Estimating changes in forest cover in the Rudsar county by using neural network and maximum likelihood methods
Subject Areas : Geospatial systems development
Seyed Reza Fatemti Talab
1
(
Assis. Prof. Department of Environment, Abadeh Branch ,Islamic Azad University
)
Morteza Madanipour Kermanshahi
2
(
Assis. Prof. Department of Forestry, Shabestar Branch, Islamic Azad University
)
Seyed Armin Hashemi
3
(
Assis. Prof. Department of Forestry, Lahijan Branch, Islamic Azad University
)
Keywords:
Abstract :
The acquisition of knowledge about the vegetation plays an important role in soil management. However, vegetation estimating in the usual way, including an overall assessment of the vegetation is time consuming and does not also provide accurate enough information. Therefore, remote sensing technology is a desirable way for reducing time and cost compared to other usual methods. In this study, forest cover maps were prepared using remote sensing techniques and LandSat ETM+ imagery of year 2000 and LandSat 8 of year 2013. The classification of the study area digital images was performed to prepare land use map classification using maximum likelihood and neural network with participation of different bands. The results showed that the best overall accuracy of image classification using neural networks ETM+ in 2000 and LandSat 8 in 2013 was 0.95 and 0.95 respectively. It was also indicated that the kappa coefficient was estimated 0.91 and 0.91 respectively. The overall accuracy of maximum likelihood method of the collected images of 2000 and 2013 was 0.95 and 0.85, but it was 0.86 and 0.84 for Kappa statistics method. The results also showed a 1054.507 and 635.319 hectares decreasing of forest cover using neural network classification and maximum likelihood classification methods respectively. According to classification accuracy and Kappa statistics, it was observed that the accuracy and kappa coefficient of neural network classification was higher than accuracy and the Kappa coefficient of maximum likelihood method.