Evalution of Landsat ETM Sensor To Separate Riparian Forest from River (Case Study: Maroon River – Khuzestan)
Journal Title: Journal of River Engineering - Year 2014, Vol 2, Issue 3
Abstract
In order to evaluate Landsat satellite imagery and remote sensing applications in separation of land from the river in riparian forest of Maroon Behbahan, a small window of multispectral images Landsat ETM+ was chosen. Selected area was 973 hectar approximately. Ground data picked randomly and was performed geometric matching using 30point and then the appropriate band was chosen. After selecting the training area was done use the classification Maximum likelihood, Minimum distance and Parallelepiped algorithm. Classified was taken to three and two user classes. For Separability classes was used Transformed Divergence and Jeffreys–Matusita. For separation vegetation from other surface cover was used NDVI. The best method of classification was Maximum Liklihood. Overall accuracy was 96% and kappa coefficient was 0/93. The results showed the ETM+ sensor imagery of landast have to a relatively good ability to separate the river from the forest but it is better to use higher resolution images.
Authors and Affiliations
M. Firoozynejad - A. Torahi - A. Abdolkhani - A. Riazi
Keywords
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