Inventory and cartography of forest variables derived from LiDAR data: comparison of methods

Authors

  • Alma Delia Ortiz-Reyes Colegio de Postgraduados, Campus Montecillo
  • J. René Valdez-Lazalde Colegio de Postgraduados, Campus Montecillo
  • Héctor M. De los Santos-Posadas Colegio de Postgraduados, Campus Montecillo
  • Gregorio Ángeles-Pérez Colegio de Postgraduados, Campus Montecillo
  • Fernando Paz-Pellat Colegio de Postgraduados, Campus Montecillo
  • Tomás Martínez-Trinidad Colegio de Postgraduados, Campus Montecillo

DOI:

https://doi.org/10.21829/myb.2015.213461

Keywords:

above-ground biomass, ratio and regression estimators, mapping, spatial modeling, total volume

Abstract

The most common method to estimate forest variables to a large or small scale is the forest inventory based on field sampling. Currently, remote sensing techniques offer a range of possibilities in forest resources estimation; this is the case of LiDAR (Light Detection And Ranging) that allows the characterization forest structure in three-dimensions. We analyzed the relationship between LiDAR and field data to estimate forest variables such as: basal area (AB), total biomass (BT), crown cover (COB) and timber volume (VOL) through four methods: 1) multiple linear regression, 2) non-linear regression, 3) ratio estimators and 4) traditional forest inventory (stratified sampling). Total estimates derived from the ratio estimator were within the 95% confidence interval calculated by traditional inventory for AB, BT and VOL; this estimator showed the closest values and precision to those obtained by traditional forest inventory. In general, estimates through non-linear models were the most optimistic compared to the traditional forest inventory. Our results indicated a good relationship (R2 > 0.50) between LiDAR metrics and field data, particularly the percentiles of height and rates of return on a defined height. From the linear models fit we generated maps for each of the forest variables analyzed.

Downloads

Download data is not yet available.

References

Aguirre S., C.A., J.R. Valdez L., G. Ángeles P., H.M. De los Santos P. y A.I. Aguirre S. 2011. Mapeo de índice de área foliar y cobertura arbórea mediante fotografía hemisférica y datos SPOT HRG: Regresión y k-nn. Agrociencia 45(1):105-119.

Aguirre S., C.A., E.J. Treviño G., O.A. Aguirre C., J. Jiménez P., M.A. González T., J.R. Valdez L., G. Sánchez D., R. Haapanen, A.I. Aguirre S. y L. Miranda A. 2014. Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico. Journal of Arid Land 6(1):80–96. DOI: https://doi.org/10.1007/s40333-013-0191-x

Ángeles P., G., C. Wayson, R. Birdsey, R. Valdez L., H. De los Santos P. y O. Plascencia E. 2011. Sitio intensivo de monitoreo de flujos de CO2 a largo plazo en bosques bajo manejo en el centro de México. In: Memorias III Simposio Internacional del Carbono en México. 12 al 14 de octubre. México. Resumen No. 5.4.

Bortolot, J.Z. y R.H. Wynne. 2005. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. Journal of Photogrammetry & Remote Sensing 59(6):342-360. DOI: https://doi.org/10.1016/j.isprsjprs.2005.07.001

Carrillo A., F., M. Acosta M. y G. Tenorio G. 2004. Tabla de volumen para Pinus patula Schl. et Cham. en el Estado de Hidalgo. Folleto Técnico No. 2. Inifap Sagarpa.16 p.

Conafor (Comisión Nacional Forestal). 2010. Inventario Nacional Forestal y de Suelos, manual y procedimientos para el muestreo de campo, remuestreo 2010. Zapopan, Jalisco, México. 140 p.

Cruz M., Z. 2007. Sistema de ecuaciones para estimación y partición de biomasa aérea en Atopixco, Zacualtipán, Hidalgo, México. Tesis de Maestría en Ciencias. División de Ciencias Forestales. Universidad Autónoma Chapingo. Texcoco, México. 39 p.

Cruz-Leyva, I.A., J.R. Valdez-Lazalde, G. Ángeles-Pérez y H.M. de los Santos-Posadas. 2010. Modelación espacial de área basal y volumen de madera en bosques manejados de Pinus patula y P. teocote en el ejido Atopixco, Hidalgo. Madera y Bosques 16(3):75-97. DOI: https://doi.org/10.21829/myb.2010.1631168

Dubayah, R.O. y J.B. Drake. 2000. LiDAR remote sensing for forestry. Journal of Forestry 98(6):44-46.

Figueroa N., C.M., G. Ángeles P., A. Velázquez M. y H.M. De los Santos P. 2011. Estimación de la biomasa en un bosque bajo manejo de Pinus patula Schltdl. et Cham. en Zacualtipán, Hidalgo. Revista Mexicana de Ciencias Forestales 1(1):105-112. DOI: https://doi.org/10.29298/rmcf.v1i1.658

Hall, S.A., I.C. Burke, D.O. Box, M.R. Kaufmann y J.M. Stoker. 2005. Estimating stand structure using discrete-return LiDAR: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management 208(1-3):189-209. DOI: https://doi.org/10.1016/j.foreco.2004.12.001

Hawbaker, T.J., T. Gobakken, A. Lesak, E. Tromborg, K. Contrucci y V. Radeloff. 2010. Light Detection and Ranging-based measures of mixed hardwood forest structure. Forest Science 56(3):313-326.

