Investigating the Land Use Changes in the City of Rasht Using Satellite Image Analysis

Document Type : Original Article

Authors

1 Master's degree in Remote Sensing and Geographical Information System, Department of Geography, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Regional Studies, Environmental Research Institute of Jihad University, Guilan, Rasht, Iran.

Abstract

Considering the extensive changes in land use and the necessity of providing awareness to managers and planners about the nature of these transformations for policy-making and problem-solving, the detection of land use changes through land cover analysis has become essential. Therefore, land use mapping is a requirement for any national and regional development planning. It empowers managers, planners, and experts to design and implement necessary actions by identifying current conditions, comparing capabilities and potentials, and addressing current and future needs and issues. Due to the high costs associated with traditional manual methods of land use mapping, remote sensing can offer engineers a faster and more accurate means for land use mapping and subsequent assessment of changes in an area. The present research aimed to investigate the land use changes in the city of Rasht from 1990 to 2020 using Landsat satellite images. Data analysis was conducted using Google Earth Engine, and the results were further analyzed and visualized using ArcGIS software. The findings indicated that over the 30-year study period, the percentage of forest and agricultural land decreased, while built-up areas, croplands, and pastures increased. The highest increase was observed in residential (urban and rural) land use, encompassing 8,080 hectares, followed by a 12,690-hectare expansion in pastureland.
 
Extended Abstract
Introduction
Having knowledge about the various land surface covers and human activities in different areas holds a significant importance as foundational information for various planning endeavors. One of the effective, valuable, and practical sources of information for identifying the land cover is remote sensing data. Remote sensing technology serves as an essential and valuable tool for assessing changes in land surface. To enhance efficiency in land use change detection, remote sensing is often integrated with Geographic Information Systems (GIS). The city of Rasht is situated in Guilān Province and stands out for its agricultural activities, particularly rice cultivation, ranking first in the country. Additionally, this region boasts pristine natural areas such as forests and pastures. Factors contributing to changes in land use within the region include population growth, the pressing need for housing, and the conversion of agricultural lands to residential for making profit. Amidst these circumstances, the main objective of this study is to investigate the land use changes in Rasht.
Methodology
In this current research, remote sensing science has been used to investigate the changes and transformations in land use and land cover within the city of Rasht. To analyze the data, images were examined using Google Earth Engine software, and the results were further analyzed in ArcGIS software, ultimately producing maps. For each year under study, the Normalized Difference Vegetation Index (NDVI) was derived from quarterly Landsat images and used as auxiliary data. The NDVI index quantifies vegetation coverage by measuring the difference between near-infrared and red wavelengths, which respectively reflect and absorb vegetation. This index was computed for four 3-month periods throughout the year.
Discussion
The changes that have occurred over a thirty-year period from 1990 to 2020 have been assessed based on available data. The images and analyses derived from change detection have revealed that the most prominent change, evident through a comparison of generated land use maps, is the continuous shift in land use patterns. This includes a reduction in forest and agricultural lands and an increase in residential and pasture lands. It's worth noting that the two classes of 'pasture' and 'bare land' should be considered cumulatively due to the fact that many areas of Guilān's soil rapidly transform into grasslands and pastures as a result of rainfall. Thus, the time of taking the satellite image and the corresponding weather conditions significantly influence the classification of these areas as either 'bare land' or 'pasture'. The extent of changes is as follows: from 1990 to 2020, the area of residential land use has increased by approximately 81 square kilometers, and the combined area of 'bare land' and 'pasture' classes has increased by around 170 square kilometers. In contrast, forest and agricultural lands have decreased by 11 and 236 square kilometers, respectively, during these years. The most significant changes in residential land use have occurred in areas that were previously designated for agriculture. This phenomenon can be attributed to the intermingling of agricultural and residential lands in the Guilān province.
Conclusion
The analysis revealed that during the 30 years under study, the percentage of forest and agricultural land has decreased, while the percentage of residential and pasture/bare land use has increased. Many agricultural lands, specifically the rice fields, have been converted to residential areas, or they are left uncultivated and are identified as bare pastures in recent satellite images. Forest coverage within Rasht is limited, primarily concentrated in the southern region, particularly in the Sarāvān area. Given the conditions of these forests, the issue of forest degradation and the prevalence of pine logging in certain parts of the county have prevented a significant decline in this land category. The reduction in orchards and agricultural lands is a significant challenge in the current land use changes. Economic issues, the commercialization of properties, recent droughts, and water shortages that have led to a decline in agricultural income at both provincial and national levels are among the factors driving rural farmers to convert their land to residential use in order to generate income and liquid assets. This trend is evident in the increased area of residential and pasture/bare land use. Although, the analysis of residential land use changes indicates an increasing trend. In recent years, inflation, capital preservation, migration, and rising housing demand within the province have driven the construction of numerous houses, apartments, residential complexes, and government housing projects in the county. Furthermore, due to the region's tourism potential, villa construction has seen a significant rise in recent years, contributing to the increasing trend of residential land use.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work. 
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the persons for scientific consulting in this paper.

