Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees
<p>DBH distribution of trees on the 25 ha of ZFDP and on the 1 ha subplot covered by TLS.</p> "> Figure 2
<p>Lateral view of a 20 m × 20 m section of ultra-high-density drone lidar in a temperate mountain forest in the south Bohemia region of the Czech Republic: (<b>A</b>) The vegetation point cloud; (<b>B</b>) Skeleton points after low-intensity filtering; (<b>C</b>) Skeleton points + stem base points after spatial filtering and before segmentation; (<b>D</b>) Segmented skeletons I + II.; (<b>E</b>) Segmented trees after proximity point assignment; (<b>F</b>) Undifferentiated points.</p> "> Figure 3
<p>Workflow diagram for individual tree segmentation using ultra-high-density drone lidar. The capital letters in parentheses refer to the individual panels in <a href="#remotesensing-12-03260-f002" class="html-fig">Figure 2</a>.</p> "> Figure 4
<p>Representative comparisons between drone lidar (black points) and TLS (green points) within 20 cm vertical slices 1.1–1.3 m aboveground.</p> "> Figure 5
<p>Relationships between DBH and height for trees segmented using high-density drone lidar and TLS. Left: The DBH relationship for individual trees segmented using drone lidar and TLS. Right: The height relationship for individual trees segmented using drone lidar and TLS. Height is derived from the segmented points that passed the intensity filter, not from all measurements acquired by drone lidar. Grey lines are the 1:1 relationship. Red lines are linear regression.</p> "> Figure 6
<p>The DBH relationship for trees segmented using high-density drone lidar and field estimates. Grey line is the 1:1 relationship. Red line is the needleleaf-tree regression. Orange line is the broadleaf-tree regression.</p> "> Figure 7
<p>Simulated error in diameter estimates using the randomized Hough transformation. (<b>A</b>) Error in diameter due to random noise in simulated point locations under four noise levels (one sigma). (<b>B</b>) Error in diameter due to variation in footprint size.</p> "> Figure 8
<p>AGB in ten diameter size classes. For each size class, the three bars compare AGB from all trees mapped in the field (left) to the subset of trees segmented using drone lidar with AGB computed using field estimates of DBH (center), and the subset of trees segmented using drone lidar with AGB computed using DBH estimates from drone lidar segmentations (right). The difference between the left and center bars is due exclusively to errors of omission. The difference between the center and right bars is due exclusively to errors in DBH estimation.</p> "> Figure A1
<p>Top view sketch of the simulation of footprint size impact to the DBH estimate. <span class="html-italic">Cs</span> is center of stem, grey points represent the stem surface, red points represent the stem surface covered by a single laser beam. Red lines show laser beam divergence, <span class="html-italic">Fw</span> is laser footprint width. <span class="html-italic">Dr.</span> is real distance from the scanner to the center of laser beam on the stem surface. <span class="html-italic">Dm</span> is measured distance from the scanner, computed as the mean distance of all red points to the scanner. <span class="html-italic">M</span> is the location of simulated point. <span class="html-italic">R</span> is the location of center of the laser beam footprint.</p> "> Figure A2
<p>Small section of the ZFDP stem position map demonstrating correspondence between locations of segmented trees and field-mapped stems. Black points are stem positions from field measurements. Blue points are stem positions from a supervised segmentation of high-density drone lidar. Note: Small positional mismatch is caused by uncertainty in field measurements. The background is a high-resolution digital terrain model from drone lidar.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Study Site and Data Collection
2.2. Drone Lidar
2.3. Sources of Uncertainty in Drone-Lidar Point Clouds
2.4. Processing Workflow of the Drone-Lidar Point Cloud
2.5. Random Forest Classification and AGB of Trees
2.6. Evaluating the Impact of Footprint Size and Noise on DBH Estimates
2.7. Statistical Analysis
3. Results and Discussion
3.1. Individual Tree Segmentations from Drone Lidar and TLS
3.2. Sources of Error in DBH Estimates from Drone Lidar
3.3. Ability of Drone Lidar to Produce a Stand-Level Tree Inventory
3.4. Aboveground Biomass Estimates from Individual Tree Segmentations
4. Conclusions and Recommendations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
