Modeling land cover changes in Golestan province using land change modeler (LCM)
Subject Areas : Geospatial systems developmentFatemeh Salarian 1 , Mohammadreza Tatian 2 * , Abdolazim Ghanghermeh 3 , Reza Tamartash 4
1 - PhD. Student of Rangeland Science, Faculty of Natural Resources, Sari University of Agriculture and Natural Resources, Sari, Iran
2 - Associate Professor, Department of Range Management, Faculty of Natural Resources, Sari University of Agriculture and Natural Resources, Sari, Iran
3 - Assistant Professor, Department of Geography, Faculty of Humanities, Golestan University, Gorgan, Iran
4 - Associate Professor, Department of Range Management, Faculty of Natural Resources, Sari University of Agriculture and Natural Resources, Sari, Iran
Keywords: Golestan province, Land change modeler (LCM), Markov chain, land use, Modeling,
Abstract :
Background and Objective In recent decades, land use change due to environmental and human factors has caused serious effects on the environment and the economy in Golestan province. On the other hand, wide rangelands and natural areas have been cultivated without observing ecological and scientific principles or have been exploited for special purposes and changing to other uses, while many of these lands are do not have the potential to become new land uses and they have a high potential for erosion, as a result of which we will see soil erosion, especially in sloping lands and the creation of destroyer floods. Therefore, it is necessary and essential to be aware of the type and manner of use and its possible changes over time, which will be important for planning and policy-making in the country. The aim of this study was to detection the land use changes in Golestan province during the years 1986 to 2019 and to predict the land use status of the region for 2050 using the Land Change Modeling (LCM) approach.Materials and Methods In order to monitor the trend of land use changes in the study area, Landsat 5 and 8 satellites (TM and OLI sensors for 1986, 2001, and 2019) were used. Interpretation and processing of satellite data were performed in ENVI software. The necessary pre-processing was performed on the images. First, the images were mosaic together and then cut according to the province boundary. In order to identify and separate the phenomena from each other, a false color image was created. Then, the supervised classification method with the maximum likelihood method was used. At this stage, five classes, including rangeland, agriculture, forestry, residential, and water areas were defined. Land use maps for 1986, 2001, and 2019 were prepared. Integration of land cover maps related to 1986, 2001, and 2019 was used as input of LCM model and digital elevation model (DEM) maps and road and stream layers to analyze area changes and prediction of land use changes of 2050. After the necessary analyzes in order to detect land use changes between the intended time periods, change maps and land use transfers were prepared. Finally, the amount of decrease and increase in each land use, the amount of net changes, the net change from other land uses to the desired class, areas without change and transfer from each land use to another land in different land cover classes in the form of maps and charts were prepared and analyzed.Results and Discussion The aim of this study was prediction and modeling of land use changes in a period of 33-years in Golestan province. According to the results during this period, the area of the rangelands has decreased a lot, equivalent to 181181.25 hectares. Much of the decline in rangelands is due to its conversion into agricultural, which can be attributed to population growth and the need to expand crop land. The area of forest lands during the mentioned years has decreased from 393018.75 to 349143.75 hectares in 2019, which has shown a decrease of 43875 hectares (2.2%). In general, the destruction of rangeland and forest areas is especially visible in developing countries due to population growth, technological growth and non-compliance with ecological principles and law enforcement. Also, the results of classified maps during the mentioned years show that the highest amount of changes in the region is related to agricultural lands, has increased to 173700 hectares equal to 8.5 % during the same period. The rate of land use changes related to the residential land class has also increased with the increasing trend from 18731.25 hectares in 1986 to 37518.75 hectares in 2019, which has increased by 18787.50 hectares (0.9%) during this period. Rapid growth of population has led to the development of residential and urban areas and the increase in this type of land use with a relatively steep slope, especially in recent years, which can be part of the government's plans for housing construction in the surrounding areas cities. This increase in the class of agricultural lands is more noticeable, especially in the central and eastern regions of the province, and can be a warning alarm for the future. It means that in an imperceptible trend, rangeland and forest lands become rainfed agricultural lands and after a while unprincipled exploitation, eventually become barren and unusable land. On the other hand, this could indicate an increase in population and demand for housing, and consequently securance of the needs of the residents of the region is a threat to rangeland lands which is necessary instead of increasing the agricultural and residential lands and turning rangeland lands into such land uses, the policy of increasing productivity in the agricultural sector should be pursued. About of water areas, it can be said that during this period, it has increased by 1.6% or 3268.75 hectares. This increase in water areas can be partly attributed to heavy rainfall and water intake and even floods in different parts of the province in 2019. Predicting the rate of land use change in 2050 indicates that in the coming years, the area of rangelands and forests will be reduced by 131906.25 and 291600 hectares, respectively, and in contrast to the area of agricultural land and residential areas will increase to 164137.50 and 25313.25 hectares, respectively. Therefore, the adoption of necessary measures and policies to further reduce forest and rangeland will be inevitable.Conclusion Understanding of the conditions of different land uses during the coming periods will facilitate planning for the future by creating information in terms of their spatial distribution pattern, but maintaining and creating sustainable conditions for the future both statistically and it is ecologically one of its limitations. These constraints play an important role in the safe use of different land uses in the planning process. Therefore, creating sustainable conditions in the region and modeling it in order to use the natural resources of a region regularly and sustainably is one of the preconditions for achieving upstream visions and documents, including the sustainable development plan.
