Effect of coastal land use change on coastline dynamics along the Nigerian Transgressive Mahin mud coast

Dada, Olusegun A.; Agbaje, Adeyeye O.; Adesina, Rasheed B.; Asiwaju-Bello, Yinusa Ayodele

doi:10.1016/j.ocecoaman.2018.11.014

Cited by 57 publications

(31 citation statements)

References 45 publications

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“…In Ghana, Benin, Togo and Nigeria, most economic activities are also located in the coastal zone. Because these urban centres stand on low-lying sandy coasts, they are particularly vulnerable to marine hazards, including sea level rise (Brown., 2011, Neumann et al, 2015Dada et al, 2019). The recently observed increase in shoreline erosion has become a major issue (Ndour et al, 2018;Almar et al, 2015;Giardino et al, 2018;Anthony et al, 2019) but it remains unclear if this is due to sea level rise or human activities (e.g., sand extraction).…”

Section: The Esa CCI Sea Level Project and Its Extension To Coastal Zmentioning

confidence: 99%

Altimetry-based sea level trends along the coasts of Western Africa

Marti

Cazenave

Birol

et al. 2021

Advances in Space Research

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We present results of contemporary coastal sea level changes along the coasts of western Africa, obtained from a dedicated reprocessing of satellite altimetry data done in the context of the ESA 'Climate Change Initiative' sea level project. High sampling rate (20 Hz) sea level data from the Jason-1 and Jason-2 missions over a 14-year-long time span (July 2002 to June 2016) are considered. The data were first retracked using the ALES adaptative leading edge subwaveform retracker. The X-TRACK processing system developed to optimize the completeness and accuracy of the corrected sea level time series in coastal ocean areas was then applied. From the 14-year long sea level time series finally obtained, we estimate sea level trends along the Jason-1 & 2 tracks covering the study region. We analyze regional variations in sea level trends, with a focus on the changes observed between the open ocean to the coastal zone. Compared to the conventional 1 Hz sea level products dedicated to open ocean applications, the retracked 20 Hz measurements used in this study allow us to retrieve valid sea level information much closer to the coast (less than 3-4 km to the coast, depending on the satellite track location). The main objective of this study is twofold: (1) provide sea level products in the coastal areas from reprocessed altimetry data and (2) check whether sea level changes at the coast differ from that reported in the open ocean with conventional altimetry products. In the selected region, results show that over the study period, sea level trends observed near the coast of western Africa are significantly different than offshore trends. In order to assess the robustness of the results, detailed analyses are performed at several locations to discriminate between possible drifts in the geophysical corrections and physical processes potentially able to explain the sea level changes observed close to the coast.

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Section: The Esa CCI Sea Level Project and Its Extension To Coastal Zmentioning

confidence: 99%

Altimetry-based sea level trends along the coasts of Western Africa

Marti

Cazenave

Birol

et al. 2021

Advances in Space Research

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“…Worldwide, LULCCs have been triggered by natural and anthropogenic factors [10,11]. The increasing pressure of anthropogenic factors has substantially altered numerous watersheds connected to coasts [12,13], particularly in developing countries due to population growth and development activities [14,15].…”

Section: Introductionmentioning

confidence: 99%

Land-Use and Land-Cover (LULC) Change Detection and the Implications for Coastal Water Resource Management in the Wami–Ruvu Basin, Tanzania

et al. 2021

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Evaluation of river basins requires land-use and land-cover (LULC) change detection to determine hydrological and ecological conditions for sustainable use of their resources. This study assessed LULC changes over 28 years (1990–2018) in the Wami–Ruvu Basin, located in Tanzania, Africa. Six pairs of images acquired using Landsat 5 TM and 8 OLI sensors in 1990 and 2018, respectively, were mosaicked into a single composite image of the basin. A supervised classification using the Neural Network classifier and training data was used to create LULC maps for 1990 and 2018, and targeted the following eight classes of agriculture, forest, grassland, bushland, built-up, bare soil, water, and wetland. The results show that over the past three decades, water and wetland areas have decreased by 0.3%, forest areas by 15.4%, and grassland by 6.7%, while agricultural, bushland, bare soil, and the built-up areas have increased by 11.6%, 8.2%, 1.6%, and 0.8%, respectively. LULC transformations were assessed with water discharge, precipitation, and temperature, and the population from 1990 to 2018. The results revealed decreases in precipitation, water discharge by 4130 m3, temperature rise by 1 °C, and an increase in population from 5.4 to 10 million. For proper management of water-resources, we propose three strategies for water-use efficiency-techniques, a review legal frameworks, and time-based LULC monitoring. This study provides a reference for water resources sustainability for other countries with basins threatened by LULC changes.

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“…), allow the choice of one or several reference lines. This results in the diversity of media and extraction techniques on which the margin of error depends upon (Bakhoum et al, 2017, Nassar et al, 2018, Moussaid et al, 2015, Dada et al, 2019Durand 2000, Byrnes 1994, Crowell et al, 1993. In this study the limit of the vegetation is used as a reference line.…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, the global error called Global Position Error of the Coastline is calculated by using the square root of the sum of the errors squared (Fletcher et al, 2003;Nassar et al, 2018, Moussaid et al, 2015, Dada et al, 2019. In this study, the value of the three errors already identified in the images has been calculated for each year.…”

Section: Estimation Of Margins Of Uncertaintymentioning

confidence: 99%