Modeling Land Use Change Process by Integrating the MLP Neural Network Model in the Central Desert Regions of Iran

Document Type : Research Paper

Authors

1 Department of management the arid and desert regions, College of Natural Resources and Desert, Yazd University, Yazd, Iran

2 Department of arid and desert regions management, College of Natural Resources and Desert, Yazd University, Yazd, Iran

3 Agriculture and Natural Resources Department, Ardakan University, Yazd, Iran

4 Yazd univerdity, Yazd, Iran

Abstract

To understand and manage the natural and human-made ecosystems and develop long-term planning, it is necessary to model Land Use Change (LUC) and predict future changes. Therefore, we used Landsat satellite imagery, Multilayer Perceptron neural network (MLP) and Markov Chain model (MCA) to monitor the regional changes over 30 years in the central arid regions of Iran. In the present research, the stratified maps derived from the object-oriented algorithm were used to detect and map the changes of land use classes from 1986 to 2016. Furthermore, the land use in 2030 was predicted using Land use Change Modeler (LCM). Slop, contour elevation lines, distance from river, road, afforestation, agricultural lands/gardens, barren lands, poor rangelands, residential lands, rocky land, and sand dunes were considered as factors influencing the changes in the ANN. The Cramer's V coefficient was employed to select appropriate parameters with the highest significant correlation. Our results showed that the sub-models performed well (75-85%). Besides, the highest and lowest accuracy of sub-models were related to the distance from barren lands and distance from residential areas (75.23 and 85.91%, respectively). The results of land use change monitoring from 2016 to 2030 revealed that land use such as forest, residential lands, gardens, and sand dunes would be increased by about 0.11, 1.53, 2.36 and 0.56 %, respectively, by 2030 compared to 2016. On the other, the area of barren land and poor rangeland would be reduced by 2.88 and 1.68 %, respectively. Our results can be used in land change evaluations, environmental studies, and integrated planning and management regarding appropriate and logical use of natural resources and reducing resource degradation.

