Open Access
Remote Sensing, volume 12, issue 7, pages 1145
Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning
Publication type: Journal Article
Publication date: 2020-04-03
General Earth and Planetary Sciences
Abstract
Wind disturbances are significant phenomena in forest spatial structure and succession dynamics. They cause changes in biodiversity, impact on forest ecosystems at different spatial scales, and have a strong influence on economics and human beings. The reliable recognition and mapping of windthrow areas are of high importance from the perspective of forest management and nature conservation. Recent research in artificial intelligence and computer vision has demonstrated the incredible potential of neural networks in addressing image classification problems. The most efficient algorithms are based on artificial neural networks of nested and complex architecture (e.g., convolutional neural networks (CNNs)), which are usually referred to by a common term—deep learning. Deep learning provides powerful algorithms for the precise segmentation of remote sensing data. We developed an algorithm based on a U-Net-like CNN, which was trained to recognize windthrow areas in Kunashir Island, Russia. We used satellite imagery of very-high spatial resolution (0.5 m/pixel) as source data. We performed a grid search among 216 parameter combinations defining different U-Net-like architectures. The best parameter combination allowed us to achieve an overall accuracy for recognition of windthrow sites of up to 94% for forested landscapes by coniferous and mixed coniferous forests. We found that the false-positive decisions of our algorithm correspond to either seashore logs, which may look similar to fallen tree trunks, or leafless forest stands. While the former can be rectified by applying a forest mask, the latter requires the usage of additional information, which is not always provided by satellite imagery.
Citations by journals
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Forests
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Forests
3 publications, 9.68%
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Remote Sensing
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Remote Sensing
3 publications, 9.68%
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ISPRS Journal of Photogrammetry and Remote Sensing
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ISPRS Journal of Photogrammetry and Remote Sensing
2 publications, 6.45%
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2 publications, 6.45%
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Science of the Total Environment
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Science of the Total Environment
1 publication, 3.23%
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Earth's Future
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Earth's Future
1 publication, 3.23%
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Animals
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Animals
1 publication, 3.23%
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Frontiers in Forests and Global Change
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Frontiers in Forests and Global Change
1 publication, 3.23%
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Dendrochronologia
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Dendrochronologia
1 publication, 3.23%
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Remote Sensing in Ecology and Conservation
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Remote Sensing in Ecology and Conservation
1 publication, 3.23%
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IEEE Transactions on Industrial Informatics
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IEEE Transactions on Industrial Informatics
1 publication, 3.23%
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Forestry
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Forestry
1 publication, 3.23%
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Integrative and Comparative Biology
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Integrative and Comparative Biology
1 publication, 3.23%
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Contemporary Problems of Ecology
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Contemporary Problems of Ecology
1 publication, 3.23%
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International Journal of Remote Sensing
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International Journal of Remote Sensing
1 publication, 3.23%
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Remote Sensing of Environment
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Remote Sensing of Environment
1 publication, 3.23%
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Journal of the Indian Society of Remote Sensing
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Journal of the Indian Society of Remote Sensing
1 publication, 3.23%
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Computers and Electronics in Agriculture
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Computers and Electronics in Agriculture
1 publication, 3.23%
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Cosmic Research
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Cosmic Research
1 publication, 3.23%
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E3S Web of Conferences
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E3S Web of Conferences
1 publication, 3.23%
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Journal of the Brazilian Society of Mechanical Sciences and Engineering
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Journal of the Brazilian Society of Mechanical Sciences and Engineering
1 publication, 3.23%
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Citations by publishers
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7
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Elsevier
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Elsevier
7 publications, 22.58%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
7 publications, 22.58%
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IEEE
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IEEE
4 publications, 12.9%
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Wiley
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Wiley
2 publications, 6.45%
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Oxford University Press
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Oxford University Press
2 publications, 6.45%
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Pleiades Publishing
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Pleiades Publishing
2 publications, 6.45%
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Springer Nature
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Springer Nature
2 publications, 6.45%
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Frontiers Media S.A.
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Frontiers Media S.A.
1 publication, 3.23%
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Taylor & Francis
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Taylor & Francis
1 publication, 3.23%
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Research Square Platform LLC
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Research Square Platform LLC, 1, 3.23%
Research Square Platform LLC
1 publication, 3.23%
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EDP Sciences
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EDP Sciences
1 publication, 3.23%
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Kislov D. E., Korznikov K. Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning // Remote Sensing. 2020. Vol. 12. No. 7. p. 1145.
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Kislov D. E., Korznikov K. Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning // Remote Sensing. 2020. Vol. 12. No. 7. p. 1145.
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TY - JOUR
DO - 10.3390/rs12071145
UR - https://doi.org/10.3390%2Frs12071145
TI - Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning
T2 - Remote Sensing
AU - Kislov, Dmitry E
AU - Korznikov, Kirill
PY - 2020
DA - 2020/04/03 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 1145
IS - 7
VL - 12
SN - 2072-4292
ER -
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@article{2020_Kislov,
author = {Dmitry E Kislov and Kirill Korznikov},
title = {Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning},
journal = {Remote Sensing},
year = {2020},
volume = {12},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {apr},
url = {https://doi.org/10.3390%2Frs12071145},
number = {7},
pages = {1145},
doi = {10.3390/rs12071145}
}
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MLA
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Kislov, Dmitry E., and Kirill Korznikov. “Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning.” Remote Sensing, vol. 12, no. 7, Apr. 2020, p. 1145. https://doi.org/10.3390%2Frs12071145.
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