عنوان مقاله [English]
Land use is one of the most important indicators of economic and social development in urban areas, and has resulted in extensive changes in available structures and procedures of these areas. Therefore, human activities are known as one of the main principles and components of change in land use. Generally, land use changes are inevitable product of interactions between human activities and environmental elements. Remote sensing technology with capabilities such as providing update and reliable information about natural and urban areas, digital processing of satellite imageries, providing the possibility of temporal and spatial comparing of different phenomena, diversity of products, and etc. is considered to be a powerful tool in improving the efficiency of urban management. Consequently, remote sensing data are used to determine type, quantity and location of land use changes. Moreover, remote sensing technology is used extensively in land use maps all over the world. Many models have been applied to predict land use changes, which due to the complex, dynamic, and non-linear nature of the issue gained little attention. However, CA-Markov model, which is a combination of Markov chain and cellular automata, is commonly considered to be an appropriate and good method for spatial-temporal modelling of land use changes. In the present study, land use changes were investigated for a 15-year period in Shiraz using object- based image analysis. Then, a land use map was produced using cellular automata-Markov (CA-Markov) model to predict land use changes in the study area in 2020.
Material & Methods
The present study includes two main phases. In the first phase, land use map of Shiraz was produced using Fuzzy object based analysis of satellite imageries. In the second phase, modeling and predicting of land use changes in 2020 were performed. Landsat imageries of the study area in 2005, 2010 & 2015 were used in this research. After preprocessing and preparing the imageries, segmentation procedure was performed as the first stage of object based classification using multiresolution segmentation algorithm. The nearest neighbor algorithm was used for object based classification of satellite imageries. Classification conditions were defined in accordance with each class properties, and classification was performed based on fuzzy operators of the classification operation. In CA-Markov model, the possibility of changing from one class of land use to another was calculated using transfer matrix table. Then, land use map of future years will be predictable in accordance with the transfer probability matrix, and desired time interval.
Result & Discussion
In this study, scale parameter of 10, shape index of 0.4, and compactness index of 0.2 were extracted as the optimum conditions for segmentation. Apart from spectral data, information regarding the location, context, texture, normalized difference vegetation index, enhanced vegetation index, and digital elevation model were also used to improve the efficiency of classification phase. The results of model validation shows an overall accuracy of 89% and kappa coefficient of 0.87. Therefore, the results of CA-Markov model shows a very good potential for predicting land use changes in Shiraz. Thus with the adjustment and calibration of model parameters and based on land use maps of 2010 and 2015, Shiraz land use in 2020 was predicted.
Due to the complexity of modeling dynamic changes in urban land use, utilizing efficient and update methods of data analysis is crucial. Therefore, satellite imageries and object based image analysis techniques were used to prepare land use map of Shiraz based on the data collected over a 15 year period. By considering the defined land use classes (residential area, barren lands, street network and urban green space), optimum image segmentation parameters were found. Then, classification conditions were defined for each class using the nearest neighbor algorithm and fuzzy operators. In this way, image classification was performed. By analyzing land use changes during the 20-year period, we understand that residential area has increased from 38 square kilometers in 2005 to 142 square kilometer in 2020. Additionally, green space area faced a reduction of 4 km in the first 5 years of the period, while in the next 15 years green space area shows an increasing trend.
1- زبردست، لعبت و همکاران (1389)، ارزیابی روند تغییرات پوشش اراضی منطقه حفاظتشده ارسباران در فاصله زمانی 2002، 2006 و 2008 میلادی با استفاده از تصاویر ماهوارهای، پژوهشهای محیط زیست، شماره1، صص 23-33.
2- سرودی و جوزی؛ منا و سید علی (1392)، سنجش از دور و اجرای مدل مارکوف برای بررسی تغییرات فضای سبز شهری (مطالعه موردی: منطقه 1 شهرداری علوی)، محیط شناسی، سال سی و نهم، شماره 1، بهار 92، صص 113-122.
3- فیضیزاده، بختیار (1386)، مقایسه روشهای پیکسل پایه و شئگرا در تهیه نقشههای کاربری اراضی مطالعه موردی جلگه شرقی دریاچه ارومیه، پایاننامه کارشناسی ارشد، دانشکده علوم انسانی و اجتماعی، دانشگاه تبریز.
4- فیضیزاده، بختیار و همکاران (1387)، کاربرد دادههای سنجش از دور در آشکارسازی تغییرات کاربریهای اراضی شهری، مجله هنرهای زیبا، سال نهم، شماره 34، صص 24-17.
