Change detection in the forest cover of Siyahmezgi watershed of Guilan using LandSat images

In recent decades Caspian forest has been attacked by human intervention. Easy access, abundance and diversity of valuable forest products led to an increase in population density, development of new residential areas and activities of deforestation. Change detection is essential in the assessment and management of natural resources. The aim of this study, was to monitor changes in forests of Siyahmezgi watershed in two time periods (2000 and 2015), using LandSat ETM (2000) and OLI (2015) images. Images were geometric corrected using 20 ground control points that are randomly taken from all over the watershed area, and topographic maps. After selection of the best indicators of using Bhattacharyya distance, image classification using an artificial neural network algorithm was performed. The results of classification of neural network method of Siyahmezgi watershed in two time periods (2000 and 2015) showed that overall accuracy is equal to 95.75% and 95.96%, respectively. The area of forest lands during 2000 and 2015 has been reduced in size 213.55 ha. In addition, in this area dense rangelands have declined, but during this period the extent of dry farming and semi-dense rangelands have 169.95 and 9.6 hectares were added, respectively.