Highlights

- Land use changes in county of Rasht during the years 1990 to 2020 have shown that the area of forest and agricultural uses has decreased and the area of residential uses and barren-pasture lands has increased.

- Preparation of land use map is a planning requirement for national and regional development.

Keywords

Main Subjects

References

Abd Al-Razzaq Abd, H., Abdulrasool Alnajjar, H. (2013). Maximum Likelihood for Land-Use/Land-Cover Mapping and Change Detection Using Landsat Satellite Images: A Case Study “South of Johor”. International Journal of Computational Engineering Research, 6(3), pp. 27- 33.
Abdi, A. M. ( 2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), pp. 1-20.
Akbari, E., Zanganeh Asadi, M. A., and Taqvi Moghadam, E. (2015). Monitoring land use changes using different methods of statistical training theory in Neishabur region. Journal of Geographical Survey of Space, (20)6, pp. 50-35. [In Persian]
Alavi Panah, K. (2011). Principles of modern remote sensing and interpretation of satellite images and aerial photographs. University of Tehran Publications. [In Persian]
Alavipanah, S. K. (2003). Application of remote sensing in earth sciences (soil sciences). Tehran: Tehran University Publications. [In Persian]
Amani, M., Ghorbanian, A., Ahmadi, S. A., and Kakoei, M. (2020). Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp. 5326-5350.
Amanpour, S., Kamelifar, M. J., and Behmai, H. (2016). An analysis of land use changes in metropolises using satellite image analysis (case study: Ahvaz metropolis). Sepehr Geographic Information, 26(102), pp. 139-150. [In Persian]
Arkhi, p. (2014). Investigating different methods of revealing land use changes using remote sensing and GIS. Natural Environment Quarterly, Natural Resources of Iran, 48(1), pp. 1-13. [In Persian]
Arulbalaji, P., Gurugnanam, B. (2014). Geospatial science for 16 years of variation in land use/land cover practice assessment around Salem District, South India. Journal of Geosciences and Geomatics, 2(1), pp. 17-20.‏
Aspinall, J.‚ Richard, H., and Michael, J. (2008). Land use change: Science, policy and management. CRC Press: Taylor and Francis Group, 185 p.
Azari, M., fani, Z., Kouzehgar, L., TavakoliNia, J., and Heidari, A. (2021), Investigating the role of key drivers in land use changes and Spatial developments of the city with a future research approach (Case study District 12 of Tehran). 25(2), pp. 121-144.
Barati, A. A., Asadi, A., Kalantari, J., Azari, H., and Mamourian, M. (2014). Analysis of the effects of changing the use of agricultural land from the point of view of the experts of the Agricultural Land Affairs Organization. Economic research and agricultural development of Iran, 45(4), pp. 639-650. [In Persian]
Barlowe, R. (1986). Land resource economics: the economics of real property. Prentice Hall, Inc., Eaglewood Cliffs, NJ.
Bateman, I. J., Harwood, A. R., Mace, G. M., Watson, R. T., Abson, D. J., Andrews, B. and Fezzi, C. (2013). Bringing ecosystem services into economic decision-making: land use in the United Kingdom. science, 341(6141), pp. 45-50.
Burkov, A. (2017). Machine learning engineering. Vol. 1. 2020: True Positive Incorporated. Keshtkar, H., W. Voigt, and E. Alizadeh, Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery. Arabian Journal of Geosciences,. 10(6), pp. 1-15.
Busko, M., Szafranska, B. (2018). Analysis of changes in land use patterns pursuant to the conversion of agricultural land to non-agricultural use in the context of the sustainable development of the Malopolska region. Sustainability, 10(1), pp. 136.
Chobkar, N., Emami Rad, A., Mohammadi Rozbahani, M., Ghaeni, M., and Rumiani, L. (2011). Application of remote sensing and GIS in natural resources management. Regional Conference of Applied Studies in Environment and Fisheries, Islamic Azad University, Kermanshah. [In Persian]
Daly, H. E., Cobb, J. B., (1989). For the common good: redirecting the economy toward community, the environment, and a sustainable future. Boston: Beacon Press.
Dong, R., Yu, L., and Liu, G. (2008). Impact of tourism development on land-cover change in a matriarchal community in the Lugu Lake area. International Journal of Sustainable Development & World Ecology, 15 (2008), pp. 28–35.
Earle, E., Pontinus, R. (2012). Land-Use and Land-cover Change. In: Encyclopedia of Earth. Eds.Culter J. Cleveland Washington, D.C. Environmental information Coalition, National Council for Science and the Environment. First published in the Encyclopedia of Earth.
