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Proceedings Paper

Object based technique for delineating and mapping 15 tree species using VHR WorldView-2 imagery
Author(s): Yaseen T. Mustafa; Hindav N. Habeeb
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Paper Abstract

Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying the Neural Network method with IOs techniques on WorldView-2 imagery.

Paper Details

Date Published: 29 October 2014
PDF: 13 pages
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390G (29 October 2014); doi: 10.1117/12.2067280
Show Author Affiliations
Yaseen T. Mustafa, Univ. of Zakho (Iraq)
Hindav N. Habeeb, Directorate of Forestry (Iraq)

Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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