- -

Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Ruiz;Hermosilla;F ...
Tamaño: 2.050Mb
Formato: PDF
Descripción: Versión del Editor
Abrir
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.contributor.author Hermosilla, T. es_ES
dc.contributor.author Mauro, Francisco es_ES
dc.contributor.author Godino, Miguel es_ES
dc.date.accessioned 2016-02-29T15:52:09Z
dc.date.available 2016-02-29T15:52:09Z
dc.date.issued 2014
dc.identifier.issn 1999-4907
dc.identifier.uri http://hdl.handle.net/10251/61252
dc.description Licencia Creative Commons: Attribution 3.0 Unported (CC BY 3.0) es_ES
dc.description.abstract This paper assesses the combined effect of field plot size and LiDAR density on the estimation of four forest structure attributes: volume, total biomass, basal area and canopy cover. A total of 21 different plot sizes were considered, obtained by decreasing the field measured plot radius value from 25 to 5 m with regular intervals of 1 m. LiDAR data densities were simulated by randomly removing LiDAR pulses until reaching nine different density values. In order to avoid influence of the digital terrain model spatial resolution, eight different resolutions were considered (from 0.25 to 2 m grid size) and tested. A set of per-plot LiDAR metrics was extracted for each parameter combination. Prediction models of forest attributes were defined using forward stepwise ordinary least-square regressions. Results show that the highest R 2 values are reached by combining large plot sizes and high LiDAR data density values. However, plot size has a greater effect than LiDAR point density. In general, minimum plot areas of 500–600 m2 are needed for volume, biomass and basal area estimates, and of 300–400 m2 for canopy cover. Larger plot sizes do not significantly increase the accuracy of the models, but they increase the cost of fieldwork. es_ES
dc.description.sponsorship The authors wish to thank the Spanish Ministry of Industry, Tourism and Trade, and the Spanish Ministry of Science and Innovation for the financial support provided in the framework of the projects InForest, and CGL2010-19591/BTE, respectively. en_EN
dc.language Inglés es_ES
dc.publisher MDPI AG, Basel, Switzerland es_ES
dc.relation.ispartof Forests es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Forest inventory es_ES
dc.subject LiDAR es_ES
dc.subject Plot size es_ES
dc.subject Data density es_ES
dc.subject Forest structure es_ES
dc.subject Forest attributes es_ES
dc.subject Remote sensing es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/f5050936
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CGL2010-19591/ES/DESARROLLO DE METODOLOGIAS INTEGRADAS PARA LA ACTUALIZACION DE BASES DE DATOS DE OCUPACION DEL SUELO/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Ruiz Fernández, LÁ.; Hermosilla, T.; Mauro, F.; Godino, M. (2014). Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates. Forests. 5(5):936-951. https://doi.org/10.3390/f5050936 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/f5050936 es_ES
dc.description.upvformatpinicio 936 es_ES
dc.description.upvformatpfin 951 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 5 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 267303 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.description.references Mäkelä, H., & Pekkarinen, A. (2004). Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management, 196(2-3), 245-255. doi:10.1016/j.foreco.2004.02.049 es_ES
dc.description.references MCROBERTS, R., & TOMPPO, E. (2007). Remote sensing support for national forest inventories. Remote Sensing of Environment, 110(4), 412-419. doi:10.1016/j.rse.2006.09.034 es_ES
dc.description.references Van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4), 749-770. doi:10.1007/s10342-010-0381-4 es_ES
dc.description.references Hyyppä, J., Yu, X., Hyyppä, H., Vastaranta, M., Holopainen, M., Kukko, A., … Alho, P. (2012). Advances in Forest Inventory Using Airborne Laser Scanning. Remote Sensing, 4(5), 1190-1207. doi:10.3390/rs4051190 es_ES
dc.description.references Lovell, J. L., Jupp, D. L. B., Newnham, G. J., Coops, N. C., & Culvenor, D. S. (2005). Simulation study for finding optimal lidar acquisition parameters for forest height retrieval. Forest Ecology and Management, 214(1-3), 398-412. doi:10.1016/j.foreco.2004.07.077 es_ES
dc.description.references Frazer, G. W., Magnussen, S., Wulder, M. A., & Niemann, K. O. (2011). Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sensing of Environment, 115(2), 636-649. doi:10.1016/j.rse.2010.10.008 es_ES
dc.description.references Martin Bollandsås, O., & Næsset, E. (2007). Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data. Scandinavian Journal of Forest Research, 22(1), 33-47. doi:10.1080/02827580601138264 es_ES
dc.description.references Solberg, S., Næsset, E., Hanssen, K. H., & Christiansen, E. (2006). Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sensing of Environment, 102(3-4), 364-376. doi:10.1016/j.rse.2006.03.001 es_ES
dc.description.references Jaskierniak, D., Lane, P. N. J., Robinson, A., & Lucieer, A. (2011). Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions. Remote Sensing of Environment, 115(2), 573-585. doi:10.1016/j.rse.2010.10.003 es_ES
dc.description.references García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114(4), 816-830. doi:10.1016/j.rse.2009.11.021 es_ES
dc.description.references Thomas, V., Treitz, P., McCaughey, J. H., & Morrison, I. (2006). Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Canadian Journal of Forest Research, 36(1), 34-47. doi:10.1139/x05-230 es_ES
dc.description.references Coops, N. C., Hilker, T., Wulder, M. A., St-Onge, B., Newnham, G., Siggins, A., & Trofymow, J. A. (Tony). (2007). Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees, 21(3), 295-310. doi:10.1007/s00468-006-0119-6 es_ES
