Predicting the unseen: A shoreline shift analysis and prediction along Mayo Bay, Davao Oriental

45%-60% of the world’s population resides in shoreline areas. Shoreline regions are one of the most vulnerable areas to the effects of global warming. Positions of shorelines are challenging to predict, but the trend of accretion and erosion can be determined using statistical and geospatial techniques. Mati City is a major tourist destination for white sand beaches and pristine waters. Shorelines along Mayo Bay are a source of income for the local community. However, Mayo Bay has been subjected to shifts due to erosion. This study aims to determine the trend of the shoreline shift in Mayo Bay from 2013 to 2023. Landsat 8 OLI satellite images are used in this study. Results reveal that most shorelines experienced erosion, with 97.26% erosion transects. The shoreline length has slightly increased by 0.08% from 2013 to 2023 and is predicted to increase by 3.57% in 2063 and 11.51% by 2100. Barangay Lucatan shows the highest shoreline expansion, while Barangays Bobon and Dahican exhibit the most erosion, with mean rates of -27.15 m/year and -23.60 m/year, respectively. With a classification accuracy of 89% and Root Mean Square Error (RMSE) of 0.05, the study provides a reliable basis for Mayo Bay’s shoreline management. The findings will inform erosion mitigation efforts and guide sustainable coastal management plans for at-risk areas.

Authors

Institutions

Publication Details

Journal
Davao Research Journal
Published
2024-11-08
DOI
https://doi.org/10.59120/drj.v15i4.269
Primary Topic
Coastal and Marine Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting the unseen: A shoreline shift analysis and prediction along Mayo Bay, Davao Oriental

Lea A. Jimenez, Fillmore D Masancay
Davao Research Journal
Coastal and Marine Dynamics
article

Predicting the unseen: A shoreline shift analysis and prediction along Mayo Bay, Davao Oriental

Lea A. Jimenez, Fillmore D Masancay
article en

Abstract

45%-60% of the world’s population resides in shoreline areas. Shoreline regions are one of the most vulnerable areas to the effects of global warming. Positions of shorelines are challenging to predict, but the trend of accretion and erosion can be determined using statistical and geospatial techniques. Mati City is a major tourist destination for white sand beaches and pristine waters. Shorelines along Mayo Bay are a source of income for the local community. However, Mayo Bay has been subjected to shifts due to erosion. This study aims to determine the trend of the shoreline shift in Mayo Bay from 2013 to 2023. Landsat 8 OLI satellite images are used in this study. Results reveal that most shorelines experienced erosion, with 97.26% erosion transects. The shoreline length has slightly increased by 0.08% from 2013 to 2023 and is predicted to increase by 3.57% in 2063 and 11.51% by 2100. Barangay Lucatan shows the highest shoreline expansion, while Barangays Bobon and Dahican exhibit the most erosion, with mean rates of -27.15 m/year and -23.60 m/year, respectively. With a classification accuracy of 89% and Root Mean Square Error (RMSE) of 0.05, the study provides a reliable basis for Mayo Bay’s shoreline management. The findings will inform erosion mitigation efforts and guide sustainable coastal management plans for at-risk areas.

Davao Research JournalVol. 15(4)
Davao Oriental State University (PH), University of Southeastern Philippines (PH)
Openalex Percentile: Top 77%
Coastal and Marine Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.