Vegetation classification by satellite image processing in a dry area of north-eastern Syria
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Masahiro Hirata, Naoki Kogab, Hitoshi Shinjo, Haruhiro Fujita, Gustave Gintzburger, Miyazaki Akira. (25/11/2010). Vegetation classification by satellite image processing in a dry area of north-eastern Syria. International Journal of Remote Sensing, 22 (4), pp. 507-516.
As vegetation classification on the highly diverse rangeland is an inevitable procedure in evaluating total forage resources and assessing human impact in large areas, a supervised classification was conducted by satellite image processing using geocoded bands 2, 3 and 4 of Landsat 5 Thematic Mapper (TM) images, dated 13 April 1994 in the Abdal Aziz Mountain study area in northeastern Syria. The rangeland was categorized into six classes according to the plant contacts of dominant shrubs (Artemisia herba-alba and Noaea mucronata) and herbaceous plants. In addition, cultivated fields were categorized into two classes. An average classification accuracy of 85% in the supervised processing and an average ground verification accuracy of 81% on the Landsat-estimated vegetation classes were achieved for the rangeland. These show that a 30m X 30m resolution of the Landsat TM image had the ability to recognize vegetation at six sub-divided community levels, and the successful classification was conducted on the whole rangeland of the study area. The distinctive feature of this work is that this vegetation classification using Landsat TM images was accomplished at the level of classifying a A. herba-alba and N. mucronata dominant community into six sub-community classes. This detailed vegetation classification was conducted with the final aim of forage resource estimation and human impact assessment in mind.