IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT (IJGEM )

E-ISSN 2504-8821
P-ISSN 2695-1878
VOL. 10 NO. 8 2024
DOI: 10.56201/ijgem.v10.no8.2024.pg29.38


Preliminary Study on the Analysis of Land Use/Land Cover of Yola North and South Local Government Areas of Adamawa State, Nigeria

Sulieman Umar Bello, Abdulqadir Abubakar Sadiq & Aisha Usman Ardo


Abstract


This present paper is an attempt on the preliminary study on the analysis of land use/land cover (LULC) of Yola North and South Local Government Areas of Adamawa State, Nigeria. Landsat 8 imagery techniques was employed in acquisition, processing and analyzing of image and algorithm classification of LULC data for the year 2023 in order to fill in the gap in the area. It was revealed that the majority of the area is covered by vegetation (46.98%) and built-up areas (23.12%), followed by water bodies (13.54%), farmland (8.14%), and bare surfaces (8.22%). In addition, the confusion matrix and ground truth information were found to allow for an assessment of the accuracy of the classification algorithm, highlighting areas of agreement and discrepancy between predicted and actual land cover classes. Similarly, commission and omission errors further elucidate the reliability of the classified results, while class accuracy metrics provides detailed evaluation of the algorithm's performance for individual land cover classes. It is therefore recommends that government and different NGOs should take steps to provide training about the impact of land use and land cover change and to foster collaboration among stakeholders for effective planning utilization and management of land resources for sustainable development.


keywords:

Analysis, Land use, Land cover, Preliminary, Yola.


References:


Aliyu, A., Ismail, M., Zubairu. S.M.,Gwio-kura. I.Y., Abdullahi, A., Abubakar B.A and Mansur.

M. (2023). Analysis of land use and land cover change using machine learning algorithm

in Yola North Local Government Area of Adamawa State, Nigeria. Environmental

Monitoring and Assessment. 195:1470. https:doi.org/10.1007/s10061-023-12112-w
Babalola, S.O., Musa, A.A., Adegboyega, I and Ezeomedo, I (2014). Analysis of land use/land

cover of Girie, Yola North and South Local Government Areas of Adamawa State,

Nigeria Using satellite imagery. FUTYJournal of the Environment. Vol.8:1. ISSN:1597-
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely

sensed data. Remote Sensing of Environment, 37(1), 35-46. https://doi.org/10.1016/0034-

4257(91)90048-B
Dogan, E., Turkekul, B., (2016). CO2 emissions, real output, energy consumption, trade,

urbanization and financial development: testing the EKC hypothesis for the USA.

Environ. Sci. Pollut. Res. 23 (2), 1203–1213. https://doi.org/10.1007/s11356-015-5323-8.

Fantaye, Y., Mohamed Motuma and Gebrie Tsegaye (2017). Land Use Land Cover Change

Analysis using Geospatial Tools in Case of Asayita District, Zone one, Afar Region,

Ethiopia. Journal of Resources Development and Management www.iiste.org ISSN 2422-

8397 An International Peer-reviewed Journal Vol.29, 2017
Fu, P., Weng, Q.H., 2016. A time series analysis of urbanization induced land use and land cover

change and its impact on land surface temperature with landsat imagery. Remote Sens.

Environ. 175 (4), 205–214. https://doi.org/10.1016/j.rse.2015.12.040.
Fung, T., and LeDrew, E. (1988). For change detection using various accuracy. Photogramm

Eng
Remote
Sens,
54(10),
1449-1454.
http://www.asprs.org/wp-

content/uploads/pers/1988journal/oct/1988_oct_1449-1454.pdf
Mahmudul Hasan, Rashidul Islam, Md. Saifur Rahman, Md. Ibrahim, Md. Shamsuzzoha, Ruma

Khanam and A. K. M. Mostafa Zaman (2021). Analysis of Land Use and Land Cover

Changing Patterns of Bangladesh Using Remote Sensing Technology. American Journal

of Environmental Sciences 2021, 17 (3): 64.74 DOI: 10.3844/ajessp.2021.64.74.
Munshisouth, J., Zolnik, C.P., Harris, S.E., 2016. Population genomics of the anthropocene:

urbanization is negatively associated with genomewide variation in white-footed mouse

populations. Evol. Appl. 9 (4), 546–564. https://doi.org/10.1111/eva.12357.
Naeem, S., Cao, C., Fatima, K., et al., 2018. Landscape greening policies based land use/land

cover simulation for Beijing and Islamabad—an implication of sustainable urban

ecosystems. Sustainability 10 (4). https://doi.org/10.3390/su10041049.
Ojima, D. S., Galvin, K. A., & Turner, B. L. (1994). The global impact of land-use change.

BioScience, 44(5), 300-304. https://doi.org/10.2307/1312379
Riggio, J., Kija, H., Masenga, E., Mbwilo, F., Van de Perre, F., & Caro, T. (2018). Sensitivity of

Africa's larger mammals to humans. Journal for Nature Conservation, 43, 136-145.

https://doi.org/10.1016/j.jnc.2018.04.001
Riggio, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover

classification using remote sensing and GIS. International Journal of Geosciences, 8(04),
https://doi.org/10.4236/ijg.2017.84033.
Sadiq, A. A, Sadiqa B. and Surayya A. (2019b).Assessment of
Substantive Causes of Soil
Degradation on Farmlands in Yola South LGA, Adamawa State. Nigeria. International
Journal of Scientific and Research Publications, Volume 9, Issue 4, April 2019 537
http://dx.doi.org/10.29322/IJSRP.9.03.2019.p8865 ISSN 2250-3153 pp
537-546.
Sikarwar, A., and Chattopadhyay, A. (2016). Change in land use-land cover and population

dynamics: A town-level Study of Ahmedabad city sub-District of Gujarat. International

Journal
of
Geomatics
and
Geosciences,
7(2),
225-234.

https://www.academia.edu/download/53442229/My_paper_IJGGS.pdf
Thenkabail, P.S., Schull, M., Turral, H., 2005. Ganges and indus river basin land use/land cover

(lulc) and irrigated area mapping using continuous streams of modis data. Remote Sens.

Environ. 95 (3), 317– 341. https://doi.org/10.1016/j.rse.2004.12.018.
Unger Holtz, T. S. (2007). Introductory digital image processing: A remote sensing perspective.

https://doi.org/10.2113/gseegeosci.13.1.89

Zhang, H.S., Lin, H., Li, Y., 2017a. Impacts of feature normalization on optical and sar data

fusion for land use/land cover classification. IEEE Geosci. Remote Sens. Lett. 12 (5),

1061–1065. https://doi.org/10.1109/ LGRS.2014.2377722.
Zhang, M., Zeng, Y., Zhu, Y., 2017b. Wetland mapping of donting lake basin based on time-

series
modis
data
and
object-oriented
method.
J.
Remote
Sens.

https://doi.org/10.11834/jrs.20176129.
Ziwei Deng, Xiang Zhu ?, Qingyun He, Lisha Tang (2019). Land use/land cover classification

using time series Landsat 8 images in a heavily urbanized area. Advances in Space

Research 63 (2019) 2144–2154


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