Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria

Abstract

This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa's fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82%, 92% and 92%, while the AA derived from the error adjusted area (EAA) method stood at 69%, 91% and 91% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11% between 1986 and 2001. In contrast, this value rose to 17% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.

Urban Expansion Occurred at the Expense of Agricultural Lands in the Tarai Region of Nepal from 1989 to 2016

“…The ML approach is one of the classical parametric statistical classifiers and is widely used for LULC classification [53]. Several studies have also evaluated alternative, recently-developed machine-learning algorithms for land-cover classification, such as support vector machine (SVM) methods [54], decision trees (DT) [55], and random-forest models (RF) [56,57]. Schneider [55] observed that the DT and SVM classifiers outperformed the ML classifier in the context of highly dynamic land-cover change and 'fuzzy' multi-signal classes around the Chinese cities of Chengdu, Xi'An, and Kunming.…”

Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

“…To evaluate the consequences of urbanization and the validity of possible NBS, social and environmental scientists are increasingly using highly detailed LULCC models [11,12]. Landcover models have been used to address general questions of landcover change and urbanization around the world [2,9,10,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]; however, only one other study models LULCCs under GI policies [28]. To predict precise landcover transitions and to answer specific questions of policy, future LULCCs need to be modeled at finer scales.…”

Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain

“…Previously, Lee [76] evaluated the SVM algorithms for landslide vulnerability mapping in the Gangwon province of Korea and the results suggested that SVM was quite suitable for a wide range of classification problems, even if the problems were of high dimension and were non-linear separable. Moreover, the approach was applied by Mahmoud et al [71] to monitor urbanization in Abuja, Nigeria. Schneider [70] evaluated the maximum likelihood classifier (ML), decision tree (DT), and SVM algorithms for monitoring land-cover change in urban and peri-urban areas using Landsat satellite data.…”

“…A supervised approach support vector machines (SVM) classifier [18,70] was employed for the classification of LULC and change analysis. Several previous authors have evaluated the performance of SVM algorithms and concluded that it is one of the flexible supervised classifier options with high accuracy [68,[70][71][72][73][74]. SVM is a supervised, non-linear, non-parametric classification technique that is widely used in the remote sensing field due to its specific capability to draw conclusions even with limited training samples [75].…”