{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:14:46Z","timestamp":1784222086579,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"A.S. (Ankur Srivastava)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha\u22121 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha\u22121. 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\u22121, while uncertainty ranged from 15.75 to 85.14 ton ha\u22121. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.<\/jats:p>","DOI":"10.3390\/rs15041143","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T01:36:37Z","timestamp":1676856997000},"page":"1143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Arunima","family":"Singh","sequence":"first","affiliation":[{"name":"Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kam\u00fdck\u00e1 129, Praha 6\u2013Suchdol, 16500 Prague, Czech Republic"},{"name":"Forestry and Ecology Department, Indian Institute of Remote Sensing, Dehradun 248001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunni Kanta Prasad","family":"Kushwaha","sequence":"additional","affiliation":[{"name":"Geomatics Group, Indian Institute of Technology, Roorkee 247667, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subrata","family":"Nandy","sequence":"additional","affiliation":[{"name":"Forestry and Ecology Department, Indian Institute of Remote Sensing, Dehradun 248001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitendra","family":"Padalia","sequence":"additional","affiliation":[{"name":"Forestry and Ecology Department, Indian Institute of Remote Sensing, Dehradun 248001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3928-2135","authenticated-orcid":false,"given":"Surajit","family":"Ghosh","sequence":"additional","affiliation":[{"name":"International Water Management Institute, 127 Sunil Mawatha, Battaramulla, Colombo 10120, Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3963-265X","authenticated-orcid":false,"given":"Ankur","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Faculty of Science, University of Technology Sydney (UTS), 745 Harris St, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikul","family":"Kumari","sequence":"additional","affiliation":[{"name":"Faculty of Science, University of Technology Sydney (UTS), 745 Harris St, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"G00E03","DOI":"10.1029\/2009JG000935","article-title":"Importance of biomass in the global carbon cycle","volume":"114","author":"Houghton","year":"2009","journal-title":"J. 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