Journal Home > Volume 5 , Issue 2
Forest Ecosystems 2018, 5(2): 10 https://doi.org/10.1186/s40663-017-0119-6
Research | Open Access | Issue | Published: 12 January 2018

Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Show Author's Information David A. Coomes1 ( ),  Daniel Šafka1,2,  James Shepherd2,  Michele Dalponte3,  Robert Holdaway4
Department of Plant Sciences, University of Cambridge, Downing Street, CB2 3EA Cambridge, UK
Landcare Research, Riddet Rd, Massey University, Palmerston North 4474 Palmerston North, New Zealand
Department of Sustainable Agro-Ecosystems and Bioresources, Reserch and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all'Adige (TN), Italy
Landcare Research, 54 Gerald Street, Lincoln 7608 Lincoln, New Zealand
Keywords:
Carbon, Remote sensing, Wind, Climate change, Forest, LiDAR, Airborne laser scanning, Cyclone, New Zealand, LUCAS
Cite this article:
Coomes DA, Šafka D, Shepherd J, et al. Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon. Forest Ecosystems, 2018, 5(2): 10. https://doi.org/10.1186/s40663-017-0119-6
Download citation
EndNote(RIS)
BibTeX

433

Views

12

Downloads

Citations

12

Crossref

N/A

WoS

16

Scopus

1

CSCD

Abstract Full text About this article
Background

Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites.

Methods

The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests.

Results

Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth.

Conclusion

Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.


menu
Abstract
Full text
Outline
About this article

Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Show Author's information David A. Coomes1 ( ), Daniel Šafka1,2, James Shepherd2, Michele Dalponte3, Robert Holdaway4
Department of Plant Sciences, University of Cambridge, Downing Street, CB2 3EA Cambridge, UK
Landcare Research, Riddet Rd, Massey University, Palmerston North 4474 Palmerston North, New Zealand
Department of Sustainable Agro-Ecosystems and Bioresources, Reserch and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all'Adige (TN), Italy
Landcare Research, 54 Gerald Street, Lincoln 7608 Lincoln, New Zealand

Abstract

Background

Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites.

Methods

The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests.

Results

Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth.

Conclusion

Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.

Keywords: Carbon, Remote sensing, Wind, Climate change, Forest, LiDAR, Airborne laser scanning, Cyclone, New Zealand, LUCAS

References(70)

Allen, CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EHTH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-HH, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259(4):660-684. doi:10.1016/j.foreco.2009.09.001.
DOI Google Scholar

Arlot, S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40-79. doi:10.1214/09-SS054.
DOI Google Scholar

Agrawal, A, Nepstad D, Chhatre A (2011) Reducing emissions from deforestation and forest degradation. Annu Rev Environ Resour 36:373-396. doi:10.1146/annurev-environ-042009-094508.
DOI Google Scholar

Asner, GP, Mascaro J (2014) Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric. Remote Sens Environ 140:614-624. doi:10.1016/j.rse.2013.09.023.
DOI Google Scholar

Asner, GP, Martin RE, Knapp DE, Tupayachi R, Anderson CB, Sinca F, Vaughn NR, Llactayo W (2017) Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355(6323):385-389.
DOI Google Scholar

Asner, GP, Martin RE, Tupayachi R, Anderson CB, Sinca F, Carranza-Jiménez L, Martinez P (2014) Amazonian functional diversity from forest canopy chemical assembly. Proc Natl Acad Sci USA 111(15). doi:10.1073/pnas.1401181111.
DOI Google Scholar

Asner, GP, Powell GVN, Mascaro J, Knapp DE, Clark JK, Jacobson J, Kennedy-Bowdoin T, Balaji A, Paez-Acosta G, Victoria E, Secada L, Valqui M, Hughes RF (2010) High-resolution forest carbon stocks and emissions in the Amazon. Proc Natl Acad Sci 107(38):16738-42. doi:10.1073/pnas.1004875107.
DOI Google Scholar

