Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Wang, Jianzhu; Maduako, Ikechukwu

doi:10.1080/22797254.2017.1419831

Cited by 101 publications

(69 citation statements)

References 52 publications

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“…Several land use change models are developed to explore the drivers of land use/land cover change and to simulate future land use patterns (e.g. Hallowell & Baran, 2013;Kryvobokov, Mercier, Bonnafous, & Bouf, 2015;Puertas, Henríquez, & Meza, 2014;Wang & Maduako, 2018). The existing modeling approaches generally adopt cellular automata (CA), Agent-based (AB), urban-economic discrete-choice and/or statistical models.…”

Section: Introductionmentioning

confidence: 99%

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

Mustafà

Rienow

Saadi

et al. 2018

European Journal of Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

Mustafà

Rienow

Saadi

et al. 2018

European Journal of Remote Sensing

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“…Over recent decades, GIS and remote sensing (RS) technologies have been used as the main components of geo-simulation modeling processes to integrate spatiotemporal data and analyze the physical growth of cities (Arsanjani, Helbich, & de Noronha Vaz, 2013;Arsanjani, Fibaek, & Vaz, 2018;Xu, Huang, Ding, Mei, & Qin, 2018). Several models have been integrated into GIS to predict land use/cover changes and urban growth; examples include artificial neural networks (ANNs) (Pijanowski, Brown, Shellito, & Manik, 2002), agent-based models (Arsanjani, Helbich, & de Noronha Vaz, 2013;Benenson, 1998), statistical models (Cheng & Masser, 2003), slope-land use-excluded-urban-transportation and hillshade (SLEUTH) (Liu et al, 2017), dynamic urban evolutionary modeling (DUEM) (Xie, 1996), conversion of land use and its effects (CLUE) (Veldkamp & Fresco, 1996), conversion of land use and its effects at small regional extent (CLUE-S) (Verburg, 2002), multi-city sustainable regional urban growth simulation (MSRUGS) (Xie & Fan, 2014), land change modeler (LCM) (Abuelaish & Olmedo, 2016;Wang & Maduako, 2018), multinomial logistic regression (Mustafa et al, 2018), Dinamica® (Rodrigues & Soares-Filho, 2018), spatially explicit regional growth model (SERGoM) (Theobald, 2005), land-use change analysis system (LUCAS) (Sleeter, Wood, Soulard, & Wilson, 2017), cellular automata (CA)-Markov (Arsanjani, Helbich, Kainz, et al, 2013;Firozjaei, Nematollahi, et al, 2019) and direction-based CA-Markov (Firozjaei, Kiavarz, Alavipanah, Lakes, & Qureshi, 2018).…”

Section: Introductionmentioning

confidence: 99%

An urban growth simulation model based on integration of local weights and decision risk values

Firozjaei

Seddighi

Jelokhani‐Niaraki

2020

Transactions in GIS

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Monitoring and managing the rapid growth of cities, especially in developing countries, highlights the need for appropriate spatio‐temporal models to predict urban growth. The parameters affecting the spatio‐temporal analysis of urban growth play a key role in the prediction results. This study proposes an urban growth simulation model by integrating local subjective–objective weights and decision risk values (ORness) into a CA–Markov model. This involves the use of ordered weighting averaging as one of the multicriteria decision analysis methods to combine the weights and degree of risk for generating a variety of risk‐averse and/or risk‐taking urban growth prediction scenarios. The proposed model has been applied to predict the physical growth of Babol city, located in Mazandaran, Iran. The results indicate that the degrees of ORness = 0.3 and ORness = 0.9 yield better prediction results in the case of using the local and global weighting strategies, respectively. Furthermore, the overall accuracy of local and global weighting strategies at different degrees of risk was 87.6 and 86.8, respectively. This implies that the use of local subjective–objective weights leads to more accurate results than the global weights for simulating urban growth.

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“…When the investigation of LC change is of concern, most studies-even recent ones-mainly focused on separately performing the classification of images with different dates and performing a change detection analysis on the thematic maps produced by the classification [28][29][30]. On the other hand, Wulder et al (2018) [31] pointed out the expansion on the availability of freely available and spatially and spectrally compatible satellite images with Landsat and Sentinel missions, which triggers high temporal and multi-sensor data analysis.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

Algancı

2019

IJGI

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Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method.

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Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Cited by 101 publications

References 52 publications

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

An urban growth simulation model based on integration of local weights and decision risk values

Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

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