Keywords

References

Al-Bakri, J., M. Duqqah, T. Brewer, 2013. Application of Remote Sensing and GIS for Modeling and Assessment of Land Use/ Cover Change in Amman/ Jordan. Journal of Geographic Information System, 509 - 519.
Amiraslani, F., D. Dragovich, 2011. Combating desertification in Iran over the last 50 years: An overview of changing approaches. Journal of Environmental Management, 1-13.
Arekhi, S., 2014. Prediction of spatial land use changes based on LCM in a GIS environment (A case study of Sarabeleh, Ilam), Iran. Iranian Journal of Forest and Range Protection Research, 1-19.
Baker, W. L., 1989. A review of models of landscape change. Landscape Ecology, 111-133.
Bakr, N., D.C.Weindorf, M.H. Bahnassy, S.M. Marei, M.M. El-Badawi,, 2010. Monitoring land cover changes in a newly reclaimed area of Egypt using multitemporal Landsat data. Applied Geography, 592-605.
Balzter, H., 2000. Markov chain models for vegetation dynamics. Ecological Modelling, 139-154.
Bello, I.E., N. Chigbu, G.I. Agbaje, 2017. Large scale mapping: an empirical comparison of pixel-based and object-based classifications of remotely sensed data. South African Journal of Geomatics, 277-293.
Blaschke. T., S. Lang, 2006. Briding remote sensing and GIS-what are the main supportive pillars. 1st International Conference on Object-based Image Analysis. pp. 6.
Chavez, P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ, 459–479.
Coppin, P., K. Nackaerts, B. Muys, E. Lambin, 2004. Review ArticleDigital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 1565-1596.
Eastman, J.R., 2006. Idrisi for windows user’s guide ver.32. Clark University.
Eastman, J. R. 2009. Idrisi Tiaga: Guide to GIS and image processing, Clark University.
Fathizad, H., M.A. Hakimzadeh Ardakani, R. Taghizadeh-Mehrjardi, H. Sodaiezadeh, 2018. Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian central desert. Journal of African Earth Sciences, 115-130.
Fonji, S. F., G. N. Taff, 2014. Using satellite data to monitor land - use land – cover change in North - eastern Latvia, Springer Plus, p. 61.
Gilks, W. R., 1996. Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC.
Gómez, C., J.C. White, M.A. Wulder, 2011. Characterizing the State and Processes of Change in a Dynamic Forest Environment Using Hierarchical Spatio-temporal Segmentation. Science of the Total Environment, 1665-1679.
Haibo, Y., D. Longjiang, G. Hengliang, Zh. Jie, 2011. Tai'an Land Use Analysis and Prediction Based on RS
and Markov Model. Procedia Environmental Sciences, 2625-2630.
Hakimzadeh Ardakan, M.A., A.R Vahdati, 2018. Monitoring of organic matter and soil salinity by using IRS-LissIII satellite data in the Harat plain, of Yazd province. Desert, 1-8.
Hamdy, O., S. Zhao, T. Osman, M.A. Salheen, Y. Y. Eid, 2017. Applying a Hybrid Model of Markov Chain and Logistic Regression to Identify Future Urban Sprawl in Abouelreesh, Aswan: A Case Study. Geosciences; p. 43.
He, Z., C. Lo, 2007. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 667-688.
Khoi, D. D., Y. Murayama, 2010. Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region. Vietnam. Remote Sensing, 1249–1272.
253
Fathizad et al. / Desert 24-2 (2019) 241-254
Kuemmerle, T., O. Chaskovskyy, J. Knorn, V.C. Radeloff, I. Kruhlov, W.S. Keeton, P. Hostert, 2009. Forest Cover Change and Illegal Logging in the Ukrainian Carpathians in the Transition Period from 1988 to 2007, Remote Sensing of Environment, 1194-1207.
Mas, J.F., K., Melanie, P. Martin, T. Mara, O. Camacho, H. Thoma, 2014. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling and Software, 94-111.
Mubea, K. W., T. G. Ngigi, C. N. Mundia, 2010. Assessing application of Markov chain analysis in Predicting land cover change: A case study of NAKURU municipality. Journal of Advanced Computer Science and Technology; p. 19.
Norris, J.R., 1997. Markov Chains. Cambridge University Press.
Oñate-Valdivieso, F., J.B. Sendra, 2010. Application of GIS and Remote Sensing Techniques in Generation of Land Use Scenarios for Hydrological Modeling. Journal of Hydrology, 256-263.
Perez-Vega, A., J. Mas, A. Ligmann-Zielinska, 2012. Comparing two approaches to land use/cover change modeling and their implications forth the assessment of biodiversity loss in the deciduous tropical forest. Environmental Modeling and Software, 11-23.
Qian, J., Q. Zhou, Q. Hou, 2007. Comparison of Pixel- based and Object-oriented Classification Methods for Extracting Built-up Areas in Aridzone, ISPRS Workshop on Updating Geo-Spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs.
Rafieyan. O., A. A. Darvishsefat, S. A. Babaii. Mataji.
2011. Evaluation of pixel-based and object-based classification methods for tree identification using aerial images (Case study: a forestation in Chamestan- Nur). Iranian Journal of Forest, 35-47.
Simbangala, M. S., B. L. Campos Cámara, L. C. Villanueva, Ó. F. Martínez, D. V. Torres, R. R. Mayo, M. E. Orozco Hernández, 2015. Using object-oriented image analysis to map and monitor land cover change in the Region Costa Maya, México: 1993-2010. Investig. Geogr. Chile, 33-50.
Thapa, R.B., Y. Murayama, 2011. Urban growth modeling of Kathmandu metropolitan region, Nepal. Computers, Environment and urban systems, p. 25-34.
Tudun-Wada1, M.I., Y.M. Tukur, Y. Hussaini, M.Z. Sani1, I. Musa, V.E. Lekwot, 2014. Analysis of forest cover changes in Nimbia Forest Reserve, Kaduna State, Nigeria using Geographic Information System and Remote Sensing techniques, International Journal of Environmental Monitoring and Analysis, 91-99.
Wang, L., W.P. Sousa, P. Gong, 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery, International journal of Remote Sensing, 5655- 5668.
Whiteside T., W. Ahmad, 2005. A comparison of object- o riented and pixel-based classification methods for mapping land cover in northern Australia. Proceedings of SSC2005 Spatial intelligence, innova tion and praxis: The national biennial Conference of the Spatial Sciences Institute.
Yan, G. 2003. Pixel Based and Object Oriented Image for Coal Fire Research.
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Desert
Volume 24, Issue 2
December 2019
Pages 241-254

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