Urban modelling for seismic prone areas: the case study of Vila Franca do Campo (Azores Archipelago, Portugal)

Martins, Válter; Cabral, Pedro; Silva, D. Sousa E

doi:10.5194/nhess-12-2731-2012

Cited by 18 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The predicted urban extent was compared with observed LULC map for 2016 using Kappa variations, i.e., K location and K quantity [54][55][56][57][58][59]. If acceptable values for these kappa variations are obtained, then we proceed to 2027 simulation [55].…”

Section: Prediction Of Urban Extent For 2027mentioning

confidence: 99%

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Padmanaban

Bhowmik

Cabral

et al. 2017

Entropy

View full text Add to dashboard Cite

Urban sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, adversely affects the provision of ecosystem services. The quantification of US is thus crucial for effective urban planning and environmental management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive US triggered by the doubling of total population over the past three decades. However, the extent and level of US has not yet been quantified and a prediction for future extent of US is lacking. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10 km suburban buffer of Chennai. The level of US was then quantified using Renyi's entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi's entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services.

show abstract

Section: Prediction Of Urban Extent For 2027mentioning

confidence: 99%

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Padmanaban

Bhowmik

Cabral

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…The severity of disasters triggered by seismic events is the outcome of the interaction between the geophysical phenomenon and the characteristics of the built environment [27,28]. Pokhara sub-metropolitan city can be considered to be moderately vulnerable to earthquakes with a probability of intensity of VIII MMI earthquake in the region.…”

Section: Earthquakesmentioning

confidence: 99%

Growing City and Rapid Land Use Transition: Assessing Multiple Hazards and Risks in the Pokhara Valley, Nepal

Rimal¹,

Baral

Stork

et al. 2015

Land

View full text Add to dashboard Cite

Pokhara is one of the most naturally beautiful cities in the world with a unique geological setting. This important tourist city is under intense pressure from rapid urbanization and population growth. Multiple hazards and risks are rapidly increasing in Pokhara due to unsustainable land use practices, particularly the increase in built-up areas. This study examines the relationship among urbanization, land use/land cover dynamics and multiple hazard and risk analysis of the Pokhara valley from 1990 to 2013. We investigate some of the active hazards, such as floods, landslides, fire, sinkholes, land subsidence and earthquakes, and prepare an integrated multiple hazard risk map indicating the highly vulnerable zones. Land use and land cover maps from 1990 and 2013 from Landsat images (30 m resolution) have been prepared and analyzed for the spatial dynamics of urbanization and the transition of land use and land cover. In the 23-year period, the built-up area more than doubled from 24.03 km² to 54.20 km². Although the landscape in the urban, peri-urban and rural areas appears to be fragmented, different drivers play pivotal roles in landscape change in these areas. The results provide OPEN ACCESSLand 2015, 4 958 substantial information for establishing innovative action plans for disaster risk management in the valley. Recommendations are made for the most suitable places for future urban expansion in the valley. This study is important for raising awareness among policy makers and other public officials to include multiple hazard risk mitigation in land use policies and plans. Establishing connections between urban expansions, escalating population growth and multiple hazards and risk assessment will also improve in modelling the latent impact of future catastrophes and emergency preparedness.

show abstract

“…To evaluate the consequences of urbanization and the validity of possible NBS, social and environmental scientists are increasingly using highly detailed LULCC models [11,12]. Landcover models have been used to address general questions of landcover change and urbanization around the world [2,9,10,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]; however, only one other study models LULCCs under GI policies [28]. To predict precise landcover transitions and to answer specific questions of policy, future LULCCs need to be modeled at finer scales.…”

Section: Introductionmentioning

confidence: 99%

“…Urban modeling studies are conducted at a variety of resolutions depending on the satellite imagery available. Most landcover models are created at a 30 m resolution using Landsat imagery [2,9,13,[15][16][17][18]27]; yet, small landcover features, like GI, require modeling at a much higher resolution, as GI projects can be smaller than 30 m. Similarly, urban models have been created at different levels of detail with varying numbers of landcover classes. Some studies present a broad overview of urbanization with only two landcover classes [10,19,[21][22][23][24][25][26][27], usually "urban" and "nature" or "nonurban."…”

Section: Introductionmentioning

confidence: 99%

“…Some studies present a broad overview of urbanization with only two landcover classes [10,19,[21][22][23][24][25][26][27], usually "urban" and "nature" or "nonurban." Other studies present more realistic models with seven to ten landcover classes representing many of the features in the urban system [13,15,16,18], such as buildings, roads, trees, and grass. A specific landcover class, such as GI, must be modeled with a larger number of landcover classes, as to accurately represent the landscape and the specific variables that effect GI's location.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Shade

Kremer

2019

Land

View full text Add to dashboard Cite

Urbanization is a rapid global trend, leading to consequences such as urban heat islands and local flooding. Imminent climate change is predicted to intensify these consequences, forcing cities to rethink common infrastructure practices. One popular method of adaptation is green infrastructure implementation, which has been found to reduce local temperatures and alleviate excess runoff when installed effectively. As cities continue to change and adapt, land use/landcover modeling becomes an important tool for city officials in planning future land usage. This study uses a combination of cellular automata, machine learning, and Markov chain analysis to predict high resolution land use/landcover changes in Philadelphia, PA, USA for the year 2036. The 2036 landcover model assumes full implementation of Philadelphia’s green infrastructure program and past temporal trends of urbanization. The methodology used to create the 2036 model was validated by creating an intermediate prediction of a 2015 landcover that was then compared to an existing 2015 landcover. The accuracy of the validation was determined using Kappa statistics and disagreement scores. The 2036 model successfully met Philadelphia’s green infrastructure goals. A variety of landscape metrics demonstrated an overall decrease in fragmentation throughout the landscape due to increases in urban landcover.

show abstract

Urban modelling for seismic prone areas: the case study of Vila Franca do Campo (Azores Archipelago, Portugal)

Cited by 18 publications

References 19 publications

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Growing City and Rapid Land Use Transition: Assessing Multiple Hazards and Risks in the Pokhara Valley, Nepal

Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Contact Info

Product

Resources

About