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Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Salim Malek (1-3), Franco Miglietta (1), Terje Gobakken (2), Erik Næsset (2), Damiano Gianelle (3), Michele Dalponte (3)   

iForest - Biogeosciences and Forestry, Volume 12, Issue 3, Pages 323-329 (2019)
doi: https://doi.org/10.3832/ifor2980-012
Published: Jun 14, 2019 - Copyright © 2019 SISEF

Research Articles


Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.

  Keywords


Aboveground Biomass, Diameter at Breast Height, Airborne Laser Scanning (ALS), Remote Sensing (RS), Support Vector Machine for Regression (SVR), Random Forests (RF)

Authors’ address

(1)
Salim Malek 0000-0002-7168-8952
Franco Miglietta 0000-0003-1474-8143
Institute of Biometeorology, CNR, 50145 Firenze (Italy)
(2)
Terje Gobakken 0000-0001-5534-049X
Erik Næsset 0000-0002-2460-5843
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 s (Norway)
(3)
Salim Malek 0000-0002-7168-8952
Damiano Gianelle 0000-0001-7697-5793
Michele Dalponte 0000-0001-9850-8985
Dept. of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, v. E. Mach 1, 38010 San Michele all’Adige, TN (Italy)

Corresponding author

 
Michele Dalponte
michele.dalponte@fmach.it

Citation

Malek S, Miglietta F, Gobakken T, Næsset E, Gianelle D, Dalponte M (2019). Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques. iForest 12: 323-329. - doi: 10.3832/ifor2980-012

Academic Editor

Carlotta Ferrara

Paper history

Received: Oct 22, 2018
Accepted: Apr 06, 2019

First online: Jun 14, 2019
Publication Date: Jun 30, 2019
Publication Time: 2.30 months

