Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran)

Abstract

Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region using an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis. At the first step, multi-temporal Landsat images (1990, 2002 and 2014) were processed using ancillary data and were classified into seven LULC categories of high density forest, low-density forest, agriculture, grassland, barren land, water and urban area. Next, LULC changes were detected for three time profiles, 1990–2002, 2002–2014 and 1990–2014. A 2014 LULC map of the study area was further simulated (for model performance evaluation) applying 1990 and 2002 map layers. In addition, a collection of spatial variables was also used for modeling LULC change processes as driving forces. The actual and simulated 2014 LULC change maps were cross-tabulated and compared to ensure model simulation success and the results indicated an overall accuracy and kappa coefficient of 97.79% and 0.992, respectively. Having the model properly validated, LULC change was predicted up to the year 2025. The results demonstrated that 992 and 1592 ha of high and low-density forests were degraded during 1990–2014, respectively, while 422 ha were added to the extent of residential areas with a growth rate of 17.58 ha per year. The developed model predicted a considerable degradation trend for the forest categories through 2025, accounting for 489 and 531 ha of loss for high and low-density forests, respectively. By way of contrast, residential area and farmland categories will increase up to 211 and 427 ha, respectively. The integrated prediction model and customary area data can be used for practical management efforts by simulating vegetation dynamics and future LULC change trajectories.

Keywords

Satellite images / Land use changes / Land change modeler / Artificial neural network / Prediction

References

Alijanpour A, Rad EJ, Shafiei AB. Investigation and comparison of two protected and non-protected forest stands regeneration diversity in Arasbaran. Iran J For, 2009, 3: 209-217.

Arsanjani JJ, Helbich M, Kainz V, Boloorani AD. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf, 2013, 21: 265-275.

Asadolahi Z, Salmanmahiny A, Sakieh Y. Hyrcanian forests conservation based on ecosystem services approach. Environ Earth Sci, 2017

Baker MM, Van Doorn AM. Farmer-specific relationships between land use change and landscape factors: introducing agents in empirical land use modelling. Land Use Policy, 2009, 26: 809-817.

Basse RM, Omrani H, Charif O, Gerber P, Bodis C. Land use changes modelling using advanced methods: cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl Geogr, 2014, 53: 160-171.

Benito RP, Cuevas JA, LaParra RB, Prieto F, Barrio JM, Zavala MA. Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through CORINE Land Cover data and Markov models. For Syst, 2010, 19(3): 315-328.

Darvishi A, Fakheran S, Soffianian A, Ghorbani M. Change detection and land use/land cover dynamics in the Arasbaran Biosphere Reserve. J Nat Environ, 2016, 68: 559-572.

Darvishsefat AA. Atlas of protected areas of Iran, 2006, Tehran: University of Tehran Press 175

Darvishsefat AA (2009) Applied GIS. Iranian student book agency

Darvishsefat AA, Pirbavegar M, Rajabpor RM. Remote sensing for GIS managers, 2011, Tehran: University of Tehran Press 720

Dezhkam S, Amiri BJ, Darvishsefat AA, Sakieh Y. Simulating urban growth dimensions and scenario prediction: a case study of Rasht County, Guilan, Iran. Geojournal, 2014, 79: 591-604.

Dezhkam S, Amiri BJ, Darvishsefat AA, Sakieh Y. Performance evaluation of land change simulation models using landscape metrics. Geocarto Int, 2016

Eastman R (2009) Idrisi Taiga version. 16.01 Clark Laboratories, Clark University, Worcester, MA

Echeverriaa C, Coomesc DA, Halld M, Newtone C. Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile. Ecol Model, 2008, 212: 439-449.

Guan DJ, Li HF, Inohae T, Su WC, Nagaie T, Hokao K. Modeling urban land use change by the integration of cellular automata and Markov model. Ecol Model, 2011, 222: 3761-3772.

Hasani M, Sakieh Y, Dezhkam S, Ardakani T, Salmanmahiny A. Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data. Environ Monit Assess, 2017

Huang CC, Yang H, Li YM, Zou J, Zhang YM, Chen X, Mi Y, Zhang ML. Investigating changes in land use cover and associated environmental parameters in Taihu Lake in recent decades using remote sensing and geochemistry. PLoS ONE, 2015, 10: 1-16.

Isek S, Kalin L, Schoonover JE, Srivastava P, Lockab G. Modeling effects of changing land use/cover on daily streamflow: an Artificial Neural Network and curve number based hybrid approach. J Hydrol, 2013, 48: 103-112.

Keersmaeker LD, Onkelinx T, Vos BD, Rogiers N, Vandekerkhove K, Thomaes A, Schrijver AD, Hermy A, Verheyen K. The analysis of spatio-temporal forest changes (1775–2000) in Flanders (northern Belgium) indicates habitat-specific levels of fragmentation and area loss. Landsc Ecol, 2015, 30: 247-259.

Lin YP, Chu HJ, Wu FC, Verburg PH. Predictive ability of logistic regression, autologistic regression and neural network models in empirical land-use change modeling—a case study. Int J Geogr Inf Sci, 2011, 25: 65-87.

