally, the region witnessed major population movements in the aftermath of the 1994 genocide against Tutsis in Rwanda. This includes the return to Rwanda of people who had ed during the war and genocide periods from 1959 and the in ux of refugees due to the civil wars in the Democratic Republic of Congo in the late 1990s and early 2000s (Bagalwa et al, 2016;Mugiraneza et al, 2019). These pressures led to an extensive conversion of forested land for settlement and agricultural expansion, to meet the growing demands for food and energy (Munanura et al, 2018;Rukundo et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In particular, the Lake Kivu (LKV) catchment, located between Rwanda and Democratic Republic of Congo, has undergone extensive LULCC in recent decades. This is partly attributed to the con icts and civil wars that happened in the region in the 1990s (Bagalwa et al, 2016;Plumptre, 2003). These changes contributed signi cantly to landscape's destruction and changes, mainly deforestation, land degradation, and uncontrolled development activities.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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 seasons are wet (September-May) and dry (June-August). Annual rainfall is about 1500 mm in the basin [45]. Rainfall and temperature variations in Lwiro station are presented in the Figure 2.…”

Section: Description Of Study Area and Sampling Stationsmentioning

confidence: 98%

“…The same authors reported that for Lake Victoria basin, vehicle emissions due to burning of fossil fuel were dominant. Lwiro and Iko are rural areas, characterized by agricultural land with high concentrations of phosphorus in soil [45] and high animal keeping by free range grazing which may contribute to increased atmospheric nutrients in the wet deposition. Mahowald et al [53] reported that atmospheric phosphorus exists almost entirely in the form of aerosols due to the low volatility of phosphorus compounds.…”

Section: Land Use/cover and Atmospheric Nutrient Depositionmentioning

confidence: 99%

Spatio-Temporal Variation of Atmospheric Nutrient Deposition in Different Land Uses/Covers around Lake Kivu

Bagalwa¹,

Majaliwa²,

Bootsma³

et al. 2021

JWARP

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Atmospheric deposition of nutrients, particularly nitrogen and phosphorus has the capacity to significantly affect the productivity and biogeochemistry of aquatic ecosystems. The objective of this study was to assess the impact of land use/cover types on the atmospheric deposition of nutrients around Lake Kivu. Dry and wet atmospheric deposition samples were collected from four different land use/cover types (forest, wetland, agricultural and urbanized area) at four stations (Goma, Lwiro, Bukavu and Iko) around the Lake Kivu basin. The highest annual loading of dry total phosphorus (TP) was recorded at a station located in an urban area at Goma (4.4 ± 3.9 µmol/m 2 /yr) and the highest dry deposition of total nitrogen (TN) was recorded at Iko (84.5 ± 41.2 µmol/m 2 /yr). High wet TP and TN were at Bukavu (0.7 ± 1.1 µmol/m 2 /yr) and Iko (21.7 ± 34.7 µmol/m 2 /yr) respectively. High dry TP loads were recorded in the forest area of Goma and the highest dry TN at Lwiro. High wet TP loads were record in agriculture at Goma and high values of wet TN in agriculture at Iko. Phosphorus and nitrogen deposition rates around Lake Kivu were similar to those reported for other African lakes. The highest rate of TP was recorded mainly in the forest area for dry deposition while high TN was recorded in all types of land use/cover in the basin. This study found out that forest traps high concentration of nutrient than other land uses/covers.

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“…This means that many of the sampled stations tended to be alkaline most probably due to underground saltwater intrusion. The High pH values witnessed could imply alkali prevalence that can be explained by the fact that huge human population is concentrated along the coastline hence intensified socio-economic activities affecting the pH of the estuarine systems [42,59,60]. High PH values could also be as a result of surface run offs originating from mature landfills.…”

Section: Water Quality Parameters In Estuarine Ecosystemsmentioning

confidence: 99%

Trends in Water Quality in a Tropical Kenyan River-estuary System: Responses to Anthropogenic Activities

Nyakeya,

Onchieku,

Masese

et al. 2024

AJOB

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Aims: To determine the spatial variation in physico-chemical water quality attributes in estuarine ecosystems of South Coast Kenya to inform its management. Study Design: We employed diagnostic research design where such factors as anthropogenic activities, hydrology, and accessibility were considered in choosing 12 sampling stations. A mixed sampling design (probability and non-probability) was used to sample. Place and Duration of Study: Four discharging rivers into estuarine system with a reference point were identified. 12 sampling points were earmarked and sampled for 12 months. Methodology: Nine selected water quality parameters were collected in triplicates monthly for 12 months. Temperature, dissolved oxygen, pH, conductivity, salinity and TDS were collected in situ using YSI Multiparameter meter (Professional plus) while nutrients were analyzed in the laboratory calorimetrically. Data was tested for normality and homogeneity of variances using Levene's and Shapiro-Wilk tests. Two-way analysis of variance (ANOVA), and Principal Component Analysis (PCA) were done using the 64-bit R Software version 4.3.0 to test for significant differences and correlate the parameters among the sampling sites respectively. Observed differences were considered statistically significant at P < 0.05. Results: The measured environmental variables differed significantly among the sampling stations (P < 0.05). temperature and salinity exhibited moderate to strong effect on water quality, while pH and nutrients (phosphates, nitrates, and ammonia) exhibited mixed effects with both negative and positive loadings on the Principal Components. It is demonstrated that the south coast estuary, Kenya is affected by two gradients: mineralization from the sea and the organic matter gradient at the upstream stations dominated by the fluvial inflows from the respective rivers. Conclusion: The south coast of Kenya estuary is impacted by natural processes and anthropogenic activities. we recommend for continued regular water quality assessment and monitoring to acquire sufficient data to shape policy frameworks towards its sustainable management.

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Land Use and Land Cover Change Detection in Rural Areas of River Lwiro Micro-catchment, Lake Kivu, Democratic Republic of Congo

Cited by 7 publications

References 0 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.

Spatio-Temporal Variation of Atmospheric Nutrient Deposition in Different Land Uses/Covers around Lake Kivu

Trends in Water Quality in a Tropical Kenyan River-estuary System: Responses to Anthropogenic Activities

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