Climate change adaptation in practice: people's responses to tidal flooding in Semarang, Indonesia

Harwitasari, Dian; Ast, Jacko van

doi:10.1111/j.1753-318x.2011.01104.x

Cited by 55 publications

(64 citation statements)

References 19 publications

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“…The poor drainage system leads to a regular occurrence of floods during the rainy season. Meanwhile, the areas adjacent to the sea are prone to the impact of tidal flood which occurs daily in line with the tides [34,35]. In this study area, four villages are located amidst extensive fishponds and rice fields.…”

Section: Study Areamentioning

confidence: 99%

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

et al. 2016

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Abstract:This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images due to its continuous variation over time; and (2) the uncertainty of the change results propagating from object extraction and implementation of shoreline change detection method. Unsupervised FCM achieved the highest kappa (κ) value when threshold (t) was set at 0. 2013-2014 (65 ha). Urban development in flood-prone areas resulted in an increase of flood hazards including inundation and erosion leading to the changes of shoreline position. The proposed methods provided an effective way to present shoreline as a line and as a margin with fuzzy boundary and its associated change uncertainty. Shoreline mapping and monitoring is crucial to understand the spatial distribution of coastal inundation including its trend.

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Section: Study Areamentioning

confidence: 99%

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

et al. 2016

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“…The alteration of sediment transport can come from changes in water flow, water level, weather events and human influence. In addition, previous studies have mentioned that this location has suffered from the changing of shorelines for more than 20 years due to coastal inundation accelerated by, for example, land subsidence, sea level rise, seaport development, and ground water extraction [16,20,38]. Many attempts have been made to combat coastal inundation and erosion along the 1.3 km coast in Demak.…”

Section: Discussionmentioning

confidence: 99%

“…These two highs and lows differ in height, whereas the average tidal range is 0.6 m. The highest tidal ranges occur in December and June during the rainy and dry seasons, while the lowest tidal ranges occur in March and September during the transitional seasons. Two types of flood regularly occur: (a) floods caused by a tidal flood occurring daily in line with tidal cycles [38,39]; and (b) floods due to poor drainage systems during rainy seasons. Extensive fishponds and rice fields are covering the study area.…”

Section: Study Areamentioning

confidence: 99%

“…Some factors such as extreme winds and waves contribute to this increase. Furthermore, in the long run, other causes such as land subsidence, sea level rise, mangrove conversion, beach reclamation and a seaport extension are potential causes for increased coastal inundation [38,[40][41][42] (Table 1). We obtained terrain corrected Landsat images (L1T product) from USGS EarthExplorer [43].…”

Section: Study Areamentioning

confidence: 99%

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Change Vector Analysis to Monitor the Changes in Fuzzy Shorelines

2017

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Abstract:Mapping of shorelines and monitoring of their changes is challenging due to the large variation in shoreline position related to seasonal and tidal patterns. This study focused on a flood-prone area in the north of Java. We show the possibility of using fuzzy-crisp objects to derive shoreline positions as the transition zone between the classes water and non-water. Fuzzy c-means classification (FCM) was used to estimate the membership of pixels to these classes. A transition zone between the classes represents the shoreline, and its spatial extent was estimated using fuzzy-crisp objects. In change vector analysis (CVA) applied to water membership of successive shorelines, a change category was defined if the change magnitude between two years, T 1 and T 2 , differed from zero, while zero magnitude corresponded to no-change category. Over several years, overall change magnitude and change directions of the shoreline allowed us to identify the trend of the fluctuating shoreline and the uncertainty distribution. The fuzzy error matrix (FERM) showed overall accuracies between 0.84 and 0.91. Multi-year patterns of water membership changes could indicate coastal processes such as: (a) high change direction and high change magnitude with a consistent positive direction probably corresponding to land subsidence and coastal inundation, while a consistent negative direction probably indicates a success in a shoreline protection scheme; (b) low change direction and high change magnitude indicating an abrupt change which may result from spring tides, extreme waves and winds; (c) high change direction and low change magnitude which could be due to cyclical tides and coastal processes; and (d) low change direction and low change magnitude probably indicating an undisturbed environment, such as changes in water turbidity or changes in soil moisture. The proposed method provided a way to analyze changes of shorelines as fuzzy objects and could be well-suited to apply to coastal areas around the globe.

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“…The area is prone to frequent inundations at high tide [52,53]. Several measures have been taken to minimize their impact, for example creation of dykes along the drainage system near the settlements, dredging the drainage channels, elevating roads and house floors, and building a permeable dam as sediment traps [54].…”

Section: Study Areamentioning

confidence: 99%

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

2017

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This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Pleiades + DTM). Both fuzzy sets and random sets model the spatial extent of shoreline including its uncertainty. Fuzzy sets represent shorelines as a margin determined by upper and lower thresholds and their uncertainty as confusion indices. They do not consider randomness. Random sets fit the mixed Gaussian model to the image histogram. It represents shorelines as a transition zone between water and non-water. Their extensional uncertainty is assessed by the covering function. The results show that fuzzy sets and random sets resulted in shorelines that were closely similar. Kappa (κ) values were slightly different and McNemar's test showed high p-values indicating a similar accuracy. Inclusion of the DTM (digital terrain model) improved the classification results, especially for roofs, inundated houses and inundated land. The shoreline model using Pleiades + DTM performed better than that of using Pleiades only, when using either fuzzy sets or random sets. It achieved κ values above 80%.

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Climate change adaptation in practice: people's responses to tidal flooding in Semarang, Indonesia

Cited by 55 publications

References 19 publications

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

Change Vector Analysis to Monitor the Changes in Fuzzy Shorelines

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

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