Year-end Sale % OFF 
Abstract
The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high financial efforts. Remotely-sensed data from satellite platforms may have the capability to provide such huge amounts of information. This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, RandomForest. The results showed the high potential of this method for an operational, large-scale and high-resolution biomass estimation over boreal forests. An RMSE from 55.2 to 63.3 m3/ha could be obtained for the annual maps. Using the multi-temporal approach, the error could be slightly reduced to 54.4 m3/ha.
Highlights
Estimating large-scale forest biomass is a crucial issue for understanding global carbon cycling, as well as for monitoring global and regional changes in vegetation due to climate change effects or changes in land use and, for local forest inventories [1]
As regression trees are grown automatically based on the features of the input data, there is no need to set training areas, and it is not necessary to figure out the parameters of regression functions manually
A larger number of trees would be more time consuming without a significant further reduction of the mean square error (MSE)
Summary
Estimating large-scale forest biomass is a crucial issue for understanding global carbon cycling, as well as for monitoring global and regional changes in vegetation due to climate change effects or changes in land use and, for local forest inventories [1]. The Russian land surface with its large forested areas, as well as peat and wetlands contains an enormous biomass reservoir. Due to the large-scale dimensions of the Russian Taiga, it is still very expensive in terms of costs and time to establish an area-covering monitoring system for the Siberian forest. Especially radar data, can assist in achieving this task. These data provide a frequent observation method for monitoring GSV decrease caused by logging, clear cutting or forest fires and for detecting forest regrowth from afforestation or forest succession processes, e.g., after fires or clear cuts [3,4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
