Mapping the current and past state of forest stands for the southern taiga of East Siberia
Vol 8, Issue 1, 2025
Abstract
Maps of forest stand condition—the current phase of the forest-forming process—will be useful for foresters in their forest management in addition to the forest planning and cartographic materials. The mapping methodology was applied in the test area of the Bolshemurtinsky forest district of the Krasnoyarsk region, which is typical for the southern taiga forests of East Siberia. Source data for mapping was obtained on the basis of descriptions of the forest subcompartments on the GIS attribute table of the forest district. Forest stand confinement to the terrain relief indicators was identified on the basis of the SRTM 55-01 digital terrain model data. Spatial analysis has been performed using the ArcGIS Spatial Analyst module. Mapping capability has been shown not only for the year of forest inventory but also for the earlier period of time. To determine the predominant species and the age of the 100-year-old forest stand, a scheme was proposed in which the conceivable options are typified depending on the succession trend, the forest stand age prior to disturbance, and the period of reforestation. Map fragments of the test area as of 2006—the year of forest inventory—and as of 1906—the year of the intensive colonization beginning in southern Siberia—are demonstrated. Maps of forest condition in the test area represent successions that are typical in the southern taiga forests of Siberia: post-harvest, pyrogenic, and biogenic. The methodology of forest condition mapping is universal.
Keywords
Full Text:
PDFReferences
1. Araza A, de Bruin S, Herold M, et al. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment. 2022; 272: 112917. doi: 10.1016/j.rse.2022.112917
2. Talucci AC, Loranty MM, Alexander HD. Siberian taiga and tundra fire regimes from 2001–2020. Environmental Research Letters. 2022; 17(2): 025001. doi: 10.1088/1748-9326/ac3f07
3. Zhang Q, Liang Y, He HS. Tree-Lists Estimation for Chinese Boreal Forests by Integrating Weibull Diameter Distributions with MODIS-Based Forest Attributes from kNN Imputation. Forests. 2018; 9(12): 758. doi: 10.3390/f9120758
4. Maksyutov S, Sedykh V, Kuzmenko EI, et al. Current state of forest mapping with Landsat data in Siberia. EGU General Assembly. 2016; 18.
5. White JC, Hermosilla T, Wulder MA, et al. Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery. Remote Sensing of Environment. 2022; 271: 112904. doi: 10.1016/j.rse.2022.112904
6. Khovratovich T, Bartalev S, Kashnitskii A, et al. Forest change detection based on sub-pixel tree cover estimates using Landsat-OLI and Sentinel 2 data. IOP Conference Series: Earth and Environmental Science. 2020; 507(1): 012011. doi: 10.1088/1755-1315/507/1/012011
7. Bartalev SA, Egorov VA, Zharko VO, et al. Satellite mapping of the vegetation cover of Russia. Moscow, Russian Federation: Space Research Institute RAS; 2016.
8. Rees WG, Tomaney J, Tutubalina O, et al. Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification. Remote Sensing. 2021; 13(21): 4483. doi: 10.3390/rs13214483
9. Khvostikov SA, Bartalev SA. Methods for Wildfire Spread Prediction and Their Integration With Remote Sensing Data. Information Technologies in Remote Sensing of the Earth - RORSE 2018; 2019.
10. Bartalev SA, Stytsenko FV. Assessment of Forest-Stand Destruction by Fires Based on Remote-Sensing Data on the Seasonal Distribution of Burned Areas. Contemporary Problems of Ecology. 2021; 14(7): 711-716. doi: 10.1134/s1995425521070027
11. Hovratovich TS, Bartalev SA. Methods of Remote Assessment of Indicators of the Tree Canopy Horizontal Structure According to the MODIS Satellite System Data. In: Proceedings of Fundamental and applied space research; 30 September - 2 October 2022; Moscow, Russian Federation.
12. Farber SK, Kuzmik NS, Bruykhanov NV. Errors in Interpretation of the Angara Region Forests by the Method of Classification of Satellite Image Pixels. Siberian Journal of Forest Science. 2016; 4: 56-67.
13. Lim K, Treitz P, Wulder M, et al. LiDAR remote sensing of forest structure. Progress in Physical Geography: Earth and Environment. 2003; 27(1): 88-106. doi: 10.1191/0309133303pp360ra
14. Pearse GD, Watt MS, Dash JP, et al. Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities. International Journal of Applied Earth Observation and Geoinformation. 2019; 78: 341-351. doi: 10.1016/j.jag.2018.10.008
15. Novakovsky BA, Kovach NS, Entin AL, Kalinovsky LV. Geoinformation Mapping of Forest Canopy Based on Airborne Laser Scanning. Geoinformatics. 2017; 1: 32-39.
16. Rogachevskaya MA. А Stolypin: Agrarian Reform and Siberia (Russian). Available online: http://econom.nsc.ru/eco/arhiv/ReadStatiy/2002_09/Rogachevska.htm (accessed on 26 December 2024).
17. Laymtsev NI. Assessment and forecast of Siberian moth mass propagation risks in the Krasnoyarsk Krai forests. News of the St. Petersburg Forestry. 2019; 228: 294-311. doi: 10.21266/2079-4304.2019.228.294-311
18. Nilsson S, Shvidenko A. Biospheric Role of Siberian Ecosystems. Laxenburg: International Institute for Applied Systems Analysis; 1993.
19. Farber SK. Forest Stand Formation in Eastern Siberia. Novosibirsk, Russian Federation: Siberian Branch of Russian Academy of Science; 2000.
20. Healey S, Cohen W, Zhiqiang Y, et al. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment. 2005; 97(3): 301-310. doi: 10.1016/j.rse.2005.05.009
DOI: https://doi.org/10.24294/sf11115
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Sergey Farber, Nastassia Sokolova, Aleksey Martynov
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.