The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach
The optimal computational capability for analyzing multispectral aerial images e.g. for fine-scale tree species mapping is often considerably constrained by their enormous data volume. This may be mainly reflected in a reduction in the speed of data processing as well as in their archiving. This res...
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Research Institute of Forests and Rangelands of Iran
2014-03-01
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doaj-fc067c837c0b42b8a9e2b65fea6cd52c2020-11-25T00:41:11ZfasResearch Institute of Forests and Rangelands of Iranتحقیقات جنگل و صنوبر ایران1735-08832383-11462014-03-0122112113210.22092/ijfpr.2013.90629062The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approachOmid Refieyan0Ali Asghar Darvishsefat1Assistant Professor, Department of Environmental Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj, Iran.The optimal computational capability for analyzing multispectral aerial images e.g. for fine-scale tree species mapping is often considerably constrained by their enormous data volume. This may be mainly reflected in a reduction in the speed of data processing as well as in their archiving. This research was conducted to explore the effect of alterations in spatial and radiometric resolutions in the quality of object-based tree species classification by UltraCamD aerial images. The study was conducted in three different study sites. Segmentation was firstly implemented on the original images featuring spatial and radiometric resolution of 7 cm and 8-bit, respectively. The optimum segmentation result was then classified. Following this, rescaling in spatial (to 14, 21, 28, 35 and 42 cm pixel size) and radiometric (16-bit to 8-bit) resolutions were conducted, which was followed by classification of the resulted images using the similar segmentation, input bands, features and training and validation data. Based on the conducted accuracy assessment of the resulted classified images, the accuracy was shown to reduce along with a decrease in the radiometric resolution for all of the three areas. However, the trend was shown to be non-uniform when reducing the spatial resolution of the input data. It is concluded that a downscaling of the pixel size down to 4 times coarser than the original pixel size does not notably affect the classification of even-aged or homogeneous forests, while it should be merely conducted with caution in case of natural stands encompassing undisturbed, heterogeneous and diverse groups of species.http://ijfpr.areeo.ac.ir/article_9062_4fd2d25f430af3f9ced7bb217171cda3.pdfUltraCamDimage segmentationobject-based classificationspatial and radiometric resolution |
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DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
Omid Refieyan Ali Asghar Darvishsefat |
spellingShingle |
Omid Refieyan Ali Asghar Darvishsefat The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach تحقیقات جنگل و صنوبر ایران UltraCamD image segmentation object-based classification spatial and radiometric resolution |
author_facet |
Omid Refieyan Ali Asghar Darvishsefat |
author_sort |
Omid Refieyan |
title |
The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach |
title_short |
The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach |
title_full |
The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach |
title_fullStr |
The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach |
title_full_unstemmed |
The effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach |
title_sort |
effect of spatial and radiometric resolutions of aerial images for tree species classification by object-based approach |
publisher |
Research Institute of Forests and Rangelands of Iran |
series |
تحقیقات جنگل و صنوبر ایران |
issn |
1735-0883 2383-1146 |
publishDate |
2014-03-01 |
description |
The optimal computational capability for analyzing multispectral aerial images e.g. for fine-scale tree species mapping is often considerably constrained by their enormous data volume. This may be mainly reflected in a reduction in the speed of data processing as well as in their archiving. This research was conducted to explore the effect of alterations in spatial and radiometric resolutions in the quality of object-based tree species classification by UltraCamD aerial images. The study was conducted in three different study sites. Segmentation was firstly implemented on the original images featuring spatial and radiometric resolution of 7 cm and 8-bit, respectively. The optimum segmentation result was then classified. Following this, rescaling in spatial (to 14, 21, 28, 35 and 42 cm pixel size) and radiometric (16-bit to 8-bit) resolutions were conducted, which was followed by classification of the resulted images using the similar segmentation, input bands, features and training and validation data. Based on the conducted accuracy assessment of the resulted classified images, the accuracy was shown to reduce along with a decrease in the radiometric resolution for all of the three areas. However, the trend was shown to be non-uniform when reducing the spatial resolution of the input data. It is concluded that a downscaling of the pixel size down to 4 times coarser than the original pixel size does not notably affect the classification of even-aged or homogeneous forests, while it should be merely conducted with caution in case of natural stands encompassing undisturbed, heterogeneous and diverse groups of species. |
topic |
UltraCamD image segmentation object-based classification spatial and radiometric resolution |
url |
http://ijfpr.areeo.ac.ir/article_9062_4fd2d25f430af3f9ced7bb217171cda3.pdf |
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