Land use/land cover and climate change interaction in the derived savannah region of Nigeria

Akintuyi, Akinlabi; Fasona, Mayowa; Ayeni, A.O.; Soneye, Alabi

doi:10.1007/s10661-021-09642-6

Cited by 11 publications

(5 citation statements)

References 63 publications

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“…This aligns with other studies, mainly in developing countries(Bongasie et al, 2024;Khwarahm et al, 2021), highlighting the role of urbanization and population growth in in uencing LULC dynamics. Terrain slope emerges as another key factor, especially in transitions between agriculture, grassland, and forest categories, con rming the signi cant role of physical landscape in determining LULCC patterns, as supported by other studies(Akintuyi et al, 2021;Mandal et al, 2023).…”

supporting

confidence: 72%

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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supporting

confidence: 72%

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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“…This agrees with Heider et al (2018) andHernández-Flores et al (2017) who reported among the main urban driving forces distance to urban areas, distance to roads and population growth rate. But they did not agree with the findings of Akintuyi et al (2021), Fathizad et al (2019 and Subiyanto et al (2019), perhaps because they analysed other land-use changes in addition to urbanisation in countries with very different conditions from the study area.…”

Section: Discussionmentioning

confidence: 57%

“…It ranges from 0 (no association) to 1 (perfect association). Values close to 0.4 show an association between variables, and values less than 0.15 indicate a low association (Akintuyi et al 2021;Fathizad et al 2019;Subiyanto et al 2019). The independent factors used were elevation, distance to previous urban land, distance to roads, population density, education years, occupants per dwelling, percentage of the dependent population, percentage of indigenous households and land use.…”

Section: Driving Socio-economic Factorsmentioning

confidence: 99%

Urban growth in peri-urban, rural and urban areas: Mexico City

Cruz-Bello

Galeana-Pizaña²,

Arellano

2023

Buildings and Cities

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This remote sensing study compares growth occurring in three urban types between 2005 and 2014: peri-urban, rural and urban, in a fast-growing metropolitan region west of Mexico City. Future urban growth for the period 2014-24 is modelled using the land-use/cover change (LUCC) model Geomod. Urban expansion is correlated with some socio-territorial factors and the impacts are assessed for the loss of biomass. In both periods, the urban zone differed the most from the other two in terms of urban expansion. The Geomod predictions overestimate the urban expansion in the urban zone and underestimate it in the peri-urban and rural zones. Significant differences exist in the average urban expansion between zones. The main urban growth drivers were elevation, population density, distance to previous urban land and distance to roads. A substantial loss of biomass is due to urban growth, including expansion and infill. The research reveals significant differences in growth between peri-urban, rural and urban areas, and contributes spatial information for designing focused land-use policies in dynamic urban contexts. POLICY RELEVANCEThis article contributes to understanding the differentiated urban growth of urban, periurban and rural areas, which can translate into more precise and effective public policies. Urban expansion and infill patterns differ. For peri-urban and rural areas, the main growth is infill, so actions should be implemented to sustainably manage the vacant or undeveloped land within an existing human settlement to prevent further expansion, but also to avoid the loss of priority areas for the provision of ecosystem services. In urban areas where the main urban increment is expansion, sensible consolidation decisions need to be taken to avoid further urban expansion and the incorporation of urban green space.*Author affiliations can be found in the back matter of this article 2 Cruz-Bello et al.

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“…Specifically, the decrease in precipitation and rise in temperatures results in changed balance of the saline-fresh water with regard to hydrology, and variations in the length of periods of dry and wet seasons with regard to soil-landscapes systems. As a consequence, this results in land cover change and deforestation [50,51]. Furthermore, environmental processes such as droughts, soil erosion, and the invasion of external species also affect the health of mangroves ecosystems [52].…”

Section: Study Focusmentioning

confidence: 99%

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

Lemenkova

Debeir

2023

JMSE

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This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.

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Land use/land cover and climate change interaction in the derived savannah region of Nigeria

Cited by 11 publications

References 63 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.

Urban growth in peri-urban, rural and urban areas: Mexico City

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

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