Identifying and reviewing the process of vegetation usage changes using time-based neural network and CA models using GIS and RS techniques (Case Study: Minoodasht County Golestan Province)

Subject Areas :

صادق شکوری ¹ , سید مسعود موسوی حسنی ² , مهسا پورعطاکش ³ , آناهیتا قربانی ⁴ , سمیرا ارنک ⁵

1 - دانشجوی دکترای جغرافیا و برنامه ریزی شهری، دانشگاه آزاد اسلامی واحد اسلامشهر
2 - گروه جغرافیا و برنامه ریزی شهری، آمایش شهری، دانشگاه آزاد اسلامی یادگار امام (شهر ری)، تهران، ایران
3 - گروه جغرافیا و برنامه ریزی شهری، آمایش شهری، دانشگاه آزاد اسلامی یادگار امام (شهر ری)، تهران، ایران
4 - گروه معماری، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران
5 - گروه شهرسازی، دانشکده هنر و معماری، واحد تهران غرب، دانشگاه آزاد اسلامی، تهران، ایران

Received: 2018-07-21 Accepted : 2018-08-26 Published : 2019-08-23

Keywords: Landsat Satellite, Temporary Neural Network, Vegetation User, CA Model, GIS &amp, RS,

Abstract :

Monitoring land use change is important in many planning and urban management activities. Due to human activities and natural phenomena, the face of the earth always changes.Therefore, for optimal management of natural areas, awareness of the land use change ratio is considered necessary. The purpose of this study was to evaluate and reveal land use changes, especially the use of vegetation cover in the Auchan region, from the functions of Minoodasht city of Golestan province in a 30-year time span using remote sensing and spatial information systems and MATLAB, ARCGIS and ENVI software.For this purpose, Landsat satellite ETM sensor was used from 1987, 1993, 1998, 2000, 2003, 2008, 2013, 2015, and 2017, and after making necessary corrections in the preprocessing stage, to monitor vegetation time changes, the index Vegetation cover (NDVI) was calculated in MATLAB software for each 9 time intervals.Then, by using the calculated images of the first 7 years and the model of the neural network (time series), the images of the eighth and ninth year were predicted and obtained, and then calculating the RMSE error between the output images of the model with the actualImages, the validation model it turned out the results show that the model with an average RMSE of about 0.13 was very good for the NDVI.The CA model was used to predict vegetation changes. The results show that the vegetation cover in the last two years, 2015 and 2017, has been upgraded by the neural network model and the study area has become greener

References:

I.

1. Yaghobzadeh, M. and Akbarpour, A., 2011, the effect of satellite image classification algorithm based on curve number runoff and maximum flood discharge using GIS and RS, Geography and Development 9 (22):5-22.


2. Tewolde, M.G., and P. Cabral, (2011), Urban Sprawl Analysis and Modeling in Asmara.


3. Subudhi, B.N., Bovolo, F., Ghosh, A., and L. Bruzzone, 2014. Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images, Optics & Laser Technology 57: 284–292.


4. Rasouli, A.A., 2008, Principles of remote sensing image processing applications, with emphasis on satellite, Tabriz University Press, 777 pages.


5. K. Sundara Kumar et al, ESTIMATION OF LAND SURFACE TEMPERATURE TO STUDY URBAN HEAT ISLAND EFFECT USING LANDSAT ETM+ IMAGE, International Journal of Engineering Science and Technology (IJEST), Vol. 4 No.02 February 2012.


6. Poulami, P. and B. Bindu. 2012, Bioinfo Publications 96 A SPATIO -TEMPORAL LA ND USE CHANGE ANALYSIS OF WAGHODIA TALUKA USING RS AND GIS, ISSN: 0976 - 9846 & E - ISSN: 0976 - 9854, Volume 3, Issue 2, 2012, and pp. – 96 – 99.