Remote sensing and image interpretation have been utilized
in forestry management for many years and offer great potential for vegetation
mapping, especially with the availability of higher-resolution imagery. In
recent years, digital cameras have expanded in their utility as an efficient
tool for forest inventory and mapping. This study is a contribution to assess
the high-resolution digital aerial imagery for semi-automatic analysis of tree
species identification. To maximize the benefit of such data, the object-based
classification was conducted in two even-aged mixed forest plantations. The
first study area, dominantly consisting of chestnut-leaved oak, loblolly pine,
with blackberry shrubs and occasionally distributed Arizona cypress trees, and
the second study area is more heterogeneous in species composition, stocking
density and canopy structure and dominantly consisting of Caspian locust,
velvet maple, white mulberry, common alder and smooth-leaf elm. Two subsets of
UltraCamD images were geometrically corrected using the aero-triangulation
method. Some appropriate transformations were performed and utilized.
Segmentation was conducted stepwise at two levels and a hierarchical image
object network was constructed. The classification hierarchy was developed and
the Nearest Neighbor classifier, using an integration of different features was
performed. Training samples and ground truth maps were prepared through
fieldwork. Accuracy assessment of the resulting maps in comparison with
reference data showed overall accuracies and Kappa Index of Agreement of 90.2%,
0.82 (Area1) and 69.8%, 0.49 (Area2), respectively. Transformed images were
advantageous to improve the results. The lower accuracy in Area 2 can be
attributed to a high diversity and heterogeneous mixture of species. Because of
the limitations of using only optical data and the potential future role of
LiDAR integration, more detailed and accurate mapping of tree species would be
fulfilled by applying precise 3D data, which are derived from LiDAR. The
accuracy of detailed vegetation classification with very high-resolution imagery
is highly dependent on the segmentation quality, sample size, sampling quality,
classification framework and ground vegetation distribution and mixture.
Author(s)details:-
O. Rafieyan
Department of Environmental Engineering, Tabriz Branch, Islamic Azad University,
Tabriz, Iran.
A. A. Darvishsefat
Department of Forestry, Faculty of Natural Resources, University of Tehran,
Karaj, Iran.
S. Babaii
Department of Forestry, Science and Research Branch, Islamic Azad
University, Tehran, Iran.
A. Mataji
Department of Forestry, Science and Research Branch, Islamic Azad
University, Tehran, Iran.
Please See the book
here :- https://doi.org/10.9734/bpi/geserh/v2/3234
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