dc.description.references Richardson, J. J., & Moskal, L. M. (2011). Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR. Remote Sensing of Environment, 115(10), 2640-2651. doi:10.1016/j.rse.2011.05.020 es_ES
dc.description.references Chasmer, L., Hopkinson, C., Smith, B., & Treitz, P. (2006). Examining the Influence of Changing Laser Pulse Repetition Frequencies on Conifer Forest Canopy Returns. Photogrammetric Engineering & Remote Sensing, 72(12), 1359-1367. doi:10.14358/pers.72.12.1359 es_ES
dc.description.references Weber, T. C., & Boss, D. E. (2009). Use of LiDAR and supplemental data to estimate forest maturity in Charles County, MD, USA. Forest Ecology and Management, 258(9), 2068-2075. doi:10.1016/j.foreco.2009.08.001 es_ES
dc.description.references Falkowski, M. J., Smith, A. M. ., Hudak, A. T., Gessler, P. E., Vierling, L. A., & Crookston, N. L. (2006). Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data. Canadian Journal of Remote Sensing, 32(2), 153-161. doi:10.5589/m06-005 es_ES
dc.description.references Falkowski, M. J., Evans, J. S., Martinuzzi, S., Gessler, P. E., & Hudak, A. T. (2009). Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA. Remote Sensing of Environment, 113(5), 946-956. doi:10.1016/j.rse.2009.01.003 es_ES
dc.description.references Erdody, T. L., & Moskal, L. M. (2010). Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737. doi:10.1016/j.rse.2009.11.002 es_ES
dc.description.references Jakubowski, M. K., Guo, Q., & Kelly, M. (2013). Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sensing of Environment, 130, 245-253. doi:10.1016/j.rse.2012.11.024 es_ES
dc.description.references Woods, M., Lim, K., & Treitz, P. (2008). Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence forest of Ontario. The Forestry Chronicle, 84(6), 827-839. doi:10.5558/tfc84827-6 es_ES
dc.description.references 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 Journal of Photogrammetry and Remote Sensing, 66(1), 28-37. doi:10.1016/j.isprsjprs.2010.08.003 es_ES
dc.description.references Bortolot, Z. J. (2006). Using Tree Clusters to Derive Forest Properties from Small Footprint Lidar Data. Photogrammetric Engineering & Remote Sensing, 72(12), 1389-1397. doi:10.14358/pers.72.12.1389 es_ES
dc.description.references Rowell, E., Seielstad, C., Vierling, L., Queen, Ll., & Shepperd, W. (2006). Using Laser Altimetry-based Segmentation to Refine Automated Tree Identification in Managed Forests of the Black Hills, South Dakota. Photogrammetric Engineering & Remote Sensing, 72(12), 1379-1388. doi:10.14358/pers.72.12.1379 es_ES
dc.description.references 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:10.1080/02827580410019472 es_ES
dc.description.references Yu, X., Hyyppä, J., Kaartinen, H., & Maltamo, M. (2004). Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment, 90(4), 451-462. doi:10.1016/j.rse.2004.02.001 es_ES
dc.description.references Hyde, P., Nelson, R., Kimes, D., & Levine, E. (2007). Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR. Remote Sensing of Environment, 106(1), 28-38. doi:10.1016/j.rse.2006.07.017 es_ES
dc.description.references Goodwin, N. R., Coops, N. C., & Culvenor, D. S. (2006). Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sensing of Environment, 103(2), 140-152. doi:10.1016/j.rse.2006.03.003 es_ES
dc.description.references Bater, C. W., Wulder, M. A., Coops, N. C., Nelson, R. F., Hilker, T., & Nasset, E. (2011). Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring. IEEE Transactions on Geoscience and Remote Sensing, 49(6), 2385-2392. doi:10.1109/tgrs.2010.2099232 es_ES
dc.description.references Magnussen, S., Næsset, E., & Gobakken, T. (2010). Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce. Remote Sensing of Environment, 114(4), 700-712. doi:10.1016/j.rse.2009.11.007 es_ES
dc.description.references Mauro, F., Valbuena, R., Manzanera, J. A., & García-Abril, A. (2011). Influence of Global Navigation Satellite System errors in positioning inventory plots for tree-height distribution studiesThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time. Canadian Journal of Forest Research, 41(1), 11-23. doi:10.1139/x10-164 es_ES
dc.description.references Gobakken, T., & Næsset, E. (2009). Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research, 39(5), 1036-1052. doi:10.1139/x09-025 es_ES
dc.description.references White, J. C., Wulder, M. A., Varhola, A., Vastaranta, M., Coops, N. C., Cook, B. D., … Woods, M. (2013). A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. The Forestry Chronicle, 89(06), 722-723. doi:10.5558/tfc2013-132 es_ES
dc.description.references Gobakken, T., & Næsset, E. (2008). Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research, 38(5), 1095-1109. doi:10.1139/x07-219 es_ES
dc.description.references Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Hermosilla, T. (2011). Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. International Journal of Digital Earth, 4(6), 521-538. doi:10.1080/17538947.2010.533201 es_ES
dc.description.references Sutton, R. N., & Hall, E. L. (1972). Texture Measures for Automatic Classification of Pulmonary Disease. IEEE Transactions on Computers, C-21(7), 667-676. doi:10.1109/t-c.1972.223572 es_ES
dc.description.references Ruiz, L. A., Recio, J. A., Fernández-Sarría, A., & Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture, 76(2), 284-296. doi:10.1016/j.compag.2011.02.007 es_ES
dc.description.references Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D., & Etheridge, D. (2012). LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada. Remote Sensing, 4(4), 830-848. doi:10.3390/rs4040830 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem