Monitoring and predicting the trend of changing rangelands using Satelite images and CA-Markov model (Case study: Noor-rood basin, Mazandaran proince)
Subject Areas : Agriculture, rangeland, watershed and forestry
Nematollah Koohestani
1
(
PhD student of Range Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
)
Shafagh Rastgar
2
(
Assistant Professor, Department of Rangeland, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
)
Ghodratollah Heidari
3
(
Associate Professor, Department of Rangeland, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
)
Shaban Shatai Joybari
4
(
Professor, Department of Forestry, Faculty of Forestry and Wood Technology, Gorgan University of Agricultural Sciences and Natural Resources, Iran
)
Hamid Amirnejad
5
(
Associate Professor, Department of Agricultural Economics, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
)
Keywords: Normalized difference vegetation index (NDVI), Landuse, Land cover, Nour-rud river basin, CA-Markov model,
Abstract :
Predicting the trend of land use/land cover chenges in natural range ecosystem via remote sensing techniques and evaluating their potentials by modeling, plays an important role in decision making. The goal of this research is monitoring and predicting land use/land cover changes in Nour-rood basin by CA-Markov in a 60 year periods (1988-2048). Landsat TM (1988, 1998, 2008) and OLI (2018) imagery of similar months (in July) were classified by maximum likelihood method algorithm. Terrestrial reality derived from topographic at scale 1:25000 and aerial photos available in the (GDNR) and (WMM) during 1988-2008 and field visits (2018) were evaluated for accuracy. The accuracy of the production maps calculated with Kappa coefficient. So that the highest and lowest ratio were related to the images of 1998 and 1988, respectively with the values of 0.86 and 0.81. The results were compared with field ground truth to determine the accuracy of results. Random matric used to convert land use classes and the map of land cover of Nour-rud basin predicted, in (2018-2028). The results showed that in (1988-2018), forests and rangelands with excellent and fair cover conditions had decreasing and ranges with good condition, rocks and residential areas had increasing trend. Total area of rangelands decreased from 116206 hectares in 1988 to 106336 hectares in 2018. Moreover, the results of Markov model with more than 85% precision showed the same trend of land use changes from 2018-2048. Excellent rangeland cover conditions, showed decreasing trend, rocky and residential areas will also have an increasing trend until 2048. Markov's prediction model also shows an accuracy of more than 85%. The trend of land use changes during 2018-2048 will be the same as in previous. In whitch case, excellent range condition will have decreasing trend; rocky and residential areas will have an increasing trend until 2048.
Askarizadeh D, Arzani H, Jaffari M, Bazeafshan J. 2018. Surveying of the past, present and future of vegetation changes in the central Alborz ranges in relation to climate change. Journal of RS and GIS for Natural Resources, 9(3): 1-18. (In Persian)
Azizi G, Rangzan K, Sadidy J, Heydarian P, Taghizadeh A. 2016. Predicting locational trend of land use changes using CA-Markov model (Case study: Kohmare Sorkhi, Fars province). Journal of RS and GIS for Natural Resources, 7(1): 59-71. (In Persian)
Chang C, Chang J. 2001. Markov model and cellular automata for vegetation. Journal of Geographical Research, 45(1): 45-57.
Eastman JR. 2006. IDRISI Andes guide to GIS and image processing. Clark University, Worcester, 328 p.
Fan F, Weng Q, Wang Y. 2007. Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7(7): 1323-1342. doi:https://doi.org/10.3390/s7071323.
FAO. 2007. State of the World's Forests. Food and Agriculture Organization of the United Nations, Rome, 144 pp.
Gilks WR. 1996. Introducing markov chain monte carlo. Markov chain Monte Carlo in practice, Chapman & Hall/CRC, 1 Edition, 512 p.
Goudarzi M, Farahpour M, Mosav A. 2006. Land cover and rangeland classification map using Land sat satellite image (TM) (Case study: Namrood watershed). Iranian Journal of Range and Desert Research, 13(3): 265-277. (In Persian)
Hernández-Guzmán R, Ruiz-Luna A, González C. 2019. Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico. Remote Sensing Applications: Society and Environment, 13: 318-327. doi:https://doi.org/10.1016/j.rsase.2018.12.005.
Jenerette GD, Wu J. 2001. Analysis and simulation of land-use change in the central Arizona – Phoenix region, USA. Landscape Ecology, 16(7): 611-626. doi:https://doi.org/10.1023/A:1013170528551.
Kamusoko C, Aniya M, Adi B, Manjoro M. 2009. Rural sustainability under threat in Zimbabwe – Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3): 435-447. doi:https://doi.org/10.1016/j.apgeog.2008.10.002.
Keshavarz E, Ebrahimi A, Naghipoor A. 2020. Comparing the accuracy of pixel and object-based classification methods in mapping vegetation types (Case study: Marjan Boroujen). Journal of Rangeland, 14(2): 272-285. (In Persian)
Khoi DD, Murayama Y. 2010. Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam. Remote sensing, 2(5): 1249-1272. doi:https://doi.org/10.3390/rs2051249.
