Quantification of the land use/land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone

Rapid and unplanned urban growth has adverse environmental and social consequences. This is prominent in sub-Saharan Africa where the urbanisation rate is high and characterised by the proliferation of informal settlements. It is, therefore, crucial that urban land use/land cover (LULC) changes be investigated in order to enhance effective planning and sustainable growth. In this paper, the spatial and temporal LULC changes in Blantyre city were studied using the integration of remotely sensed Landsat imageries of 1994, 2007 and 2018, and a geographic information system (GIS). The supervised classification method using the support vector machine algorithm was applied to generate the LULC maps. The study also analysed the transition matrices derived from the classified map to identify prominent processes of changes for planning prioritisation. The results showed that the built-up class, which included urban structures such as residential, industrial, commercial and public installations, increased in the 24-year study period. On the contrary, bare land, which included vacant lands, open spaces with little or no vegetation, hilly clear-cut areas and other fallow land, declined over the study period. This was also the case with the vegetation class (i.e., forests, parks, permanent tree-covered areas and shrubs). The post-classification results revealed that the LULC changes during the second period (2007–2018) were faster compared to the first period (1994–2007). Furthermore, the results revealed that the increase in built-up areas systematically targeted the bare land and avoided the vegetated areas, and that the vegetated areas were systematically cleared to bare land during the study period (1994–2018). The findings of this study have revealed the pressure of human activities on the land and natural environment in Blantyre and provided the basis for sustainable urban planning and development in Blantyre city.

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Employing Spatial Metrics in Urban Land-use/Land-cover Mapping

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.73.12.1403 ◽

2007 ◽

Vol 73 (12) ◽

pp. 1403-1415 ◽

Cited By ~ 15

Author(s):

Soe W. Myint ◽

Elizabeth A. Wentz ◽

Sam J. Purkis

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Land ◽

Urban Land Use ◽

Land Cover Mapping ◽

Land Use Land Cover ◽

Spatial Metrics

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Use of Landsat Thematic Mapper for urban land use/land cover mapping

Journal of the Indian Society of Remote Sensing ◽

10.1007/bf02995834 ◽

1989 ◽

Vol 17 (3) ◽

pp. 85-99 ◽

Cited By ~ 1

Author(s):

A. P. Subudhi ◽

N. D. Sharma ◽

Debajit Mishra

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Land ◽

Urban Land Use ◽

Thematic Mapper ◽

Land Cover Mapping ◽

Landsat Thematic Mapper ◽

Land Use Land Cover

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Machine Learning Algorithms for Urban Land Use Planning: A Review

Urban Science ◽

10.3390/urbansci5030068 ◽

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.

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Correction to: Spatiotemporal trends of urban land use/land cover and green infrastructure change in two Ethiopian cities: Bahir Dar and Hawassa

ENVIRONMENTAL SYSTEMS RESEARCH ◽

10.1186/s40068-018-0114-0 ◽

2018 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Kassahun Gashu ◽

Tegegne Gebre‑Egziabher

Keyword(s):

Land Use ◽

Land Cover ◽

Green Infrastructure ◽

Urban Land ◽

Urban Land Use ◽

Bahir Dar ◽

Land Use Land Cover ◽

Spatiotemporal Trends

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Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2020.05.07 ◽

2020 ◽

Vol 11 (5) ◽

pp. 529-535

Author(s):

Dan Abudu ◽

Nigar Sultana Parvin ◽

Geoffrey Andogah

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Urban Areas ◽

Land Use Changes ◽

Land Cover Classification ◽

Urban Land ◽

Urban Land Use ◽

Land Use Land Cover ◽

Sensing Technology

Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets.

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