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Yuan F, Sawaya K E, Loeffelholz B C, Bauer M E. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98:317-328. doi:http://dx.doi.org/10.1016/j.rse.2005.08.006.
_||_Abbas I, Muazu K, Ukoje J. 2010. Mapping land use-land cover and change detection in Kafur local government, Katsina, Nigeria (1995-2008) using remote sensing and GIS. Research Journal of Environmental and Earth Sciences, 2(1): 6-12.
Afifi M E. 2020. Modeling Land use changes using Markov chain model and LCM model. Journal of Applied Researches in Geographical Sciences, 20 (56):141-158. doi:http://doi.org/10.29252/jgs.20.56.141. (In Persian).
Bakr N, Weindorf D C, Bahnassy M H, Marei S M, El-Badawi M M. 2010. Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography, 30(4):592-605. doi:http://doi.org/10.1016/j.apgeog.2009.10.008.
Battsengel V, Tsolmon D, Byambakhuu G, Myagmartseren P, Otgonbayar L, Falin W. 2020. Spatiotemporal monitoring and prediction of land use/land cover changes using CA-Markov chain model: a case study in Orkhon Province, Mongolia. Proc. SPIE 11535, Remote Sensing Technologies and Applications in Urban Environments V, 115350E (20 September 2020). https://doi.org/10.1117/12.2574032.
Eastman J R. 2006. IDRISI Andes. Guide to GIS and Image Processing, Clark Labs, Clark University,
Worcester, MA.
Eastman J R. 2009. Idrisi taiga manual. Clark Lab. Clark University. Worcester, USA.333 p.
Eastman J R. 2014. Idrisi TerrSet 18.00. Clark University, Worcester, MA, USA. 392 p.
Fang S, George Z, Gertnera G Z, Sun Z, Andersonc A. 2005. The Impact of Interactions in Spatial Simulation of the Dynamics of Urban Sprawl, Landscape and Urban Planning, 73: 294–306. doi:http://dx.doi.org/10.1016/j.landurbplan.2004.08.006.
Farajollahi A, Asgari H, Ownagh M, Mahboubi M, Salman Mahini A. 2015. Monitoring and prediction of spatial and temporal changes of landuse/ cover (Case study: Marave Tappeh region, Golestan). Journal of RS and GIS for Natural Resources, 6(4), 1-14. (In Persian). http://girs.iaubushehr.ac.ir/article_518869.html?lang=en.
Fathollahi roudbary S, Nasirahmadi K, khanmohamadi M. 2018. land use change modeling using LCM module (Case study: NEKA region). Journal of Natural Ecosystem of Iran, 9(1): 53-69. http://neijournal.iaunour.ac.ir/article_544280_8c028323a531b13ee04e4dd4d45ae804.pdf. (In Persian).
Gholamalifard M, Mirzayi M, Joorabian Shooshtari S. 2014. Land use change modeling using artificial neural network and markov chain (Case study: Middle Coastal of Bushehr province). Journal of RS and GIS for Natural Resources, 5(1), 61-74. (In Persian). http://girs.iaubushehr.ac.ir/article_516599.html?lang=en.
