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Integrating area-based and individual tree detection approaches for estimating tree volume in plantation inventory using aerial image and airborne laser scanning data

Emily T Shinzato (1)   , Yosio E Shimabukuro (1), Nicholas C Coops (2), Piotr Tompalski (2), Esthevan AG Gasparoto (3)

iForest - Biogeosciences and Forestry, Volume 10, Issue 1, Pages 296-302 (2016)
doi: https://doi.org/10.3832/ifor1880-009
Published: Dec 15, 2016 - Copyright © 2016 SISEF

Research Articles


Remote sensing has been increasingly used to assist forest inventory. Airborne Laser Scanning (ALS) systems can accurately estimate tree height in forests, and are being combined with more traditional optical images that provide further details about the horizontal structure of forests. To predict forest attributes two main techniques are applied to process ALS data: the Area Based Approach (ABA), and the Individual Tree Detection (ITD). The first part of this study was focused on the effectiveness of integrating ALS data and aerial imagery to estimate the wood volume in Eucalyptus urograndis plantations using the ABA approach. To this aim, we analyzed three different approaches: (1) using only ALS points cloud metrics (RMSE = 6.84%); (2) using only the variables derived from aerial images (RMSE = 8.45%); and (3) the integration of both 1 and 2 (RMSE = 5.23%), which underestimated the true volume by 2.98%. To estimate individual tree volumes we first detected individual trees and corrected the density estimate for detecting mean difference, with an error of 0.37 trees per hectare and RMSE of 12.68%. Next, we downscaled the total volume prediction to single tree level. Our approach showed a better result of the overall volume in comparison with the traditional forest inventory. There is a remarkable advantage in using the Individual Tree Detection approach, as it allows for a spatial representation of the number of trees sampled, as well as their volume per unit area - an important metric in the management of forest resources.

  Keywords


Forest Inventory, Airborne Laser Scanning, Treetop Detection, Eucalyptus Plantation, Area-based Approach, LiDAR

Authors’ address

(1)
Emily T Shinzato
Yosio E Shimabukuro
Department of Remote Sensing, National Institute for Space Research - INPE, São Paulo (Brazil)
(2)
Nicholas C Coops
Piotr Tompalski
Department of Forest Resources Management, University of British Columbia, Victoria, BC (Canada)
(3)
Esthevan AG Gasparoto
Department of Forest Sciences, Luiz de Queiroz College of Agriculture, University of São Paulo , Piricicaba (Brazil)

Corresponding author

 

Citation

Shinzato ET, Shimabukuro YE, Coops NC, Tompalski P, Gasparoto EAG (2016). Integrating area-based and individual tree detection approaches for estimating tree volume in plantation inventory using aerial image and airborne laser scanning data. iForest 10: 296-302. - doi: 10.3832/ifor1880-009

Academic Editor

Piermaria Corona

Paper history

Received: Sep 21, 2015
Accepted: Aug 26, 2016

First online: Dec 15, 2016
Publication Date: Feb 28, 2017
Publication Time: 3.70 months