5- مرکز آمار ایران (1392)، سالنامه آماری استان فارس.
6- نیکورزم و جوزی؛ یاسمن و سید علی (1394)، بررسی تغییرات فضای سبز با مدل مارکوف و شاخص NDVI و تبیین راهبردها با مدل SWOT، مطالعه موردی: مناطق 18، 19 و 21 شهرداری تهران، دو فصلنامه پژوهشهای بوم شناسی شهری، سال ششم، شماره 1، صص 53-72.
7- Anderson, J & et al (1975), A Land Cover Classification System for Use with Remote Sensor Data, United States Government Printing Office, Washington.
8- Baatz, M & Schäpe, A (1999), Object-Oriented and Multi-Scale Image Analysis in Semantic Networks, 2nd International Symposium on Operationalization of Remote Sensing, Enschede, ITC.
9- Benito, P.R & et al (2010), Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through CORINE Land Cover data and Markov models. Forest Systems 19: 315–328.
10- Berberoğlu, S & et al (2016), Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: A comparative approach. Landscape and Urban Planning 153: 11-27.
11- Blaschke, T (2010), Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing 65(17): 2-16.
12- Chavez, P.S (1998), An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data, Remote Sensing of Environment 24(3): 459-479.
13- Chen, M & et al (2009), Comparison of pixel-based and object-oriented knowledge- based classification methods using spot5 imagery, swears transactions on information science and applications 6(3): 477- 489.
14- Duro, D.C & et al (2012), A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery, Remote Sensing of Environment 118 : 259-272.
15- Eastman, J.R., (2009). IDRISI Selva Manual, Accessed in IDRISI 17, Worcester, MA: Clark University, PP. 230-237.
16- Feizizadeh, B & et al (2014), Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, IEEE Journal of selected topics in applied earth observations and remote sensing 7(12): 4806-4817.
17- He, C.Y & et al (2008), Modeling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape Urban Plan 86: 79-91.
18- Huang, L & Ni, L (2008), Object-oriented classification of high resolution satellite image for better accuracy, Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Shanghai, P. R. China, PP: 211-218.
19- Huishi, D & et al (2012), Land Coverage Changes in the Hulun Buir Grassland of China Based on the Cellular Automata-Markov Model, International Conference on Geological and Environmental Sciences IPCBEE, Vol.36, IACSIT Press, Singapore.
20- Kamusoko, C & et al (2009), Rural Sustainability under Threat in Zimbabwe – Simulation of Future Land Use/cover Changes in the Markov- Cellular Automata Model, Applied Geography 29: 435-447.
21- Li, H & Reynolds, J.F (1997), Modeling Effects of Spatial Pattern, Drought, and Grazing on Rates of Rangeland Degradation: A Combined Markov and Cellular Automaton Approach, In Quattro chi, D. A., & Godchild, M. F. (eds.), Scale in Remote Sensing and GIS, Boca Raton, Florida: Lewis Publishers, PP. 211-230.
22- Liu, D & et al (2010), Assessing object-based classification: advantages and limitations. Remote Sensing Letters 1(4):187–194.
23- Liu, X & et al (2010), A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology 25(5): 671-682.
24- Macleod, R.S & Congalton R.G (1998), A quantitative comparison of change detection algorithms for monitoring grass from remotely sensed data, Photogrammetric and Engineering Remote Sensing 64(3): 207-216.
25- Martellozzo, F & et al (2011), Measuring urban sprawl, coalescence, and dispersal: a case study of Pordenone, Italy. Environment and Planning-Part B 38(6): 1085.
26- Moghadam, H. S & et al (2013), spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography 40: 140-149.
27- Peterson, L. K & et al (2009), Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region, Forest Ecology and Management 257: 911-922.
28- Rasuly, A & et al (2013), Simulation of land use/cover change dynamics in future based on Markov-CA model, ISPRS International Journal of Geo-Information 2: 1-13.
29- Stevens, D & et al (2007), “iCity: A GIS- CA modeling tool for urban planning and decision making”, Environmental Modelling & Software 22: 761-773.
Tso, B & Mather, P.M (2001), Classification Methods for Remotely Sensed Data, 2th Edition, Taylor and Francis Publication, London.
30- Walter, V (2004), Object-based classification of remote sensing data for change detection. ISPRS Journal of photogrammetry and remote sensing 58(3): 225-238.
31- Wood, E. C & et al. (1997), The development of a land cover change model for southern Senegal, In: Land use modeling workshop, June 5–6, Sioux Falls, SD. Santa Barbara, CA: National Center for Geographic Information and Analysis.