Mahiny AS, Clarke KC. Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: towards dynamic sustainable land-use planning. Environ Plan, 2012, 39: 925-944.

Martin F, Alegria C, Gil A. Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal. For Syst, 2016 25 3 078

Mayes M, Spiota EM, Syzmanski L, Erdogan MA, Ozdogan M, Clayton M. Soil type mediates effects of land use on soil carbon and nitrogen in the Konya Basin, Turkey. Geoderma, 2014, 232: 517-527.

Mialhe F, Gunnel Y, Ignacio FA, Delbart N, Ogania J, Henry S. Monitoring land-use change by combining participatory land-use maps with standard remote sensing techniques: showcase from a remote forest catchment on Mindanao, Philippines. Int J Appl Earth Obs Geoinf, 2015, 36: 69-82.

Mishra VN, Rai PK, Mohan K. Prediction of land use changes based on land change modeler (LCM) using remote sensing, a case study of Muzaffarpur (Bihar), India. Orig Sci Pap, 2014, 64: 111-127.

Moreno JL, Zabalza J, Serrano VSM, Revueltu J, Gilabberte M, Molina C, Tejeda EM, Ruiz JM, Tague C. Impact of climate and land use change on water availability and reservoir management: scenarios in the Upper Aragón River, Spanish Pyrenees. Sci Total Environ, 2014, 493: 1222-1231.

Olmedo MTC, Pontius RG, Paegelow M, Mas JF. Comparison of simulation models in terms of quantity and allocation of land change. Environ Model Softw, 2015, 69: 214-221.

Parker DC, Manson SM, Janssen M, Hoffmann MJ, Deadman PJ. Multi-agent systems for the simulation of land use and land cover change: a review. Ann Assoc Am Geogr, 2003, 93: 314-337.

Parsa VA, Yavari A, Nejadi A. Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. Model Earth Syst Environ, 2016

Pontius RJ Jr. Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens, 2002, 66: 1011-1016.

Pontius RG Jr, Chen H. GEOMOD modeling, 2006, Worcester: Clark Lab, Clark University.

Qiang Y, Lam SNN. Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular Automata. Environ Monit Assess, 2015 187 3 57

Rasuly A, Naghdifar R, Rasoli M. Detecting of Arasbaran forest changes applying image processing procedures and GIS techniques. Proc Environ Sci, 2010, 2: 454-464.

Rivero PC, Mendoza GG, Siller AM, Mas JF. Deforestation rates in the Mexican Huasteca region (1976–2011). Agric Sci Technol, 2014, 3: 1.

Rubio L, Freire RM, Sunchez MC, Estrigul C, Saura S. Sustaining forest landscape connectivity under different land cover change scenarios. For Syst, 2012, 21(2): 223-235.

Sakieh Y, Salmanmahiny A, Jafarnezhad J, Mehri A, Kamyab H, Galdavi S. Evaluating the strategy of decentralized urban land-use planning in a developing region. Land Use Policy, 2015, 48: 534-551.

Sakieh Y, Gholipour M, Salmanmahiny A. An integrated spectral-textural approach for environmental change monitoring and assessment: analyzing the dynamics of green covers in a highly developing region. Environ Monit Assess, 2016

Samal RD, Gedam SS. Monitoring land use changes associated with urbanization: an object based image analysis approach. Eur J Remote Sens, 2015, 48: 85-99.

Sang LL, Zhang C, Yang JY, Zhu DH, Yun WJ. Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model, 2011, 54: 938-943.

Sarhangzade J, Makhdom M. Land use planning for forest catchments of Arasbaran. J Environ Stud, 2001, 26: 31-41.

Singh B, Jeganathan C. Spatio-temporal forest change assessment using time series satellite data in Palamu District of Jharkhand, India. J Indian Soc Remote Sens, 2016, 44: 573-581.

Swetnam RD, Fisher B, Mbilinyi PB, Munishi PKT, Willcock S, Ricketts T, Mwakalila S, Balmford A, Burgess ND, Marshall AR, Lewis SL. Mapping socio-economic scenarios of land cover change: a GIS method to enable ecosystem service modelling. J Environ Manag, 2011, 92: 563-574.

Valdivieso FO, Sendra JB. Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. J Hydrol, 2010, 395: 256-263.

Wyman MS, Stein VT. Modeling social and land-use/land-cover change data to assess drivers of smallholder deforestation in Belize. Appl Geogr, 2010, 30: 329-342.

Zarandian A, Baral H, Stork NE, Ling MA, Yavari AR, Jafari HR, Amirnejad H. Modeling ecosystem services informs spatial planning in lands adjacent to Sarvelat and Javaherdasht protected area in northern Iran. Land Use Policy, 2017, 61: 487-500.

Zebardast L, Jafari H, Badehyan Z, Asheghmoala M. Assessment of the trend of changes in land cover of Arasbaran Protected Area using satellite images of 2002, 2006 and 2008. Environ Res, 2009, 1: 23-33.