Vulnerability Assessment of Soil Erosion Based on Topography and Vegetation Cover in a Developing City of Orlu L.G.A, South East Nigeria

Iwuji, M; Iroh, O; Njoku, John Didacus; Anyanwu, S; Amangabara, Gordon T.; Ukaegbu, K

doi:10.9734/ajee/2017/34143

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Agronomy 2021, 11, x FOR PEER REVIEW 2 of 13 and taking into account the canopy and understory vegetation, together with the Covered Cavity Fraction being very difficult to study with an accuracy of 10 m pixels. Therefore, different calculation methods have been used for this factor: vegetation cover [17], a radiometer [18] or from NDVI applied to other fields such as erosion in war zones [19].…”

Section: Study Areamentioning

confidence: 99%

“…For the calculation of erosion the most used method is the USLE (Universal Soil Loss Equation) or the RUSLE (Revised Universal Soil Loss Equation) with the disadvantage of using the latter for forest fires being that the dynamic calculation of the C factor is very complicated and very costly because it is necessary to revisit the study area continuously, and taking into account the canopy and understory vegetation, together with the Covered Agronomy 2021, 11, 1459 2 of 13 Cavity Fraction being very difficult to study with an accuracy of 10 m pixels. Therefore, different calculation methods have been used for this factor: vegetation cover [17], a radiometer [18] or from NDVI applied to other fields such as erosion in war zones [19].…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas

2021

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Soil erosion is one of the most important environmental problems of the moment, especially in areas affected by wildfires. In this paper, we study pre-fire and post-fire erosion using remote sensing techniques with Sentinel-2 satellite images and LiDAR. The Normalized Burn Ratio is used to determine the areas affected by the fire that occurred on 18 August 2016 in the Natural Reserve of Garganta de los Infiernos (Cáceres). To calculate the erosion, the multi-criteria analysis is carried out from the RUSLE. Once all calculations were performed, there was a considerable increase in sediment production from 16 June 2016 (pre-fire) with an erosion of 31 T/ha·year to 16 June 2017 of 74 T/ha·year for areas of moderate fire severity, and an increase from 11 T/ha·year in 2016 to 70 T/ha·year for areas with a very high severity. From the NDVI, it was possible to verify that this also affected the recovery of post-fire vegetation, decreasing the NDVI index 0.36 in areas of moderate severity and 0.53 in areas of very high severity.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas

2021

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Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing

Samimi

2019

Land

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Uyo is one of the fastest-growing cities in Nigeria. In recent years, there has been a widespread change in land use, yet to date, there is no thorough mapping of vegetation change across the area. This study focuses on land use change, urban development, and the driving forces behind natural vegetation loss in Uyo. Based on time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) image data, the relationships between urban land development and its influencing factors from 1985 to 2018 were analyzed using remote sensing (RS) and time series data. The results show eight land use cover classes. Three of these (forest, swamp vegetation, and mixed vegetation) are related to natural vegetation, and three (sparse built-up, dense built-up, and borrow pit) are direct consequences of urban infrastructure development changes to the landscape. Swamp vegetation, mixed vegetation, and forest are the most affected land use classes. Thus, the rapid growth of infrastructure and industrial centers and the rural and urban mobility of labor have resulted in an increased growth of built-up land. Additionally, the growth pattern of built-up land in Uyo corresponds with socioeconomic interviews conducted in the area. Land use changes in Uyo could be attributed to changes in economic structure, urbanization through infrastructure development, and population growth. Normalized difference vegetation index (NDVI) analysis shows a trend of decreasing vegetation in Uyo, which suggests that changes in economic structure represent a key driver of vegetation loss. Furthermore, the implementation of scientific and national policies by government agencies directed at reducing the effects of urbanization growth should be strengthened, in order to calm the disagreement between urban developers and environmental managers and promote sustainable land use.

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Urban Growth Monitoring and Prediction Using Remote Sensing Urban Monitoring Indices Approach and Integrating CA-Markov Model: A Case Study of Lagos City, Nigeria

Gilbert,

Shi

2023

Sustainability

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As one of the swiftly advancing megacities globally, Lagos faces significant challenges in managing its urban expansion. Mainly, this study focuses on monitoring and predicting urban growth using a comprehensive approach incorporating Global Land 30 (GL30), satellite-based nighttime light observations, and built-up and population density data. The application of remote sensing techniques, combined with utilizing the GL30 dataset, provides an effective means to monitor and predict urban growth trends and patterns. The major patterns occurred from 2000 to 2020, including increased cultivated land; reductions in grasslands, shrublands, and wetlands; and major urbanization. Predictive models indicate that urbanization will continue. Furthermore, employing the Cellular Automata (CA) Markov model in land-use and land-cover (LULC) change prediction. The findings revealed significant changes in LULC over the two decades. Particularly, the percentage of artificial terrain increased from 17.016% to 25.208%, and the area under cultivation increased significantly, rising from 46,771 km2 (1.238%) in 2000 to 75,283 km2 (1.993%) in 2020. Grasslands fell from 7.839% to 1.875%, while forest cover somewhat increased, climbing from 39.319% to 43.081%. Additionally, marshes fell from 9.788% to 5.646%, while shrublands decreased from 4.421% to 2.640%. Surprisingly, bare ground decreased sharply from 0.677% to 0.003%. To forecast future LULC changes, the study also used a Markov Chain Transition Matrix. According to the data, there is a 3.54% chance that agricultural land will become urban, converting it from being used for agriculture to urban development. On the other hand, just 1.05% of forested regions were likely to become municipal areas. This study offers foundations for the upcoming research to enhance urban growth models and sustainability strategies in the face of rising urbanization and environmental concerns in the region, as well as laying the groundwork for informed decision-making in the region.

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Vulnerability Assessment of Soil Erosion Based on Topography and Vegetation Cover in a Developing City of Orlu L.G.A, South East Nigeria

Cited by 4 publications

References 5 publications

Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas

Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas

Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing

Urban Growth Monitoring and Prediction Using Remote Sensing Urban Monitoring Indices Approach and Integrating CA-Markov Model: A Case Study of Lagos City, Nigeria

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