Volume II-2/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W2, 175-181, 2015
https://doi.org/10.5194/isprsannals-II-2-W2-175-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W2, 175-181, 2015
https://doi.org/10.5194/isprsannals-II-2-W2-175-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Oct 2015

19 Oct 2015

IDENTIFICATION AND MAPPING OF TREE SPECIES IN URBAN AREAS USING WORLDVIEW-2 IMAGERY

Y. T. Mustafa1, H. N. Habeeb2, A. Stein3, and F. Y. Sulaiman2 Y. T. Mustafa et al.
  • 1Faculty of Science, University of Zakho, Kurdistan Region of Iraq
  • 2Directorate of Forestry, Duhok, Kurdistan Region of Iraq
  • 3Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, the Netherlands

Keywords: Urban tree species, Supervised classification, VHR imagery, Kurdistan Region of Iraq

Abstract. Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree species were identified and mapped at a satisfactory accuracy in urban areas of this study.