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Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data (2017)

  • Authors:
  • USP affiliated author: RODRIGUEZ, LUIZ CARLOS ESTRAVIZ - ESALQ
  • School: ESALQ
  • DOI: 10.1186/s13021-017-0081-1
  • Subjects: CICLO DO CARBONO; EUCALIPTO; INVENTÁRIO FLORESTAL; TECNOLOGIA LIDAR
  • Agências de fomento:
  • Language: Inglês
  • Imprenta:
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  • Online source accessDOI
    Informações sobre o DOI: 10.1186/s13021-017-0081-1 (Fonte: oaDOI API)
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    • ABNT

      SILVA, Carlos Alberto; HUDAK, Andrew Thomas; KLAUBERG, Carine; et al. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data. Carbon Balance and Management, London, BioMed Central, v. 12, n. 1, 2017. Disponível em: < http://dx.doi.org/10.1186/s13021-017-0081-1 > DOI: 10.1186/s13021-017-0081-1.
    • APA

      Silva, C. A., Hudak, A. T., Klauberg, C., Vierling, L. A., Gonzalez-Benecke, C., Carvalho, S. de P. C., et al. (2017). Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data. Carbon Balance and Management, 12( 1). doi:10.1186/s13021-017-0081-1
    • NLM

      Silva CA, Hudak AT, Klauberg C, Vierling LA, Gonzalez-Benecke C, Carvalho S de PC, Rodriguez LCE, Cardil A. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data [Internet]. Carbon Balance and Management. 2017 ; 12( 1):Available from: http://dx.doi.org/10.1186/s13021-017-0081-1
    • Vancouver

      Silva CA, Hudak AT, Klauberg C, Vierling LA, Gonzalez-Benecke C, Carvalho S de PC, Rodriguez LCE, Cardil A. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data [Internet]. Carbon Balance and Management. 2017 ; 12( 1):Available from: http://dx.doi.org/10.1186/s13021-017-0081-1

    Referências citadas na obra
    IPCC. Climate change 2013: The physical science basis. contribution of working group I to the Fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK, and New York, USA. http://www.ipcc.ch/report/ar5/wg1/ . Accessed 10 Jan 2016.
    IPCC. Climate change 2001: The scientific basis; Cambridge: Cambridge University Press; 2016. http://www.gridla.no/climate/ipcc_tar/wg1/pdf/wg1_tar-front.pdf . Accessed 10 Jan 2016.
    Binkley CS, Apps MJ, Dixon RK, Kauppi P, Nilsson LO. Sequestering carbon in natural forests. Crit Rev Environ Sci Technol. 1998;27:23–45.
    Fan S, Gloor M, Mahlman J, Pacala S, Sarmiento JL, Takahashi T, Tans P. The North American Sink. Science. 1815;1999:283. doi: 10.1126/science.283.5409.1813q .
    Houghton RA. Interannual variabiity in the global carbon cycle. J Geophys Res. 2000;105:20121–30.
    Gálvez FB, Hudak AT, Byrne JC, Crookston NL, Keefe RF. Using climate-FVS to project landscape-level forest carbon stores for 100 years from field and LiDAR measures of initial conditions. Carbon Balance Manag. 2014;9:1–22. doi: 10.1186/1750-0680-9-1 .
    García M, Riaño D, Chuvieco E, Danson FM. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens Environ. 2010;114:816–30. doi: 10.1016/j.rse.2009.11.021 .
    Bellassen V, Luyssaert S. Carbon sequestration: managing forests in uncertain times. Nature. 2014;506:153–5.
    Stape JL, Binkley D, Ryan MG. Production and carbon allocation in a clonal Eucalyptus plantation with water and nutrient manipulations. For Ecol Manag. 2008;255:920–30. doi: 10.1016/j.foreco.2007.09.085 .
    Du H, Zeng F, Peng W, Wang K, Zhang H, Liu L, Song T. Carbon Storage in a Eucalyptus Plantation chronosequence in Southern China. Forests. 2015;6:1763–78. doi: 10.3390/f6061763 .
    Booth TH. Eucalypt plantations and climate change. For Ecol Manag. 2013;301:28–34. doi: 10.1016/j.foreco.2012.04.004 .
    Abá. Brazilian tree industry. 2015. http://www.iba.org/images/shared/iba_2015.pdf .
    Gonçalves JLDM, Alvares CA, Higa AR, Silva LD, Alfenas AC, Stahl J, Ferraz SFDB, Lima WDP, Brancalion PHS, Hubner A, Bouillet JPD, Laclau JP, Nouvellon Y, Epron D. Integrating genetic and silvicultural strategies to minimize abiotic and biotic constraints in Brazilian eucalypt plantations. For Ecol Manag. 2013;301:6–27. doi: 10.1016/j.foreco.2012.12.030 .
    Silva CA, Klauberg C, Pádua SCD, Piccolo M, Rodriguez LCE. Estoque de carbono na biomassa aérea florestal em plantações comerciais de Eucalyptus spp. Sci For. 2015;43:135–46.
    Silva CA, Klauberg C, Carvalho SPC, Hudak A, Rodriguez LCE. Mapping aboveground carbon stocks using LiDAR data in Eucalyptus spp. plantations in the state of São Paulo, Brazil. Sci For. 2014;42:591–604.
    Laurin VG, Chen Q, Lindsell JA, Coomes DA, Del Frate F, Guerriero L, Pirotti F, Valentini R. Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS J Photogramm Remote Sens. 2014;89:49–58. doi: 10.1016/j.isprsjprs.2014.01.001 .
    Hudak AT, Evans JS, Smith AMS. LiDAR utility for natural resource managers. Remote Sens. 2009;1:934–51. doi: 10.3390/rs1040934 .
    Næsset E. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J Photogramm Remote Sens. 1997;52:49–56. doi: 10.1016/S0924-2716(97)83000-6 .
    Næsset E, Bjerknes KO. Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sens Environ. 2001;78:328–40. doi: 10.1016/S0034-4257(01)00228-0 .
    Næsset E, Økland T. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens Environ. 2002;79:105–15. doi: 10.1016/S0034-4257(01)00243-7 .
    Gobakken T, Næsset E. Weibull and percentile models for lidar-based estimation of basal area distribution. Scand J For Res. 2005;20:490–502. doi: 10.1080/02827580500373186 .
    Hudak AT, Crookston NL, Evans JS, Falkowski MJ, Smith AMS, Gessler P. Regression modeling and mapping of coniferous forest basal area and tree density from discrete- return LiDAR and multispectral satellite data. Can J Remote Sens. 2006;32:126–38. doi: 10.5589/m06-007 .
    Nelson R, Krabill W, Tonelli J. Estimating forest biomass and volume using airborne laser data. Remote Sens Environ. 1988;24:247–67. doi: 10.1016/0034-4257(88)90028-4 .
    Oderwald R, Popescu S. A simplified method of predicting percent volume in log portions. South J Appl. 2003;27:149–52.
    Holmgren J. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scand J For Res. 2004;19:543–53. doi: 10.1080/02827580410019472 .
    Tesfamichael SG, Van Aardt JAN, Ahmed F. Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data. Prog Phys Geogr. 2010;34:515–40. doi: 10.1177/0309133310365596 .
    Næsset E. Estimation of above- and below-ground biomass in boreal forest ecosystems. Int Arc Photogramme Remote Sens Spat Inf Sci. 2004;36:145–8.
    Næsset E, Gobakken T. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens Environ. 2008;112:3079–90.
    Hudak AT, Strand EK, Vierling LA, Byrne JC, Eitel JUH, Martinuzzi S, Falkowski MJ. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sens Environ. 2012;123:25–40. doi: 10.1016/j.rse.2012.02.023 .
    Mascaro J, Detto M, Asner GP, Muller-Landau HC. Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sens Environ. 2011;115:3770–4. doi: 10.1016/j.rse.2011.07.019 .
