NOVEL APPROACH FOR FOREST ALLOMETRIC EQUATION MODELLING WITH RANSAC SHAPE DETECTION USING TERRESTRIAL LASER SCANNER

Abstract. Forest biomass quantification is highly crucial for the maintenance of global carbon cycle. Hence, the accurate estimation of tree parameters is of utmost requirement nowadays. The hypothesis of this research is to formulate a volumetric equation which will be focussed on the structure of the tree and not the species. To serve this purpose, 13 plots were scanned in the Barkot forest range of Uttarakhand, India, and the plots were scanned using Terrestrial Laser Scanner (TLS). The tree attribute such as radius of the stem and tree height were estimated using Random Sample Consensus (RANSAC) algorithm and line primitive vector, respectively. The correlation was established using multiple linear regression using the TLS derived tree parameters and field estimated tree parameters. The R2 obtained for field estimated biomass is 0.91 with radius and 0.73 with the tree height. The radius of the stem and tree height were used for the calculation of volume of all the trees and the R2 value obtained for field estimated volume and TLS derived volume is 0.95. Then the stem volume of the trees were calculated based on the new volume equation. The statistical analysis was done, and ANOVA test was performed indicating high F value (25.4) which shows that the regression model is significant. The predicted biomass correlation coefficient (R2 = 0.97) was obtained. It represents the significance of the model for the estimation of volume and biomass is high. The equation can be implemented in any forest type irrespective of the forest structure.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: NOVEL APPROACH FOR FOREST ALLOMETRIC EQUATION MODELLING WITH RANSAC SHAPE DETECTION USING TERRESTRIAL LASER SCANNER ; volume:XLVIII-4/W4-2022 ; year:2022 ; pages:133-138 ; extent:6 ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-4/W4-2022 (2022), 133-138 (gesamt 6)

Contributors, Places and Time

• Creator: Singh, A. ; Kushwaha, S. K. P. ; Nandy, S. ; Padalia, H.

Further information

• DOI: 10.5194/isprs-archives-XLVIII-4-W4-2022-133-2022
• URN: urn:nbn:de:101:1-2022102005381447069950
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:35 AM CEST