Urban land use/cover mapping and change detection analysis using time series satellite images

Document Type : Original Article

Authors

1 Academic Member/University of Tabriz

2 Department of Remote Sensing and GIS. Faculty of Planning and Environmental Sciences. University of Tabriz

3 M.A Student in Remote Sensing and Geographic Information System, University of Tabriz

10.22034/rsgi.2023.16823

Land use and land cover change have been among the most important perceptible changes taking place around us. Although perceptible, the magnitude, variety and the spatial variability of the changes taking place has made the quantification and assessment of land use and land cover changes a challenge to scientists. Furthermore, since most of the land use and land cover changes are directly influenced by human activities, they rarely follow standard ecological theories. The Remote Sensing and Geographic Information System has proved to be very important in assessing and analyzing land use and land cover changes. Satellite-based Remote Sensing, by virtue of its ability to provide synoptic information of land use and land cover at a particular time and location, has revolutionized the study of land use and land cover change. The temporal information on land use and land cover helps identify the areas of change in a region. The use of Geoinformatics has enabled us to assign spatial connotations to land use land cover changes, namely, population pressure, climate, terrain, etc which drive these changes. This has helped scientists to quantify these tools and to predict various scenarios. The purpose of this paper is to detect and evaluate land use and land cover changes (LULCC) of  Khanaqin urban area over 20 years using remote sensing techniques and Landsat dataset for years 2000, 2010, and 2020. For this purpose supervised classification algorithm and maximum likelihood method has been used. Results show that Water lands, Built-up area, and Vegetation areas increased from 2000 to 2020 in the last 20 years while Barren lands, and Agricultural mixed lands had decreased. According to the analysis and results obtained, this research can be useful in the field of regional and environmental management in the city of Khanaqin and in the field of urban planning and management and research decisions in this region can be used.

