Land-Use/Land-Cover Dynamics in Calabar Metropolis Using a Combined Approach of Remote Sensing and GIS

Journal of Geographic Information System, 2018, 10, 398-414 http://www.scirp.org/journal/jgis ISSN Online: 2151-1969 ISSN Print: 2151-1950 Land-Use/Land-Cover Dynamics in Calabar Metropolis Using a Combined Approach of Remote Sensing and GIS M. E. Awuh1*, M. C. Officha2, A. O. Okolie3, I. C. Enete1 1Department of Geography and Meteorology, NAU, Awka, Nigeria 2Department of Architecture, NAU, Awka, Nigeria 3Department of Architecture, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria How to cite this paper: Awuh, M.E., Of- Abstract ficha, M.C., Okolie, A.O. and Enete, I.C. (2018) Land-Use/Land-Cover Dynamics in This paper assessed the dynamics in the land use/land cover (LULC) within Calabar Metropolis Using a Combined patterns of the land use/land cover (LULC) in Calabar metropolis. The ther- Approach of Remote Sensing and GIS. mal imageries for 2002, 2006, 2008, 2010, 2012, 2014 and 2016 were obtained Journal of Geographic Information System, 10, 398-414. and processed using remote sensing and Arc GIS software package in order to https://doi.org/10.4236/jgis.2018.104021 determine the changes that have occurred in the LULC in study area. The result of the LULC thematic maps overall accuracies was computed above 80 Received: June 19, 2018 percent, which indicates an almost perfect agreement. The findings of this Accepted: August 14, 2018 Published: August 17, 2018 study reveal that, LULC classes by the year 2016 have assumed different di- mensions of change from the sizes of their previous sizes in comparison to Copyright © 2018 by authors and their current sizes. Land-use pattern changes in the study area were characte- Scientific Research Publishing Inc. rized by an increase in the built up class, waterbody (though with a slightly This work is licensed under the Creative Commons Attribution International negative change from 2010 to 2012) and a predominant negative trend in License (CC BY 4.0). dense vegetation and bare land classes; thus, indicating that the future changing http://creativecommons.org/licenses/by/4.0/ trends will pose a depleting threat to the overall LULC. This study has shown Open Access that, the changing land use pattern of the area is capable affecting certain cha- racteristics of the environment such as surface temperature. The study re- commends that effort should be made by the government to increase urban vegetation around city centers and outliers by embarking on reforestation. Keywords Remote Sensing, GIS, Calabar, Land Use/Cover Change 1. Introduction Land use/cover (LULC) is an important component to understand the changes DOI: 10.4236/jgis.2018.104021 Aug. 17, 2018 398 Journal of Geographic Information System M. E. Awuh et al. in the environment triggered by the interaction between human and the environment. Population increased to major cities has resulted in urban sprawl at an unprecedented rate, which according to [1] analysis and prediction report, is projected to continue into the next era. The geometric increase in the global population has necessitated the building of services such as, settlements, to ac- commodate the growing population. These activities result in land conversion, such as, forest or plantations, agricultural lands and grasslands to grow imper- vious surfaces such as roads, sidewalks, parking lots, rooftops and bare lands [2] [3] [4]. According to numerous studies [5] [6] [7] [8], land conversion to imper- vious surfaces is one of the main contributors to climate change and variability in different parts of the world which potentially affects the health of urban dwel- lers living in localities that continue to experience LULC changes. More also, land-use/land-cover change contributes significantly to earth-atmosphere interactions, forest fragmentation, and biodiversity loss and has become a major issues for environmental change monitoring and natural resource management [3] [4]. Therefore monitoring land cover dynamics in the urban area, in an ap- propriate and cost effective manner, is very important to local communities and decision makers. It enables the planning, management and conservation of natural resources and the environment. 