scholarly journals Urban growth dynamics based on surface Albedo changes in Petrolina, Brazil

2020 ◽  
Vol 42 ◽  
pp. e46270
Author(s):  
Michele Laurentino de Oliveira ◽  
Iana Alexandra Alves Rufino ◽  
John Elton de Brito Leite Cunha ◽  
Rochele Sheila Vasconcelos ◽  
Higor Costa de Brito
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Growth ◽  
Land Use Planning ◽  
Quantitative Methods ◽  
Surface Albedo ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Urban Land ◽  
Urban Land Use

Cities keep growing, and in most of the cases this expansion process is hard to model and describe for planning actions. Quantitative methods are increasingly used to help planning, monitoring, and regulating urban land-use processes. Remote sensing images series are making possible different types of spatial-temporal analysis of the Earth surface. Surface albedo is a remote sensing product acquired in a long series of satellite images such as Landsat (more than 40 years of observation). Those analyses allow measuring waterproofed areas for urban drainage studies, as well as monitoring urban spreading patterns, growth vectors, and issues related to comfort and environmental quality, as well as about land use and land-use planning (directives for master plans) among others. This article shows the direct applicability of surface albedo changes as an indicator of urban land-cover changes. The current study analyzed the urban area of Petrolina County (PE) in the following periods: 2001 and 2006, 2006 and 2011, and 2011 and 2017. Such analysis uses the surface albedo variation along the time and results showed a strong correlation between increased surface albedo and urban expansion. Besides, it enabled to observe the relation between the high urban growth in the 2011-2017 period and the urban spot expansion by 14% (approximately 590 thousand square meters of territorial extension). The Urban development stood out in the Northern and Southwestern regions of Petrolina County.

scholarly journals Machine Learning Algorithms for Urban Land Use Planning: A Review

Urban Science ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Vineet Chaturvedi ◽  
Walter T. de Vries
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Land Use Planning ◽  
Pattern Analysis ◽  
Urban Land ◽  
Earth Observation ◽  
Urban Land Use

Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

scholarly journals Analyzing Urban Growth Pattern and Driving Factors Using Remote Sensing and GIS: A Case Study of Banepa Municipality, Nepal

2020 ◽  
Vol 20 (1) ◽  
pp. 9-18
Author(s):  
Rabina Twayana ◽  
Sijan Bhandari ◽  
Reshma Shrestha
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Growth ◽  
Growth Pattern ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Driving Factors ◽  
Rapid Urbanization ◽  
The Impact

Nepal is considered one of the rapidly urbanizing countries in south Asia. Most of the urbanization is dominated in large and medium cities i.e., metropolitan, sub-metropolitan, and municipalities. Remote Sensing and Geographic Information System (GIS) technologies in the sector of urban land governance are growing day by day due to their capability of mapping, analyzing, detecting changes, etc. The main aim of this paper is to analyze the urban growth pattern in Banepa Municipality during three decades (1992-2020) using freely available Landsat imageries and explore driving factors for change in the urban landscape using the AHP model. The Banepa municipality is taken as a study area as it is one of the growing urban municipalities in the context of Nepal. The supervised image classification was applied to classify the acquired satellite image data. The generated results from this study illustrate that urbanization is gradually increasing from 1992 to 2012 while, majority of the urban expansion happened during 2012-2020, and it is still growing rapidly along the major roads in a concentric pattern. This study also demonstrates the responsible driving factors for continuous urban growth during the study period. Analytical Hierarchy Process (AHP) was adopted to analyze the impact of drivers which reveals that, Internal migration (57%) is major drivers for change in urban dynamics whereas, commercialization (25%), population density (16%), and real estate business (5%) are other respective drivers for alteration of urban land inside the municipality. To prevent rapid urbanization in this municipality, the concerned authorities must take initiative for proper land use planning and its implementation on time. Recently, Nepal Government has endorsed Land Use Act 2019 for preventing the conversion of agricultural land into haphazard urban growth.

