Prediction of flood occurrences and magnitude in Hadejia-Jama’are river basin, Nigeria

Babati, Abu-Hanifa; Abdussalam, Auwal F.; Baba, Saadatu Umaru; Isa, Zaharaddeen

doi:10.1007/s40899-022-00781-3

Cited by 3 publications

(2 citation statements)

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“…This may influence the soil water balance. National Soil Survey Center (NSSC, 2008) attributed the salinization processes to involve hydrological processes, changing climate, irrigation, drainage, plant cover and to large extent farming practices (A.-H. Babati et al, 2022). Food and Agriculture Organization (FAO, 2013) stressed that soil salinization is one among many environmental issues in many areas of the world.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is imperative to continuously monitor agricultural lands with the sole purpose of identifying saline agricultural lands for the sustenance of soil fertility, (Baba et al, 2022) reduction in soil degradation and resultant high crop production (Zewdu et al, 2017). Geographic information systems (GIS) and remote sensing techniques (RS) have become tools for identifying and classifying saline soils (Zurqani et al, 2012) and are very useful for monitoring and controlling salinity problems, especially in arid and semi-arid environments with sparse vegetation cover (Babati et al, 2021;Bashariya et al, 2022;Zaharaddeen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Variations of Soil Salinity in Gusau Local Government Area, Zamfara State, Nigeria

Musa¹,

Atiyong²,

Isa³

et al. 2022

FJS

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This paper assessed the spatio-temporal variations of soil salinity in Gusau Local Government Area of Zamfara State. Modis and Landsat images of three epochs were used to examine the effective area from 2000 to 2020.The study employed an index-based approach of RS and GIS to monitor the variations and changes in salinity. NDVI from Modis data with a threshold of -1 to 1 was used to measure salinity based on crop response to various degrees of salinity, while NDSI was used to measure salinity directly from the surface salt reflectance using Landsat images. The NDVI was classified into three classes; bare surfaces from (-0.46 to 0.2), sparse vegetation (0.2 to 0.5) and dense vegetation (0.5 to 0.7). The result revealed that shows three salinity classes low (0.3 to 0.5), moderate (0.5 to 0.7) and high salinity (0.7 to 1). In addition, it showed a positive trend (an increase of 0.0013) in the vegetation of the study period, while that of the salinity has a negative trend (decrease of -0.0014) over the study period. The result revealed that the area with a bare surface has high salinity compared to the other classes in the study area. The study recommends the enhancements of models in the prediction and monitoring of further salinity implications.

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Section: Introductionmentioning

confidence: 99%