Lee JK, Acharya TD, Lee DH. 2018. Exploring land cover classification accuracy of Landsat 8 image using spectral index layer stacking in hilly region of South Korea. Sensors and Materials, 30(12): 2927-2941. doi:https://doi.org/:10.18494/SAM.2018.1934.
Lourdes L, Karina Z, Pedro L, Héctor M, Néstor M. 2011. A dynamic simulation model of land cover in the Dulce Creek Basin, Argentina. Procedia Environmental Sciences, 7: 194-199. doi:https://doi.org/10.1016/j.proenv.2011.07.034.
Mir Alizadehfard SR, Alibakhshi SM. 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): 33-45. (In Persian)
Muller MR, Middleton J. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2): 151-157. doi:10.1007/BF00124382.
Otukei JR, Blaschke T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12: S27-S31. doi:https://doi.org/10.1016/j.jag.2009.11.002.
Pain WJ. 2007. Landcover Classification and Change Detection Analysis Using High-resolution IKONOS Imagery for the Bayview Bog Wetland, Ontario. Ontario Doctoral dissertation, Queen's University, 278 p.
Pontius GR, Malanson J. 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2): 243-265. doi:https://doi.org/10.1080/13658810410001713434.
Richards John A, Xiuping J. 1999. Remote sensing digital image analysis: an introduction. Springer-Verlag, Berlin, https://doi.org/10.1007/978-3-642-30062-2.
Salehi N, Ekhtesasi M, Talebi A. 2019. Predicting locational trend of land use changes using CA-Markov model (Case study: Safarod Ramsar watershed). Journal of RS and GIS for Natural Resources, 10(1): 106-120. (In Persian)
Shahabi H, Ahmad BB, Mokhtari MH, Zadeh MA. 2012. Detection of urban irregular development and green space destruction using normalized difference vegetation index (NDVI), principal component analysis (PCA) and post classification methods: A case study of Saqqez city. International Journal of Physical Sciences, 7(17): 2587-2595. doi:https://doi.org/10.5897/IJPS12.009.
Wang SQ, Zheng XQ, Zang XB. 2012. Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13: 1238-1245. doi:https://doi.org/10.1016/j.proenv.2012.01.117.
Weng Q. 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64(3): 273-284. doi:https://doi.org/10.1006/jema.2001.0509.
Wu Q, Li H-q, Wang R-s, Paulussen J, He Y, Wang M, Wang B-h, Wang Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78(4): 322-333. doi:https://doi.org/10.1016/j.landurbplan.2005.10.002.
Ye B, Bai Z. 2008// 2008. Simulating Land Use/Cover Changes of Nenjiang County Based on CA-Markov Model. In: Li D (ed) Computer And Computing Technologies In Agriculture, Volume I, Boston, MA. Springer US, pp 321-329. https://doi.org/310.1007/1978-1000-1387-77251-77256_77235.
Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2): 317-328. doi:https://doi.org/10.1016/j.rse.2005.08.006.
Zareh Garizi A, Bardi Sheikh V, Sadodin A, Salman Mahini A. 2012. Application of logistic regression to model spatial pattern of vegetation change (Case study: Chehel Chai basin, Golestan province). Journal of Geographical Research, 37: 55-68. (In Persian). doi:https://doi.org/10.22092/ijfpr.2015.13174.
Zhou Y, Xiao X, Qin Y, Dong J, Zhang G, Kou W, Jin C, Wang J, Li X. 2016. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. International Journal of Applied Earth Observation and Geoinformation, 46: 1-12. doi:https://doi.org/10.1016/j.jag.2015.11.001.
Zubair AO. 2006. Change detection in land use and Land cover using remote sensing data and GIS (A case study of Ilorin and its environs in Kwara State). Department of Geography, University of Ibadan, 176 p.
_||_Askarizadeh D, Arzani H, Jaffari M, Bazeafshan J. 2018. Surveying of the past, present and future of vegetation changes in the central Alborz ranges in relation to climate change. Journal of RS and GIS for Natural Resources, 9(3): 1-18. (In Persian)
Azizi G, Rangzan K, Sadidy J, Heydarian P, Taghizadeh A. 2016. Predicting locational trend of land use changes using CA-Markov model (Case study: Kohmare Sorkhi, Fars province). Journal of RS and GIS for Natural Resources, 7(1): 59-71. (In Persian)
Chang C, Chang J. 2001. Markov model and cellular automata for vegetation. Journal of Geographical Research, 45(1): 45-57.
Eastman JR. 2006. IDRISI Andes guide to GIS and image processing. Clark University, Worcester, 328 p.
Fan F, Weng Q, Wang Y. 2007. Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7(7): 1323-1342. doi:https://doi.org/10.3390/s7071323.
FAO. 2007. State of the World's Forests. Food and Agriculture Organization of the United Nations, Rome, 144 pp.
Gilks WR. 1996. Introducing markov chain monte carlo. Markov chain Monte Carlo in practice, Chapman & Hall/CRC, 1 Edition, 512 p.