Ellis, E., Pontius. R. (2007). Landuse and landcover change. In: Encyclopedia of earth. (Eds). Cutler, J. Cleveland. Washington, D.C.: Environmental Information Coalition, National Council for Science and Environment. Available at: http://www.eoearth.org/view/article/15414 3/.
Ely, R. T., Wehrwein, G. S. (1948). Land Economics. New York: Macmillan Company.
Esbahi, H., Erdogan, M.A., and Tanriover, A. A. (2011). Cellular automata -Markov chain and Landscape metrics for Landscape planning. ITU A/Z, 8 (2), p. 63.
Falahatkar, S., Saffianian, A., Khajeddin, S. J., and Ziaei, S. J. (2009). Isfahan land cover change detection in the past 4 decades using remote sensing. Journal of science technology of agriculture and natural resources, water and soil science, 13 (47), pp. 381-395. [In Persian]
Feyisa, G. L., Palao, L. K., Nelson, A.,, Gumma, M. K., Paliwal, A., Win, K. T., Htar Nge,  K., and Johnson, D. E. (2020). Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices. Computers and electronics in agriculture, 175, p. 105595.
Foroghifar, A., Dashti, S. (2019). Revealing land use changes using satellite images (case study: Shush city). Journal of Physical Development Planning Scientific, 5(3), pp. 101-114. [In Persian]
Ge, G., She, Z., Zhu, Y., and Yang, X. (2020). Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22, p. 00971.
Groos, J. E., Goetz, S. J., and Cihlar, J. (2009). Application of remote sensing to parks and protected area monitoring: introduction to the special issue. Remote sensing of environment, 113, pp. 1343-1345.
Haghighi Zaidehi, B., Jabarian Amiri, B., and Ebrahimpour, R. )2012(. Investigating the land use changes f Lahijan city by using remote sensing technique and IDRISI software, the third international onference on environmental planning and management, University of Tehran. ‌[In Persian]
Hegazy, I., Kaloop, M. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Dagahlia governorate Egypt. International Journal of Sustanable Built Environment, 4, pp. 117-124.
Henderson, V. (2017). Medium Size Cities. Regional Science and Urban Economics, No. 6, pp. 583-612.
Hersperger, A. M., Mueller, G., Knopfel, M., Kienast, F. (2017). Evaluating Outcomes in Planning: Indicators and Reference Values for Swiss Landscapes. Ecol. Indic, 77, pp. 96–104.
Jia, M., Wang, Z., Mao, D., Ren, C., Wang, C., and Wang, Y. (2021). Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 255: p. 112285.
Karimi, K., Bayram Komaki, C. h. (2014). Monitoring, evaluation and prediction of spatial changes in land use/land cover using Markov chain model (case study: Bastaq plain, South Khorasan). remote sensing and geographic information system in natural resources, 6(2), pp. 75-88. [In Persian]
Kavzoglu, T., Colkesen, I., and Tonbul. H. (2019). Agricultural crop type mapping using object-based image analysis with advanced ensemble learning algorithms. in the 40th Asian Conference on Remote Sensing (ACRS 2019) October.
Kolhe, N. P., Kumar, D. K. (2016). Rurban Centers: The New Dimension of Urbanism. Procedia, No. 24, pp.1699-1705.
Lambin, E. F., Geist, H. J. (2008). Land-use and land-cover change: local processes and global impacts. Springer Science & Business Media.
Linderman, M.A., An, L., and Bearer, S. (2005). Modeling the spatio-temporal dynamics and interactions of households, landscapes, and giant panda habitat. Ecol.Model, 183 (1), pp. 47–65.
Mahmoudzadeh, H. (2016). Application of artificial neural network in modeling and predicting land use change in Sardrood. Geography and Planning, No. 60, pp. 221-237. [In Persian]
Malekpour, b., Ahmadi, T., and Kazemi Mazandarani, S. (2012). The effect of pasture land use change on the physical and chemical properties of soil in the old Lashek Kejur area of ​​Nowshahr city. Quarterly Journal of Natural Resources Sciences and Techniques, 6(3), pp. 115-126. [In Persian]
 Manouchehri Miandoab, A., Rahnamaei, M. (2019). An Analysis of the Process of Production of Public Capitalist Space in Iran Case: Tehran. Journal of Spatial Planning, No. 1, pp. 16-186. [In Persian]
Marraccini, E., Debolini, M., Moulery, M., Abrantes, P., Bouchier, A., Chery, J. P., Sanz, E., Sabbatini, T., and Napoleone, C. (2015). Common features and different trajectories of land cover changes in six Western Mediterranean urban regions. Applied Geography, 62, pp. 347-356. DOI: 10.1016/j.apgeog.2015.05.004
