Abstract
Accurate assessment of forest structure and biomass is hampered by extensive field measurements that are time-consuming, costly, and inefficient. This is especially true in mangrove forests that have developed complex above-ground root structures for stability and survival in the harsh, anaerobic, and reducing conditions of water-logged sediments. These diverse structures can differ even among similar species, providing complex three dimensional structures and making them difficult to accurately assess using traditional allometric methods. Terrestrial laser scanners (TLS) have been used widely in collecting forest inventory information in recent years, mainly due to their fine-scale, detailed spatial measurements and rapid sampling. In this work we detected stems and roots in TLS data from three mangrove forests on Pohnpei Island in Micronesia using 3D classification techniques. After removing noise from the point cloud, the training set was acquired by filtering the facets of the point cloud based on angular orientation. However, many mangrove trees contain above-ground roots, which can incorrectly be classified as stems. We consequently trained a supporting classifier on the roots to detect omitted root returns (i.e., those classified as stems). Consistency was assessed by comparing TLS results to concurrent field measurements made in the same plots. The accuracy and precision for TLS stem classification was 82% and 77%, respectively. The same values for TLS root detection were 76% and 68%. Finally, we simulated the stems using alpha shapes for volume estimation. The average consistency of the TLS volume assessment was 85%. This was obtained by comparing the plot-level mean stem volume (m3/ha) between field and TLS data. Additionally, field-measured diameter-at-breast-heights (DBH) were compared to the lidar-derived DBH using the reconstructed stems, resulting in 74% average accuracy and an RMSE of 7.52 cm. This approach can be used for automatic structural evaluation, and could contribute to more accurate biomass assessment of complex mangrove forest environments as part of forest inventories or carbon stock assessments.
Similar content being viewed by others
References
Akkiraju N, Edelsbrunner H, Facello M, Fu P, Mucke EP, Varela C (1995) Alpha shapes: definition and software. In: Proceedings of the 1st international computational geometry software workshop, vol 63, p 66
Alexander C, Smith-Voysey S, Jarvis C, Tansey K (2009) Integrating building footprints and LiDAR elevation data to classify roof structures and visualise buildings. Comput Environ Urban Syst 33(4):285–292
Baltsavias EP (1999) Airborne laser scanning: basic relations and formulas. ISPRS J photogramm Remote Sens 54(2–3):199–214
Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517
Bertelli L, Yu T, Vu D, Gokturk B (2011) Kernelized structural SVM learning for supervised object segmentation. In: CVPR 2011. IEEE, pp 2153–2160
Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: Sensor fusion IV: control paradigms and data structures. International Society for Optics and Photonics, vol 1611, pp 586–607
Bucksch A, Lindenbergh R, Rahman MZA, Menenti M (2013) Breast height diameter estimation from high-density airborne LiDAR data. IEEE Geosci Remote Sens Lett 11(6):1056–1060
Calders K, Origo N, Disney M, Nightingale J, Woodgate W, Armston J, Lewis P (2018) Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. Agric For Meteorol 252:231–240
Clough BF, Scott K (1989) Allometric relationships for estimating above-ground biomass in six mangrove species. For Ecol Manag 27(2):117–127
Cole TG, Ewel KC, Devoe NN (1999) Structure of mangrove trees and forests in Micronesia. For Ecol Manag 117(1–3):95–109
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Côté JF, Widlowski JL, Fournier RA, Verstraete MM (2009) The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sens Environ 113(5):1067–1081
Côté JF, Fournier RA, Egli R (2011) An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR. Environ Model Softw 26(6):761–777
Delagrange S, Rochon P (2011) Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology. Ann Bot 108(6):991–1000
Dewez TJ, Girardeau-Montaut D, Allanic C, Rohmer J (2016) Facets: a cloudcompare plugin to extract geological planes from unstructured 3D point clouds. Int Arch Photogramm Remote Sens Spat Inf Sci, vol 41
Dix M, Abd-Elrahman A, Dewitt B, Nash L Jr (2011) Accuracy evaluation of terrestrial LiDAR and multibeam sonar systems mounted on a survey vessel. J Surv Eng 138(4):203–213
Duke NC (1992) Mangrove floristics and biogeography. In: Robertson AI, Alongi DM (eds) Tropical mangrove ecosystems. American Geophysical Union, Washington, DC, pp 63–100
Edelsbrunner H, Kirkpatrick D, Seidel R (1983) On the shape of a set of points in the plane. IEEE Trans Inf Theory 29(4):551–559
Fafard A, Rouzbeh Kargar A, van Aardt J (2020) Weighted spherical sampling of point clouds for forested scenes. Photogramm Eng Remote Sens (in-press)
Feliciano EA, Wdowinski S, Potts MD (2014) Assessing mangrove above-ground biomass and structure using terrestrial laser scanning: a case study in the Everglades National Park. Wetlands 34(5):955–968
Forsman P, Halme A (2005) 3-D mapping of natural environments with trees by means of mobile perception. IEEE Trans Rob 21(3):482–490
Fromard F, Puig H, Mougin E, Marty G, Betoulle JL, Cadamuro L (1998) Structure, above-ground biomass and dynamics of mangrove ecosystems: new data from French Guiana. Oecologia 115(1–2):39–53
Hilker T, Frazer GW, Coops NC, Wulder MA, Newnham GJ, Stewart JD et al (2013) Prediction of wood fiber attributes from LiDAR-derived forest canopy indicators. For Sci 59(2):231–242
Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition. IEEE, vol 1, pp 278–282
