ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 2, pp. 75-93

Use of convolutional neural networks for geospatial modelling of species structure and taxation characteristics of forests (case study of Khanty-Mansi Autonomous Okrug – Yugra)

M.D. Moskovchenko 1 
1 Tyumen State University, Tyumen, Russia
Accepted: 30.03.2023
DOI: 10.21046/2070-7401-2023-20-2-75-93
Until recently, forest inventory data was the only reliable data source in forestry. Nowadays, correction of forest inventory data became possible, including correction at the local per-community level, using remote sensing data and geospatial modeling methods. This study assessed the possibility of using convolutional neural networks for geospatial modelling of the species structure and taxation characteristics of forests on the example of the territory of Khanty-Mansi Autonomous Okrug – Yugra. Forest inventory data of the Khanty-Mansi Autonomous Okrug – Yugra was used as a target variable, remote sensing data (Sentinel-2 satellite imagery), digital elevation model ASTER GDEM, ESA WorldCover landcover data and Global Forest Change dataset were used as independent variables. The model of the vegetation of the Khanty-Mansi Autonomous Okrug (of its dominant species, ground cover and forest site index classes) based on DeepLab architecture achieved an accuracy of 88 % on both train, validation and test datasets and made it possible to create an up-to-date digital map of vegetation in the region with a resolution of 10 m. Using the pre-trained models for modelling forest characteristics in the territories of adjacent regions — Yamalo-Nenets Autonomous Okrug (modeling accuracy was 85 %) and the Krasnoyarsk Krai (modeling accuracy was from 62 to 67 %) — showed that they can be used in adjacent territories of the other regions of Western Siberia but are not applicable for modeling forest characteristics in Eastern Siberia.
Keywords: neural networks, modelling, U-Net, DEEPLAB, forest management, forest mapping, Western Siberia
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