Insights to urban dynamics through landscape spatial pattern analysis

Ramachandra, TV; Aithal, Bharath H.; Sanna, Durgappa D.

doi:10.1016/j.jag.2012.03.005

Cited by 234 publications

(43 citation statements)

References 44 publications

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“…The spatial time-series data provided by Remote Sensing (RS) systems serve as an excellent source to analyse and quantify the changes over time in different urban cover and other land cover types. The repetitive coverage, cost effectiveness and accessibility of RS data offer great potential in terms of identifying and modelling long-term urban changes [12][13][14]. A number of studies have utilized bi-temporal RS data to detect the spatial patterns and overall rate of urban cover change for selected periods of time.…”

Section: Introductionmentioning

confidence: 99%

Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

2016

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This study analyses the expansion of urban growth and land cover changes in five Saudi Arabian cities (Riyadh, Jeddah, Makkah, Al-Taif and the Eastern Area) using Landsat images for the 1985, 1990, 2000, 2007 and 2014 time periods. The classification was carried out using object-based image analysis (OBIA) to create land cover maps. The classified images were used to predict the land cover changes and urban growth for 2024 and 2034. The simulation model integrated the Markov chain (MC) and Cellular Automata (CA) modelling methods and the simulated maps were compared and validated to the reference maps. The simulation results indicated high accuracy of the MC-CA integrated models. The total agreement between the simulated and the reference maps was >92% for all the simulation years. The results indicated that all five cities showed a massive urban growth between 1985 and 2014 and the predicted results showed that urban expansion is likely to continue going for 2024 and 2034 periods. The transition probabilities of land cover, such as vegetation and water, are most likely to be urban areas, first through conversion to bare soil and then to urban land use. Integrating of time-series satellite images and the MC-CA models provides a better understanding of the past, current and future patterns of land cover changes and urban growth in this region. Simulation of urban growth will help planners to develop sustainable expansion policies that may reduce the future environmental impacts.

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Section: Introductionmentioning

confidence: 99%

Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

2016

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“…High population growth, dramatic land use change and socioeconomic transformations have brought the inconsistency of rapid urbanization and environmental consequences in Kathmandu valley [16,17]. For the management of rapid urbanization, the development plan needs to be integrated in order to maintain the equilibrium amongst resources, users, managers and the environment [18,19]. We hypothesize that the ongoing urbanization in Kathmandu valley is discordant with its geology and geography, and the ongoing LULC change foments the jeopardy of the human-nature integrity.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Urban Growth and the Nepal Earthquake 2015 for Sustainability of Kathmandu Valley, Nepal

Rimal

Zhang

et al. 2017

Land

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Abstract:The exodus of people from rural areas to cities brings many detrimental environmental, social and cultural consequences. Monitoring spatiotemporal change by referencing the historical timeline or incidence has become an important way to analyze urbanization. This study has attempted to attain the cross-sectional analysis of Kathmandu valley that has been plagued by rampant urbanization over the last three decades. The research utilizes Landsat images of Kathmandu valley from 1976 to 2015 for the transition analysis of land use, land cover and urban sprawl for the last four decades. Results showed that the urban coverage of Kathmandu valley has tremendously increased from 20.19 km 2 in 1976 to 39.47 km 2 in 1989 to 78.96 km 2 in 2002 to 139.57 km 2 in 2015, at the cost of cultivated lands, with an average annual urban growth rate of 7.34%, 7.70% and 5.90% in each temporal interval, respectively. In addition, the urban expansion orientation analysis concludes the significant urban concentration in the eastern part, moderately medium in the southwest and relatively less in the western and northwest part of the valley. Urbanization was solely accountable for the exploitation of extant forests, fertile and arable lands and indigenous and cultural landscapes. Unattended fallow lands in suburban areas have compounded the problem by welcoming invasive alien species. Overlaying the highly affected geological formations within the major city centers displays that unless the trend of rapid, unplanned urbanization is discontinued, the future of Kathmandu is at the high risk. Since land use management is a fundamental part of development, we advocate for the appropriate land use planning and policies for sustainable and secure future development.

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“…In the case of landscape ecology, configuration and composition landscape have important roles in determining natural habitats, hydrological processes, energy, and the nutrient cycle (Lee et al, 2009). Many studies showed an increase in the use of landscape metrics for monitoring of patterns of land cover changes (Lausch and Herzog, 2002;Antwi et al, 2008;Ramachandra et al, 2012;Plexida et al, 2014;Joorabian Shooshtari and Gholamalifard, 2015;Sakieh and Salmanmahiny, 2016;García et al, 2017). Boentje and Blinnikov (2007) assessed forest loss in the Green Belt around Moscow, Russia during the 1991 to 2001 period.…”

Section: Introductionmentioning

confidence: 99%

Land cover change modelling in Hyrcanian forests, Northern Iran: a landscape pattern and transformation analysis perspective

Shooshtari

Shayesteh

Gholamalifard

et al. 2018

CIG

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The main objective of this study is to analyze the spatio-temporal changes in land cover and land use, (1984–2010), as well to simulate future land cover for 2030 in the Neka River Basin, including the Hyrcanian forest, in northern Iran. For this purpose, we used detailed land cover maps for the years 1984, 2001 and 2010. The results showed that the highest deforestation occurred in the boundaries between forest and agriculture areas between 1984 and 2010. Comparing the observed and predicted land cover in 2010 yielded agreement of 96.41%. From 1984 to 2010, landscape metrics showed that the forest area evolved to more fragmentation, with less shape complexity and less connectivity. Projections for the future are consistent with observed changes for the Neka landscape, with a tendency to continue disaggregating and increasing diversity in a number of different patch types. Between 2010 and 2030, we observed the arrival of new crops, rangelands, and urban areas within the remaining areas of homogeneous forest. Changes in the Hyrcanian forest will cause alteration in ecosystem services, such as erosion control, water yield, timber harvest, and ground water reservation. Results of this work may represent a useful tool to provide strategies and territorial planning for sustainable management of the fragile Hyrcanian forest ecosystems in the Neka Basin.

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Insights to urban dynamics through landscape spatial pattern analysis

Cited by 234 publications

References 44 publications

Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

Monitoring Urban Growth and the Nepal Earthquake 2015 for Sustainability of Kathmandu Valley, Nepal

Land cover change modelling in Hyrcanian forests, Northern Iran: a landscape pattern and transformation analysis perspective

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