Tree Cover detection through Maxlike Classification of Land sat ETM + Images of the Year 2001 in Golestan Province

Subject Areas : environmental management

Abdorrasoul Salman Mahini ¹ , Azade Nadali ² , Jahangir i Feghhi ³ , Borhan Riyazi ⁴

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Received: 2007-10-30 Accepted : 2008-01-14 Published : 2012-04-02

Keywords: Maxlike Classifier, Training Sites, Unsupervised Classification, Randomisation, Ground Control Point,

Abstract :

Sparse vegetation gives rise to increased overland water flow, soil erosion, water pollution and decreased soil fertility. Golestan Province has witnessed a relatively extensive forest clearing during recent years causing intensified flooding.  We used ETM+ land sat imagery to classify forest cover of the Golestan Province using Max like classification and assessed its accuracy. Land uses and land covers were distinguished on the color composite images of the area and used as training sites for image classification that included all six bands of the imagery. We also used an ISO-Cluster unsupervised classification to derive 100 clusters for purifying initial training sites. Accuracy assessment was implemented through test set pixels that were randomized and set aside from the training set pixels. We also used a LISS III imagery to assess the accuracy of the classification. Our assessment proved the classification to be of high accuracy.