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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

2072-4292

Svazek periodika

15

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

000942290100001

EID výsledku v databázi Scopus

2-s2.0-85149252994