Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: A case study of Banda Aceh coast of Indonesia

Meilianda, Ella; Pradhan, Biswajeet; Syamsidik, Syamsidik; Comfort, Louise K.; Alfian, Dedy; Juanda, R; Syahreza, Saumi; Munadi, Khairul

doi:10.1016/j.ijdrr.2019.101292

Cited by 36 publications

(26 citation statements)

References 25 publications

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“…Long-term and NRT publicly available RS datasets within GEE along with its high-performance computing promote this cloud-based platform for monitoring, forecasting, prevention, vulnerability, and resilience studies of natural disasters. In particular, GEE was utilized for drought monitoring [129], [130], flood mapping and flood risk assessment [131], [132], wildfire severity mapping [133], [134], landslides analyses [135], hurricane studies [136], and tsunami studies [137]. For instance, MODIS and meteorological datasets were employed within GEE to study the temporal and spatial variations of drought events in Potohar Plateau of Punjab, Pakistan between 2000 and 2015 [129].…”

Section: F Natural Disastermentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

597

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: F Natural Disastermentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

597

216

View full text Add to dashboard Cite

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“…After the tsunami, the along‐shore continuity of the sandy coastline was re‐established through the closure of the numerous breaches that had been opened across the beach ridges (Figure 5, lower panel; Figures 7, 2004/2019). Lagoons became isolated from the open sea, and fish ponds were soon rebuilt (Daly et al, 2017; Griffin et al, 2013; Meilianda et al, 2019). Seaward progradation of the coastline occurred locally on either side of the delta, but, overall, the post‐tsunami evolution is characterized by a continuation of the previous trend of coastal retreat.…”

Section: Discussionmentioning

confidence: 99%

“…Indonesia hosts several of the most densely populated deltas in the world (Caldwell et al, 2019; Chaussard et al, 2013). People on these deltas acutely experience the adverse effects of sea‐level rise, floods, storms, and tsunamis (Fuchs et al, 2011; Meilianda et al, 2019). The decadal (Meilianda et al, 2010) to centennial evolution (Storms et al, 2005) of some of these deltas has been investigated to assess shoreline sensitivity to natural disturbances (Meilianda et al, 2010), and the vulnerability of coastal communities to shoreline evolution (Daly et al, 2017; Meilianda et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Fluvial and coastal landform changes in the Aceh River delta (northern Sumatra) during the century leading to the 2004 Indian Ocean tsunami

Chapkanski

Brocard

Lavigne³

et al. 2022

Earth Surf Processes Landf

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River deltas are strongly affected by demographic growth and by the intensification of land use. The migration of deltaic coastlines is often rapid, threatening urban settlements, coastal farming, and coastal biotopes. Some deltas benefit from centuries of monitoring, such that the evolution of their coastline is well documented. For most deltas, however, such long records do not exist. The study of their geomorphological evolution can benefit from overlapping maps drafted over time, combined with aerial photographs and satellite images, to track the evolution of fluvial and coastal landforms. Both fluvial and coastal landforms are sensitive to variations in water and sediment supply, such that covariations in the evolution of these landforms, or the lack thereof, provide clues on the contribution of water and sediment supply to delta evolution. We document the evolution of river channels and coastlines in the delta of the Aceh River in northwest Sumatra, by overlying maps, ortho‐rectified aerial photographs, and satellite images covering the past 130 years. We assess the accuracy of the overlays, and then use multivariate statistics to analyze the co‐evolution of fluvial and coastal landforms. We propose that a progressive decrease in sediment supply spurred river channel lengthening and narrowing, landward migration of the shoreline, and narrowing of beach ridges. The 2004 Indian Ocean tsunami generated an instantaneous retreat of the coastline that amounts to ∼53% of the coastal retreat from 1884 to 2019 ce. Post‐tsunami evolution is marked by an irreversible acceleration of previous trends. Beach ridges located up‐drift of rivers and tidal channel mouths are more sensitive to long‐term landward retreat and tsunamigenic erosion.

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“…Systematic utilization of vulnerability information based on factors for development of tsunami risk reduction in the DRM is still limited, e.g., in areas of Galle; there are many studies on assessment methodologies of risk, however, if no use is made of them, for example, at the local level, then development is of little help [ 32 ]. One important issue is to link post-disaster reconstruction activities, along with the key factors to either prevent and decision-making by stakeholders [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

The Point Bonitation Method and Its Adaptation in Risk Studies: A Case Study in Sri Lanka’s Cities in the Coastal Zone

Rucińska

Zagrzejewska

2021

IJERPH

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Article proposes using weighting method named the Point Bonitation Method, a popular interdisciplinary method, especially in the tourism and socio-economic geography, for giving optional direction to further researching tsunami risk. This method qualifies and quantifies those factors that lead to natural disasters so that it is possible to make comparisons with their roles in disaster areas. This case study in Sri Lanka shows a specific result that is quantification of vulnerability by regions and can be used and developed locally for disaster risk management and reduction. This paper presents discussion about other possible reasons of high risk in regions.

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Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: A case study of Banda Aceh coast of Indonesia

Cited by 36 publications

References 25 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Fluvial and coastal landform changes in the Aceh River delta (northern Sumatra) during the century leading to the 2004 Indian Ocean tsunami

The Point Bonitation Method and Its Adaptation in Risk Studies: A Case Study in Sri Lanka’s Cities in the Coastal Zone

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