Inventory of Single Oak Trees Using Object-Based Method on WorldView-2 Satellite Images and Earth


1 Ph.D. Student of forestry in Ilam University

2 Associate Professor and Faculty Member of Forest Sciences Department in University of Ilam

3 Associate Professor, Department of Natural Resources and Environment, College of Agriculture, Shiraz University, Shiraz, Iran


Remote sensing provides data types and useful resources for forest mapping. Today, one of the most
commonly used application in forestry is the identification of single tree and tree species compassion
using object-based analysis and classification of satellite or aerial images. Forest data, which is derived
from remote sensing methods, mainly focuses on the mass i.e. parts of the forest that are largely
homogeneous, in particular, interconnected) and plot-level data. Haft-Barm Lake is the case study which
is located in Fars province, representing closed forest in which oak is the valuable species. High
Resolution Satellite Imagery of WV-2 has been used in this study. In this study, A UAV equipped with a
compact digital camera has been used calibrated and modified to record not only the visual but also the
near infrared reflection (NIR) of possibly infested oaks. The present study evaluated the estimation of
forest parameters by focusing on single tree extraction using Object-Based method of classification with a
complex matrix evaluation and AUC method with the help of the 4th UAV phantom bird image in two
distinct regions. The object-based classification has the highest and best accuracy in estimating single-tree
parameters. Object-Based classification method is a useful method to identify Oak tree Zagros Mountains
forest. This study confirms that using WV-2 data one can extract the parameters of single trees in the
forest. An overall Kappa Index of Agreement (KIA) of 0.97 and 0.96 for each study site has been
achieved. It is also concluded that while UAV has the potential to provide flexible and feasible solutions
for forest mapping, some issues related to image quality still need to be addressed in order to improve the
classification performance.