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Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F23%3A97023" target="_blank" >RIV/60460709:41320/23:97023 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.3390/rs15041143" target="_blank" >http://dx.doi.org/10.3390/rs15041143</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/rs15041143" target="_blank" >10.3390/rs15041143</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

  • Original language description

    Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R-2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha(-1) for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R-2 value for ANN was 0.77, with an RMSE of 98.46 ton ha(-1). The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha(-1), while uncertainty ranged from 15.75 to 85.14 ton ha(-1). The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10511 - Environmental sciences (social aspects to be 5.7)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Remote Sensing

  • ISSN

    2072-4292

  • e-ISSN

    2072-4292

  • Volume of the periodical

    15

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    17

  • Pages from-to

    1-17

  • UT code for WoS article

    000942290100001

  • EID of the result in the Scopus database

    2-s2.0-85149252994