A Giraldo,M.Giraldo, M., S Chaudhari,L., O Schulz,L.(2012). Land-use and land-cover assessment for the study of lifestyle change in a rural Mexican community: The Maycoba Project. Giraldo et al. International Journal of Health Geographics.
Abd El-Kawy, O. R., Rød, J. K., Ismail, H. A., & Suliman, A. S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied geography, 31(2), 483-494.
Alawamy, J. S., Balasundram, S. K., & Boon Sung, C. T. (2020). Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya using time-series landsat data from 1985 to 2017. Sustainability, 12(11), 4490.
Al-doski, J., Mansor, S. B., & Shafri, H. Z. M. (2013). Change detection process and techniques. Civil and Environmental Research, 3(10).
Alqurashi, A. F., Kumar, L., & Sinha, P. (2016). Urban land cover change modelling using timeseries satellite images: A case study of urban growth in five cities of Saudi Arabia. Remote Sensing, 8(10), 838.
Chang,Y.Chang, Y., Hou,K.Hou, K., Li,X., Zhang,Y., Chen,P.(2018). Review of Land Use and Land Cover Change research progress.OPprogress. OP Conf. Series: Earth and Environmental Science 113. 
Erasu, D. (2017). Remote sensing-based urban land use/land cover change detection and monitoring. Journal of Remote Sensing & GIS, 6(2), 5.
Feizizadeh, B., Mohammadzade Alajujeh, K., Lakes, T., Blaschke, T., & Omarzadeh, D. (2021). A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment. GIScience & Remote Sensing, 58(8), 1543-1570. Grigorescu, I., Kucsicsa, G., Popovici, E. A., Mitrică, B., Mocanu, I., & Dumitraşcu, M. (2021). Modelling land use/cover change to assess future urban sprawl in Romania. Geocarto International, 36(7), 721-739.
Hao, S., Zhu, F., & Cui, Y. (2021). Land use and land cover change detection and spatial distribution on the Tibetan Plateau. Scientific Reports, 11(1), 1-13.
Hashim, B. M., Sultan, M. A., Attyia, M. N., Al Maliki, A. A., & Al-Ansari, N. (2019). Change detection and impact of climate changes to Iraqi southern marshes using Landsat 2 Mss, Landsat 8 Oli and sentinel 2 Msi data and Gis applications. Applied Sciences, 9(10).
Hassan, Z., Shabbir, R., Ahmad, S. S., Malik, A. H., Aziz, N., Butt, A., & Erum, S. (2016). Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan. SpringerPlus, 5(1), 1-11.
Hemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021). A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth. Remote Sensing, 13(15), 2869.
Hu,Y.Hu, Y., Batunacun., Zhen,L.Zhen, L., Zhuang,D.(2019). Assessment of Land-Use and Land-Cover Change in Guangxi, China. Scientific Reports, 9:2189.
Kafi, K. M., Shafri, H. Z. M., & Shariff, A. B. M. (2014, June). An analysis of LULC change detection using remotely sensed data; A Case study of Bauchi City. In IOP conference series: Earth and environmental science (Vol. 20, No. 1, p. 012056). IOP Publishing.
Leta,K,M., Demissie,T,A., Tranckner ,J. (2021). Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia. Sustainability, 13, 3740
Li, B., & Zhou, Q. (2009). Accuracy assessment on multitemporal landcover change detection using a trajectory error matrix. International Journal of Remote Sensing, 30(5), 1283-1296.
Mustafa, Y. T., Ali, R. T., & Saleh, R. M. (2012). Monitoring and evaluating land cover change in the Duhok city, Kurdistan regionRegion-Iraq, by using remote sensing and GIS. International Journal of Engineering Inventions, 1(11), 28-33.
Näschen, K., Diekkrüger, B., Evers, M., Höllermann, B., Steinbach, S., & Thonfeld, F. (2019). The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios. Sustainability, 11(24), 7083.
Nistor, C., Vîrghileanu, M., Cârlan, I., Mihai, B. A., Toma, L., & Olariu, B. (2021). Remote Sensing-Based Analysis of Urban Landscape Change in the City of Bucharest, Romania. Remote Sensing, 13(12), 2323.
Norovsuren, B., Tseveen, B., Batomunkuev, V., Renchin, T., Natsagdorj, E., Yangiv, A., & Mart, Z. (2019, November). Land cover classification using maximum likelihood method (2000 and 2019) at Khandgait valley in Mongolia. In IOP Conference Series: Earth and Environmental Science (Vol. 381, No. 1, p. 012054). IOP Publishing.
Othman, A. A., Al-Saady, Y. I., Al-Khafaji, A. K., & Gloaguen, R. (2014). Environmental change detection in the central part of Iraq using remote sensing data and GIS. Arabian Journal of Geosciences, 7(3), 1017-1028.
Pickering, J., Tyukavina, A., Khan, A., Potapov, P., Adusei, B., Hansen, M. C., & Lima, A. (2021). Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of
PlanetScope Imagery                for Cropland and Tree-Cover Loss Area Estimation. Remote
Sensing, 13(11), 2191.
Roy,PRoy, P.S, Roy,A.Roy, A.(2010). Land Use and Land Cover Change: A Remote Sensing & GIS Perspective. Journal of the Indian Institute of Science VOL 90:4 Oct–Dec 2010 journal.library.iisc.ernet.in
Shivakumar, B. R., & Rajashekararadhya, S. V. (2018). Investigation on land cover mapping capability of maximum likelihood classifier: a case study on North Canara, India. Procedia computer science, 143, 579-586.
Stephens, D., & Diesing, M. (2014). A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PloS one, 9(4), e93950.
 
کاربرد سنجش از دور و GIS در علوم محیطی، شماره 5، سال دوم، زمستان 1401، صص
 Application of remote sensing and GIS in environmental sciences, Vol 2, No. 5, Winter 2023, pp. 112-133  24
Tesfaw, A. T., Pfaff, A., Kroner, R. E. G., Qin, S., Medeiros, R., & Mascia, M. B. (2018). Landuse and land-cover change shape the sustainability and impacts of protected areas. Proceedings of the National Academy of Sciences, 115(9), 2084-2089.
Tewabe, D., & Fentahun, T. (2020). Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science, 6(1), 1778998.
Traore,T.Traore, T., Lee,MLee, M,S., Rasul,A., Balew,A.(2021). Assessment of land use/land cover changes and their impacts on land surface temperature in Bangui (the capital of Central African Republic). Environmental Challenges 4.
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC medical informatics and decision making, 19(1), 1-16.
Viana, C. M., Oliveira, S., Oliveira, S. C., & Rocha, J. (2019). Land use/land cover change detection and urban sprawl analysis. In Spatial modeling in GIS and R for earth and environmental sciences (pp. 621-651). Elsevier.
Volume 2, Issue 5 - Serial Number 5
March 2023
Pages 135-112
  • Receive Date: 24 July 2023
  • Revise Date: 10 August 2023
  • Accept Date: 27 August 2023