Coastal evolution assessment and prediction using remotely sensed front vegetation line along the Nigerian Transgressive Mahin mud coast

Daramola, Samuel; Li, Huan; Otoo, Ebenezer; Idowu, Temitope Ezekiel; Gong, Zheng

doi:10.1016/j.rsma.2022.102167

Cited by 9 publications

(8 citation statements)

References 76 publications

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“…The population that will be affected is approximately 461,491 while the cost of livelihood and economic activity expressed with GDP will be USD 99.6 million (N57.8 billion). The observed patterns of shoreline changes in the study area are due to waves, rising tides, and anthropogenic forces along the coast which corroborates with findings from Dada et al (2020) and Daramola et al (2022). The Landsat dataset used to generate proxies for the shorelines and the ALOS PALSAR digital elevation model may not have sufficient spatial resolution, but they are useful for monitoring coastal changes due to erosion and sea-level rise along the transgressive mud coast.…”

Section: Discussionsupporting

confidence: 88%

“…km. The reasons for the variations include the period they considered to be shorter compared to this study, the duration of assessments, and the vegetation line proxy used in Daramola et al (2022). The noticeable accretion in the western section of the coast is due to the longshore drift of sediment from the Barrier-Lagoon coast from Lagos, rising sea levels, and the deposition of sand and other sediments along the shore.…”

Section: Discussionmentioning

confidence: 90%

“…This study has been able to show that coastal retrogradation has been a recurring occurrence along the Transgressive mud coast over the last 35 years through shoreline change and rising sea levels. Many intervention approaches have been suggested for managing the Transgressive shoreline such as identification of potential threats to hazards, flood defenses, land use management, community education and evacuation plan, and the adoption of the managed realignment approach (Daramola et al, 2022;Komolafe et al, 2020). These approaches are good, but unless they are integrated with a planning framework such as an SMP, they are not sufficient for effective shoreline management.…”

Section: Shoreline Management Plan (Smp)mentioning

confidence: 99%

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Spatio-Temporal Assessment of Shoreline Changes and Management of the Transgressive Mud Coast, Nigeria

Popoola

2022

ESJ

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This study investigated changes due to erosion and the consequences of rising sea levels on the Transgressive mud coast of Nigeria using multispectral Landsat images and ALOS PALSAR (AW3D30) elevation models with the view of proffering a management strategy for a sustainable coast. Endpoint Rate (EPR) and Linear Regression Rate (LRR) techniques within the Digital Shoreline Analysis System (DSAS) were used to assess the rates of changes along the shoreline between 1986 and 2021. Inundation models were developed in line with sea-level rise scenarios of the Green House Gas emissions (SSP5-8.5) with GIS to assess sea-level rise’s impact on land and structures. Likewise, spatially disaggregated population and economic activity datasets projected to the year 2100 were overlaid on the inundation models to generate exposure indices for the coast. The study revealed that 49.8 km (64.67%) of the shoreline experienced retreat over the entire study period. This rapid rate of shoreline retreat has caused a land loss of 15.1 sq. km to the Atlantic over the last 35 years, which could trigger an additional 1.26 sq. km by the year 2032. Furthermore, the impact of sea-level rise is severe on the transgressive coast triggering submergence of the mud coast by inundation to the extent of 201 sq. km which will increase to 551 sq. km by 2100. Losses in terms of structures, population, and economic activity are recorded. To curtail the ongoing coastal changes, the study recommends the full adoption of the shoreline management plan (SMP) for sustainable shoreline management.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 90%

Section: Shoreline Management Plan (Smp)mentioning

confidence: 99%

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Spatio-Temporal Assessment of Shoreline Changes and Management of the Transgressive Mud Coast, Nigeria

Popoola

2022

ESJ

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“…The shoreline extraction uncertainty value was set regarding the pixel size at 30m, 10m, and 1 m for the Landsat, the Sentinel-2 and the GE images respectively. The Linear Regression statistical rates (LRR) provided by the Digital Shoreline Analysis System (DSAS) [13,14] tool, were computed and compared utilizing the R-squared, the Root Mean Square Error (RMSE), and the Mean Annual Error (MAE) coefficient methods [15,16] . These methods and their equations have been already described in detail in previous studies [17,18] .…”

Section: Methodsmentioning

confidence: 99%

Quantitative comparison of temporal changes in coastline utilizing Google Earth, Landsat and Sentinel-2 historical time series case study: the Mytikas coast, Aitoloakarnania Prefecture, Greece

