Investigation on the capability of digital data of ETM+ sensor in seperating of forest types (Case study: Lafoor area of Savadkooh)

• Published in: Taḥqīqāt-i jangal va ṣanubar-i Īrān Vol. 17; no. 1; pp. 63 - 51
• Main Authors: Farahnaz Rashidi, Sasan Babaie Kafaki, Ja'far Oladi
• Format: Journal Article
• Language: Persian
• Published: Research Institute of Forests and Rangelands of Iran 01.03.2009
• Subjects: Classification; ETM; forest typemapping; geometric correction; ground truth; Kappa coefficient; overall accuracy
• ISSN: 1735-0883; 2383-1146

This study was carried out in order to investigate the capability of digital data of ETM+ sensor in separation of forest types in Gazoo district of Lafoor area in Savadkooh. The bands were controlled according to radiometric and geometric errors, separately. Band 1, was omitted because of the existence of radiometric error and its less importance in vegetation cover study. Geometric correction was performed by 21 ground control points with DEM, up to ortho rectification level with precision of less than half pixel (0.3 pixel). The supervised classification was performed by using basic and synthetic bands to 6 classes, (pure beech type, mixed beech type, mixed hornbeam, road and non covered area, persimmon, mixed broad leaf). Ground truth map prepared through sampling in 24% of whole area. The highest overall accuracy was belong to maximum likelihood classification for 6 classes which was 38.29% and Kappa coefficient was 27.7%. Six vegetation types were merged because of radiometric mixing, therefore classification with 5 classes was performed again. Accuracy assessment of classification results indicated that the highest overall accuracy and Kappa coefficient were 53.22% and 34.71%, respectively. Results showed that the ML classification increases %15 of overall accuracy and %7 in Kappa coefficient. Overall, using ETM+ data is not so appropriate in the studies which the map type is considered as a base map with maximum number of existing type in the area. In order to increase the classification accuracy, using of other classification methods like object-base method and the other information and multitemporal data is suggestible.