Avitabile, V, Herold M, Heuvelink GBM, Lewis SL, Phillips OL, Asner GP, Armston J, Asthon P, Banin LF, Bayol N, Berry NJ, Boeckx P, de Jong BHJ, DeVries B, Girardin CAJ, Kearsley E, Lindsell JA, Lopez-Gonzalez G, Lucas R, Malhi Y, Morel A, Mitchard ETA, Nagy L, Qie L, Quinones MJ, Ryan CM, Slik F, Sunderland T, Vaglio Laurin G, Valentini R, Verbeeck H, Wijaya A, Willcock S, Ashton PS, Banin LF, Bayol N, Berry NJ, Boeckx P, de Jong BHJ, DeVries B, Girardin CAJ, Kearsley E, Lindsell JA, Lopez-Gonzalez G, Lucas R, Malhi Y, Morel A, Mitchard ETA, Nagy L, Qie L, Quinones MJ, Ryan CM, Ferry SJW, Sunderland T, Laurin GV, Gatti RC, Valentini R, Verbeeck H, Wijaya A, Willcock S (2016) An integrated pan-tropical biomass map using multiple reference datasets. Glob Chang Biol 22(4):1406-1420. doi:10.1111/gcb.13139.
DOI Google Scholar

Baccini, A, Goetz SJ, Walker WS, Laporte NT, Sun M, Sulla-Menashe D, Hackler J, Beck PSA, Dubayah R, Friedl Ma, Samanta S, Houghton RA (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Chang 2(1):1-4. doi:10.1038/nclimate1354.
DOI Google Scholar

Beets, PN, Kimberley MO, Oliver GR, Pearce SH, Graham JD, Brandon A (2012) Allometric Equations for Estimating Carbon Stocks in Natural Forest in New Zealand. Forests 3(4):818-839. doi:10.3390/f3030818.
DOI Google Scholar
Bivand, R, Rundel C (2016) rgeos: Interface to Geometry Engine - Open Source (GEOS). https://cran.r-project.org/package=rgeos. Accessed 1 Oct 2016.
Bivand, R, Keitt T, Rowlingson B (2016) rgdal: Bindings for the Geospatial Data Abstraction Library. https://cran.r-project.org/package=rgdal. Accessed 1 Oct 2016.

Bradford, JB, Birdsey RA, Joyce LA, Ryan MG (2008) Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests. Glob Chang Biol 14(12):2882-97. doi:10.1111/j.1365-2486.2008.01686.x.
DOI Google Scholar
Bunting, P, Armston J, Clewley D, Lucas R, et al (2011) The Sorted Pulse Data Software Library (SPDLib): Open source tools for processing LiDAR data In: Proceedings of SilviLaser 2011, 11th International Conference on LiDAR Applications for Assessing Forest Ecosystems, University of Tasmania, Australia, 16-20 October 2011. Conference Secretariat, 1-11.

Chen, Q, Baldocchi D, Gong P, Kelly M (2006) Isolating individual trees in a savanna woodland using small footprint lidar data. Photogramm Eng Remote Sens 72(8):923-932.
DOI Google Scholar

Chen, Q, Vaglio Laurin G, Valentini R (2015) Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels. Remote Sens Environ 160:134-143. doi:10.1016/j.rse.2015.01.009.
DOI Google Scholar

Coomes, DA, Allen RB, Bentley WA, Burrows LE, Canham CD, Fagan L, Forsyth DM, Gaxiola-Alcantar A, Parfitt RL, Ruscoe WA, Wardle DA, Wilson DJ, Wright EF (2005) The hare, the tortoise and the crocodile: the ecology of angiosperm dominance, conifer persistence and fern filtering. J Ecol 93(5):918-935. doi:10.1111/j.1365-2745.2005.01012.x.
DOI Google Scholar
Coomes, DA, Allen RBR, Scott NNA, Goulding C, Beets P (2002) Designing systems to monitor carbon stocks in forests and shrublands. For Ecol Manag 164(1-3):89-108. doi:16/S0378-1127(01)00592-8.https://doi.org/10.1016/S0378-1127(01)00592-8
DOI
Coomes, DA, Dalponte M, Jucker T, Asner GP, Banin LF, Burslem DFRP, Lewis SL, Nilus R, Phillips O, Phuag MH, Qiee L (2017) Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests with airborne laser scanning data. Remote Sens Environ.https://doi.org/10.1016/j.rse.2017.03.017
DOI

Coomes, DA, Holdaway RRJ, Kobe RK, Lines ER, Allen RB (2012) A general integrative framework for modelling woody biomass production and carbon sequestration rates in forests. J Ecol 100(1):42-64. doi:10.1111/j.1365-2745.2011.01920.x.
DOI Google Scholar
Dalponte, M, Coomes DA (2016) Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol Evol. doi: 10.1111/2041-210X.12575.https://doi.org/10.1111/2041-210X.12575
DOI

