Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches

Thapa, Rajesh; Murayama, Yuji

doi:10.3390/rs1030534

Cited by 153 publications

(104 citation statements)

References 28 publications

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“…In recent years, considerable attention has been paid to change occurring in land use/cover because of urbanization and industrialization [1]. Land use/cover change has widespread influence in many fields, not only in urban planning, but also in transportation, environment, policy, and economics [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

2018

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Abstract:Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

2018

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“…Based on these parameters (i.e., proportion correct and Kappa), the accuracy levels are within the range reported by some other LUCC studies that used medium-resolution satellite images (i.e., Landsat TM and ETM+) [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] like the ones used in this study. The reported overall accuracies of these studies [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] range from 82% to 99% with an average of 89%. The number of land cover classes ranges from three to nine with an average of six.…”

Section: Accuracy Assessmentmentioning

confidence: 65%

“…Some recently published studies relating to LUCC analysis and modelling using medium-resolution satellite images like Landsat images include the following references [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Some of them fall under specific research topics like; urban growth analysis and modelling [17,18,[21][22][23]30,31], LUCC mapping, analysis and modelling [25][26][27][28][30][31][32], landscape fragmentation analysis [24,29], and deforestation driving forces analysis and forest conversions forecasting [19,20].…”

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confidence: 99%

“…Some of them fall under specific research topics like; urban growth analysis and modelling [17,18,[21][22][23]30,31], LUCC mapping, analysis and modelling [25][26][27][28][30][31][32], landscape fragmentation analysis [24,29], and deforestation driving forces analysis and forest conversions forecasting [19,20]. These studies were conducted in various parts of the world.…”

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Prioritizing Areas for Rehabilitation by Monitoring Change in Barangay-Based Vegetation Cover

Estoque

Estoque²,

Murayama³

2012

IJGI

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Analysis of spatial and temporal changes of vegetation cover using remote sensing (RS) technology, in conjunction with Geographic Information Systems (GIS), is becoming increasingly important in environmental conservation. The objective of this study was to use RS data and GIS techniques to assess the vegetation cover in 1989 and 2009, in the barangays (smallest administrative units) of the city of San Fernando, La Union, the Philippines, for planning vegetation rehabilitation. Landsat images were used to prepare both the 1989 and 2009 land cover maps, which were then used to detect changes in the vegetation cover for the barangays. In addition to conventional accuracy assessment parameters such as; proportion correct, and standard Kappa index of agreement, two other parameters; quantity, and allocation disagreements were used to assess the accuracy of the land cover classification. Results revealed that there were gains and losses of vegetation cover in most of the barangays, but overall vegetation cover increased by 11% (around 625 ha) based on the original extent of 1989. Those barangays that showed substantial net losses in vegetation cover need to be prioritised for rehabilitation planning. As exemplified in this study, the collection, processing and analysis of relevant RS and GIS information, can facilitate priority-setting in the planning of environmental rehabilitation and conservation by the local government at both city and barangay levels.

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“…In this paper In recent decades, remote sensing data and Geospatial Information System (GIS) have been widely applied for identification and analyses of land use change in metropolitan area [15,16,17,18,19,20,21]. Remote sensing imageries data have been used in urban growth modeling, urban morphology and land uses [22,23,24], quantifying land use dynamics and urban growth [25,26,27,28]. Geospatial Information Systems (GIS) are widely used to represent, analyze, and display various spatial data such as remote sensing, topography, soil type, rainfall and vegetation [29].…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Modeling Using Anfis Algorithm: A Case Study for Sanandaj City, Iran

Mohammady

Delavar

Pijanowski

2013

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

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ABSTRACT:Global urban population has increased from 22.9% in 1985 to 47% in 2010. In spite of the tendency for urbanization worldwide, only about 2% of Earth's land surface is covered by cities. Urban population in Iran is increasing due to social and economic development.The proportion of the population living in Iran urban areas has consistently increased from about 31% in 1956 to 68.4% in 2006.Migration of the rural population to cities and population growth in cities have caused many problems, such as irregular growth of cities, improper placement of infrastructure and urban services. Air and environmental pollution, resource degradation and insufficient infrastructure, are the results of poor urban planning that have negative impact on the environment or livelihoods of people living in cities. These issues are a consequence of improper land use planning.Models have been employed to assist in our understanding of relations between land use and its subsequent effects. Different models for urban growth modeling have been developed. Methods from computational intelligence have made great contributions in all specific application domains and hybrid algorithms research as a part of them has become a big trend in computational intelligence. Artificial Neural Network (ANN) has the capability to deal with imprecise data by training, while fuzzy logic can deal with the uncertainty of human cognition. ANN learns from scratch by adjusting the interconnections between layers and Fuzzy Inference Systems (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy logic, and fuzzy reasoning. Fuzzy logic has many advantages such as flexibility and at the other sides, one of the biggest problems in fuzzy logic application is the location and shape and of membership function for each fuzzy variable which is generally being solved by trial and error method. In contrast, numerical computation and learning are the advantages of neural network, however, it is not easy to obtain the optimal structure. Since, in this type of fuzzy logic, neural network has been used, therefore, by using a learning algorithm the parameters have been changed until reach the optimal solution. Adaptive Neuro Fuzzy Inference System (ANFIS) computing due to ability to understand nonlinear structures is a popular framework for solving complex problems. Fusion of ANN and FIS has attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013 SMPR 2013, 5 -8 October 2013 This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract. 493In this research, an ANFIS method has been developed for modeling land use change and interpreting the relationship between the drivers of urbanization. Our study area is the city of Sanandaj located in the west of Iran. Landsat imag...

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Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches

Cited by 153 publications

References 28 publications

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Prioritizing Areas for Rehabilitation by Monitoring Change in Barangay-Based Vegetation Cover

Urban Growth Modeling Using Anfis Algorithm: A Case Study for Sanandaj City, Iran

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