Monitoring Urban Spatial Growth in Harare Metropolitan Province, Zimbabwe

Kamusoko, Courage; Gamba, Jonah; Murakami, Hitomi

doi:10.4236/ars.2013.24035

Cited by 66 publications

(54 citation statements)

References 34 publications

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“…Currently, the population of Epworth district is estimated to be 161,840 [48]. Given this rapid population growth and the ensuing urbanisation [49], we selected Harare Metropolitan Province to test the RF-CA model. In addition, Harare Metropolitan Province is characterised by urban growth patterns such as extension, infill and leapfrog developments, which are also observed in other cities in sub-Saharan Africa [17].…”

Section: Study Area and Datamentioning

confidence: 99%

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Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

Kamusoko¹,

Gamba

2015

IJGI

Self Cite

165

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Sustainable urban planning and management require reliable land change models, which can be used to improve decision making. The objective of this study was to test a random forest-cellular automata (RF-CA) model, which combines random forest (RF) and cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and components of agreement and disagreement statistics were used to validate the RF-CA model. Furthermore, the RF-CA model was compared with support vector machine cellular automata (SVM-CA) and logistic regression cellular automata (LR-CA) models. Results show that the RF-CA model outperformed the SVM-CA and LR-CA models. The RF-CA model had a Kappa simulation (KSimulation) accuracy of 0.51 (with a figure of merit statistic of 47%), while SVM-CA and LR-CA models had a KSimulation accuracy of 0.39 and −0.22 (with figure of merit statistics of 39% and 6%), respectively. Generally, the RF-CA model was relatively accurate at allocating "non-built-up to built-up" changes as reflected by the correct "non-built-up to built-up" components of agreement of 15%. The performance of the RF-CA model was attributed to the relatively accurate RF transition potential maps. Therefore, this study highlights the potential of the RF-CA model for simulating urban growth.

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Section: Study Area and Datamentioning

confidence: 99%

“…Land use/cover maps were classified from Landsat imagery for 1984,2002,2008,2013 and validated using anniversary and near-anniversary reference data [49]. Overall accuracy levels for the four dates range from 86% to 93% [49].…”

Section: Study Area and Datamentioning

confidence: 99%

Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

Kamusoko¹,

Gamba

2015

IJGI

Self Cite

165

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“…Unlike in Freetown, the growth of Bo town has led to a reduction in the area of the densely vegetated LULC category between 1985 and 2015. As observed in Bo town, in most studies, the proportion of total area occupied by dense vegetation decreases with continuous urban expansion [26,27,53,54]. Kamusoko et al [54] observed that the expansion of built-up areas in Harare Zimbabwe pushed most dense vegetation locations outwards to the peripheries of the city.…”

Section: Discussionmentioning

confidence: 86%

Comparative Analysis of Responses of Land Surface Temperature to Long-Term Land Use/Cover Changes between a Coastal and Inland City: A Case of Freetown and Bo Town in Sierra Leone

Tarawally

Hou

et al. 2018

Remote Sensing

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Urban growth and its associated expansion of built-up areas are expected to continue through to the twenty second century and at a faster pace in developing countries. This has the potential to increase thermal discomfort and heat-related distress. There is thus a need to monitor growth patterns, especially in resource constrained countries such as Africa, where few studies have so far been conducted. In view of this, this study compares urban growth and temperature response patterns in Freetown and Bo town in Sierra Leone. Multispectral Landsat images obtained in 1998, 2000, 2007, and 2015 are used to quantify growth and land surface temperature responses. The contribution index (CI) is used to explain how changes per land use and land cover class (LULC) contributed to average city surface temperatures. The population size of Freetown was about eight times greater than in Bo town. Landsat data mapped urban growth patterns with a high accuracy (Overall Accuracy > 80%) for both cities. Significant changes in LULC were noted in Freetown, characterized by a 114 km 2 decrease in agriculture area, 23 km 2 increase in dense vegetation, and 77 km 2 increase in built-up area. Between 1998 and 2015, built-up area increased by 16 km 2 , while dense vegetation area decreased by 14 km 2 in Bo town. Average surface temperature increased from 23.7 to 25.5 • C in Freetown and from 24.9 to 28.2 • C in Bo town during the same period. Despite the larger population size and greater built-up extent, as well as expansion rate, Freetown was 2 • C cooler than Bo town in all periods. The low temperatures are attributed to proximity to sea and the very large proportion of vegetation surrounding the city. Even close to the sea and abundant vegetation, the built-up area had an elevated temperature compared to the surroundings. The findings are important for formulating heat mitigation strategies for both inland and coastal cities in developing countries.

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“…Additionally, the variations in the level of complexity of urban land use/cover (LUC) among different localities limits most methods from being applied under a different set of conditions [5]. For example, most of the noted improvements in urban land use/cover classification using advanced classification techniques have been conducted in developed countries, which are characterised by a highly developed urban built-up environment and well-planned ULU system [13,14]. On the other hand, SSA cities are characterised by urban environments with a complex mix of ULUs, usually displaying chaotic spatial patterns dominated by unplanned/informal settlements haphazardly located close to urban growth centres, such as the Central Business District and commercial and industrial areas.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, despite ULU classification being an important issue in the remote sensing literature [12] and detailed ULU information being important to urban planners and policy makers [19], very few urban studies have addressed ULU in SSA cities. Actually, most of the urban studies using remote sensing and GIS techniques in SSA consider all ULUs as one individual category, often referred to as built-up land (e.g., [13,14,20].) The broad simplification of different ULU types into one class severely limits information that is essential for urban development planning.…”

Section: Introductionmentioning

confidence: 99%

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

Simwanda

Murayama

2017

IJGI

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For most sub-Saharan African (SSA) cities, in order to control the historically unplanned urban growth and stimulate sustainable future urban development, there is a need for accurate identification of the past and present urban land use (ULU). However, studies addressing ULU classification in SSA cities are lacking. In this study, we developed an integrated approach of remote sensing and Geographical Information System (GIS) techniques to classify ULU in the developing SSA city of Lusaka. First, we defined six ULU classes (i.e., unplanned high density residential; unplanned low density residential; planned medium-high density residential; planned low density residential; commercial and industrial; public institutions and service areas). ULU parcels, created using road networks as homogenous units separating ULU classes, were used to classify ULU. We utilised the combined detail of cadastral and land use data plus high-resolution Google Earth imagery to infer ULU and classify the parcels. For residential ULU, we also created density thresholds for accurate separation of the classes. We then used the classified ULU parcels for post-classification sorting of built-up pixels extracted from three Landsat TM/ETM+ imageries (1990, 2000, and 2010) into respective ULU classes. Three ULU maps were produced with overall accuracy values of 84.09% to 85.86%. The maps provide information that is relevant to urban planners and policy makers for sustainable future urban planning of Lusaka City. The study also provides an insight for ULU classification in SSA cities with complex urban landscapes similar to Lusaka.

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Monitoring Urban Spatial Growth in Harare Metropolitan Province, Zimbabwe

Cited by 66 publications

References 34 publications

Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

Comparative Analysis of Responses of Land Surface Temperature to Long-Term Land Use/Cover Changes between a Coastal and Inland City: A Case of Freetown and Bo Town in Sierra Leone

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

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