Duncanson, LI, Cook BD, Hurtt GC, Dubayah RO (2014) An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sens Environ 154:378-386.
DOI Google Scholar

Duncanson, LI, Dubayah RO, Cook BD, Rosette J, Parker G (2015) The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sens Environ 168:102-112. doi:10.1016/j.rse.2015.06.021.
DOI Google Scholar

Duncanson, L, Rourke O, Dubayah R (2015) Small sample sizes yield biased allometric equations in temperate forests. Sci Rep 5:17153. doi:10.1038/srep17153.
DOI Google Scholar

Elder, NL (1965) Vegetation of the Ruahine Range: an introduction. Trans R Soc N Z (Botany) 3:13-66.
Google Scholar

Eysn, L, Hollaus M, Lindberg E, Berger F, Monnet JM, Dalponte M, Kobal M, Pellegrini M, Lingua E, Mongus D, et al (2015) A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space. Forests 6(5):1721-1747.
DOI Google Scholar

Ferraz, A, Saatchi S, Mallet C, Meyer V (2016) Lidar detection of individual tree size in tropical forests. Remote Sens Environ 183:318-333. doi:10.1016/j.rse.2016.05.028.
DOI Google Scholar

Ferry, B, Morneau F, Bontemps JD, Blanc L, Freycon V (2010) Higher treefall rates on slopes and waterlogged soils result in lower stand biomass and productivity in a tropical rain forest. J Ecol 98(1):106-116. doi:10.1111/j.1365-2745.2009.01604.x.
DOI Google Scholar

Getzin, S, Fischer R, Knapp N, Huth A (2017) Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro. Landscape Ecol 32(9):1881-1894.
DOI Google Scholar

Gibbs, HK, Brown S, Niles JO, Foley JA (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2(4):045023. doi:10.1088/1748-9326/2/4/045023.
DOI Google Scholar

Gobakken, T, Næsset E (2009) Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J For Res 39(5):1036-1052. doi:10.1139/X09-025.
DOI Google Scholar

Harcombe, PA, Allen RB, Wardle JA, Platt KH (1998) Spatial and Temporal Patterns in Stand Structure, Biomass, Growth, and Mortality in a Monospecific Nothofagus solandri var. cliffortioides (Hook, f.) Poole Forest in New Zealand. J Sustain For 6(3-4):313-345.
DOI Google Scholar
Hijmans, RJ (2015) raster: Geographic Data Analysis and Modeling. https://cran.r-project.org/package=raster. Accessed 1 Oct 2016.
Holdaway, RJ, Easdale TA, Carswell FE, Richardson SJ, Peltzer DA, Mason NWH, Brandon AM, Coomes DA (2016) Nationally Representative Plot Network Reveals Contrasting Drivers of Net Biomass Change in Secondary and Old-Growth Forests. Ecosystems: 1-16. doi: 10.1007/s10021-016-0084-x.https://doi.org/10.1007/s10021-016-0084-x
DOI
Holdaway, RJ, Easdale TA, Mason NWH, Carswell FE (2014) LUCAS Natural Forest Carbon Analysis. Prepared for the Ministry for the Environment by Landcare Research. Wellington Technical report. Landcare Research New Zealand.
Holdaway, RJ, Mason NWH, Easdale T, Dymond J, Betts H, Wakelin SJ, Moore JR (2014) Annual carbon emissions associated with natural disturbance in New Zealand's natural and planted forests. Technical report. New Zealand Government.

Holdaway, RJ, McNeill SJ, Mason NWH, Carswell FE (2014) Propagating uncertainty in plot-based estimates of forest carbon stock and carbon stock change. Ecosystems 17(4):627-640.
DOI Google Scholar

Houghton, RA, Byers B, Nassikas AA (2015) A role for tropical forests in stabilizing atmospheric CO2. Nat Clim Chang 5(12):1022-1023. doi:10.1038/nclimate2869.
DOI Google Scholar

Hyyppä, J, Kelle O, Lehikoinen M, Inkinen M (2001) A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. Geosci Remote Sens IEEE Trans 39(5):969-975.
DOI Google Scholar

Jones, PB (1972) A Comparison of the Precision of Traverses Adjusted by Bowditch Rule and by Least Squares. Surv Rev 21(164):253-273.
DOI Google Scholar
Jucker, T, Asner GP, Dalponte M, Brodrick P, Philipson CD, Vaughn N, Brelsford C, Burslem DFRP, Deere NJ, Ewers RM, Kvasnica J, Lewis SL, Malhi Y, Milne S, Nilus R, Pfeifer M, Phillips O, Qie L, Renneboog N, Reynolds G, Riutta T, Struebig MJ, Svátek M, Teh YA, Turner EC, Coomes DA (2017) A regional model for estimating the aboveground carbon density of Borneo's tropical forests from airborne laser scanning.

Jubanski, J, Ballhorn U, Kronseder K, Franke J, Siegert F (2013) Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR. Biogeosciences 10(6):3917-3930. doi:10.5194/bg-10-3917-2013.
DOI Google Scholar
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 (2016) Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Chang Biol. doi: 10.1111/gcb.13388.https://doi.org/10.1111/gcb.13388
DOI

Korner, C, Körner C (2003) Slow in, Rapid out-Carbon Flux Studies and Kyoto Targets. Science 300(5623):1242-1243. doi:10.1126/science.1084460.
DOI Google Scholar

Lefsky, M, Cohen W, Acker S, Parker G (1999) Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sens 70(3):339-361.
DOI Google Scholar
Longo, M, Keller MM, Dos-Santos MN, Leitold V, Pinagé ER, Baccini A, Saatchi S, Nogueira EM, Batistella M, Morton DC (2016) Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob Biogeochem Cycles: 10-10022016005465. doi: 10.1002/2016GB005465.https://doi.org/10.1002/2016GB005465
DOI
Mason, NWH, Carswell FE, Overton JM, Briggs CM, Hall GMJ (2012) Estimation of current and potential carbon stocks and Kyoto-compliant carbon gain on conservation land. Sci Conserv Rep 317: 39.

Morsdorf, F, Meier E, Kötz B, Itten KI, Dobbertin M, Allgöwer B (2004) LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sens Environ 92(3):353-362.
DOI Google Scholar

Müller, MU, Shepherd JD, Dymond JR (2015) Support vector machine classification of woody patches in New Zealand from synthetic aperture radar and optical data, with LiDAR training. J Appl Remote Sens 9(1):95984. doi:10.1117/1.JRS.9.095984.
DOI Google Scholar

Nelson, R, Krabill W, Tonelli J (1988) Estimating forest biomass and volume using airborne laser data. Remote Sens Environ 24(2):247-267. doi:10.1016/0034-4257(88)90028-4.
DOI Google Scholar

Pan, Y, Birdsey RA, Fang J, Houghton RA, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D, Canadell JG, Khatiwala S, Primeau F, Hall T, Quéré CL, Dixon RK, Kauppi PE, Kurz WA, Stinson G, Rampley GJ, Dymond CC, Neilson ET, Stinson G, Birdsey RA, Pregitzer K, Lucier A, Kauppi PE, Pan Y, Pan Y, Birdsey RA, Hom J, McCullough K, Mantgem PJv, Breshears DD, Ciais P, Fang J, Chen A, Peng C, Zhao S, Ci L, Lewis SL, Phillips OL, Gloor M, Lewis SL, Lloyd J, Sitch S, Mitchard ETA, Laurance WF, Houghton RA, Friedlingstein P, Tarnocai C, Hooijer A, Page SE, Rieley JO, Banks CJ, McGuire AD, Goodale CL, Sarmiento JL, Schulze ED, Pacala SW, Phillips OL, Metsaranta JM, Kurz WA, Neilson ET, Stinson G, Zhao M, Running SW, Houghton RA (2011) A Large and Persistent Carbon Sink in the World's Forests. Science 333(6045):988-993. doi:10.1126/science.1201609.
DOI Google Scholar

Pan, Y, Birdsey RA, Phillips OL, Jackson RB (2013) The structure, distribution, and biomass of the world's forests. Ann Rev Ecol Evol Syst 44(1):593-622. doi:10.1146/annurev-ecolsys-110512-135914.
DOI Google Scholar
Payton, IJ, Newell CL, Beets PN (2004) New Zealand Carbon Monitoring System. Indigenous forest and shrubland data collection manual. Manaaki Whenua Landcare Research, Lincoln.
Platt, I, Griffiths A, Wootton M (2014) Assessment of Cyclone Ita Wind-blow Damage to West Coast Indigenous Forests. Technical report. Ministry for Primary Industries, Wellington, New Zealand. http://www.mpi.govt.nz/news-resources/publications.aspx. Accessed Oct 2016.
Popescu, SC, Zhao K, Neuenschwander A, Lin C (2011) Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens Environ: 1-12. doi: 10.1016/j.rse.2011.01.026.https://doi.org/10.1016/j.rse.2011.01.026
DOI