Verrou,

Apostolopoulos,

Nikolakopoulos

2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Google Earth (GE) is a useful, free of charge platform which provide images from the Earth’s surface during time. The present study investigates the usage of the GE integrated timeline module, to assess the shoreline temporal displacement. The results were compared to the respective results derived from Landsat and Sentinel-2 images. The littoral area of the bay of Mytika situated in the Prefecture of Aitoloakarnania’s, Greece, has been used as test site. Past shoreline positions for the 2003-2022 period have been demarcated, through on-screen digitizing method, from the GE images in a Geographic Information System (G.I.S.) environment, using the wetted boundaries that where visible on the images. Moreover, automatic indices such as NDWI, NDVI and MNDWI were used to delineate the land from the water from the satellite images respectively. The shoreline evolution was estimated based on the cross-shore transects method and compared for each dataset via the Linear Regression Rate (LRR). In addition, the future shoreline position (forecasted shoreline), in ten and 20 years from 2022, was estimated using the integrated forecasting function of the fifth version of the Digital Shoreline Analysis System (DSAS) tool of the ArcGIS software. The mean value of declination among the future shorelines emerged, were statistically compared via the Shoreline Change Envelope (SCE) rates. The satellite images have been orthorectified and georeferenced to the Hellenic Geodetic Reference System of 1987 (Greek Grid) using the Leica Photogrammetry Suite (LPS), while perpendicular to the coast lines (transects) were created every 30 meters spanning along the coastline and used for the measurements. The results revealed that the NDVI predicted shoreline based on the Sentinel-2 images, yielded the lower value of declination from the GE shores, for both time periods (ten - and 20- year), ranging from 4.45 m to 7.37 m respectively, while the Landsat datasets emerged high rates of declination.

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“…al., 2022). A linha de vegetação é um indicativo atrativo para os estudos de preservação ambiental (Bush et al, 1999;Sharples, 2006;De Farias e Maia, 2010;Martins, 2015;Daramola et. al., 2022) além de ser um indicativo importante para monitorar a evolução espaço-temporal da linha de costa.…”

Section: Introductionunclassified

O uso do sensoriamento remoto para identificar através de índices a linha de vegetação

Amancio

Gonçalves

Holanda

et al. 2023

Rev. Bras. Geog. Fis.

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A linha de vegetação é uma feição presente na geomorfologia costeira. Pode ser considerada como um indicativo da posição espacial da linha de costa. Existem diversas formas para mapear a linha de vegetação, cada qual com sua respectiva resolução espacial. Nesta contribuição foram extraídas a linha de vegetação por diferentes métodos, entre eles o uso de índices de vegetação tais como o VARI (Visual Atmospheric Resistance Index) obtidos pelo mosaico de ortofotos digitais a partir do RPAS (Remotely Piloted Aircraft System); o NDVI (Normalized Difference Vegetation Index) através de imagens de satélites artificiais oriundas do Landsat 8, Sentinel 2 e Planet e o método de posicionamento relativo com GNSS (Global Navigation Satellite Systems). Os objetivos são: (i) identificar a linha de vegetação a partir do VARI obtido por RPAS e (ii) a partir do NDVI utilizando o Landsat 8, Sentinel 2 e Planet e (iii) comparar as respectivas acurácias encontradas tendo como valor verdadeiro a linha de vegetação obtida por GNSS. Os resultados obtidos foram representados em ordem de acurácia seguidas das respectivas médias em metros e desvio padrão: Landsat 8 (-13,30 ±4,99), Sentinel 2 (2,08 ±2,92), Planet (0,10 ±1,39) e VARI (-0,11 ±1,15). Tais resultados evidenciaram a aplicabilidade e limitações de cada método em função da resolução espacial e qualidade posicional para a extração da linha de vegetação.Palavras-chave: Linha de Vegetação; Visual Atmospheric Resistance Index (VARI); Normalized Difference Vegetation Index (NDVI); Remotely Piloted Aircraft System (RPAS); Global Navigation Satellite Systems (GNSS).The remote sensing usage to identify through indices the vegetation lineA B S T R A C TThe vegetation line is a feature present in coastal geomorphology. It can be considered as an indication of the shoreline spatial position. There are several approaches to map the vegetation line, considering its respective spatial resolution. In this contribution, the vegetation line was extracted by different methods, including the use of vegetation indices such as the VARI (Visual Atmospheric Resistance Index) obtained by the mosaic of digital orthophotos from RPAS (Remotely Piloted Aircraft System); the NDVI (Normalized Difference Vegetation Index) through artificial satellite images from Landsat 8, Sentinel 2 and Planet and the relative positioning method with GNSS (Global Navigation Satellite Systems). The aims are: (i) to identify the vegetation line from VARI obtained by RPAS and from NDVI using Landsat 8, Sentinel 2 and Planet and (ii) to compare the respective accuracies found having as true value the vegetation line obtained by GNSS. The results obtained were represented in order of accuracy followed by the respective averages in meters and standard deviation: Landsat 8 (-13.30 ±4.99), Sentinel 2 (2.08 ±2.92), Planet (0.10 ±1.39) and VARI (-0.11 ±1.15). These results showed the applicability and limitations of each method in terms of spatial resolution and positional quality for extracting the vegetation line.Keywords: Vegetation Line; Visual Atmospheric Resistance Index (VARI); Normalized Difference

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Coastal evolution assessment and prediction using remotely sensed front vegetation line along the Nigerian Transgressive Mahin mud coast

Cited by 9 publications

References 76 publications

Spatio-Temporal Assessment of Shoreline Changes and Management of the Transgressive Mud Coast, Nigeria

Spatio-Temporal Assessment of Shoreline Changes and Management of the Transgressive Mud Coast, Nigeria

Quantitative comparison of temporal changes in coastline utilizing Google Earth, Landsat and Sentinel-2 historical time series case study: the Mytikas coast, Aitoloakarnania Prefecture, Greece

O uso do sensoriamento remoto para identificar através de índices a linha de vegetação

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