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List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Batista JLF (2014)
Quantificação dos recursos florestais - árvores, arvoredos e florestas [Quantification of forest resources - trees, grove and forests]. Oficina de Textos, São Paulo, Brazil, pp. 384. [in Portuguese]
Gscholar
(2)
Brandtberg T, Warner TA, Landenberger RE, Mcgraw JB (2003)
Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sensing of Environment 85: 290-303.
CrossRef | Gscholar
(3)
Breindenbach J, Astrup R (2014)
The semi-individual tree crown approach. In: “Forestry Applications of Airborne Laser Scanninng: Concepts and Case Studies” (Maltamo M ed). Springer Science + Business Media, Dordrecht, Netherlands, pp. 113-133.
Gscholar
(4)
Campbell JB, Wynne RH (2011)
Introduction to remote sensing. The Guildford Press, New York, USA, pp. 667.
Online | Gscholar
(5)
CEPAGRI (2014)
Clima dos municípios paulistas [Climate of the municipalities of São Paulo]. Web site. [in Portuguese]
Online | Gscholar
(6)
Curtis R (1967)
Height-diameter and height-diameter-age equations for second-growth Douglas-fir. Forest science 13 (4): 365-375.
Online | Gscholar
(7)
Ferreira M (1979)
Escolha de espécies de eucalipto [Choosing Eucalyptus species]. Circular Técnica IPEF, Piracicaba, Brazil, vol. 47, pp. 1-30. [in Portuguese]
Gscholar
(8)
Hudak AT, Lefsky M, Cohen WB, Berterretche M (2002)
Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment 82: 397-416.
CrossRef | Gscholar
(9)
IBA (2014)
Industria Brasileira de Árvore [Brazilian Industry of trees]. IBA, Brasília, Brazil, pp. 100. [in Portuguese]
Online | Gscholar
(10)
James G, Witten D, Hastie T, Tibshirani R (2014)
An introduction to statistical learning with applications in R. Springer Science+Business Media, New York, USA, pp. 426.
Gscholar
(11)
Kaartinen H, Hyppä J, Yu X, Vastaranta M, Hyppä H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Naesset E, Pitkänen J, Popescu S, Solberg BMW, Wu J (2012)
An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing 4: 950-974.
CrossRef | Gscholar
(12)
Köppen W, Geiger R (1954)
Köppen W, Geiger R (1954): Klima der Erde (Climate of the earth). Wall Map 1:16 Mill. Klett-Perthes, Gotha, Germany.
Online | Gscholar
(13)
Kumar V (2012)
Forest inventory parameters and carbon mapping from airborne LiDAR. Dissertation, The University of Twente, Enschede, The Netherlands, pp. 89.
Gscholar
(14)
Lumley T (2014)
LEAPS: R package version 2.9 for regression subset selection including exhaustive search. University of Washington, Washington, DC, USA, pp. 8.
Online | Gscholar
(15)
Mccullagh MJ (1988)
Terrain and surface modelling systems: theory and practice. Photogrammetric Record 2 (72): 747-749.
CrossRef | Gscholar
(16)
McGaughey RJ (2013)
FUSION/LDV: software for LiDAR data analysis and visualization. Pacific Northwest Research Station, USDA Forest Service, WA, USA, pp. 150.
Online | Gscholar
(17)
Mehtätalo L, Nyblom J, Virolainen A (2014)
A model-based approach for the recovery of forest attributes using airborne laser scanning data. In: “Forestry Applications of Airborne Laser Scanninng: Concepts and Case Studies” (Maltamo M ed). Springer Science + Business Media, Dordrecht, Netherlands, pp. 193-211.
CrossRef | Gscholar
(18)
Monnet J, Mermim E, Chanussot J, Berger F (2010)
Tree top detection using local maxima filtering: a parameter sensitivity analysis. In: Proceedings of the “10th International Conference on LiDAR Application for Assessing Forest Ecossystems”. Freiburg (Germany) Sep 2010. Silvaser 2010, Freiburg, Germany, pp. 9.
Online | Gscholar
(19)
Næsset E (2002)
Predicting forest stand characteristics with airborne laser using a practical two-stage procedure field data. Remote Sensing of Environment 80: 88-90.
CrossRef | Gscholar
(20)
Packalén P, Mehtätalo L, Maltamo M (2011)
ALS-based estimation of plot volume and site index in a Eucalyptus plantation with a nonlinear mixed-effect model that accounts for the clone effect. Annals of Forest Science 68: 1085.
CrossRef | Gscholar
(21)
Paris C, Bruzzone LA (2015)