Haibo Y, Longjiang D, Hengliang G, Jie Z. 2011. Tai'an land use Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences, 10:2625- 2610. doi:http://dx.doi.org/10.1016/j.proenv.2011.09.408.
Hasan S, Shi W, Zhu X, Abbas S, Khan H U A. 2020. Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data. Sustainability, 12(11): 1-24. doi: http://dx.doi.org/10.3390/su12114350.
Jokar Arsanjani J, Kainz W, Mousivand A. 2011. Tracking Dynamic Land Use Change Using Spatially Explicit Markov Chain Based on Cellular Automata: the Case of Tehran. International Journal of Image and Data Fusion, 2: 329-345. doi:https://doi.org/10.1080/19479832.2011.605397.
Kalnay E. Cai M. 2003. Impact of urbanization and land-use change on climate. Nature, 423(6939): 528- 531. doi:http://dx.doi.org/10.1038/nature01675.
Kindu M, Schneider T, Teketay D, Knoke T. 2016. Changes of ecosystem service values in response to land use/land cover dynamics in Munessa–Shashemene landscape of the Ethiopian highlands. Science of The Total Environment, 547: 137-147. doi:http://dx.doi.org/10.1016/j.scitotenv.2015.12.127.
Lu D, Weng Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 5: 823–870. doi:http://dx.doi.org/10.1080/01431160600746456.
Martínez M L, Pérez-Maqueo O, Vázquez G, Castillo-Campos G, García-Franco J, Mehltreter K, Landgrave R. 2009. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. Forest Ecology and Management, 258(9): 1856-1863. doi:http://dx.doi.org/10.1016/j.foreco.2009.02.023.
Mishra V, Rai P, Mohan K. 2014. Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute "Jovan Cvijic", SASA, 64(1): 111–127. doi:http://dx.doi.org/10.2298/IJGI1401111M.
Mir Alizadehfard S, Alibakhshi S. 2016. Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam). Journal of RS and GIS for Natural Resources, 7(2), 33-46. (In Persian). http://girs.iaubushehr.ac.ir/article_524153.html?lang=en
Mosaedi A, Sharifan H, Shahabi M. 2007. Risk Management by identification of microclimates in Golestan province. Applied research report, Iran Meteorological Organization, 171 p. (In Persian).
Nazari Samani A, Ghorbani M, Kohbanani H R. 2010. Landuse changes in taleghan watershed from 1987 to 2010. Rangeland, 4(3): 442-451. (In Persian).
Onate-vadiieso F, sendra J B. 2010. Aplication of GIS and Remote sensing technequs in generation of landuse scenario for hidrological modeling. Journal of Hydrology, 395 (4): 256-264. doi:https://doi.org/10.1016/j.jhydrol.2010.10.033.
Parker D C, Manson S M, Janssen M A, Hoffmann M J, Deadman M J. 2003. Multi agent systems for the simulation of land use and land cover change: A Review, Annals of the Association of American Geographers, 93(2): 314–337. doi:https://doi.org/10.1111/1467-8306.9302004.
Sari, F. 2020. Assessment of land use change effects on future beekeeping suitability via CA-Markov prediction model, Journal of Apicultural Science, 64(2): 263-276. doi:https://doi.org/10.2478/jas-2020-0020.
Szumacher I, Pabjanek P. 2017. Temporal changes in ecosystem services in european cities in the continental Biogeographical region in the period from 1990–2012. Sustainability, 9(4): 665. doi:http://dx.doi.org/10.3390/su9040665.
Vaclavik T, Rogan J. 2010. Identifying trends in land use/land cover changes in the context of post-socialist transformation in Central Europe: A case study of the greater Olomouc region, Czech Republic. GIS Science and Remote Sensing, 46 (1):54-76. doi:https://doi.org/10.2747/1548-1603.46.1.54.
Wang R, Murayama Y. 2017. Change of land use/cover in Tianjin city Based on the Markov and Cellular Automata models. ISPRS International Journal of Geo-Information, 6: 150. doi:https://doi.org/10.3390/ijgi6050150.
Wang SW, Gebru B M, Lamchin M, Kayastha R B, Lee W K. 2020. Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and GIS. Sustainability. 12(9):3925. doi:https://doi.org/10.3390/su12093925.
Yuan F, Sawaya K E, Loeffelholz B C, Bauer M E. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98:317-328. doi:http://dx.doi.org/10.1016/j.rse.2005.08.006.