Utilizing Hyperspectral Remote Sensing Imagery for Afforestation Planning of Partially Covered Areas

Abstract

In this study, a supportive method for afforestation planning process of partially forested areas using hyperspectral remote sensing imagery has been proposed. The algorithm has been tested on a scene covering METU campus area that is acquired by high resolution hyperspectral push-broom sensor operating in visible and NIR range of the electromagnetic spectrum. The main contribution of this study to the literature is segmentation of partially forested regions with a semi-supervised classification of specific tree species based on chlorophyll content quantified in hyperspectral scenes. In addition, the proposed method makes use of various hyperspectral image processing algorithms to improve identification accuracy of image regions to be planted.