Three-dimensional model-based approach to the estimation of the tree top height by fusing low-density LiDAR data and very high resolution optical images. IEEE Transactions on Geoscience and Remote Sensing 53 (1): 467-480.
CrossRef | Gscholar
(22)
Payn T, Carnus J, Freer-smith P, Kimberley M, Kollert W, Liu S, Orazio C, Rodriguez L, Silva LN (2015)
Changes in planted forests and future global implications. Forest Ecology and Management 352: 57-67.
CrossRef | Gscholar
(23)
Peña EA, Slate EH (2006)
Global validation of linear model assumptions. Journal of the American Statistical Association 101 (473): 341-354.
CrossRef | Gscholar
(24)
Peña EA, Slate EH (2014)
Gvlma: Global validation of linear models assumptions. R package version 1.0.0.2.
Online | Gscholar
(25)
Pereira JP (2014)
Mensuração automática de copas de Araucaria angustifolia (Bertol.) Kuntze a partir de dados LiDAR para estimativa de variáveis dendrométricas [Automatic measurement of Araucaria angustifolia’s crowns (Bertol.) Kuntze from LiDAR data to estimate dendrometric variables]. Dissertation, State University of Santa Catarina, Brazil, pp. 174. [in Portuguese]
Gscholar
(26)
Persson A, Holmgren J, Söderman U (2002)
Detecting and measuring individual trees using airborne laser scanner. Photogrammetric Engineering and Remote Sensing 68 (9): 925-932.
Gscholar
(27)
Popescu SC, Wynne RH, Nelson RF (2002)
Estimating plot level tree heights with lidar: local filtering with canopy-height based variable window size. Computers and Electronics 37: 71-95.
CrossRef | Gscholar
(28)
R Developing Core Team (2007)
R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Online | Gscholar
(29)
Roberts SD, Dean TJ, Evans DL, Mcombs JW, Harrington RL, Glass PA (2005)
Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions. Forest Ecology and Management 213: 54-70.
CrossRef | Gscholar
(30)
Saarela S, Grafström A, Stahl G, Kangas A, Holopainen M, Tuominen S, Nordkvist K, Hyyppä J (2015)
Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information. Remote Sensing of Environment 158: 431-440.
CrossRef | Gscholar
(31)
Schöepfer W (1966)
Automatisierung des massen, sorten und wertberechnung stenender waldbestände Schriftenreihe Bad [Automation of the mass, varieties and value for calculating forest stock series]. Wurtt-Forstl, Koblenz, Germany.
Gscholar
(32)
Silva CA, Klauberg C, Carvalho SPC, Hudak AT, Rodriguez LCE (2014)
Mapping aboveground carbon stocks using LiDAR data in Eucalyptus spp. plantations in the state of São Paulo, Brazil. Scientia Forestalis 42 (104): 591-604.
Online | Gscholar
(33)
Simões D (2008)
Avaliação econômica de dois sistemas de colheita florestal mecanizada de eucalipto [Economic evaluation of two systems of mechanized forest harvest of Eucaliptus]. Master Thesis, Faculty of Agronomic Sciences Ciências, UNESP, Botucatu, SP, Brazil, pp. 105. [in Portuguese]
Online | Gscholar
(34)
Vastaranta M, Holopainen M, Yu X, Hyppä J, Mäkinen A, Rasinmäki J, Melkas T, Kaartinen H, Hyyppä H (2011)
Effects of individual tree detection error sources on forest management planning calculations. Remote Sensing 3: 1614-1626.
CrossRef | Gscholar
(35)
Wang L, Gong P, Biging GS (2004)
Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery. Photogrammetric Engineering and Remote Sensing 70 (3): 351-357.
CrossRef | Gscholar
(36)
White JC, Wulder MA, Varhole A, Vastaranta M, Coops NC, Cook DC, Pitt D, Woods M (2013)
A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (Version 2.0). Information Report FI-X-010, Natural Resources Canada, Canadian Forest Service and Canadian Wood Fiber Centre, Victoria, BC, Canada, pp. 41.
Online | Gscholar
(37)
Wulder M, Niemann KO, Goodenough DG (2000)
Local Maximum Filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of Environment 73: 103-114.
CrossRef | Gscholar
(38)
Wulder M, Seemann D (2003)
Forest inventory height update through the integration of lidar data with segmented Landsat imagery. Canadian Journal of Remote Sensing 29 (5): 536-543.
CrossRef | Gscholar
(39)
Zhu X, Liu D (2015)
Improving forest aboveground biomass using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing 102: 222-231.
CrossRef | Gscholar
 

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