Using SRTM and GDEM2 data for assessing vulnerability to coastal flooding due to sea level rise in Lagos: a comparative study

Climate change and its associated sea level rise is one of the recent challenging global issues especially in coastal areas, where a large percentage of the world population resides. Sealevel rise (SLR) is expected to increase coastal inundation and erosion. This may disrupt the physical and human processes including economic systems and social structures in coastal regions, which are densely populated. Digital Elevation Model (DEM) especially Shuttle Radar Topography Mission (SRTM) is a common source of elevation data for assessing the risk of flooding due to sea level rise. Recently, a new Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) Global DEM Version 2 (GDEM2) has been released to the public. This paper compares the flood risk estimations of SRTM and GDEM2. It examines different scenarios of sea level rise and its consequences on flooding in Mainland Lagos. It uses high resolution remote sensing data within Geographic Information System (GIS) environment to visualize the scenarios. The result shows that Lagos Mainland is vulnerable to sea level rise and SRTM (RMSE = 1.98) gives better flood risk estimations than GDEM2 (RMSE = 10.09).Keywords: geospatial techniques; sea level rise, coastal flooding, SRTM, ASTER GDEM2 and flood risk estimation

Field identification of Typha species in Hadeja Gashua Nguru wetlands

Field identification of Typha species in Hadeja Gashua Nguru wetlands Nigeria between January/June 2008. and July/October 2008. The study was conducted at Gashua Nguru wetland (Yobe and Jigawa states), Nigeria. The three sampling stations were established. Aquatic Plant Control information system Table (1996), for identify the species of Typha species was used, in three sampling station. Two Typha species were identified T. latifolia and T. angustifolia. Data analysis showed that, there was significant difference between T. latifolia and T. angustifolia (P<0.05)

Typha latifolia infestation and fish species migration in Hadejia Nguru wetland

The river was divided into two, within each sampling station. Open water and Typha latifolia infested area. The extent of coverage of Typha latifolia each year were determined using line transect. Experimental gill nets were used for the experiment in each location, and the data were collected every three months for the period of two years. The numbers of fish caught and their species in the three sampling sites were recorded. Seventy-four different species of fish were caught in the first year in open water while sixty one different species were caught in Typha latifolia infested area. In the second year seventy-four different species of fish were caught, while forty-five different species of fish were caught in Typha latifolia infested area. Typha latifolia proliferation affects all physio-chemical parameter in water

Changes in air quality associated with mobility trends and meteorological conditions during COVID-19 lockdown in Northern England, UK

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter