Use of LiDAR data during multi-annual periods for estimating forestry variables

Manuel A. Valbuena, Esperanza Mateos, Francisco Rodríguez

Abstract


Aim of study: To test the use of LiDAR data from a single acquisition in order to estimate volume overbark variations ina 5-yr period of Pinus radiata D. Don.

Area of study: Province of Bizkaia in the Autonomous Community of the Basque Country (Spain).

Material and methods: Two field plot measurements were made in 2011 and 2015 and two wood volume models (one for each year) were fitted using the metric variables of the 2012 LiDAR points cloud. The models were applied to a 26.59 m raster covering the study area and the increase in volume at each pixel was calculated by subtraction.

Main results: The increase in estimated wood volume, when added to the volume of timber extracted in the area during the 5-yr period under consideration, yielded an average increase of 13.74 m3 ha-1 yr-1, which corresponds to the average growth of the P. radiata in that area. The harvest area estimated using this procedure largely coincides with the actual harvest area in the same period. The value of R2 (85%) of the wood volume model for 2011 is similar to that obtained in other studies. However, as expected, the one obtained for the wood volume model for 2015 (80%) is significantly lower.

Research highlights: The increase in wood volume can be estimated using a single LiDAR flight and field data from the 5-yr period provided that data from plots subjected to this kind of harvest is included in the models.

Keywords


LiDAR forest inventory; wood volume increase; wood volume LiDAR model

Full Text:

PDF

References


Babcock C, Finley AO, Bradford JB, Kolka R, Birdsey R, Ryan MG, 2015. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients Remote Sens Environ 169: 113-127. https://doi.org/10.1016/j.rse.2015.07.028

Brandtberg T, 2007. Classifying individual tree species under leaf-off and leaf-onconditions using airborne LiDAR. ISPRS J Photogramm. Remote Sens 61: 325-340. https://doi.org/10.1016/j.isprsjprs.2006.10.006

Breusch TS, Pagan AR, 1979. A simple test for heteroscedaticity and random coefficient variation. Econometrica 47: 1287-1294. https://doi.org/10.2307/1911963

Cao L, Coops NC, Innes JL, Sheppard SR, Fu L, Ruan H, She G, 2016. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data. Remote Sens Environ 178: 158-171. https://doi.org/10.1016/j.rse.2016.03.012

Condes Ruiz S, Fernández-Landa A, Rodríguez F, 2013. Influencia del inventario de campo en el error de muestreo obtenido en un inventario con tecnología Lidar. Actas del 6º Congreso Forestal Español, Vitoria-Gasteiz, Spain.

Dalponte M, Coops N, Bruzzone L, Gianelle D, 2009. Analysis on the use of multiple returns LiDAR data for the estimation of tree stems volume. IEEE J-STARS 2: 310-318. https://doi.org/10.1109/JSTARS.2009.2037523

Dean TJ, Cao QV, Roberts SD, Evans DL, 2009. Measuring heights to crownbase and crown median with LiDAR in a mature even-aged loblolly pine stand. Forest Ecol Manag 257: 126-133. https://doi.org/10.1016/j.foreco.2008.08.024

Durbin J, Watson GS, 1971. Testing for serial correlation in least squares regression. III Biometrika 58: 1-19. https://doi.org/10.2307/2334313

Fekety PA, Falkowski MJ, Hudak AT, 2014. Temporal transferability of LiDAR-based imputation of forest inventory attributes. Can J Forest Res 45 (4): 422-435. https://doi.org/10.1139/cjfr-2014-0405

Giannico V, Lafortezza R, John R, Sanesi G, Pesola L, Chen J, 2016. Estimating stand volume and above-ground biomass of urban forests using LiDAR. Remote Sens 8 (4): 339. https://doi.org/10.3390/rs8040339

Holmgren J, 2004. Prediction of tree height basal area and stem volume in forest stands using airborne laser scanning. Scand J Forest Res 19: 543-553. https://doi.org/10.1080/02827580410019472

Hopkinson C, Chasmer L, Hall RJ, 2008. The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets. Remote Sens Environ 112 (3): 1168-1180. https://doi.org/10.1016/j.rse.2007.07.020

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. IEEE T Geosci Remote 39 (5): 969-975. https://doi.org/10.1109/36.921414

Jochem A, Hollaus M, Rutzinger M, Höfle B, 2010. Estimation of aboveground biomass in alpine forests: A semi-empirical approach considering canopy transparency derived from airborne LiDAR data. Sensors 11 (1): 278-295. https://doi.org/10.3390/s110100278

Koenig K, Höfle B, 2016. Full-waveform airborne laser scanning in vegetation studies-A review of point cloud and waveform features for tree species classification. Forests 7 (9): 198. https://doi.org/10.3390/f7090198

Latifi H, Nothdurft A, Koch B, 2010. Non-parametric prediction and mapping of standing wood volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry 83 (4): 395-407. https://doi.org/10.1093/forestry/cpq022

Leiterer R, Furrer R, Schaepman ME, Morsdorf F, 2015. Forest canopy-structure characterization: A data-driven approach. Forest Ecol Manag 358: 48-61. https://doi.org/10.1016/j.foreco.2015.09.003

Li R, Weiskittel AR, 2010. Comparison of model forms for estimating stem taper and volume in the primary conifer species of the North American Acadian Region. Ann For Sci 67 (3): 302. https://doi.org/10.1051/forest/2009109

Lindner M, Karjalainen T, 2007. Carbon inventory methods and carbon mitigation potentials of forests in Europe: a short review of recent progress. Eur J Forest Res 126: 149-156. https://doi.org/10.1007/s10342-006-0161-3

Magnussen S, Næsset E, Gobakken T, 2010. Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce. Remote Sens Environ 114 (4): 700-712. https://doi.org/10.1016/j.rse.2009.11.007

Mallows CL, 1973. Some comments on CP. Technometrics 15 (4): 661-675.

Maltamo M, Packalén P, Peuhkurinen J, Suvanto A, Pesonen A, Hyyppä J, 2007. Experiences and possibilities of ALS based forest inventory in Finland. Proc ISPRS Workshop on Laser Scanning, Spoo, Finland. pp: 270-279.

Maltamo M, Næsset E, Vauhkonen J, 2014. Forestry applications of airborne laser scanning. Concepts and case studies. Manag For Ecosys 27: 460.

Mateos E, Garrido F, Ormaetxea L, 2016. Assessment of biomass energy potential and forest carbon stocks in Biscay (Spain). Forests 7 (4): 75-89. https://doi.org/10.3390/f7040075

Montaghi A, Corona P, Dalponte M, Gianelle D, Chirici G, Olsson H, 2013. Airborne laser scanning of forest resources: an overview of research in Italy as a commentary case study. Int J Appl Earth Obs 23: 288-300. https://doi.org/10.1016/j.jag.2012.10.002

Næsset E, Gobakken T, 2005. Estimating forest growth using canopy metricsderived from airborne laser scanner data. Remote Sens Environ 96: 453-465. https://doi.org/10.1016/j.rse.2005.04.001

Næsset E, 2011. Estimating above-ground biomass in young forests with airborne laser scanning. Int J Remote Sens 32: 473-501. https://doi.org/10.1080/01431160903474970

Nilsson M, 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56: 1-7. https://doi.org/10.1016/0034-4257(95)00224-3

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. Ann Forest Sci 68 (6): 1085-1092. https://doi.org/10.1007/s13595-011-0124-9

Pan Y, Birdsey R, Fang J, Houghton R, Kauppi P, Kurz W, Phillips O, Shvidenko A, Lewis S, et al A large and persistent carbon sink in the world's forests. Science 333: 988-993. https://doi.org/10.1126/science.1201609

Popescu SC, 2007. Estimating biomass of individual pine trees using airborne Lidar. Biomass Bioenerg 31: 646-655. https://doi.org/10.1016/j.biombioe.2007.06.022

Popescu SC, Zhao K A, 2008. Voxel-based LiDAR method for estimating crownbase height for deciduous and pine trees. Remote Sens Environ 112: 767-781. https://doi.org/10.1016/j.rse.2007.06.011

Popescu SC, Wynne RH, Nelson RF, 2003. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can J Remote Sens 29 (5): 564-577. https://doi.org/10.5589/m03-027

Riaño D, Chuvieco E, Condés S, González-Matesanz J, Ustin SL, 2004. Generation of crown bulk density for Pinus sylvestris L from lidar. Remote Sens Environ 92 (3): 345-352. https://doi.org/10.1016/j.rse.2003.12.014

Rodríguez F, Broto M, Lizarralde I, 2008. CubiFOR: Herramienta para cubicar clasificar productos y calcular biomasa y CO2 en masas forestales de Castilla y León. Montes 95: 33-39.

Song Y, Imanishi J, Sasaki T, Ioki K, Morimoto Y, 2016. Estimation of broad-leaved canopy growth in the urban forested area using multi-temporal airborne LiDAR datasets. Urban For Urban Gree 16: 142-149. https://doi.org/10.1016/j.ufug.2016.02.007

Stone C, Penman TD, Turner R, 2011. Determining an optimal model for processing lidar data at the plot level: results for a Pinus radiata plantation in New South Wales Australia. NZ J For Sci 41: 191-205.

Tonolli S, Dalponte M, Vescovo L, Rodeghiero M, Bruzzone L, Gianelle D, 2011. Mapping and modeling forest tree volume using forest inventory and airborne laser scanning. Eur J For Res 130 (4): 569-577. https://doi.org/10.1007/s10342-010-0445-5

Torres AB, Marchant R, Lovett JC, Smart JC, Tipper R, 2010. Analysis of the carbon sequestration costs of afforestation and reforestation agroforestry practices and the use of cost curves to evaluate their potential for implementation of climate change mitigation. Ecol Econ 69 (3): 469-477. https://doi.org/10.1016/j.ecolecon.2009.09.007

Vepakomma U, St-Onge B, Kneeshaw D, 2011. Response of a boreal forest to canopy opening: assessing vertical and lateral tree growth with multi-temporal LiDAR data. Ecol Appl 21: 99-121. https://doi.org/10.1890/09-0896.1

Watt M, Meredith A, Watt P, Gunn A, 2013. Use of LiDAR to estimate stand characteristics for thinning operations in young Douglas-fir plantations. NZ J For Sci 43 (18): 1-10. https://doi.org/10.1186/1179-5395-43-1

Yu X, Hyyppä J, Kaartinen H, Maltamo M, 2004. Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sens Environ 90: 451-462. https://doi.org/10.1016/j.rse.2004.02.001

Yu X, Hyyppä J, Kukko A, Maltamo M, Kaartinen H, 2006. Change detection techniques for canopy height growth measurements using airborne laser scanner data Photogramm. Eng Remote Sens 72: 1339-1348. https://doi.org/10.14358/PERS.72.12.1339

Yu X, Hyyppä J, Holopainen M, Vastaranta M, 2010. Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sens 6: 1481-1495. https://doi.org/10.3390/rs2061481

Zhang J, Ge Y, Chang J, Jiang B, Jiang H, Peng C, Zhu J, Yuan W, Qi L, Yu S, 2007. Carbon storage by ecological service forests in Zhejiang Province subtropical China. For Ecol Manag 245: 64-75.

Zhang Z, Kazakova A, Moskal LM, Styers DM, 2016. Object-based tree species classification in urban ecosystems using LiDAR and hyperspectral data. Forests 7 (6): 122. https://doi.org/10.3390/f7060122

Zianis D, Muukkonen P, Mäkipää R, Mencuccini M, 2005. Biomass and stem volume equations for tree species in Europe. Silva Fennica Monographs 4, 63 pp.

Zolkos SG, Goetz SJ, Dubayah RA, 2013. Meta-analysis of terrestrial aboveground biomass estimation using LiDAR remote sensing. Remote Sens Environ 128: 289-298. https://doi.org/10.1016/j.rse.2012.10.017




DOI: 10.5424/fs/2017263-11468

Webpage: www.inia.es/Forestsystems