Matinfar, H., Maghsodi, Z., Mousavi, S, R, and Jalali, M. (2021). Evaluation of Machine Learning Methods in Digital Mapping of Soil Organic Carbon (part of Khorramabad Plain). JWSS-Isfahan University of Technology, 24(4), pp. 327-342.
Mokhtari Gerchkani, P., Ayubi, S. A., Mossadeghi, M. R., and Malekian, M. (2016) The effect of slope and land use change on soil organic matter reserves in particle size components and some physical and chemical properties of soil in Tepe Mahor Lordegan. Journal of Soil Management and Sustainable Production, 1(1), pp. 41-23. [In Persian]
Mousavi, M., Yazdani Chaharbarj, R. (2012). land use suitability analysis for the development of Tabriz city using AHP-OWA model. Geographical Researches of Urban Planning, 3(3), pp. 361-381. [In Persian]
Nalina, P., Meenambal, T., and Sridhar, R. S. (2014). Land use land cover dynamics of Nilgiris district, India inferred from satellite imageries. American Journal of Applied Sciences, 11(3), p. 455.‏
Nikkho, Nava, Ilderami, Alireza and Nouri, Hamid. (2013). Developments of land use in Malair city using remote sensing. Quarterly Journal of Environmental Studies, 30, pp. 63-86. [In Persian]
Niknehad Kormakher, H., Maramai, M. (2017). Studying the effects of land use change on soil properties, a case study of small area. Journal of soil management and sustainable production, 1(2). [In Persian]
Peel, D., Lloyd, M. G. (2007). Neo-traditional planning. Towards a new methods for land use planning. Land Use Policy, 24(2), pp. 396-403.
Petropoulos, G. P. K. (2012). Arvanitis, and N. Sigrimis, Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Expert systems with Applications, 39(3), pp. 3800-3809.
Polanyi, K. (1957). The great transformation. New York: Farrar & Rinehart.
Randall, A., Castle, E. N. (1985). Land resources and land markets. Handbook of natural resource and energy economics, Vol. 1, Elsevier Science Publishers.
Renne, R. R. (1947). Land economics: principles, problems, and policies in utilizing land resources, New York: Harper & Brothers.
Salemi Abbasi, M. (2015). Monitoring land use changes using remote sensing technique (case study: Azadarvand region), Master's thesis, Department of Environment, Islamic Azad University, Ahvaz branch. [In Persian]
Seidifar, L., Amiri, M. J., Daraei, H., Karbasi, A., and Agha Mohammadi, H. (2019). Prediction of land use change in Tabriz city, using SLEUTH model. Journal of Physical Development Planning Scientific, 5th year, 3(19), pp. 21-33. [In Persian]
Shakri, F. (2011). Investigating the effects of urban and industrial development on the desertification process in Lar city, Fars province. Master's Thesis, Faculty of Natural Resources and Desertology, Department of Desert Management. [In Persian]
Shatrian, M., Mousavi, H., and Momenbeyk, Z. (2017). The application of remote sensing data in revealing urban land use changes (Kord city). Scientific-Research Quarterly of Geographical Information, (28)111, pp. 250-235. [In Persian]
Soltani Moghads, R. (2018). Spatial Consequences of Land Use Change in Rural Settlements (Case Study: Qarchak City, Tehran Province). Physical Development Planning Quarterly, 4(14), pp. 79-94. [In Persian]
Taleshi, M., Rostami, S. h., Ali Akbari, E., and Vojdani, H. (2017). Explaining the basic effective factors on land use changes with a political ecology approach (Case study: Hamedan city). journal of scientific-research journal of the program Physical development, 4(2), pp. 95-110. [In Persian]
Van Vliet, J., Bregt, A. K., Brown, D. G., van Delden, H., Heckbert, S., and Verburg, P.H. (2016). A review of current calibration and validation practices in land-change modeling. Environmental Modelling & Software‚ Vol 82‚ pp. 174-182.
Wang, S. Q., Zheng, X. Q., and Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, pp. 1238 - 1245.‏
Wijesekara, G. N., Gupta, A., and Valeo, C. (2012). Assessing the impact of future land-use changes on hydrological processes in the Elbow River watershed in southern Alberta, Canada. J. Hydrol. 412–413, pp. 220–232.
Zandi, R., Kerami, M., and Taheri, J. (2018). The role of land use changes on the spatial form of thermal islands in the city of Mashhad. Scientific-Research Journal of Physical Development Planning, 4(16), pp. 104-93. [In Persian]
Zurqani, H. A., Post, C., Mikhailova, E. A., and Cope, M. (2020). Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine. Scientific Reports, 10(1), pp. 1-16.
Geographical Studies of Coastal Areas Journal
Volume 4, Issue 3 - Serial Number 14
September 2023
Pages 69-88

Files

Share

How to cite

Statistics