Investigating Land Use Changes in Herat City Using Satellite Images during 2015 and 2022

Document Type : Original Article

Authors

1 Dep. of Geodesy, Faculty of Geosciences, Bamyan University, Bamyan, Afghanistan

2 College of Engineering, University of Tehran, Tehran, Iran

Abstract

Introduction: In recent years, land use changes, especially in urban areas, have been recognized as one of the major challenges in the field of urban planning, natural resource management, and sustainable development. With the increase in population, expansion of construction, and the growing demand for urban land, the need for accurate monitoring and analysis of land use changes is felt more than ever before in order to optimally manage resources and formulate efficient urban development policies. In this regard, the present study aimed to investigate the trend of land use changes in Herat city during the years 2015 to 2022. For this purpose, Landsat 8 satellite images from the OLI sensor were used, which, due to their suitable spectral and spatial characteristics, have a high capability in analyzing land use changes.
Materials and Methods: First, the satellite images collected for the two mentioned years were subjected to the necessary pre-processing, including radiometric correction, geometric correction, and atmospheric correction, in order to increase the accuracy of subsequent analyses. After preprocessing, four land use classes including soil, vegetation, residential areas, and water areas were identified in the study area. In order to classify these images and extract information related to land use changes, two widely used classification algorithms including Maximum Likelihood Classification and Artificial Neural Network were used, and the accuracy of the results of these two methods was evaluated and compared.
Results and Discussion: The results of the analyses showed that the Maximum Likelihood method performed better in classifying satellite images than the Artificial Neural Network method. The values ​​of the Kappa coefficient and overall accuracy used to evaluate the classification accuracy were 0.75 and 85 percent for 2015 and 0.96 and 97 percent for 2022. These values ​​indicate the high accuracy and reliability of the Maximum Likelihood method in separating different land use classes in the study area. Analysis of the classification results in the period 2015 to 2022 indicates significant changes in the spatial structure of land use in Herat city. According to the results, during this period, the land area has decreased by 4.0 square kilometers. Also, the water area has decreased by 1.62 square kilometers. On the other hand, the land area related to residential areas has increased by 1.39 square kilometers and the vegetation area has increased by 4.59 square kilometers.
Conclusion: These changes indicate the trend of urban development in Herat city during this period and indicate an increase in human interventions in land use, especially in the form of the development of residential areas and the increase in green space or vegetation. In summary, the findings of this study indicate that urban development in Herat city has caused a decrease in natural lands such as land and water resources, and on the contrary, uses with human interventions such as residential areas and vegetation have increased. Given the decrease in water resources and natural lands, it is essential that future planning of the city focuses on sustainable development, protection of natural resources, and especially effective management of water resources. This management can be achieved through precise policies, imposing restrictions on excessive urban development, and raising public awareness of the importance of natural resources. This research, using remote sensing data and scientific analysis of land use changes, has provided valuable information for decision-makers and urban planners. The results of this study can be used as a basis for developing scientific solutions in the field of urban land management, natural resource conservation, and sustainable development in Herat city. Therefore, the use of modern technologies in monitoring and evaluating land use changes should be considered as one of the basic requirements in the decision-making process and urban policies, and it is suggested that urban development should proceed with better management of water resources.

