尼日利亚东北部博尔诺大都会环境因子与洪水发生的地理空间评价（1987-2024）

IF 2.9 4区环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES

Environmental Monitoring and Assessment Pub Date : 2025-04-30 DOI:10.1007/s10661-025-14050-1

Sadegh Mokhtarisabet, Akus Kingsley Okoduwa

{"title":"尼日利亚东北部博尔诺大都会环境因子与洪水发生的地理空间评价（1987-2024）","authors":"Sadegh Mokhtarisabet, Akus Kingsley Okoduwa","doi":"10.1007/s10661-025-14050-1","DOIUrl":null,"url":null,"abstract":"<div>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),\\) 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.</div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 5","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geospatial assessment of environmental factors and flooding occurrences in Borno Metropolis, Northeastern Nigeria (1987–2024)\",\"authors\":\"Sadegh Mokhtarisabet, Akus Kingsley Okoduwa\",\"doi\":\"10.1007/s10661-025-14050-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>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),\\\\) 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.</div>\",\"PeriodicalId\":544,\"journal\":{\"name\":\"Environmental Monitoring and Assessment\",\"volume\":\"197 5\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Monitoring and Assessment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10661-025-14050-1\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Monitoring and Assessment","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10661-025-14050-1","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

在人类和自然过程的驱动下，气候变化增加了洪水的频率，影响了基础设施和资源。本研究探讨了尼日利亚博尔诺大都会在2024年洪水期间土地利用/土地覆盖（LULC）变化、降雨模式和洪水之间的关系。利用谷歌Earth Engine （GEE）对1987 ～ 1990年、2013 ～ 2014年和2024年的Landsat影像进行分析，计算土壤调整植被指数（SAVI）、归一化差水指数（NDWI）和归一化差建筑指数（NDBI）等环境指数。Sentinel-1合成孔径雷达（SAR）图像识别了2024年的洪水灾区。利用Mann-Kendall和Sen 's斜率试验分析了CHIRPS（1987-2024）的降雨数据。基于规则的分类确定了环境变化，并应用Pearson、Spearman、Kendall和点双序列等统计检验来评估气候和环境因素与洪水之间的关系。所有的分析都使用Python。调查结果显示，330平方公里(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 \({(r}_{pb}=0.15, p=<0.001),\) 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.

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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 \({(r}_{pb}=0.15, p=<0.001),\) 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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Environmental Monitoring and Assessment 环境科学-环境科学

CiteScore

4.70

自引率

6.70%

发文量

1000

审稿时长

7.3 months

期刊介绍： Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.