Classification and Assessment of the land use changes using Landsat satellite imagery (Case Study: Rey Plain)

Subject Areas : environmental management

pegah mohammadpour ¹ , reza Arjmandi ² , Amir Hesam Hasani ³ , Jamal Ghoddousi ⁴

1 - PH. D Student Department of Environmental Management, Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Associate Professor, Department of Environmental Management, Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran .*(Corresponding Author)
3 - Professor of Environmental Engineering, Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Associate Professor, Faculty Member of Soil and Watershed Management Institute. Tehran, Iran.

Received: 2022-04-17 Accepted : 2022-06-29 Published : 2022-09-23

Keywords: Rey Plain, maximum likelihood algorithm, Remote sensing, Supervised Classification, land use,

Abstract :

Background and Purpose :Land use change due to human activities is one of the important issues in regional and development planning. Lack of attention to land use changes in recent decades has created many environmental problems such as pollution of water resources, soil, etc. Therefore, the study and analysis of land use at different scales with the aim of sustainable development in the proper management of the environment and natural resources is essential. Remote sensing and GIS provide the necessary and sufficient facilities for extracting and updating land use maps and determining its amount. This study aims to investigate changes in land use conversion using remote sensing technology and satellite images for four periods It has been done for 3 years, from 2008 to 2020 in Rey plain. Material and Methodology: TM and OLI satellite images of Landsat 5 and 8 satellites were used to prepare land use maps for the studied years. Then the satellite images were monitored by classification method and were classified using the maximum neighborhood probability algorithm with an overall accuracy of 87.39 to 95.78% and a kappa coefficient of 85 to 93% in four user classes.. In the next step, land use maps were compared. Results: Based on the analysis, it was found that in the period under study, 26.07 square kilometers of Barren lands in this area has changed to agricultural, industrial and residential lands. As a result, the area of Barren lands has decreased and other uses have increased during the studied years. , So that the area of land with agricultural, industrial and residential use has increased by 14.66 square kilometers, 9.77 square kilometers, 1.64 square kilometers, respectively. Discussion and Conclusion: The results of the research show that the most important factor in land use change in the region is human activities that have caused many changes in land use. Analysis of the area of these uses showed that the level of agricultural land has increased significantly, mainly this increase. The result is the conversion of agricultural land use. Finally, the results of this study indicate that the combination of remote sensing techniques and GIS in the implementation of models for assessing spatial-temporal changes in land use, in order to know the type and percentage of land use and the extent of their changes, is very effective. The title of a management parameter can help planners of different executive departments in monitoring and managing the environment.  

References:

• Rawat, J., Manish Kumar, b., 2015. National Authority for Remote Sensing and Space Sciences. The Egyptian Journal of Remote Sensing and Space Sciences, vol. 18, pp. 77–84


• Mesbahzadeh, T., Soleimani Sardoo, F., 2019. Effects of land use change on agricultural water quality in Kerman Plain using remote sensing technique. Environmental Sciences Journal, vol.16, pp. 33-46


• Lynn, I., Manderson, A., Page, M., Harmsworth, G., Eyles, G., Douglas, G., Mackay, A., 2009. Land Use Capability Survey Handbook. New Zealand handbook for the classification of land, pp. 8-12


• Assefa, b., 2010. Analysis of Impact of Resettlement on Land Use and Land Cover Dynamics and Change Modeling: The Case of Selected Resettlement Kebeles in Gimbo Woreda, Kafa Zone. A Thesis Submitted to the School of Graduate Studies of Addis Ababa University for the Degree of Master of Science in Environmental Science, pp. 5-18


• Akhtar Alam, M., Sultan Bhat, M., 2020. Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal, vol. 85, pp. 1529–1543


• Feizizadeh, B., 2017. Modeling the Trends of the Land Use/Cover Change and Its Impacts on the Erosion System of the Allavian Dam Based on the Remote Sensing and GIS Techniques. Journal of Hydrogeomorphology, Vol. 3(11), pp.21-38


• Coppin, P., 2014. Review Article Digital change detection methods in ecosystem monitoring. International Journal of Remote Sensing, Vol. 25, pp. 1565-1596


• Brian, W., Michael, B., 2011. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, vol. 31, pp. 525-532