Keywords

References

Abdolalizadeh, Z., Ebrahimi, A. & Mostafazadeh, R., 2019, Landscape Pattern Change in Marakan Protected Area, Iran, Regional Environmental Change, 19, PP. 1683-1699, https://doi.org/10.1007/s10113-019-01504-9.
Aghaei, M., Khavarian, H. & Mostafazadeh, Z., 2022, Prediction and Detection of Land Use Changes Using CA-Markov and LCM Models in Kouzeh-Tepraqi Watershed, Ardabil Province, Watershed Research, 33(3), PP. 91-107, https://doi.org/10.22092/ wmej.2019.128009.1267.
Ahani, H., Ghorbani, A., Rostegarmoghadam, M., Fallahshamsi, S.R. & Bagharnajad, M., 2010, Land Use Change Assessment Using Satellite Images: A Case Study of Tang-Sorkh Watershed, Shiraz, Agricultural and Natural Resources Sciences, (2), PP. 242-252.
Alavi, S.F. & Tanaka, T., 2023, Analyzing the Role of Identity Elements and Features of Housing in Historical and Modern Architecture in Shaping Architectural Identity: The Case of Herat City, Architecture, 3, 548-577, https://doi.org/ 10.3390/architecture3030030.
Arghandwal, N.A., 2021, The Land Use Change Analysis of the Walled City in Afghanistan, Urban, Planning and Transport Research, 9, PP. 306-318, https://doi.org/ 10.1080/21650020.2021.1943511.
Arkhi, S., 2015, Detecting Land Cover/Land Use Changes by Object-Oriented Processing of Satellite Images Using IdrisiSelva Software (Case Study: Abdanan Region), Geographical Data (SEPEHR), 24(95), PP. 51-62.
Arkhi, S. & Fathizad, H., 2010, Evaluation of Desertification Trends and Spatial Modeling of Land Use Change Patterns in the Dehloran Desert Region, Ilam Province, Using Landsat Satellite Images, First National Conference on Desert (Science, Technology, and Sustainable Development), PP. 46-68.
Arkhi, S., Shahkouei, E. & Ata, B., 2022, Evaluation of Land Cover/Use Change Detection Techniques Using Satellite Imagery and GIS (Case Study: Gorganrud Basin), Physical Development Planning, (2), PP. 41-60.
Banzhaf, E., Kabisch, S., Knapp, S., Rink, D., Wolff, M. & Kindler, A., 2017, Integrated Research on Land-Use Changes in the Face of Urban Transformations–an Analytic Framework for Further Studies, Land Use Policy, 60, PP. 403-407, https://doi.org/ 10.1016/j.landusepol.2016.11.012.
Bolstad, P. & Lillesand, T.M., 1991, Rapid Maximum Likelihood Classification, Photogrammetric Engineering and Remote Sensing, 57, PP. 67-74.
Changiz, M., Savari, A., Dashti, S., Orak, N. & Karimi-Orghani, F., 2020, Detection of Natural Land Use Changes in Shadegan Wetland before and after Flooding Using Satellite Images and GIS, Wetland Ecobiology, (50), PP. 19-32.
Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., Xu, X., Liao, W., Qiu, Y. & Wu, Q., 2020, Global Projections of Future Urban Land Expansion under Shared Socioeconomic Pathways, Nature Communi-cations, 11, P. 537.
Danesh, A. & Ilanlou, M., 2020, Land Use Change Assessment and Urban Area Horizontal Expansion Using Satellite Images (Case Study: Mahshahr, 1956–2016), Environmental Planning, (49), PP. 135-153.
 
