Investigating forest level changes using LCM model in satellite images in northern Iran with human geography approach
Subject Areas :
امین خادمی
1
,
مرتضی معدنی پور کرمانشاهی
2
,
سیدآرمین هاشمی
3
,
بهروز کرد
4
1 - گروه فضای سبز، واحدملایر، دانشگاه آزاد اسلامی، ملایر، ایران
2 - گروه محیط زیست، واحد پرند، دانشگاه آزاد اسلامی، پرند، ایران
3 - گروه جنگلداری، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران
4 - گروه فضای سبز، واحدملایر، دانشگاه آزاد اسلامی، ملایر، ایران
Keywords: Forest changes, Remote sensing, Talesh city, Dense forest.,
Abstract :
Abstract
Introduction: The purpose of this research was to compare the forest level changes using the LCM model in satellite images in Talash forests. Studying the amount of changes and destruction of resources in the past years and the feasibility and prediction of these changes in the coming years can be the necessity of conducting research in line with planning and optimal use of natural resources.
Materials and Methods: The trend of usage changes in the study area was processed using the satellite images of TM, ETM, and OLI sensors in the years 2000, 2010, and 2020 with the supervised neural network method. Land use classification map including residential use, agriculture, pastures, dense forest, semi-dense forest, and thin forest was identified in the region. For this purpose, Google Earth software images and field visits were used to prepare educational points. Multi-layer perceptron neural network was used for image classification.Finally, the accuracy matrix was formed for each map. Modeling with TerrSet2020 software has made it possible to investigate the changes in land use between the researched years and display the results with different charts and maps in a quantitative and qualitative manner.
Results and Discussion: The changes in land use from 2000 to 2020 with the artificial neural network method showed that residential areas increased by 210% and agricultural use by 1500%, semi-dense forest use by 200%, and thin forest by 340% of hectares, and dense forest use by 50% and pasture by 30%. had. The accuracy of the classification maps in this research was 0.88, 0.8950, and 0.9150 respectively in 2000, 2010, and 2020, which was acceptable.
Conclusion: The research showed that the expansion of agricultural activities and the construction of residential areas on pasture and forest ecosystems have turned pastures and thin forests into less valuable land. In general, in this research, it can be concluded that there have been changes in use in the studied area, and according to the results of the studies, the main changes are related to the change of use of pasture to agricultural and residential land, the change of untouched forests and It has been dense to semi-dense and thin forests. Using images with higher resolution in the scope of the research and comparing it with the research results of other researchers can be useful. Therefore, it is necessary to monitor user changes and control user changes within the scope of studies to be paid attention to by officials and planners.
Acknowledgements: The authors of this article appreciate Islamic Azad University for their support.
References
Ahdnejad Roshti, M., Zolfi, A., Shokripour, M. and Dizaj, H. (2011). Evaluation and Prediction of Physical Expansion of Cities Using Multi-Time Satellite Imaging and Geographic Information System, Case Studies: Ardabil, 1984-2006. Quarterly Journal of Geography Environment Preparation, 15: 107-124.2(In persian)
Batty, M. (2005). Agents, cells, and cities: new representational models for simulating multiscale urban dynamics. Environment and Planning, 37: 1373-1394.
Brinkmann, K., Schumacher, J., Dittrich, A., Kadaore, I. and Buerkert, A. (2012). Analysis of Landscape Transformation Processes in and around Four West African Cities over the Last 50 Years. Landscape and Urban Planning, 105: 94–105.
Chavez PS. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3): 459- 479.
Darvishsefat, A., Ghaffari Dafchah, f. and Bonyad, A. (2014). Feasibility of satellite imagery for poplar plantation mapping (Case study: Sowme`eh Sara). Iranian journal of poplar research, 22(3): 392-401. (In persian)
Dehghani, T., Ahmadpari, H., Amini, A.(2023).Assessment of land use changes using multispectral satellite images and artificial neural network. Water and Soil Management and Modeling, 3(2): 18-35(In persian)
Fatemti Talab, S. R., Madanipour Kermanshahi, M. and Hashemi, S. A.( 2015). Estimating changes in forest cover in the Rudsar county by using neural network and maximum likelihood methods. Journal of RS and GIS for Natural Resources, 6(2):33-43. (In persian)
Girma, R., Fürst, C., Moges, A. (2022).Land Use Land Cover Change Modeling by Integrating Artificial Neural Network with Cellular Automata-Markov Chain Model in Gidabo River Basin, Main Ethiopian Rift. Environ. Chall. 6, 100-114
Guo, H., Cai, Y., Yang, Z., Zhu, Z., Ouyang, Y. (2021).Dynamic Simulation of Coastal Wetlands for Guangdong-Hong Kong-Macao Greater Bay Area Based on Multi-Temporal Landsat Images and FLUS Model. Ecol. Indic. 2021, 125, 107559.