Goudarzi M, Farahpour M, Mosav A. 2006. Land cover and rangeland classification map using Land sat satellite image (TM) (Case study: Namrood watershed). Iranian Journal of Range and Desert Research, 13(3): 265-277. (In Persian)
Hernández-Guzmán R, Ruiz-Luna A, González C. 2019. Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico. Remote Sensing Applications: Society and Environment, 13: 318-327. doi:https://doi.org/10.1016/j.rsase.2018.12.005.
Jenerette GD, Wu J. 2001. Analysis and simulation of land-use change in the central Arizona – Phoenix region, USA. Landscape Ecology, 16(7): 611-626. doi:https://doi.org/10.1023/A:1013170528551.
Kamusoko C, Aniya M, Adi B, Manjoro M. 2009. Rural sustainability under threat in Zimbabwe – Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3): 435-447. doi:https://doi.org/10.1016/j.apgeog.2008.10.002.
Keshavarz E, Ebrahimi A, Naghipoor A. 2020. Comparing the accuracy of pixel and object-based classification methods in mapping vegetation types (Case study: Marjan Boroujen). Journal of Rangeland, 14(2): 272-285. (In Persian)
Khoi DD, Murayama Y. 2010. Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam. Remote sensing, 2(5): 1249-1272. doi:https://doi.org/10.3390/rs2051249.
Lee JK, Acharya TD, Lee DH. 2018. Exploring land cover classification accuracy of Landsat 8 image using spectral index layer stacking in hilly region of South Korea. Sensors and Materials, 30(12): 2927-2941. doi:https://doi.org/:10.18494/SAM.2018.1934.
Lourdes L, Karina Z, Pedro L, Héctor M, Néstor M. 2011. A dynamic simulation model of land cover in the Dulce Creek Basin, Argentina. Procedia Environmental Sciences, 7: 194-199. doi:https://doi.org/10.1016/j.proenv.2011.07.034.
Mir Alizadehfard SR, Alibakhshi SM. 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): 33-45. (In Persian)
Muller MR, Middleton J. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2): 151-157. doi:10.1007/BF00124382.
Otukei JR, Blaschke T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12: S27-S31. doi:https://doi.org/10.1016/j.jag.2009.11.002.
Pain WJ. 2007. Landcover Classification and Change Detection Analysis Using High-resolution IKONOS Imagery for the Bayview Bog Wetland, Ontario. Ontario Doctoral dissertation, Queen's University, 278 p.
Pontius GR, Malanson J. 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2): 243-265. doi:https://doi.org/10.1080/13658810410001713434.
Richards John A, Xiuping J. 1999. Remote sensing digital image analysis: an introduction. Springer-Verlag, Berlin, https://doi.org/10.1007/978-3-642-30062-2.
Salehi N, Ekhtesasi M, Talebi A. 2019. Predicting locational trend of land use changes using CA-Markov model (Case study: Safarod Ramsar watershed). Journal of RS and GIS for Natural Resources, 10(1): 106-120. (In Persian)
Shahabi H, Ahmad BB, Mokhtari MH, Zadeh MA. 2012. Detection of urban irregular development and green space destruction using normalized difference vegetation index (NDVI), principal component analysis (PCA) and post classification methods: A case study of Saqqez city. International Journal of Physical Sciences, 7(17): 2587-2595. doi:https://doi.org/10.5897/IJPS12.009.
Wang SQ, Zheng XQ, Zang XB. 2012. Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13: 1238-1245. doi:https://doi.org/10.1016/j.proenv.2012.01.117.
Weng Q. 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64(3): 273-284. doi:https://doi.org/10.1006/jema.2001.0509.
Wu Q, Li H-q, Wang R-s, Paulussen J, He Y, Wang M, Wang B-h, Wang Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78(4): 322-333. doi:https://doi.org/10.1016/j.landurbplan.2005.10.002.
Ye B, Bai Z. 2008// 2008. Simulating Land Use/Cover Changes of Nenjiang County Based on CA-Markov Model. In: Li D (ed) Computer And Computing Technologies In Agriculture, Volume I, Boston, MA. Springer US, pp 321-329. https://doi.org/310.1007/1978-1000-1387-77251-77256_77235.
Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2): 317-328. doi:https://doi.org/10.1016/j.rse.2005.08.006.
Zareh Garizi A, Bardi Sheikh V, Sadodin A, Salman Mahini A. 2012. Application of logistic regression to model spatial pattern of vegetation change (Case study: Chehel Chai basin, Golestan province). Journal of Geographical Research, 37: 55-68. (In Persian). doi:https://doi.org/10.22092/ijfpr.2015.13174.
Zhou Y, Xiao X, Qin Y, Dong J, Zhang G, Kou W, Jin C, Wang J, Li X. 2016. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. International Journal of Applied Earth Observation and Geoinformation, 46: 1-12. doi:https://doi.org/10.1016/j.jag.2015.11.001.
Zubair AO. 2006. Change detection in land use and Land cover using remote sensing data and GIS (A case study of Ilorin and its environs in Kwara State). Department of Geography, University of Ibadan, 176 p.