    Patenaude G, Hill R, Milne R, Gaveau DLA, Briggs BBJ, Dawson TP. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sens Environ. 2004;93:368–80. doi: 10.1016/j.rse.2004.07.016 .
    Hummel S, Hudak AT, Uebler EH, Falkowski MJ, Megown KA. A Comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur National Forest. J For. 2011;109:267–73.
    Watt MS, Adams T, Aracil SG, Marshall H, Watt P. The influence of LiDAR pulse density and plot size on the accuracy of New Zealand plantation stand volume equations. N Z J For Sci. 2013;43:1–10. doi: 10.1186/1179-5395-43-15 .
    Magnussen S, Næsset E, Gobakken T. Reliability of LiDAR derived predictors of forest inventory attributes: a case study with Norway spruce. Remote Sens Environ. 2010;114:700–12.
    Magnussen S, Boudewyn P. Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Can J Remote Sens. 1998;28:1016–31.
    Ruiz L, Hermosilla T, Mauro F, Godino M. Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests. 2014;5:936–51.
    Leitold V, Keller M, Morton DC, Cook BD, Shimabukuro YE. Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+. Carbon Balance Manag. 2015;. doi: 10.1186/s13021-015-0013-x .
    Watt MS, Meredith A, Watt P, Gunn A. The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions. N Z J For Sci. 2014;44:18. doi: 10.1186/s40490-014-0018-3 .
    Strunk J, Temesgen H, Andersen HE, Flewelling JP, Madsen L. Effects of LiDAR pulse density and sample size on a model-assisted approach to estimate forest inventory variables. Can J Remote Sens. 2012;38:644–54.
    Treitz P, Lim K, Woods M, Pitt D, Nesbitt D, Etheridge D. LiDAR sampling density for forest resource inventories in Ontario. Can J Remote Sens. 2012;4:830–48. doi: 10.3390/rs4040830 .
    Koppen W, Geiger R. Klimate der Erde. Gotha: Verlag Justus Perthes. Wall-map 150cm×200cm. 1928.
    White JC, Wulder MA, Varhola A, Vastaranta M, Coops N, Cook BD, Pitt D, Woods M. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Nat For Chron. 2013;89:5.
    Hayashi R, Weiskittel A, Kershaw JA. Influence of prediction cell size on LiDAR-derived area-based estimates of total volume in mixed species and multi-cohort forests in north eastern North America. Can J Remote Sens. 2016;41:473–88.
    Mcgaughey RJ. FUSION/LDV: Software for LiDAR data analysis and visualization. 3rd ed. USDA, Forest Service Pacific Northwest Research Station, Seattle; 2015.
    Kraus K, Pfeifer N. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens. 1998;53:193–203.
    Kraus K, Mikhail EM. Linear least squares interpolation. Photogramm Eng. 1972;38:1016–29.
    Silva CA, Klauberg C, Hudak TA, Vierling LA, Carvalho SP, Rodriguez LC. A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data. Forestry. 2016;1:412. doi: 10.1093/forestry/cpw016 .
    Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
    Liaw A, Wiener M. Classification and regression by random-forest. R News. 2002;2:18–22.
    R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing Access: http://www.Rproject.org . 2015.
    Evans JS, Cushman SA. Gradient modeling of conifer species using random forest. Landsc Ecol. 2009;24:673–83.
    Evans JS, Murphy MA, Holden ZA, Cushman SA. Modeling species distribution and change using Random Forests. In: Drew CA, Huettmann F, Wiersma Y, editors. Predictive Modeling in Landscape Ecology. New York: Springer; 2011. p. 139–59.
    Robinson AP, Froese RE. Model validation using equivalence tests. Ecol Mod. 2004;176:349–58.
    Robinson AP, Duursma RA, Marshall JD. A regression-based equivalence test for model validation: shifting the burden of proof. Tree Physiol. 2005;25:903–13.
    Smith AMS, Falkowski MJ, Hudak AT, Evans JS, Robinson AP, Steele CM. A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Can J Remote Sens. 2009;35(5):447–59.
    Robinson, A. Equivalence: Provides Tests and Graphics for Assessing Tests of Equivalence, Version 0.7.2. https://cran.r-project.org/web/ packages/ equivalence/ (accessed on 20 January, 2016).
    Silva CA, Hudak A, Vierling LA, Loudermilk L, O’brien JJ, Hiers J, Jack J, Gonzalez-Benecke CA, Lee H, alkowskie MJ, Khosravipour A. Imputation of individual longleaf pine ( Mill.) Tree attributes from field and LiDAR Data. Can J Remote Sens. 2016;42:554–73.
    Singh KK, Chen G, McCarter JB, Meentemeyer RK. Effects of LiDAR point density and landscape context on estimates of urban forest biomass. ISPRS J Photogramm Remote Sens. 2015;101:310–22. doi: 10.1016/j.isprsjprs.2014.12.021 .
    Packalen P, Maltamo M, Mehtatalo L. 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. Ann For Sci. 2011;2011(68):1085–92.
    Batista JLF, Couto HTZ, Silva Filho DF. Quantificação de Recursos Florestais: árvores, Arvoredos e Florestas. 1st edn. Oficina de Textos; 2014.
    Gonzalez P, Asner GP, Battles JJ, Lefsky MA, Waring KM, Palace M. Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sens Environ. 2010;114:1561–75.
    Brubaker KM, Johnson SE, Brinks J, Leites LP. Estimating canopy height of deciduous forests at a regional scale with leaf-off, low point density LiDAR. Can J Remote Sens. 2014;40:123–34.
    Wulder MA, White JC, Bater CW, Coops NC, Hokinson CH, Chen G. Lidar plots a new large-area data collection option: context, concepts, and case study. Can J Remote Sens. 2012;38:600–18.

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