Comparison of support vector machine and artificial neural network classification methods to produce landuse maps (Case study: Bojagh National Park)

National parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classify satellite imagery in remote sensing, selecting an appropriate classification algorithm is very important in achieving the accurate results. In this research, a more accurate algorithm was determined by comparing the classification accuracy of two artificial neural network and support vector machine algorithms, and it was used to examine the process of the land use changes. The present study was performed in Boujagh National Park, in the Guilan Province, during the years 2000 to 2017, using satellite imagery ETM and OLI of Landsat 7 and 8. The results of the research revealed that the support vector machine algorithm with overall accuracy and Kappa coefficient of 86.42 and 0.83 respectively for the year 2000 and, 90.65 and 0.88 for the year 2017, classified the satellite images more precisely, in comparison with the artificial neural network algorithm with overall accuracy and Kappa coefficient of 83.71 and 0.80 respectively for the year 2000 and overall accuracy and Kappa coefficient of 89.25 and 0.87 for the year 2017. Therefore, the land use maps of the support vector machine algorithm were used to determine the land use changes. The study of land use change by this method concluded that the areas of the waterbody, sea, grassland and agriculture have decreased and marshland, woody and bare lands classes showed an increase during the study period.