| Issue |
E3S Web Conf.
Volume 75, 2019
Regional Problems of Earth Remote Sensing (RPERS 2018)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 6 | |
| Section | Methods and Algorithms for Image Processing | |
| DOI | https://doi.org/10.1051/e3sconf/20197501003 | |
| Published online | 14 January 2019 | |
Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images
1
Institute of Numerical Mathematics of Russian Academy of Sciences, ul. Gubkina 8, 119333 Moscow, Russia
2
M.V. Lomonosov Moscow State University, Leninskiye Gory 1, 119991 Moscow, Russia
3
Federal Forestry Agency ROSLESINFORG, Volgogradsky Prospect, 45-1, 109316 Moscow, Russia
4
Mytischi Branch of Bauman Moscow State Technical University, 1st Institutskaya street 1, 141005 Mytischi, Moscow region, Russia
5
Laboratoire de PhysicoChimie de l'Atmosphère Université du Littoral Cote d'Opale, Avenue Maurice Schumann 189A, 59140 Dunkerque, France
* Corresponding author: yegor@mail.ru
A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.