 
Dehghani, T., Ahmadpari, H. & Amini, A., 2023, Land Use Change Assessment Using Multispectral Satellite Images and Artificial Neural Networks, Water and Soil Modeling and Management, (2), PP. 18-35, https://doi.org/10.22098/mmws.2022.11279.1114.
Delgarm, S., Ganjalikhani, M. & Bakhtiari, B., 2023, Land Use Change Detection Using Satellite Images in Zarand-Kerman County, Agricultural Meteorology, (1), PP. 64-72, https://doi.org/10.22125/agmj.2023. 320893.1128.
Gomroki, M., Hasanlou, M. & Reinartz, P., 2022, IUNet-UCD: Improved U-Net with Weighted Binary Cross-Entropy Loss Function for Urban Change Detection of Sentinel-2 Satellite Images, https://doi.org/10.21203/rs.3.rs-1668171/v1.
Gomroki, M., Hasanlou, M. & Chanussot, J., 2023a, Automatic 3D Multiple Building Change Detection Model Based on Encoder-Decoder Network Using Highly Unbalanced Remote Sensing Datasets, https://doi.org/10.1109/JSTARS.2023.3328561.
Gomroki, M., Hasanlou, M. & Reinartz, P., 2023b, STCD-EffV2T Unet: Semi Transfer Learning EfficientNetV2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images, Remote Sensing, 15, P. 1232, https://doi.org/10.3390/ rs15051232.
Hekmat, H., Ahmad, T., Singh, S.K., Kanga, S., Meraj, G. & Kumar, P., 2023, Land Use and Land Cover Changes in Kabul, Afghanistan Focusing on the Drivers Impacting Urban Dynamics during Five Decades 1973-2020, Geomatics, 3, PP. 447-464, https://doi.org/ 10.3390/geomatics3030024.
Hersini, I., Kaboli, M., Faghihi, J., Taherzadeh, A. & Asadi, A., 2014, Land Use/Cover Change Trends in Hamadan Province over the Past Three Decades Using Satellite Images, Natural Environment and Natural Resources of Iran, (1), PP. 1-12.
Hu, Y., Raza, A., Syed, N.R., Acharki, S., Ray, R.L., Hussain, S., Dehghanisanij, H., Zubair, M. & Elbeltagi, A., 2023, Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province, Sustainability, 15, P. 3572, https://doi.org/10.3390/su15043572.
Jafarian, N., Mirzaei, J. & Karami, O., 2020, Land Use Change Trends in Manesht and Qalarang Protected Area Using Satellite Images, Iranian Journal of Forest and Rangeland Protection Research, (1), PP. 14-30.
Karimian, K., Amini, A., Bagheri-Abadi, M. & Ghayoumi-Mohammadi, H., 2020, Monitoring Land Use Changes Using Landsat Satellite Images (Case Study: Khan Mirza Plain), Human Geography, (2), PP. 419-436.
Khalighi, S., Mahdavi, M. & Sagafian, B., 2005, Investigating the Effect of Land Use on Flood Potential Using the NRCS Model: A Case Study of Barandoozchai Watershed, West Azerbaijan, Natural Environment, (4), PP. 733-742.
Lippmann, R., 1987, An Introduction to Computing with Neural Nets, IEEE Assp Magazine, 4, PP. 4-22, https://doi.org/10.1145/ 44571.44572.
Mahdizadeh-Karizaki, M., Alizadeh, A. & Ansari, H., 2019, Land Use Change Analysis in the Karde Dam Basin Using Intensity Analysis, Geography and Environmental Hazards, (30), PP. 75-95.
Mojarad, K. & Kamanroudi, M., 2018, Land Use and Land Cover Change Detection Using Satellite Images and GIS (Case Study: Mazandaran Province), Applied GIS and Remote Sensing in Planning, (1), PP. 66-73.
Mousavi, S.H., Ranjbar, A. & Haseli, M., 2016, Monitoring and Trending of LandUse Changes in Abarkooh Basin using Satellite Images (1976-2014), Geographical Data (SEPEHR), 25(97), PP. 129-146. https://doi.org/10.22131/sepehr.2016.20141.
Oluseyi, O.F., 2006, Urban Land Use Change Analysis of a Traditional City from Remote Sensing Data: The Case of Ibadan Metropolitan Area, Nigeria, Humanity & Social Sciences Journal, 1, PP. 42-64.
Padgett, C. & Cottrell, G.W., 2022, A Simple Neural Network Models Categorical Perception of Facial Expressions, Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, Routledge, PP. 806-811.
Pakkhosal, E., Ghadikolaei, J., Jalilvand, H. & Akbari, H., 2021, Land Use Change Detection Using Remote Sensing Data (Case Study: Tehran), Geography and Regional Planning, (4), PP. 287-298, https://doi.org/ 10.22034/jgeoq.2021.136722.
Pijanowski, B.C., Brown, D.G., Shellito, B.A. & Manik, G.A., 2002, Using Neural Networks and GIS to Forecast Land Use Changes: A Land Transformation Model, Computers, Environment and Urban Systems, 26, PP. 553-575, https://doi.org/10.1016/S0198-9715 (01)00015-1.
Rezaei, R., Ghadousi, J., Hosseini, A.H., Arjamandi, R. & Vafainejad, A., 2020, Classification and Evaluation of Land Use Changes Using Landsat Images (Case Study: Qazvin Plain Aquifer), Geographical Space, (72), PP. 185-204.
Saadian, A. & Shafizadeh-Moghadam, H., 2021, Land Use Change Assessment in the Karkheh Watershed from 1990 to 2020 Using Google Earth Engine and Landsat Imagery, Iranian Journal of Water and Soil Research, (10), PP. 2569-2580.
Shanai, H.S.M. & Zarei, H., 2016, Land Use Change Analysis over Two Decades (Case Study: Abolabbas Watershed), Watershed Management, (14), PP. 237-244.
Sharafat, A., Toghani, N. & Sirat, M.K., 2024, Investigating Changes in Snow Cover in the Center of Bamyan Province Using Satellite Images, Sprin Multidisciplinary Journal in Pashto, Persian & English, 2(01), PP. 5-11, https://doi.org/10.55559/smjppe.v2i01.222.
Shehu, P., Rikko, L.S. & Azi, M.B., 2023, Monitoring Urban Growth and Changes in Land Use and Land Cover: A Strategy for Sustainable Urban Development, Int. J. Hum. Cap. Urban Manag., 8, PP. 111-126.
Statistical Yearbook of Afghanistan, 2022.
Sundarakumar, K., Harika, M., Begum, S.A., Yamini, S. & Balakrishna, K., 2012, Land Use and Land Cover Change Detection and Urban Sprawl Analysis of Vijayawada City Using Multitemporal Landsat Data, International Journal of Engineering Science and Technology, 4, PP. 170-178.
Talebi Khiavi, H., Mostafazadeh, R., Asaadi, M.A. & Asbaghian Namini, S.K., 2022, Temporal Land Use Change and Its Economic Values under Competing Driving Forces in a Diverse Land Use Configuration, Arabian Journal of Geosciences, 15, P. 1597.
Tarko, A.P., 2023, Maximum Likelihood Method of Estimating the Conflict-Crash Relationship, Accident Analysis & Prevention, 179, P. 10687, https://doi.org/10.1016/j.aap. 2022.106875.
 

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