2. Materials and Method 2.1. Study Area Calabar Metropolis, the study area, is the capital of Cross River State, Nigeria, located at the southern part of the State. It encompasses of Calabar Municipality and Calabar South Local Government Areas and lies between latitudes 4˚50'N and 5˚10'N and longitudes 8˚17'E and 8˚20'E; bounded to the north by Odukpa- ni Local Government Area (LGA) and to the East by Akpabuyo LGA. Calabar Metropolis is sandwiched between the Great Kwa River to the East and the Ca- labar River to the West. The present of urban area is on the eastern bank of the Calabar River. Its growth of the southern part is hindered by the mangrove swamps. It covers an estimated land area of about 274.593 km2 (Figure 1). Cala- bar falls within tropical equatorial (Af) climate of high temperature, high relative humidity and abundant annual rainfall [9]. The annual rainfall is 2750 mm and mean annual average temperature is 26.1˚C. The study area has witnessed a tre- mendous increase in the population of 10,000 estimated at the pre-colonial, to 99,352 in 1993; 328,876, in 1991. The last census in 2006 put the population to 371,022 [10]. The population growth of Calabar has been followed by the expan- sion of its physical boundaries. This increase in the physical boundaries implies a corresponding loss of vegetation and land in the area thereby a direct impact on the micro-climate [11]. 2.2. Image and Pre-Processing Landsat cloud-free imagery were acquired from the NASA web site which DOI: 10.4236/jgis.2018.104021 399 Journal of Geographic Information System M. E. Awuh et al. Figure 1. Map of the study area. comprised of the Thematic Mapper (TM), Enhance Thematic Mapper plus (ETM+) image and the Operational Land Imager (OLI) to determine LULC change within Calabar Metropolis between “2002 and 2016”. The imagery downloaded covered a period of 15 years at an interval of 2 years. The software employed for desktop analysis was ArcGIS. Identifying the study area was the first step of this research which was achieved with the use of an administrative map of Calabar, showing Calabar Municipality and Calabar South LGA. 2.3. Image and Pre-Processing A systematically geometric, radiometric correction was performed to the image data using the Calibration Parameter File (CPF) released by the Earth Resources Observation Systems (EROS) Data Center (EDC), USGS before the satellite image were delivered. The quality of Landsat images were in 1B level. The Landsat images, including the thermal bands, were further rectified to Universal Trans- verse Mercator coordinate system and were re-sampled using the nearest neigh- bor algorithm with a pixel size of 30 by 30 ms for all bands and the Resultant Root Mean Square Error (RMSE) less than 0.5 pixels. A supervised classification DOI: 10.4236/jgis.2018.104021 400 Journal of Geographic Information System M. E. Awuh et al. was performed by creating a training sample and based on spectral signature curves; various land use types and cover classes were identified. Five land use/cover classes where identified (Table 1). The land use was calculated using the LULC profile generation by the SVM algorithm. The urban size, development area and water proportion were ex- tracted directly from the classification images. Here the urban size and development area of Calabar South and Calabar Municipality can be easily calculated from the sum of corresponding land-use/land-cover pixels in the classification images, while the water proportion (the ratio of water area against the total of urban area, including both land and water areas), was computed using the fol- lowing equation: PS= water( S water+ S urban ) (1) where P is the water proportion, Swater is the pixel area of water; Surban is the pixel area of urban-used land. 2.4. Change Detection Cross tabulation was employed to determine quantities of conversions from a particular land cover to another land cover category at a later date [12]. The change matrices based on post classification comparison were obtained. Change that occurred over the study period 2002-2016 (15 years) was analyzed. The ex- tent of the land use and land covers change in the study period was also calculated, the results were presented in maps, charts and tables. 2.5. Land Use Land Cover (LULC) Accuracy Assessment Accuracy assessment tasks were performed on the 2002, 2006, 2008, 2010, 2012, 2014 and 2016 images using Kappa statistics. The Kappa statistic is generally accepted as a measure of classification accuracy for both the model as well as user of the model of classification [13]. These tasks consisted of an overall classification accuracy, Kappa statistics, and error matrix reports Kappa values are cha- racterized as <0 as indicative of no agreements and 0 - 0.2 as slight, 0.2 - 0.41 as fair, 0.41 - 1160.60 as moderate, 0.60 - 0.80 as substantial and 0.81 - 1.0 as almost perfect agreement [13]. The classification accuracy report calculates the statistics of percentages of accuracy relative to error matrix results. The error matrix report simply compares the historical (reference) values to the assigned class Table 1. Land use/cover of calabar south and calabar metropolis. No. Class Name 1 Waterbody 2 Sparse Vegetation 3 Dense Vegetation 4 Built-Up 5 Barelands DOI: 10.4236/jgis.2018.104021 401 Journal of Geographic Information System M. E. Awuh et al. values. Kappa statistics measure the ability to provide information about a single matrix as well as to compare matrices [14].

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