Estimating forest area using remote sensing and regression estimator

Area estimates using remotely sensed data is an important subject that has been investigated around the world during the last decade. It plays an important role in the production of vegetation statistic when area frame sample design is used using regression estimator. This technique is used widely in estimation of crop area and yield. This work is carried out utilizing the same method but tested for the tropical forest in Malaysia. The estimates have been conducted using direct expansion from sample survey and regression estimator approaches. The latter result using regression of ground data and satellite data seem more reliable when training pixels are chosen at random subset of the area sampling frame. The regression analyses showed all the land cover class had a very high correlation (r2 = 0.86 to 0.89). This method is not only practical with accurate estimation for this task but also does not haveany additional time and cost implications.

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