E3S Web Conf.
Volume 223, 2020Regional Problems of Earth Remote Sensing (RPERS 2020)
|Number of page(s)||6|
|Section||Models and Methods of Remote Sensing Data Processing|
|Published online||21 December 2020|
Remote spectral methods for detecting stress coniferous
1 A.N. Sevchenko Institute of Applied Physical Problems of Belarusian State University, Minsk, 220045, the Republic of Belarus
2 S.P. Korolev Rocket and Space Public Corporation Energia, Korolev, 141070, Russia
* Corresponding author: firstname.lastname@example.org
The article presents investigation of the possibility of drying coniferous forest areas detecting by multispectral satellite data in the visible and NIR spectral range with low spatial resolution, obtained by the imaging systems of three satellites - the Belarusian spacecraft (BS), Landsat 8 and Sentinel 2. A forest area in the south of Belarus was considered as a test site. High-resolution multispectral airborne data and, in part, ground measurements were used as reference ground data by which training samples were formed. Most of the known classical methods of supervised classification have been tested, the maximum likelihood method turned out to be the best for this task. In order to improve the accuracy of identifying the drying areas of coniferous forests on multispectral images, parametric transformations of images in the spectral space are proposed, which should lead to an increase in initial small spectral differences. The methodological issues of assessing the accuracy of the satellite images classification are considered using the result of the classification of airborne image with high spatial resolution as a ground truth image. The assessment of the classification accuracy, both visually and using the obtained confusion matrices, allows us to conclude that the images of the BS, Landsat 8 and Sentinel 2 can be used to detect drying area of coniferous forests as well as the expediency of carrying out the proposed transformations of the original images.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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