Abrasion and accretion dynamics as impact of climate change in coastal area of Yogyakarta

Susanti, Indah; Nurlatifah, Amalia; Martono,; Maryadi, Edy; Slamet, S. Lilik; Siswanto, Bambang; Suhermat, Mamat

doi:10.1063/5.0042005

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…At this time, many changes in coastal areas can disrupt the activities of residents around the area. Changes in coastal areas occur due to various natural events such as wind, waves, and currents (Guerrera et al, 2021;Susanti et al, 2021). In addition, anthropogenic (human) effects are one of the causes of coastal changes (Goldberg et al 2020;Ning et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Ihsan,

Harto,

Sakti

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The coastal area is an area that has a dense population with a lot of human activities that occur there. Due to environmental changes and human activities, changes often occur in coastal areas ranging from erosion and sedimentation. Changes must continuously be monitored to plan countermeasures due to the occurring phenomena. This study aims to create a website-based application to monitor coastal areas. This study will use Landsat data 5,7,8, and 9 to see changes in coastal areas. The analysis can be provided from 1985 until recent data by integrating four Landsat satellites. The NDWI index (Normalized Difference Wetness Index) analyzes changes occurring in coastal areas and differentiates between water and land area. The analysis is not only in the form of changes that occur in coastal areas but also in time series analysis, and trends that occur at a point can be analyzed using land trend analysis. The resulting website based on Cloud Computation in Google Earth Engine can be seen at the link https://bit.ly/MonitoringPesisir. This website can automatically update, and users can choose the location to monitor. This research is expected to be used by policymakers to monitor and plan the development and regulation of coastal areas.

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Section: Introductionmentioning

confidence: 99%

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Ihsan,

Harto,

Sakti

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Bayesian Network Analysis for Shoreline Dynamics, Coastal Water Quality, and Their Related Risks in the Venice Littoral Zone, Italy

Pham,

Dal Barco,

Pourmohammad Shahvar

et al. 2024

JMSE

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The coastal environment is vulnerable to natural hazards and human-induced stressors. The assessment and management of coastal risks have become a challenging task, due to many environmental and socio-economic risk factors together with the complex interactions that might arise through natural and human-induced pressures. This work evaluates the combined effect of climate-related stressors on low-lying coastal areas by applying a multi-risk scenario analysis through a Bayesian Network (BN) approach for the Venice coast. Based on the available open-source and remote sensing data for detecting shoreline changes, the developed BN model was trained and validated with oceanographic variables for the 2015–2019 timeframe, allowing us to understand the dynamics of local-scale shoreline erosion and related water quality parameters. Three “what-if” scenarios were carried out to analyze the relationships between oceanographic boundary conditions, shoreline evolution, and water quality parameters. The results demonstrate that changes in sea surface height and significant wave height may significantly increase the probability of high-erosion and high-accretion states. Moreover, by altering the wave direction, the water quality variables show significant changes in the higher-risk class. The outcome of this study allowed us to identify current and future coastal risk scenarios, supporting local authorities in developing adaptation plans.

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Abrasion and accretion dynamics as impact of climate change in coastal area of Yogyakarta

Cited by 2 publications

References 18 publications

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Bayesian Network Analysis for Shoreline Dynamics, Coastal Water Quality, and Their Related Risks in the Venice Littoral Zone, Italy

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