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Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas

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Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas

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dc.contributor.author Estornell Cremades, Javier es_ES
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.contributor.author Velázquez Martí, Borja es_ES
dc.contributor.author Hermosilla Gómez, Txomin es_ES
dc.date.accessioned 2013-12-11T07:57:00Z
dc.date.available 2013-12-11T07:57:00Z
dc.date.issued 2012
dc.identifier.issn 1931-3195
dc.identifier.uri http://hdl.handle.net/10251/34428
dc.description.abstract Shrub vegetation is a key element of Mediterranean forest areas and it is necessary to develop tools that allow a precise knowledge of this vegetation. This study aims to predict shrub volume and analyze the factors affecting the accuracy of these estimations in small stands using airborne discrete-return LiDAR data. The study was performed over 83 circular stands with 0.5 m radius located in Chiva (Spain) mainly occupied by Quercus coccifera. The vegetation inside each area was clear cut, and the height and the diameter of each plant was measured to compute the volume of shrub vegetation per stand. Volume values were related with maximum height values derived from LiDAR data reaching a coefficient of determination value R2=0.26. Afterwards, factors affecting the quality of volume estimations were analyzed, i.e., vegetation type, LiDAR density, and accuracy of the digital terrain model (DTM). Significant accuracy improvements (R2=0.71) were detected for stands with 0.5 m, LiDAR data density greater than 8 points/m2, vegetation Q. coccifera, and error associated to the DTM less than 0.20 m. These results show the feasibility of using LiDAR data to predict shrub volume under certain conditions, which can contribute to improved forest management and characterization. es_ES
dc.description.sponsorship The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion in the framework of the project CGL2010-19591. en_EN
dc.language Inglés es_ES
dc.publisher Society of Photo-optical Instrumentation Engineers (SPIE) es_ES
dc.relation.ispartof Journal of Applied Remote Sensing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Shrub es_ES
dc.subject Volume es_ES
dc.subject LiDAR data es_ES
dc.subject Digital Terrain Model (DTM) es_ES
dc.subject.classification INGENIERIA AGROFORESTAL es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1117/1.JRS.6.063544
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.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Rural y Agroalimentaria - Departament d'Enginyeria Rural i Agroalimentària es_ES
dc.description.bibliographicCitation Estornell Cremades, J.; Ruiz Fernández, LÁ.; Velázquez Martí, B.; Hermosilla Gómez, T. (2012). Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas. Journal of Applied Remote Sensing. 6:1-10. https://doi.org/10.1117/1.JRS.6.063544 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1117/1.JRS.6.063544 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.relation.senia 222973
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.description.references Velázquez-Martí, B., Fernández-González, E., Estornell, J., & Ruiz, L. A. (2010). Dendrometric and dasometric analysis of the bushy biomass in Mediterranean forests. Forest Ecology and Management, 259(5), 875-882. doi:10.1016/j.foreco.2009.11.027 es_ES
dc.description.references Mundt, J. T., Streutker, D. R., & Glenn, N. F. (2006). Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications. Photogrammetric Engineering & Remote Sensing, 72(1), 47-54. doi:10.14358/pers.72.1.47 es_ES
dc.description.references Riaño, D., Chuvieco, E., Ustin, S. L., Salas, J., Rodríguez-Pérez, J. R., Ribeiro, L. M., … Fernández, H. (2007). Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging. International Journal of Wildland Fire, 16(3), 341. doi:10.1071/wf06003 es_ES
dc.description.references Riaño, D., Chuvieco, E., Condés, S., González-Matesanz, J., & Ustin, S. L. (2004). Generation of crown bulk density for Pinus sylvestris L. from lidar. Remote Sensing of Environment, 92(3), 345-352. doi:10.1016/j.rse.2003.12.014 es_ES
dc.description.references Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4), 441-449. doi:10.1016/j.rse.2004.10.013 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 Næsset, E. (2004). Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research, 19(6), 554-557. doi:10.1080/02827580410019544 es_ES
dc.description.references Breidenbach, J., Nothdurft, A., & Kändler, G. (2010). Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. European Journal of Forest Research, 129(5), 833-846. doi:10.1007/s10342-010-0384-1 es_ES
dc.description.references Gonzalez, P., Asner, G. P., Battles, J. J., Lefsky, M. A., Waring, K. M., & Palace, M. (2010). Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sensing of Environment, 114(7), 1561-1575. doi:10.1016/j.rse.2010.02.011 es_ES
dc.description.references Wulder, M. A., White, J. C., Stinson, G., Hilker, T., Kurz, W. A., Coops, N. C., … Trofymow, J. A. (Tony). (2009). Implications of differing input data sources and approaches upon forest carbon stock estimation. Environmental Monitoring and Assessment, 166(1-4), 543-561. doi:10.1007/s10661-009-1022-6 es_ES
dc.description.references Dubayah, R. O., Sheldon, S. L., Clark, D. B., Hofton, M. A., Blair, J. B., Hurtt, G. C., & Chazdon, R. L. (2010). Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. Journal of Geophysical Research: Biogeosciences, 115(G2), n/a-n/a. doi:10.1029/2009jg000933 es_ES
dc.description.references (2010). Journal of Ecology, 98(3). doi:10.1111/jec.2010.98.issue-3 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 Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008). Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29(5), 1339-1366. doi:10.1080/01431160701736489 es_ES
dc.description.references Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655. doi:10.1016/j.biombioe.2007.06.022 es_ES
dc.description.references Holopainen, M., Mäkinen, A., Rasinmäki, J., Hyyppä, J., Hyyppä, H., Kaartinen, H., … Kangas, A. (2009). Effect of tree-level airborne laser-scanning measurement accuracy on the timing and expected value of harvest decisions. European Journal of Forest Research, 129(5), 899-907. doi:10.1007/s10342-009-0282-6 es_ES
dc.description.references Dalponte, M., Bruzzone, L., & Gianelle, D. (2011). A System for the Estimation of Single-Tree Stem Diameter and Volume Using Multireturn LIDAR Data. IEEE Transactions on Geoscience and Remote Sensing, 49(7), 2479-2490. doi:10.1109/tgrs.2011.2107744 es_ES
dc.description.references Hill, R. A., & Thomson, A. G. (2005). Mapping woodland species composition and structure using airborne spectral and LiDAR data. International Journal of Remote Sensing, 26(17), 3763-3779. doi:10.1080/01431160500114706 es_ES
dc.description.references Mutlu, M., Popescu, S. C., & Zhao, K. (2008). Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps. Forest Ecology and Management, 256(3), 289-294. doi:10.1016/j.foreco.2008.04.014 es_ES
dc.description.references Su, J. G., & Bork, E. W. (2007). Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data. Applied Vegetation Science, 10(3), 407-416. doi:10.1111/j.1654-109x.2007.tb00440.x es_ES
dc.description.references Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Fernández-Sarría, A. (2011). Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecology and Management, 262(9), 1697-1703. doi:10.1016/j.foreco.2011.07.026 es_ES
dc.description.references Takhtajan, A. ,Floristic regions of the world, University of California Press, Los Angeles (1986). es_ES
dc.description.references Gómez, F. ,Los bosques ibéricos, Editorial Planeta, Barcelona (1998). 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 Reitberger, J., Schnörr, C., Krzystek, P., & Stilla, U. (2009). 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 561-574. doi:10.1016/j.isprsjprs.2009.04.002 es_ES
dc.description.references Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery. Photogrammetric Engineering & Remote Sensing, 72(7), 799-811. doi:10.14358/pers.72.7.799 es_ES


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