Current Science

Open Access Open Access  Restricted Access Subscription Access

Automatic Estimation of Tree Stem Attributes Using Terrestrial Laser Scanning in Central Indian Dry Deciduous Forests


Affiliations
1 Forestry and Ecology Group, National Remote Sensing Centre, Hyderabad 500 037, India
2 Lab of Spatial Informatics, Indian Institute of Information Technology, Hyderabad 500 032, India
 

Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level (e.g. tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single- and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betul, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single- and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R2 = 0.96) and multiple scans (R2 = 0.99). The DBH estimates from multi-scan TLS data resulted in low ischolar_main-meansquare error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R2 = 0.98; N = 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with reliable accuracy at individual tree level.

Keywords

DBH, Forest Inventory Parameters, Multiscan, Single-Scan, Terrestrial Laser Scanner.
User
Notifications
Font Size

  • Wang, B., Huang, J., Yang, X., Zhang, B. and Liu, M., Estimation of biomass, net primary production and net ecosystem production of China’s forests based on the 1999–2003 National Forest Inventory. Scand. J. For. Res., 2010, 25, 544–553.

  • Maas, H.-G., Bienert, A., Scheller, S. and Keane, E., Automatic forest inventory parameter determination from terrestrial laser scanner data. Int. J. Remote Sensing, 2008, 29, 1579–1593.

  • Bienert, A., Scheller, S., Keane, E., Mohan, F. and Nugent, C., Tree detection and diameter estimations by analysis of forest terrestrial laserscanner point clouds. In ISPRS Workshop on Laser Scanning, 2007, pp. 50–55.

  • Xu, W. et al., Comparison of conventional measurement and LiDAR-based measurement for crown structures. Comput. Electron. Agric., 2013, 98, 242–251.

  • Olofsson, K., Holmgren, J. and Olsson, H., Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sensing, 2014, 6, 4323–4344.

  • Antonarakis, A. S., Richards, K. S., Brasington, J. and Muller, E., Determining leaf area index and leafy tree roughness using terrestrial laser scanning. Water Resour. Res., 2010, 46.

  • Hackenberg, J., Spiecker, H., Calders, K., Disney, M. and Raumonen, P., SimpleTree – an efficient open source tool to build tree models from TLS clouds. Forests, 2015, 6, 4245–4294.

  • Zheng, G., Moskal, L. M. and Kim, S.-H., Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Trans. Geosci. Remote Sensing, 2013, 51, 777–786.

  • Thies, M. and Spiecker, H., Evaluation and future prospects of terrestrial laser scanning for standardized forest inventories. Forest, 2004, 2, 1.

  • Huang, H. et al., Automated methods for measuring DBH and tree heights with a commercial scanning lidar. Photogramm. Eng. Remote Sensing, 2011, 77, 219–227.

  • Simonse, M., Aschoff, T., Spiecker, H. and Thies, M., Automatic determination of forest inventory parameters using terrestrial laser scanning. In Proceedings of the Scand Laser Scientific Workshop on Airborne Laser Scanning of Forests, 2003, pp. 252–258.

  • Astrup, R., Ducey, M. J., Granhus, A., Ritter, T. and von Lüpke, N., Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode. Can. J. For. Res., 2014, 44, 666–676.

  • Kankare, V. et al., Diameter distribution estimation with laser scanning based multisource single tree inventory. ISPRS J. Photogramm. Remote Sensing, 2015, 108, 161–171.

  • Fischler, M. A. and Bolles, R. C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981, 24, 381–395.

  • Chum, O., Two-view geometry estimation by random sample and consensus, Czech Technical University in Prague, 2005.

  • Strahler, A. H. et al., Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna{®}). Can. J. Remote Sensing, 2008, 34, S426–S440.

  • Liang, X. et al., Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Trans. Geosci. Remote Sensing, 2012, 50, 661–670.

  • Liang, X. et al., Terrestrial laser scanning in forest inventories. ISPRS J. Photogramm. Remote Sensing, 2016, 115, 63–77.


Abstract Views: 838

PDF Views: 118




  • Automatic Estimation of Tree Stem Attributes Using Terrestrial Laser Scanning in Central Indian Dry Deciduous Forests

Abstract Views: 838  |  PDF Views: 118

Authors

R. Suraj Reddy
Forestry and Ecology Group, National Remote Sensing Centre, Hyderabad 500 037, India
Rakesh
Forestry and Ecology Group, National Remote Sensing Centre, Hyderabad 500 037, India
C. S. Jha
Forestry and Ecology Group, National Remote Sensing Centre, Hyderabad 500 037, India
K. S. Rajan
Lab of Spatial Informatics, Indian Institute of Information Technology, Hyderabad 500 032, India

Abstract


Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level (e.g. tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single- and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betul, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single- and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R2 = 0.96) and multiple scans (R2 = 0.99). The DBH estimates from multi-scan TLS data resulted in low ischolar_main-meansquare error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R2 = 0.98; N = 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with reliable accuracy at individual tree level.

Keywords


DBH, Forest Inventory Parameters, Multiscan, Single-Scan, Terrestrial Laser Scanner.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi01%2F201-206