Climate change, driven by human and natural processes, has increased flood frequency, impacting infrastructure, and resources. This study explores the relationship between land use/land cover (LULC) changes, rainfall patterns, and floods in Borno Metropolis, Nigeria, during the 2024 floods. Using Google Earth Engine (GEE), Landsat images from 1987 to 1990, 2013 to 2014, and 2024 were analyzed to calculate environmental indices, including the soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). Sentinel-1 Synthetic Aperture Radar (SAR) images identified flood-affected areas in 2024. Rainfall data from CHIRPS (1987–2024) were analyzed using Mann–Kendall and Sen’s slope tests. Rule-based classification identified environmental changes, and statistical tests such as Pearson, Spearman, Kendall, and point–biserial were applied to assess relationships between climatic and environmental factors and floods. Python was used for all analyses. The findings revealed that 330 km2 (12.6%) of the total area experienced flooding in 2024. Vegetation cover decreased by 16.1 km2 (0.61%) in 2024 compared to 1987–1990, and non-vegetated areas increased significantly, reaching 19.5 km2 in 2024. Built-up/bareland areas expanded by 59.4 km2 (2.39%) from 2013–2014 to 2024. Spearman analysis effectively highlighted non-linear relationships between indices and floods. Point–biserial tests confirmed correlations between rainfall and flooding <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10661_2025_14050_Article_IEq1.gif" Format="GIF" Height="22" Rendition="HTML" Resolution="72" Type="Linedraw" Width="176" /> </InlineMediaObject> <EquationSource Format="TEX">${(r}_{pb}=0.15, p=<0.001),$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mrow> <mo stretchy="false">(</mo> <mi>r</mi> </mrow> <mrow> <mi mathvariant="italic">pb</mi> </mrow> </msub> <mrow> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <mi>p</mi> <mo>=</mo> <mo><</mo> <mn>0.001</mn> <mo stretchy="false">)</mo> <mo>,</mo> </mrow> </mrow> </math></EquationSource> </InlineEquation> indicating that higher rainfall levels increase flood likelihood. The heavy rainfall of 863 mm in 2024 was a key factor in increasing runoff and intensifying floods. This study highlights critical flood-affected areas, providing valuable insights for flood management planning to help governments and local communities reduce risks.

Geospatial assessment of environmental factors and flooding occurrences in Borno Metropolis, Northeastern Nigeria (1987–2024)

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发表日期：

2025年4月30日

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Sadegh Mokhtarisabet,
Akus Kingsley Okoduwa

摘要

Climate change, driven by human and natural processes, has increased flood frequency, impacting infrastructure, and resources. This study explores the relationship between land use/land cover (LULC) changes, rainfall patterns, and floods in Borno Metropolis, Nigeria, during the 2024 floods. Using Google Earth Engine (GEE), Landsat images from 1987 to 1990, 2013 to 2014, and 2024 were analyzed to calculate environmental indices, including the soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). Sentinel-1 Synthetic Aperture Radar (SAR) images identified flood-affected areas in 2024. Rainfall data from CHIRPS (1987–2024) were analyzed using Mann–Kendall and Sen’s slope tests. Rule-based classification identified environmental changes, and statistical tests such as Pearson, Spearman, Kendall, and point–biserial were applied to assess relationships between climatic and environmental factors and floods. Python was used for all analyses. The findings revealed that 330 km² (12.6%) of the total area experienced flooding in 2024. Vegetation cover decreased by 16.1 km² (0.61%) in 2024 compared to 1987–1990, and non-vegetated areas increased significantly, reaching 19.5 km² in 2024. Built-up/bareland areas expanded by 59.4 km² (2.39%) from 2013–2014 to 2024. Spearman analysis effectively highlighted non-linear relationships between indices and floods. Point–biserial tests confirmed correlations between rainfall and flooding ${(r}_{pb}=0.15, p=<0.001),$ ${(r}_{pb} = 0.15, p = < 0.001),$ indicating that higher rainfall levels increase flood likelihood. The heavy rainfall of 863 mm in 2024 was a key factor in increasing runoff and intensifying floods. This study highlights critical flood-affected areas, providing valuable insights for flood management planning to help governments and local communities reduce risks.