Estimating Above-Ground Biomass of the Regional Forest Landscape of Northern Western Ghats Using Machine Learning Algorithms and Multi-sensor Remote Sensing Data-Reference-Cited by-同舟云学术

Estimating Above-Ground Biomass of the Regional Forest Landscape of Northern Western Ghats Using Machine Learning Algorithms and Multi-sensor Remote Sensing Data

Published:2024-02-23 Issue:4 Volume:52 Page:885-902
ISSN:0255-660X
Container-title:Journal of the Indian Society of Remote Sensing
language:en
Short-container-title:J Indian Soc Remote Sens

Author:

Sainuddin Faseela V.^ORCID,Malek Guljar,Rajwadi Ankur,Nagar Padamnabhi S.,Asok Smitha V.,Reddy C. Sudhakar

Funder

Department of Biotechnology, Ministry of Science and Technology, India

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12524-024-01836-y.pdf

Reference90 articles.

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2. Antropov, O., Rauste, Y., Ahola, H., & Hame, T. (2013). Stand-level stem volume of boreal forests from spaceborne SAR imagery at L-band. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 35–44. https://doi.org/10.1109/JSTARS.2013.2241018

3. Ayushi, K., Babu, K. N., Ayyappan, N., Nair, J. R., Kakkara, A., & Reddy, C. S. (2024). A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats India. Ecological Informatics, 20, 102479. https://doi.org/10.1016/j.ecoinf.2024.102479

4. Behera, D., Kumar, V. A., Rao, J. P., Padal, S. B., Ayyappan, N., & Reddy, C. S. (2023). Estimating aboveground biomass of a regional forest landscape by integrating textural and spectral variables of sentinel-2 along with ancillary data. Journal of the Indian Society of Remote Sensing, 14, 1–13. https://doi.org/10.1007/s12524-023-01740-x

5. Bhandari, S. K., & Nandy, S. (2023). Forest aboveground biomass prediction by integrating terrestrial laser scanning data, Landsat 8 OLI-derived forest canopy density and spectral indices. Journal of the Indian Society of Remote Sensing, 18, 1–12. https://doi.org/10.1007/s12524-023-01687-z

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