Discovering and Recovering the Changes in Land Use and Land Cover Using Remote Sensing and GIS (Case Study Heev Village, Alborz Province)

Journal of Radar and Optical Remote Sensing and GIS

مقاله 3، دوره 4، شماره 3، آذر 2021، صفحه 17-28   

نویسندگان

Zahra talebi banizi^* ؛ Zahra Naserian؛ Mohammad Mazrae Molaei

GIS expert of Yazd Municipality

چکیده

Detecting changes is one of the basic needs in the management and evaluation of natural resources. Modeling the process of land cover changes over time using multi- time data in the GIS environment can act as one of the most important factors in managing the mentioned changes. In order to modeling the process of land cover changes and to investigating the possibility of predicting it in the future, land change modeling (lcm) has been used.  Therefore, the Landsat TM5 analyzer data of Heev village in Alborz province for the years 1985T 2000 and 2011 were analyzed. Next, using the maximum similarity method, land cover maps of each image for the mentioned years, was extracted and categorized into five layers of vegetation, city, asphalt, barren lands (soil) and rocks and cliffs. The extracted accuracy evaluation coefficients (overall accuracy and kappa coefficient) indicate the high accuracy of this classification method. The analysis of the results obtained from the studies conducted on the two periods of 1985-2000 and 2000-2011 shows an increase in urban construction with a decrease in vegetation, and even in some areas, the disappearance of vegetation, while the village is expanding towards the mountainside. Using the combination of Markov model and automatic cell maps land use prediction maps for the next 16 years were obtained, while the kappa coefficient was used to determine the prediction compliance, and comparing them with the actual map

کلیدواژه‌ها

LCM؛ Markov Chain؛ Automated Cells؛ Kappa Coefficient

مراجع

Arkhi, S. (2014). Predicting the trend of spatial land use change using LCM model in GIS environment (Case study: Sarableh area). Iranian Journal of Forests and Rangelands Protection, 12(1), 1-19.

Salman Mahini, A,. ZareGarizi, A., Saaduddin, A., & Sheikh Vahed, B. )2011(. Spatio-temporal simulation of forest area changes in ChehelChay watershed of Golestan province using an integrated model of automatic cells and Markov chain. Iranian Forest and Poplar Research, 2, 273-285.

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 Multitemporal Landsat data. Applied Geography, 30(4), 592-605.

Congalton, R. G.(1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 42, 35-37. Doi:http://dx.doi.org/10.1016/0034-4257(91)90048-B

Dawelbaita. F., Morari. (2012). Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. Journal of Arid Environments, 80, 45-55. Doi:http://dx.doi.org/10.1016/j.jaridenv.2011.12.011

Eastman, J. R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA.

Fan, F., Wang, Q., & Wang, Y. (2007). Land use and land cover change in Guangzhou, Chaina, from 1998 based on land sat TM/ETM+ imagery. Sensors. Doi:https://doi.org/10.3390/s7071323

Ghanbari, F., & Shetabi, Sh. (2010). Investigating the trend of forest surface changes using aerial photographs and ASTER images (Case study: forests on the southern and southwestern outskirts of Gorgan). Journal of Wood and Forest Science and Technology Research, 17(4), 1-18.

Ghahraman, J. (2004(. Application of Markov chains in human resource planning (a practical approach for human resource managers in social organizations). Administrative Transformation, 43-44, 75-93.

Hashimoto, M., Nose, T., & Muriguchi, Y. (2002). Wood products: potential carbon sequestration and impact on net carbon emissions of industrialized countries. Environmental Science & Policy, 5, 183-193.

Kevin O'Donnell, T., Goyne, K. W., Miles, R. J., Baffaut, C., Anderson., S. H., & Sudduth, K. A. (2010). Identification and quantification of soil redoximorphic features by digital image processing. Geoderma, 157, 86–96.

Lamchin, M., Lee, J. Y., Lee, K., Lee, Y., Kim, M., Lim, H., Choi, H., & Kim, S. (2016). Assessment of landcover change and desertification using remote sensing technology in a local region of Mongolia. Advances in space research, 57, 64-77. Doi: https://doi.org/10.1016/j.asr.2015,10,006

Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological indicator, 2(1-2), 3-15.

Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25, 2365-2401.  

Mas, J., Kolb, F., Paegelow, M., Camacho Olmedo, M., & Houet, T. (2014). Inductive pattern-based land use/cover change models: a comparison of four software packages. Journal of Environmental Modelling and Software, 51(13), 94-111.

Perma, R., & Shetabi, Sh. (2010). Investigation of the possibility of preparing diversity and canopy density maps of Zagros forests using ETM + sensor images (Case study of Qalajeh forests of Kermanshah province). Iranian Forest Journal, 2(3), 242-231.

Prabaharan, S., Raju, K. S., Lakshumanan, C., & Ramalingam, M. (2010). Remote sensing and GIS applications on change detection study in coastal zone using multi temporal satellite data. International Journal of Geomatics and Geosciences, 1(2), 159.

Russell-Smith, J., Yates, C., Edwards, A., Allen, G. E., Cook, G. D., Cooke, P., Craig, R., Heath, B., & Smith, R. (2003). Contemporary fire regimes of northern Australia, 1997–1380: change since Aboriginal occupancy, challenges for sustainable management. International Journal of Wild land Fire, 12, 283-297.

Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54, 938-943. Doi: https://doi.org/10,1016/j.mcm.2010, 11,019

Singh, A. (1989). Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing, 10, 983-1003. Doi: https://doi.org/10,1080/01431168908903939

Vaclavik, T., & Rogan, J. (2009). 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 & Remote Sensing, 46(1), 54–76.

YaghmaeianMahabadi, N., NaderiKhorasgani, M., & Givi, J. (2011(. Percentage of land degradation changes in Ardestan region of Isfahan province in the last three decades using remote sensing technology. Journal of Agricultural Science and Technology and Natural Resources, Soil Science. 15(58), 82-71.

Zhang, F., Tiyip, T., Feng, Z., Kung, H. T., Johnson, V., Ding, J., Tashpolat, N., Sawut, M., & Gui, D. (2015). Spatio‐Temporal Patterns of Land Use/Cover Changes Over the Past 20 Years in the Middle Reaches of the Tarim River, Xinjiang, China. Land Degradation & Development, 26(3), 284-299.