• Shanani Hoveyzeh, S., Zarei, H., 2017. Investigation of Land Use Changes During the Past Two Last Decades (Case Study: Abolabas Basin). Journal of Watershed Management Research, vol. 7 (14), pp. 237-244


• Singh, SK., Mustak, S., Srivastava, PK., Szabó, S., Islam, T., 2015. Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Journal of Environmental Processes, vol. 2(1), pp. 107-115


• Hosseini, S.B., Saremi, A., Noori Gheydari, M., Sedghi, H., Firoozfar, A., Nikbakht, J., 2019. Pixel Based Classificatrion Analyisis of Land Use Land Cover in Tarom Basin. Journal of Soil and Water Resources Conservation, Vol. 8(4), pp. 135-151


• Farokhnia, A., Morid, S., Delavar, M., 2018. Study of Land Use Change in the Urmia Lake Water Shed Based on Landsat-TM Images and

 

Pixel-Based and Object-Based Classification Techniques, Iran J Irrig. Drain, Vol.4(12), pp. 823-839

• Rodríguez Echeverry, J., Echeverría, C., Oyarzún, C., Morales, L., 2018. Impact of land-use change on biodiversity and ecosystem services in the Chilean temperate forests. Landscape Ecology, Vol. 33, pp 439-453


• Kiani salmi, E., Ebrahimi, A., 2018. cover changes in the city of Shahrekord, and predicting its future status, using remote-sensing data and CA-Markov. Spatial Planning, Vol. 8 (1), pp.71-88


• Sabzghabaei, G., Raz, S., Dashti, S., Yousefi Khanghah, S., 2017. Study the Changes of Land Use by the Help of GIS & RS Case Study: AndimeshkCity. Iranian Journal of Geography And Development, vol. 15 (46), pp.35-42


• Mazaheri, M.R., Esfandyari, M., Masihabadi, M. H., Kamali, A., 2014. Monitoring time changes in land use using remote sensing techniques and GIS (Case study: Jiroft, Kerman). Journal of RS and GIS for Natural Resources,Vol. 4 (2), pp. 25-39


• Pandian, M., Rajagopal, N., Sakthivel, G., Amrutha, D.E., 2014. Land use and land cover change detection using remote sensing and GIS in parts of Coimbatore and Tiruppur districts, Tamil Nadu, India. International Journal of Remote Sensing & Geoscience, Vol. 3 (1), pp. 15-20


• Yousefi, M., Farsi, J., 2014. Detection of land -use changes using remote sensing data (Case study: Bojnourd plain). Journal of Geography and Environmental Studies, vol. 2 (7), pp. 95-106


• Mallupattu, P.K., Sreenivasula Reddy, J.R., 2013. Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal, pp.1-6


• Aldoski, J., Mansor, S.B., MohdShafri, H.Z., 2013. Monitoring Land Cover Changes in Halabja City Iraq. International Journal of Sensor and Related Networks, Vol.1, pp. 20-30


• Haque, M.I., Basak, R., 2017. Land cover change detection using GIS and remote sensing techniques: A spatiotemporal study on Tanguar Haor Bangladesh. J.Rem. Sens Space Sci, vol. 20(2), pp. 251-263


• Erener, A., Düzgün,S., Yalciner, A.C., 2012. Evaluating land use/cover change with temporal satellite data and information systems. Procedia Technology, Vol. 1, pp. 385 – 389


• Rafi sharif abad,J., 2015. Investigating the trend of land use changes on the quality of underground water in Yazd-Ardakan Plain. Scientific Research Quarterly of Geography and Regional Planning, Vol 7, Number 1, pp. 189-199.(In Persian)


• Sabzghabaei, G., Raz, S., Dashti, S., Yousefi Khanghah, S., 2017. Study the Changes of Land Use by the Help of GIS & RS Case Study: AndimeshkCity. Iranian Journal of Geography And Development, vol. 15 (46), pp.35-42


• Nasrollahi, M., Investigating the trend of changes in land use and land cover on the status of underground water using satellite images (case study: Dasht Gilangharb). Quarterly Scientific Research Journal of Geographical Information (Sephr), Vol. 23, Number 91, Page 97-89(In Persian)


• Jensen, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd Edition. Prentice Hall: Saddle River


• Song, C., Woodcodk, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effect. Remote Sensing of Environment, vol.75, pp. 230–244


• Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, Vol. 113, PP.893-903


• Kerle, N., janssen. L., huurneman, c., 2004. Principles of remote sensing, 3th edition. issn 1567-5777, Netherland, 540


• Shalaby, A., Tateishi, R., 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography,vol. 27 (1), pp. 28-41


• San, B. T., Suzan, M.L., 2010. Evaluation of different atmospheric correction algorithms for EO-1 Hyperion imagery. International Archives of the Photogrammetry Remote Sensing and Spatial Information Science, Vol. 8, pp. 392-397


• Shanani Hoveyzeh, S.M., Zarei, H., 2017. Investigation of Land Use Changes During the Past Two Last Decades (Case Study: Abolabas Basin). Journal of Watershed Management Research, vol. 7 (14), pp. 237-244


• Yang, X., Lo, C.P., 2002. Using A Time Series Of Satelite Imagery To Detect Land Use And Land Cover Change In The Atlanta, Georgia Metropolitan Area. International Journal of Remote Sensing, vol. 29, pp.1775-1798


• Matthew, M.W., Adler Golden, S.M., Berk, A., Felde, G., Anderson, G.P., Gorodetzky, D., Paswaters, S., Shippert, M., 2002. Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with a Viris data.


• 20-Kaufman, Y.J., Wald, A.E., Remer, L.A., Gao, B.C., Li, R.R., Flynn, L., 1997. The Channel Correlation with Visible Reflectance for Use in Remote Sensing of Aerosol. IEEE Transactions On Geoscience And Remote Sensing, Vol. 35, pp. 1286-1298


• Mountrakis G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66(3), pp. 247-259


• Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B., Bandopadhyay, S., 2008. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences, Vol. 40(4), pp. 409-424


• Omidvar, K., Narangifard, M., Abbasi, H., 2015. Detecting the Changes of land uses and vegetation cover using remote sensing in Yasooj city.Geography and Territorial Spatial Arrangement5, Vol. 16, pp. 111-126


• Chen, J., Zhu, X., Vogelmann, J.E., Gao, F., Jin, S., 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote sensing of environment, Vol. 115, PP 1053-1064


• Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., 2007. Top 10 algorithms in data mining. J Knowl Inf Syst, pp.1–37


• Myint, S.W., Gober, P., Brazel, A., Grossman Clarke, S., Weng, Q., 2011. Per-pixel vs. objectbased classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment1, Vol. 15(5), pp. 1145-1161


• Wijaya, A., Budiharto, R.S., Tosiani, A., Murdiyarso, D., Verchot, L.V., 2015. Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia. The International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences, Vol. 40(7),pp. 557-573


• Rezaei Moghadam, M.H., Andaryani, S., Valizadeh, K., Almaspor, F., 2016. Determine the best algorithm for land use and land cover extraction and changes detecting from Landsat satellite images(Case Study: Sufi chay Basin of Maragheh). Journal of Geographic Space, Vol. 16 (55), pp. 65-85


• Teixeira, L.A., Oliveira, A .L., 2010. A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Systems with Applications, Vol. 37, pp. 6885–6890


• Fan, F., Weng, Q., Wang, Y., 2007. Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+imagery.Sensors,Vol. 7, pp.1323-1342


• Dewan, A.M., Yamaguchi, Y., 2009. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, Vol. 29, PP. 390-401


• OnateValdivieso, F., Sendra, J. B., 2010. Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. Journal of Hydrology, Vol. 395(3-4), pp.256-263


• Ward, D., Phinn, S.R., Murray, A.T., 2000. Monitoring growth in rapidly urbanizing areas using remotely sensed data. ProfessionalGeographer, vol. 52(3), 371-386


• Lunetta, R.S., Elvidge, C.D., 1998. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Ann Arbor Press, Chelsea, MI, pp. 318


• Niyazi,Y., 2019. Comparison of two methods of maximum likelihood classification and artificial neural network in extracting land use map (case study of Sedailam area). Journal of Geography and Development, No. 20, pp. 119-133(In Persian)


• Feizizadeh, B., 2017. Modeling the Trends of the Land Use/Cover Change and Its Impacts on the Erosion System of the Allavian Dam Based on the Remote Sensing and GIS Techniques. Journal of Hydrogeomorphology, Vol. 3 (11) , pp.21-38


• Jensen, J.R., 2004. Digital change detection. Introductory digital image processing: A remote sensing perspective, pp.467-494