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(1)
Asner GP, Mascaro J, Muller-Landau HC, Vieilledent G, Vaudry R, Rasamoelina M, Hall JS, Van Breugel M (2012)
A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168 (4): 1147-1160.
CrossRef | Gscholar
(2)
Breiman L (2001)
Random forests. Machine Learning 45 (1): 5-32.
CrossRef | Gscholar
(3)
Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, Duque A, Eid T, Fearnside PM, Goodman RC, Henry M, Martínez-Yrízar A, Mugasha WA, Muller-Landau HC, Mencuccini M, Nelson BW, Ngomanda A, Nogueira EM, Ortiz-Malavassi E, Pélissier R, Ploton P, Ryan CM, Saldarriaga JG, Vieilledent G (2014)
Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology 20: 3177-3190.
CrossRef | Gscholar
(4)
Dalponte M, Coomes DA (2016)
Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods in Ecology and Evolution 7 (10): 1236-1245.
CrossRef | Gscholar
(5)
Dalponte M, Frizzera L, Orka HO, Gobakken T, Naesset E, Gianelle D (2018)
Predicting stem diameters and aboveground biomass of individual trees using remote sensing data. Ecological Indicators 85: 367-376.
CrossRef | Gscholar
(6)
Gobakken T, Naesset E (2004)
Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scandinavian Journal of Forest Research 19: 529-542.
CrossRef | Gscholar
(7)
Hannan MA, Ali JA, Mohamed A, Uddin MN (2017)
A random forest regression based space vector PWM inverter controller for the induction motor drive. IEEE Transactions on Industrial Electronics 64 (4): 2689-2699.
CrossRef | Gscholar
(8)
Hauglin M, Dibdiakova J, Gobakken T, Naesset E (2013)
Estimating single-tree branch biomass of Norway spruce by airborne laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 79: 147-156.
CrossRef | Gscholar
(9)
Hauglin M, Gobakken T, Astrup R, Ene L, Naesset E (2014)
Estimating single-tree crown biomass of Norway spruce by airborne laser scanning: a comparison of methods with and without the use of terrestrial laser scanning to obtain the ground reference data. Forests 5: 384-403.
CrossRef | Gscholar
(10)
Jucker T, Caspersen J, Chave J, Antin C, Barbier N, Bongers F, Dalponte M, Van Ewijk KY, Forrester DI, Haeni M, Higgins SI, Holdaway RJ, Iida Y, Lorimer C, Marshall PL, Momo S, Moncrieff GR, Ploton P, Poorter L, Rahman KA, Schlund M, Sonké B, Sterck FJ, Trugman AT, Usoltsev VA, Vanderwel MC, Waldner P, Wedeux BMM, Wirth C, Wöll H, Woods M, Xiang W, Zimmermann NE, Coomes DA (2017)
Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Global Change Biology 23: 177-190.
CrossRef | Gscholar
(11)
Jucker T, Asner GP, Dalponte M, Brodrick PG, Philipson CD, Vaughn NR, Teh YA, Brelsford C, Burslem DFRP, Deere NJ, Ewers RM, Kvasnica J, Lewis SL, Malhi Y, Milne S, Nilus R, Pfeifer M, Phillips OL, Qie L, Renneboog N, Reynolds G, Riutta T, Struebig MJ, Svátek M, Turner EC, Coomes DA (2018)
Estimating aboveground carbon density and its uncertainty in Borneo’s structurally complex tropical forests using airborne laser scanning. Biogeosciences 15: 3811-3830.
CrossRef | Gscholar
(12)
Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST (2002)
Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography 11: 393-399.
CrossRef | Gscholar
(13)
Liaw A, Wiener M (2002)
Classification and regression by random forest. R News 2 (3): 18-22.
Online | Gscholar
(14)
Liu M, Liu X, Liu D, Ding C, Jiang J (2015)
Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm. Computers and Geosciences 75: 44-56.
CrossRef | Gscholar
(15)
Mareya HT, Tagwireyi P, Ndaimani H, Gara TW, Gwenzi D (2018)
Estimating tree crown area and aboveground biomass in Miombo woodlands from high-resolution RGB-only imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (3): 868-875.
CrossRef | Gscholar
(16)
Marklund LG (1988)
Biomass functions for pine, spruce and birch in Sweden. Report 45, Department of Forest Survey, Swedish University for Agricultural Sciences, Uppsala, Sweden, pp. 73.
Gscholar
(17)
Mensah S, Veldtman R, du Toit B, Kakaï RG, Seifert T (2016)
Aboveground biomass and carbon in a South African mistbelt forest and the relationships with tree species diversity and forest structures. Forests 7: 1-17.
CrossRef | Gscholar
(18)
Peng S, He N, Yu G, Wang Q (2017)
Aboveground biomass estimation at different scales for subtropical forests in China. Botanical Studies 58: 45.
CrossRef | Gscholar
(19)
Slik JWF, Aiba SI, Brearley FQ, Cannon CH, Forshed O, Kitayama K, Nagamasu H, Nilus R, Payne J, Paoli G, Poulsen AD, Raes N, Sheil D, Sidiyasa K, Suzuki E, Van Valkenburg JLCH (2010)
Environmental correlates of tree biomass, basal area, wood specific gravity and stem density gradients in Borneo’s tropical forests. Global Ecology and Biogeography 19: 50-60.
CrossRef | Gscholar
(20)
Smola AJ, Schölkopf B (2004)
A tutorial on support vector regression. Statistics and Computing 14 (3): 199-222.
CrossRef | Gscholar
(21)
Vapnik VN (1998)
Statistical learning theory, vol. 1. Wiley, New York, USA, pp. 1-768.
Gscholar
(22)
Weibull W (1951)
A statistical distribution function of wide applicability. Journal of Applied Mechanics 18: 293-297.
Online | Gscholar
(23)
Zhang Y, Chen HYH, Taylor AR (2016)
Aboveground biomass of understorey vegetation has a negligible or negative association with overstorey tree species diversity in natural forests. Global Ecology and Biogeography 25: 141-150.
CrossRef | Gscholar
 

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