Lim, K. y P. Treitz. 2004. Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scandinavian Journal of Forest Research 19(6):558-570. DOI: https://doi.org/10.1080/02827580410019490

Holmgren, J. 2004. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research 19(6):543-553. DOI: https://doi.org/10.1080/02827580410019472

Hudak, A.T., J.S. Evans y A.M.S. Smith. 2009. LiDAR Utility for natural resource managers. Remote Sensing 1:934-951. DOI: https://doi.org/10.3390/rs1040934

Laes, D., S.T. Reutebunch, R. McGaughey y B. Mitchell. 2011. Guidelines to estimate forest inventory parameters from LiDAR and field plot data. Forest Service. Pacific Northwest Research Station. United States Department of Agriculture. 21 p.

Martínez M., M.A. 2004. Nuevos registros de aves en el bosque mesófilo de montaña del noreste de Hidalgo, México. Huitzil 5(2):12-19. DOI: https://doi.org/10.28947/hrmo.2004.5.2.40

Means, J.E., S.A. Acker, D.J. Harding, J.B. Blair, M.A. Lefsky, W.B. Cohen, M.E. Harmon y W.A. McKee. 1999. Use of large-footprint scanning airborne LiDAR to estimate forest stand characteristics in the western Cascades of Oregon. Remote Sensing of Environment 67(3):298-308. DOI: https://doi.org/10.1016/S0034-4257(98)00091-1

McGaughey, R.J. 2012. FUSION/LDV: Software for LIDAR data analysis and visualization. Forest Service. Pacific Northwest Research Station. United States Department of Agriculture. 171 p.

Mora, B., M.A. Wulder, J.C. White y G. Hobart. 2013. Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LiDAR. Remote Sensing 5:2308-2326. DOI: https://doi.org/10.3390/rs5052308

Muñoz-Ruiz, M.A., J.R. Valdez-Lazalde, H.M. De los Santos-Posadas, G. Ángeles-Pérez, A.I. Monterroso-Rivas. 2014. Inventario y mapeo del bosque templado de Hidalgo, México mediante datos del satélite SPOT y de campo. Agrociencia 48:847-862.

Næsset E. 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote sensing of environment 80(1):88-99. DOI: https://doi.org/10.1016/S0034-4257(01)00290-5

Næsset, E. 2004. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research 19(2):164-179. DOI: https://doi.org/10.1080/02827580310019257

Navarro C., R.M., M. Sánchez, J. Gómez, A. García, R. Hernández y S. Lanjeri. 2010. Aplicación de imágenes LiDAR para la estimación del índice de superficie foliar (LAI) en encinas [Quercus ilex L. subsp. ballota (Desf.) Samp]. Forest Systems 19(1)61-69. DOI: https://doi.org/10.5424/fs/2010191-01167

Persson, A., J. Holmgren y U. Söerman. 2002. Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering & Remote Sensing 68(9):925–32.

Popescu, S.C. 2007. Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy 31(9):646-655. DOI: https://doi.org/10.1016/j.biombioe.2007.06.022

Soriano-Luna, M.A., G. Ángeles-Pérez, T. Martínez-Trinidad, F.O. Plascencia-Escalante y R. Razo-Zárate. 2015. Estimación de biomasa aérea por componente estructural en Zacualtipán, Hidalgo, México. Agrociencia 49:423-438.

Van Aardt, J., R. Wynne y J. Scrivani. 2008. LiDAR-based mapping of forest volume and biomass by taxonomic group using structurally homogenous segments. Photogrammetric Engineering & Remote Sensing 74(8):1033–1044. DOI: https://doi.org/10.14358/PERS.74.8.1033

SAS, Institute Inc. 2002. SAS/IML Users guide, Version 9.0. Cary, N.C. SAS Institute Inc. 1031 p.

Scheaffer, L.R., W. Mendenhall y L. Ott. 1986. Elementary survey sampling. PWS Publishers. EUA. 320 p.

Valdez L., J.R. y C.A. Aguirre S. 2013. Active microwave remote sensing for forest resources management: current use in the world and potential use in Mexico. In: Aplicaciones de geomática en la región central de México. M. Escalona M., Y. Fernández-Ordoñez y M.J. Jiménez-Moreno, eds. Colegio de Postgraduados. p:155-178.

Downloads

Published

2016-02-25

How to Cite

Ortiz-Reyes, A. D., Valdez-Lazalde, J. R., De los Santos-Posadas, H. M., Ángeles-Pérez, G., Paz-Pellat, F., & Martínez-Trinidad, T. (2016). Inventory and cartography of forest variables derived from LiDAR data: comparison of methods. Madera Y Bosques, 21(3). https://doi.org/10.21829/myb.2015.213461
Metrics
Views/Downloads
  • Abstract
    3136
  • PDF (Español)
    595
  • HTML (Español)
    1085

Issue

Vol. 21 No. 3 (2015): Otoño

Section

Research papers

License

Licencia Creative Commons

Madera y Bosques by Instituto de Ecología, A.C. is licensed under a Creative Commons Atribution-Non-Commercial-Share-alike 4.0 International.

Metrics

Most read articles by the same author(s)

<< < 1 2 3 4 > >>