Abstract
Forest structure plays a crucial role in maintaining the ecosystem balance. All the biogeochemical cycles need trees for the successful execution of the processes. Nowadays, one of the most critical concerns is the accurate and precise assessment of forest biomass. The biomass assessment can be done by knowing the canopy metrics, stem volume, and specific wood gravity. This research used a terrestrial laser scanner (TLS) to retrieve tree parameters, providing point cloud data (PCD). The parameters derived from PCD are diameter at breast height (DBH) and tree height using randomized Hough transformation (RHT). With these tree parameters, the stem volume of the tree was calculated and correlated with the Forest Survey of India (FSI) equation. The radius, DBH, tree height, and stem volume were also obtained using the Random Sample Consensus (RANSAC) algorithm. The volume calculated using the RANSAC algorithm is statistically analyzed with the volume calculated with the FSI equations available for specific tree species. The R2 value obtained for the volume calculated by the RANSAC and FSI equations is 0.95. In contrast, the correlation value obtained for the volume calculated by RHT and FSI equations is 0.99. Therefore, it shows that both algorithms are highly correlated and can be used as an alternative method for stem volume calculation, which will be less time-consuming and more accurate as well as precise. This method tries to explain the alternative method to calculate tree stem volume without using the species-specific FSI equations, which may sometimes produce biases and uncertainty in calculating stem volume and biomass.
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Acknowledgements
This research was supported by the Indian Institute of Remote Sensing (IIRS)/ISRO, Dehradun, Uttarakhand, India. We would like to thank Dr. Prakash Chauhan, Director of IIRS, for his continuous support and motivation. We thank the Forestry and Ecology Department for providing infrastructure and hardware support.
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Singh, A., Kushwaha, S.K.P., Nandy, S. et al. An approach for tree volume estimation using RANSAC and RHT algorithms from TLS dataset. Appl Geomat 14, 785–794 (2022). https://doi.org/10.1007/s12518-022-00471-x
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DOI: https://doi.org/10.1007/s12518-022-00471-x