Hopkinson C, Chasmer L, Young-Pow C, Treitz P (2004) Assessing forest metrics with a ground-based scanning lidar. Can J For Res 34(3):573–583
Janssen LLF, Gorte BGH (2004) Digital image classification. In: Principles of remote sensing: an introductory textbook. ITC, Chicago, pp 193–204
Kelbe D, van Aardt J, Romanczyk P, van Leeuwen M, Cawse-Nicholson K (2015) Single-scan stem reconstruction using low-resolution terrestrial laser scanner data. IEEE J Sel Top Appl Earth Obs. Remote Sens. 8(7):3414–3427
Kelbe D, van Aardt J, Romanczyk P, van Leeuwen M, Cawse-Nicholson K (2016) Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics. IEEE Trans Geosci Remote Sens 54(7):4314–4330
Komiyama A, Ong JE, Poungparn S (2008) Allometry, biomass, and productivity of mangrove forests: a review. Aquat Bot 89:128–137
Krauss KW, Cahoon DR, Allen JA, Ewel KC, Lynch JC, Cormier N (2010) Surface elevation change and susceptibility of different mangrove zones to sea-level rise on Pacific high islands of Micronesia. Ecosystems 13(1):129–143
Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST (2002) Lidar remote sensing of above-ground biomass in three biomes. Glob Ecol Biogeogr 11(5):393–399
Li W, Guo Q (2013) A new accuracy assessment method for one-class remote sensing classification. IEEE Trans Geosci Remote Sens 52(8):4621–4632
Li S, Dai L, Wang H, Wang Y, He Z, Lin S (2017) Estimating leaf area density of individual trees using the point cloud segmentation of terrestrial LiDAR data and a voxel-based model. Remote Sens 9(11):1202
Liang X, Hyyppä J, Kaartinen H, Holopainen M, Melkas T (2012) Detecting changes in forest structure over time with bi-temporal terrestrial laser scanning data. ISPRS Int J Geo-Inf 1(3):242–255
SICK, LMS100/111/120/151 Laser Measurement Systems Operating. Instructions (2009) SICK AG Waldkirch: Reute, Germany
Olagoke A, Proisy C, Féret JB, Blanchard E, Fromard F, Mehlig U et al (2016) Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data. Trees 30(3):935–947
Olofsson K, Holmgren J (2014) Forest stand delineation from lidar point-clouds using local maxima of the crown height model and region merging of the corresponding Voronoi cells. Remote Sens Lett 5(3):268–276
Page S, Hosciło A, Wösten H, Jauhiainen J, Silvius M, Rieley J, ... Limin S (2009) Restoration ecology of lowland tropical peatlands in Southeast Asia: current knowledge and future research directions. Ecosystems 12(6):888–905
Paynter I, Genest D, Peri F, Schaaf C (2018) Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems. Interface Focus 8(2):20170043
Rouzbeh Kargar A, MacKenzie R, Asner GP, Van Aardt J (2019) A density-based approach for leaf area index assessment in a complex forest environment using a terrestrial laser scanner. Remote Sens 11(15):1791
Rusu RB, Marton ZC, Blodow N, Dolha M, Beetz M (2008) Towards 3D point cloud based object maps for household environments. Robot Auton Syst 56(11):927–941
Saenger P, Snedaker SC (1993) Pantropical trends in mangrove above-ground biomass and annual litterfall. Oecologia 96(3):293–299
Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci 93(4):1591–1595
Shapovalov R, Velizhev A (2011) Cutting-plane training of non-associative markov network for 3D point cloud segmentation, 3D imaging. modeling, processing, visualization and transmission (3DIMPVT), vol 8
Stephens MA (1974) EDF statistics for goodness of fit and some comparisons. J Am Stat Assoc 69(347):730–737
Stovall AE, Vorster AG, Anderson RS, Evangelista PH, Shugart HH (2017) Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sens Environ 200:31–42
Thies M, Pfeifer N, Winterhalder D, Gorte BG (2004) Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Scand J For Res 19(6):571–581
van Aardt J, Fafard A, Kelbe D, Giardina C, Selmants P, Litton C, Asner GP (2017) A terrestrial lidar’s assessment of climate change impacts on forest structure. Silvilaser 2017. October 10–12, 2017; Blacksburg, VA
Van der Zande D, Hoet W, Jonckheere I, van Aardt J, Coppin P (2006) Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agric For Meteorol 141(2–4):147–160
Vonderach C, Voegtle T, Adler P (2012) Voxel-based approach for estimating urban tree volume from terrestrial laser scanning data. Int Arch Photogramm Remote Sens Spat Inf Sci 39:451–456
Wuttke S, Schilling H, Middelmann W (2012). Reduction of training costs using active classification in fused hyperspectral and lidar data. In: Image and signal processing for remote sensing XVIII. International Society for Optics and Photonics, vol 8537, p 85370 M
Yao T, Yang X, Zhao F, Wang Z, Zhang Q, Jupp D (2011) Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar. Remote Sens Environ 115(11):2965–2974
Zai D, Li J, Guo Y, Cheng M, Huang P, Cao X, Wang C (2017) Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game. ISPRS J Photogramm Remote Sens 134:15–29
Acknowledgements
This research was funded in part by United States Forest Service, Pacific Southwest Research Station, and United States Geological Survey (USGS; award # G19AC00355). The opinions expressed are those of the authors, and not those of either the USFS or USGS.
Funding
This research was funded in part by United States Forest Service, Pacific Southwest Research Station, and United States Geological Survey (USGS; Award # G19AC00355). The opinions expressed are those of the authors, and not those of either the USFS or USGS.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rouzbeh Kargar, A., MacKenzie, R.A., Apwong, M. et al. Stem and root assessment in mangrove forests using a low-cost, rapid-scan terrestrial laser scanner. Wetlands Ecol Manage 28, 883–900 (2020). https://doi.org/10.1007/s11273-020-09753-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11273-020-09753-w