Hashemi, S.A., Fatemi Talab, S.R., Kavousi, H. and Madanipour, M.(2017). Change detection in the forest cover of Siyahmezgi watershed of Guilan using LandSat images. Journal of RS and GIS for Natural Resources, 7(24):78-88. (In persian)
Hashemi, SA., Khademi A., Madanipour Kermanshahi, M., Kord, B.(2022). Investigation of forest area using support vector machine and provide a model for predicting level of changes. Journal of RS and GIS for Natural Resources, 13(3): 23-26. (In persian)
Khoi, D. and Murayama, Y. (2011). Modeling Deforestation Using a Neural Network-Markov Model. Spatial Analysis and Modeling in Geographical Transformation Process, 169-190.
Li, J. J., Wang, X. R., Wang, X. J., Ma, W. C. and Zhang, H.( 2009). Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecological Complexity, 6(4): 413-420.
Mahdavi, M. (2015). The geography of Gilan. Nashre Farhange Ilia Publication, Rasht. (In persian)
Mahmoudzadeh, H. (2010). Application of Artificial Neural Network in Modeling and Forecasting Land Use Changes in Sardroud City (1984-2031). Geography and planning, 21(60): 221-237.
Masoumi, H., Jamali, A.A., Khabazi M.(2014). Investigation of Role of Slope, Aspect and Geological Formations of Landslide Occurrence Using Statistical Methods and GIS in Some Watersheds in Chahar Mahal and Bakhtiari Province. Journal of Applied Environmental and Biological Sciences, 4(9)121-129.
Maviza, A. and Fethi, A. (2020). Analysis of past and future multi-temporal land use and land cover changes in the semi-arid Upper-Mzingwane sub-catchment in the Matabeleland south province of Zimbabwe. International Journal of Remote Sensing, 41: 5206-5227.
Mesgari, I. and Jabalameli, M. (2017). Modeling Land Use Change: Categorizing Concepts and Models and Explaining Research Areas. Journal of Geography and Environmental Planning, 64: 75-92. (In persian)
Mirhosseini, S. M., Jamali, A. and Hosseini, S. Z. (2016). Investigating and Predicting the Extension of Dunes Using Land Change Modeler (LCM) in the North West of Yazd, Iran. Journal of Desert, 21(1 ): 76-90. (In persian)
Mirzai, V. and Mahdavi, A. (2014). Investigating the role of human factors and physiography on the reduction of forest cover, a case study of the watershed of Arquaz, Malekshahi, Ilam province. Geographical space, 15(52): 75-96. (In persian)
Muller, D. and Zeller, M. (2002). Land use dynamics in the central highlands of Vietnam: A spatial model combining village survey data with satellite imagery interpretation. Agricultural Economics, 27: 333- 354.
Nazari Samani, A., Heravi, H., Panahi, M. and Shalamzari, Masoud. 2013. Effect of Land-use and Precipitation Changes on Sediment Yield (Case Study: Taleghan Watershed).Journal of range and weatershed management, 66(1):157-165. (In persian)
Perez-Vega, A., Mas, J. and Ligmann-Zielinska, A. (2011). Comparing two approaches to land use/cover change modeling and their implication for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modeling & Software, 29(1): 11-23.
Saadani, S., Laajaj, R., Maanan, M., Rhinane, H. and Aaroud, A. (2020). Simulating spatial–temporal urban growth of a Moroccan metropolitan using CA–Markov model, Journal of Spatial Information Science, 28(7):67-75
Shahi, E., Karimi, S. & Jafari, H.R. (2020). Monitoring and modeling land use/cover changes in Arasbaran protected Area using and integrated Markov chain and artificial neural network. Model. Earth Syst. Environ. 6, 1901–1911.
Talebi Khiavi H. Mostafazadeh R., Asaadi M.A., Namini ,SK.( 2022).Temporal land use change and its economic values under competing driving forces in diverse land use configuration. Arabian Journal of Geosciences ,15:1597:3-14
Vasile, A. J., Popescu, C., Ion, R. A., and Dobre, I.( 2015). From conventional to organic in Romanian agriculture- Impact assessment of land use changing paradigm. Land Use Policy, 46: 258-266.
Verburg, P. H., Tabeau, A. and Hatna, E. (2013). Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: A study for land use in Europe. Journal of Environmental Management, 127: 132-144.
Masoumi, H., Jamali, A.A., Khabazi M.2014. Investigation of Role of Slope, Aspect and Geological Formations of Landslide Occurrence Using Statistical Methods and GIS in Some Watersheds in Chahar Mahal and Bakhtiari Province. Journal of Applied Environmental and Biological Sciences, 4(9)121-129.
