Land use and land cover change Assessment in Kaduna metropolis, northern Nigeria (1995 – 2015)

2017 ◽  
Vol 6 (8) ◽  
Author(s):  
Abdu Yaro ◽  
Lawal Abdurrashid ◽  
Yahaya Sani ◽  
Mukhtar Usman ◽  
Jerome John
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Rapid Changes ◽  
Gis Environment ◽  
Economic Developments ◽  
Urban Population Growth ◽  
Gis Software

Land use/land cover changes were studied in Kaduna metropolis from 1995 to 2015 through the application of remote sensing data in GIS environment. The data used comprise of Landsat TM of 1995, ETM+ of 2005 and OLI of 2015. Colour composites using Bands 4, 3 and 2 were made for TM and ETM+ while bands 5, 4 and 3 was made for OLI image. The images were resampled to a common UTM and were radiometrically calibrated using the Chavez's Cos (t) model. A total of 220 GCPs were used for supervised classification of identified LULC classes. Maximum likelihood algorithm was used in Ilwis 3.7 GIS software for the classification of the three datasets before changes were estimated and compared among the datasets. Results revealed that vegetation was estimated as the largest land cover during 1995 and 2005 with about 783.0 km2 (65.51%) and 538.9km2 (45.08%) respectively, but had been converted to urban built-up by 2015 by about 320.3km2 (26.80%). This translates to about 462.7km2 (38.71%) of vegetation conversion to urban built-up and related uses within a period of 20 years only. The study concluded that rapid changes observed in LULC in Kaduna metropolis were largely occasioned by rapid urban population growth, urbanization, migration and socio-economic developments witnessed during the last two decades of political dispensation.

scholarly journals Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

10.29007/jvz3 ◽  
2018 ◽  
Author(s):  
Mohamed Mostafa Mohamed ◽  
Samy Elmahdy
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Confusion Matrix ◽  
Land Development ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Spectral Angle Mapper ◽  
Fast Pace

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals Assessment of Change in the Built-Up Index of Uyo Metropolis and Its Environs Using Remote Sensing

2019 ◽  
Vol 4 (6) ◽  
pp. 84-89 ◽  
Author(s):  
Aniekan Effiong Eyoh ◽  
Akwaowo Ekpa
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Quantitative Analysis ◽  
Land Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Positive Growth ◽  
Bare Land ◽  
Erdas Imagine

The research was aim at assessing the change in the Built-up Index of Uyo metropolis and its environs from 1986 to 2018, using remote sensing data. To achieve this, a quantitative analysis of changes in land use/land cover within the study area was undertaken using remote sensing dataset of Landsat TM, ETM+ and OLI sensor images of 1986, 2000 and 2018 respectively. Supervised classification, using the maximum likelihood algorithm, was used to classify the study area into four major land use/land cover types; built-up land, bare land/agricultural land, primary swamp vegetation and secondary vegetation. Image processing was carried out using ERDAS IMAGINE and ArcGIS software. The Normalised Difference Built-up Index (NDBI) was calculated to obtain the built-up index for the study area in 1986, 2000 and 2018 as -0.20 to +0.45, -0.13 to +0.55 and -0.19 to +0.63 respectively. The result of the quantitative analysis of changes in land use/land cover indicated that Built-up Land had been on a constant and steady positive growth from 6.76% in 1986 to 11.29% in 2000 and 44.04% in 2018.

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