- Disney, M.; Burt, A.; Calders, K.; Schaaf, C.; Stovall, A. Innovations in ground and airborne technologies as reference and for training and validation: Terrestrial laser scanning (TLS). Surv. Geophys. 2019, 40, 937–958. [Google Scholar] [CrossRef] [Green Version]
- Disney, M.I.; Boni Vicari, M.; Burt, A.; Calders, K.; Lewis, S.L.; Raumonen, P.; Wilkes, P. Weighing trees with lasers: Advances, challenges and opportunities. Interface Focus 2018, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Raumonen, P.; Kaasalainen, M.; Åkerblom, M.; Kaasalainen, S.; Kaartinen, H.; Vastaranta, M.; Holopainen, M.; Disney, M.; Lewis, L. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens. 2013, 5, 491–520. [Google Scholar] [CrossRef] [Green Version]
- Brede, B.; Calders, K.; Lau, A.; Raumonen, P.; Bartholomeus, H.M.; Herolda, M.; Kooistra, L. Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR. Remote Sens. Environ. 2019, 233, 111355. [Google Scholar] [CrossRef]
- Kellner, J.R.; Armston, J.; Birrer, M.; Cushman, K.C.; Duncanson, L.; Eck, C.; Falleger, C.; Imbach, B.; Král, K.; Krůček, M.; et al. New opportunities for forest remote sensing through ultra-high-density drone lidar. Surv. Geophys. 2019, 40, 959–977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Calders, K.; Newnham, G.; Burt, A.; Murphy, S.; Raumonen, P.; Herold, M.; Culvenor, D.; Avitabile, V.; Disney, M.; Armston, J.; et al. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol. Evol. 2015, 6, 198–208. [Google Scholar] [CrossRef]
- Brede, B.; Lau, A.; Bartholomeus, H.M.; Kooistra, L. Comparing RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with terrestrial LiDAR. Sensors 2017, 17, 2371. [Google Scholar] [CrossRef] [PubMed]
- Wieser, M.; Mandlburger, G.; Hollaus, M.; Otepka, J.; Glira, P.; Pfeifer, N. A case study of UAS borne laser scanning for measurement of tree stem diameter. Remote Sens. 2017, 9, 11. [Google Scholar] [CrossRef] [Green Version]
- Burt, A.; Disney, M.; Calder, K. Extracting individual trees from lidar point clouds using treeseg. Methods Ecol. Evol. 2019, 10, 438–445. [Google Scholar] [CrossRef] [Green Version]
- De Tanago, J.G.; Lau, A.; Bartholomeus, H.; Herold, M.; Avitabile, V.; Raumonen, P.; Martius, C.; Goodman, R.C.; Disney, M.; Manuri, S.; et al. Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol. Evol. 2018, 9, 223–234. [Google Scholar] [CrossRef] [Green Version]
- Duncanson, L.; Armston, J.; Disney, M.; Avitabile, V.; Barbier, N.; Calders, K.; Carter, S.; Chave, J.; Herold, M.; Crowther, T.W.; et al. The importance of consistent global forest aboveground biomass product validation. Surv. Geophys. 2019, 40, 979–999. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dubayah, R.; Blair, J.B.; Goetz, S.; Fatoyinbo, L.; Hansen, M.; Healey, S.; Hofton, M.; Hurtt, G.; Kellner, J.; Luthcke, S.; et al. The global ecosystem dynamics investigation: High-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 2020, 1, 100002. [Google Scholar] [CrossRef]
- Scipal, K.; Arcioni, M.; Chave, J.; Dall, J.; Fois, F.; LeToan, T.; Lin, C.-C.; Papathanassiou, K.; Quegan, S.; Rocca, F.; et al. The BIOMASS Mission—An ESA Earth Explorer Candidate to Measure the BIOMASS of the Earth’s Forests. In Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010; IEEE: New York, NY, USA, 2010; pp. 52–55. [Google Scholar]
- Kellogg, K.; Hoffman, P.; Standley, S.; Shaffer, S.; Rosen, P.; Edelstein, W.; Dunn, C.; Baker, C.; Barela, P.; Shen, Y.; et al. NASA-ISRO synthetic aperture radar (NISAR) mission. In Proceedings of the 2020 IEEE Aerospace Conference, Big Sky, MT, USA, 7–14 March 2020. [Google Scholar]
- Trochta, J.; Krůček, M.; Vrška, T.; Král, K. 3D Forest: An. application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE 2017, 12, e0176871. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Aubry-Kientz, M.; Dutrieux, R.; Ferraz, A.; Saatchi, S.; Hamraz, H.; Williams, J.; Coomes, D.; Piboule, A.; Vincent, G. A comparative assessment of the performance of individual tree crowns delineation algorithms from ALS data in tropical forests. Remote Sens. 2019, 11, 1086. [Google Scholar] [CrossRef] [Green Version]
- Qin, Y.; Ferraz, A.; Mallet, C.; Iovan, C. Individual tree segmentation over large areas using airborne LiDAR point cloud and very high resolution optical imagery. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014. [Google Scholar]
- ForestGEO. Zofin. 2017. Available online: https://forestgeo.si.edu/sites/europe/zofin (accessed on 5 October 2020).
