This paper presents an analysis of ICESat-2 data capabilities for forest-height estimation and assessment of its spatial distribution over Russia. A brief overview of available current and historic lidar satellite-data products is carried out. The physical basis of the ATLAS instrument operation, as well as an approach to forming an ATL08 data product with information on vertical structure of vegetation, are described. An approach for automated download and preprocessing of ATL08 product is implemented, including filtering of missing/corrupted data, conversion to vector format, and delineation of borders of aggregated lidar footprints for which characteristics of vegetation-height distribution are provided. All the available ATL08 data over Russia for the period from October 14, 2018, to May 13, 2020, was processed to form a dataset of over 125 million lidar measurements of vegetation vertical structure, including over 50 million measurements of forest height. This study was performed at local and national spatial levels. Local-scale accuracy assessment of the ATL08 product (at the level of forest stands) included actualization of field survey data using models of forest growth, and satellite-data-based estimation of mean forest height and its uncertainty considering uncertainties of input lidar measurements and forest cover heterogeneity. Results of the local-scale accuracy assessment of ATL08 data are presented, showing agreement between ground-based and satellite measurements of mean forest height at the level of R2 = 0.67 and RMSE = 3.79 m. National level analysis included examples of potential use of the resulting dataset to study height and productivity of Russian forests. Estimates of mean forest-height spatial distribution over Russia, as well as distribution of mean height for different forest types, are presented.


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    Title :

    Investigation of ICESat-2 Data Capabilities for Forest-Height Estimation over Russia


    Additional title:

    Cosmic Res


    Contributors:

    Published in:

    Cosmic Research ; 60 , Suppl 1 ; S10-S17


    Publication date :

    2022-12-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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