scholarly journals Spatio-temporal Analysis of South Western States of Nigeria using Remote Sensing Techniques

2020 ◽  
Vol 4 (2) ◽  
pp. 298-313
Author(s):  
A. E. Oseni ◽  
G. O. Ode
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Change Detection ◽  
Urban Growth ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Bare Soil ◽  
Development Planning ◽  
Sustainable Urban Development ◽  
Remote Sensing Techniques

The south western states of Nigeria have witnessed urban growth over time and the effect of this is urban growth has resulted in loss of vegetation, waterbody, bare soil, mangroves and gain in built up area for residential and commercial purposes. This research utilizes Remote Sensing techniques in mapping of Land Use/Land Cover changes that has taken place in south western states of Nigeria between a period of 15 years from 2003 to 2018 at a five year interval using Multi temporal Landsat satellite images (MSS, TM, and ETM+).Using supervised classification algorithm, the images were classified into bare soil, built-up area, vegetation and water body, which was used to carry out change detection analysis or time series analysis. Change detection analyses were carried out on the imageries to obtain the physical expansion of the area due to various land use. Results obtained from the analysis of built-up area dynamics for fifteen years revealed that the states have been undergoing urban expansion processes at the detriment of other landcover. The expansion of the built-up area from the analysis shows that the urban center is spreading to adjoining non-built-up areas in all directions. The analysis and quantification of the spatial trend revealed that urban expansion patterns and developmental processes of the past trends and present trends can provide better understanding of the dynamics of spatial increase in built up area and guide for sustainable urban development planning for future urban growth.

The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses

10.1068/a3496 ◽  
2002 ◽  
Vol 34 (8) ◽  
pp. 1443-1458 ◽  
Author(s):  
Martin Herold ◽  
Joseph Scepan ◽  
Keith C Clarke
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Landscape Metrics ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Region ◽  
Santa Barbara

Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, the determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial form—landscape metrics—to describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed ‘landscape metric signatures’ for the land-use categories. The analysis shows the importance of the spatial measurements as second-order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern.

IMPACTS OF URBAN LAND USE ON CRIME PATTERNS THROUGH GIS APPLICATION

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Ahmad Nazri Muhamad Ludin ◽  
Norsiah Abd. Aziz ◽  
Nooraini Hj Yusoff ◽  
Wan Juliyana Wan Abd Razak
Keyword(s):  
Land Use ◽  
Drug Abuse ◽  
Land Use Planning ◽  
Urban Areas ◽  
Spatial Relationship ◽  
Police Department ◽  
Strong Relationship ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Patterns

Land use planning plays a crucial role in creating a balance between the needs of society, physical development and the ecosystem. However, most often poor planning and displacement of land uses particularly in urban areas contribute to social ills such as drug abuse and criminal activities. This research explains the spatial relationship of drug abuse and other criminal activities on urban land use planning and their implications on the society at large. Spatial statistics was used to show patterns, trends and spatial relationships of crimes and land use planning. Data on crime incidents were obtained from the Royal Malaysia Police Department whilst cases of drug abuse were collected from the National Anti-Drug Agency (AADK). Analysis of the data together with digital land use maps produced by Arnpang Jaya Municipal Council, showed the distribution of crime incidents and drug abuse in the area. Findings of the study also indicated that, there was a strong relationship between petty crimes, drng abuse and land use patterns. These criminal activities tend to concentrate in residential and commercial areas of the study area.

scholarly journals Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Growth ◽  
Smart City ◽  
Urban Land ◽  
Urban Land Use ◽  
Growth Simulation ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

scholarly journals From centralised planning to collaborative urban land use planning: The case of Wat Ket, Chiang Mai, Thailand

2021 ◽  
Vol 4 (1) ◽  
pp. 100154
Author(s):  
Somporn Sangawongse ◽  
Robert Fisher ◽  
Sidhinat Prabudhanitisarn
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Urban Land ◽  
Urban Land Use ◽  
Chiang Mai
Sign in / Sign up

Export Citation Format

Share Document