- Janík, D.; Král, K.; Adam, D.; Hort, L.; Samonila, P.; Unara, P.; Vrska, V.; McMahon, S. Tree spatial patterns of Fagus sylvatica expansion over 37 years. For. Ecol. Manag. 2016, 375, 134–145. [Google Scholar] [CrossRef] [Green Version]
- Anderson-Teixeira, K.J.; Davies, S.J.; Bennett, A.C.; Gonzalez-Akre, E.B.; Muller-Landau, H.C.; Wright, S.J.; Salim, K.A.; Almeyda Zambrano, A.M.; Alonso, A.; Baltzer, J.L.; et al. CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change. Glob. Chang. Biol. 2015, 21, 528–549. [Google Scholar] [CrossRef] [Green Version]
- Condit, R. Environmental intelligence unit. In Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama, and a Comparison with Other Plots; Springer: Berlin/Heidelberg, Germany, 1998. [Google Scholar]
- Kellner, J.R.; Clark, D.B.; Hofton, M.A. Canopy height and ground elevation in a mixed-land-use lowland neotropical rain forest landscape. Ecology 2009, 90, 3274. [Google Scholar] [CrossRef] [Green Version]
- Calders, K.; Disney, M.I.; Armston, J.; Burt, A.; Brede, B.; Origo, N.; Muir, J.; Nightingale, J. Evaluation of the range accuracy and the radiometric calibration of multiple terrestrial laser scanning instruments for data interoperability. IEEE Trans. Geosci. Remote Sens. 2017, 55, 2716–2724. [Google Scholar] [CrossRef]
- VUKOZ-OEL/3DForest. GitHub. Available online: https://github.com/VUKOZ-OEL/3DForest (accessed on 5 October 2020).
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Muukkonen, P. Generalized allometric volume and biomass equations for some tree species in Europe. Eur. J. For. Res. 2007, 126, 157–166. [Google Scholar] [CrossRef]
- Hancock, S.; Armston, J.; Hofton, M.; Sun, X.; Tang, H.; Duncanson, L.I.; Kellner, J.R.; Dubayah, R. The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions. Earth Space Sci. 2019, 6, 294–310. [Google Scholar] [CrossRef] [PubMed]
- Cottingham, K.L.; Lennon, J.T.; Brown, B.L. Knowing when to draw the line: Designing more informative ecological experiments. Front. Ecol. Environ. 2005, 3, 145–152. [Google Scholar] [CrossRef]
- Neter, J. Applied Linear Statistical Models; Irwin: Chicago, IL, USA, 1996. [Google Scholar]
- Lutz, J.A.; Furniss, T.J.; Johnson, D.J.; Davies, S.J.; Allen, D.; Alonso, A.; Anderson-Teixeira, K.J.; Andrade, A.; Baltzer, J.; Becker, K.M.L.; et al. Global importance of large-diameter trees. Glob. Ecol. Biogeogr. 2018, 27, 849–864. [Google Scholar] [CrossRef] [Green Version]
- Vicari, M.B.; Disney, M.; Wilkes, P.; Burt, A.; Calders, K.; Woodgate, W. Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods Ecol. Evol. 2019, 10, 680–694. [Google Scholar] [CrossRef] [Green Version]
- McRoberts, R.E.; Næsset, E.; Gobakken, T.; Chirici, G.; Condés, S.; Hou, Z.; Saarela, S.; Chen, Q.; Ståhl, G.; Walters, B.F. Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications. Can. J. For. Res. 2018, 48, 642–649. [Google Scholar] [CrossRef]
- Patterson, P.; Healey, S.P.; Ståhl, G.; Saarela, S.; Holm, S.; Andersen, H.-E.; Dubayah, R.O.; Duncanson, L.; Hancock, S.; Armston, J.; et al. Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation. Environ. Res. Lett. 2019, 14, 065007. [Google Scholar] [CrossRef]
- Saarela, S.; Holm, S.; Healey, S.P.; Andersen, H.-E.; Petersson, H.; Prentius, W.; Patterson, P.L.; Næsset, E.; Gregoire, T.G.; Stahl, G. Generalized hierarchical model-based estimation for aboveground biomass assessment using GEDI and Landsat data. Remote Sens. 2018, 10, 1832. [Google Scholar] [CrossRef] [Green Version]
DBH Class | 15–20 | 20–30 | 30–40 | 40–50 | 50–60 | 60–70 | 70–80 | 80–90 | 90–100 | >100 | Total |
---|---|---|---|---|---|---|---|---|---|---|---|
No. field | 1157 | 886 | 536 | 355 | 286 | 306 | 295 | 272 | 170 | 149 | 4412 |
No. segmented | 78 | 281 | 332 | 268 | 240 | 267 | 267 | 234 | 144 | 126 | 2237 |
% segmented | 6.74% | 31.72% | 61.94% | 75.49% | 83.92% | 87.25% | 90.51% | 86.03% | 84.71% | 84.56% | 50.70% |
Field Reference | ||||||
---|---|---|---|---|---|---|
Broadleaf | Needleleaf | Total | Omission Error | Commission Error | ||
Classification | Broadleaf | 1526 | 52 | 1578 | 15.2% | 3.3% |
Needleleaf | 273 | 465 | 738 | 10.1% | 36.9% | |
Total | 1799 | 517 | Overall accuracy = 85.9% |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Krůček, M.; Král, K.; Cushman, K.; Missarov, A.; Kellner, J.R. Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees. Remote Sens. 2020, 12, 3260. https://doi.org/10.3390/rs12193260
Krůček M, Král K, Cushman K, Missarov A, Kellner JR. Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees. Remote Sensing. 2020; 12(19):3260. https://doi.org/10.3390/rs12193260
Chicago/Turabian StyleKrůček, Martin, Kamil Král, KC Cushman, Azim Missarov, and James R. Kellner. 2020. "Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees" Remote Sensing 12, no. 19: 3260. https://doi.org/10.3390/rs12193260