Reitberger, J, Schnörr C, Krzystek P, Stilla U (2009) 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS J Photogramm Remote Sens 64(6):561-574.
DOI Google Scholar

Réjou-Méchain, M, Tymen B, Blanc L, Fauset S, Feldpausch TR, Monteagudo A, Phillips OL, Richard H, Chave J (2015) Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest. Remote Sens Environ 169:93-101. doi:10.1016/j.rse.2015.08.001.
DOI Google Scholar

Seidl, R, Schelhaas MJ, Lexer MJ (2011) Unraveling the drivers of intensifying forest disturbance regimes in Europe. Glob Chang Biol 17(9):2842-2852. doi:10.1111/j.1365-2486.2011.02452.x.
DOI Google Scholar

Singh, M, Evans D, Coomes DA, Friess DA, Suy Tan B, Samean Nin C (2016) Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia. PLoS One 11(5):0154307. doi:10.1371/journal.pone.0154307.
DOI Google Scholar

Shugart, HH, Asner GP, Fischer R, Huth A, Knapp N, Le Toan T, Shuman JK (2015) Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models. Front Ecol Environ 13(9):503-511. doi:10.1890/140327.
DOI Google Scholar
Spriggs, R (2015) Robust methods for estimating forest stand characteristics across landscapes using airborne LiDAR. PhD thesis. University of Cambridge.

Vauhkonen, J, Ene L, Gupta S, Heinzel J, Holmgren J, Pitkanen J, Solberg S, Wang Y, Weinacker H, Hauglin KM, Lien V, Packalen P, Gobakken T, Koch B, Naesset E, Tokola T, Maltamo M (2012) Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85(1):27-40. doi:10.1093/forestry/cpr051.
DOI Google Scholar

Vincent, G, Sabatier D, Blanc L, Chave J, Weissenbacher E, Pélissier R, Fonty E, Molino JF, Couteron P (2012) Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sens Environ 125(null):23-33. doi:10.1016/j.rse.2012.06.019.
DOI Google Scholar
Wardle, P (2002) Vegetation of New Zealand. Blackburn Press.

Wiser, SK, Hurst JM, Wright EF, Allen RB (2011) New Zealand's forest and shrubland communities: a quantitative classification based on a nationally representative plot network. Appl Veg Sci 14(4):506-523. doi:10.1111/j.1654-109X.2011.01146.x.
DOI Google Scholar

Wulder, MA, White JC, Nelson RF, Næsset E, Ørka HO, Coops NC, Hilker T, Bater CW, Gobakken T (2012) Lidar sampling for large-area forest characterization: A review. Remote Sens Environ 121:196-209. doi:10.1016/j.rse.2012.02.001.
DOI Google Scholar

Yu, X, Hyyppä J, Vastaranta M, Holopainen M, Viitala R (2011) Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J Photogramm Remote Sens 66(1):28-37.
DOI Google Scholar

Zeide, B (2005) How to measure stand density. Trees-Structure Funct 19(1):1-14.
DOI Google Scholar
Zotov, VD, Elder NL, Beddie AD, Sainsbury GOK, Hodgson EA (1938) An outline of the vegetation and flora of the Tararua mountains 68: 239-324.

Zolkos, SG, Goetz SJ, Dubayah R (2013) A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens Environ 128:289-298. doi:10.1016/j.rse.2012.10.017.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 14 September 2017
Accepted: 10 December 2017
Published: 12 January 2018
Issue date: June 2018

Copyright

© The Author(s) 2018.

Acknowledgements

Acknowledgements

We are grateful to the New Zealand Ministry for the Environment for permission to use the LiDAR survey and LUCAS natural forest plots for this work, as retrieved from the National Vegetation Survey (NVS) Databank. We thank the many researchers involved in creating the LUCAS, NVS and LCDB datasets. This research was supported by Ministry of Business, Innovation and Employment core funding to Crown Research Institutes. Peter Bellingham provided helpful insights into the ecology of the Wellington Region.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Return