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Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

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Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

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Medina, N.; Vidal, P.; Cifuentes, R.; Torralba, J.; Keusch, F. (2018). Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales. Revista de Teledetección. (52):41-53. doi:10.4995/raet.2018.10916

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/114905

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Title: Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales
Secondary Title: Evaluation of the health status of Araucaria araucana trees using hyperspectral images
Author:
Issued date:
Abstract:
[EN] The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing ...[+]


[ES] La Araucaria araucana es una especie endémica de Chile y Argentina, presenta un alto valor biológico, científico, cultural y desde el año 2016 ha evidenciado una severa afección del daño foliar en algunos individuos, ...[+]
Subjects: Imágenes hiperespectrales , Araucaria araucana , Respuesta espectral , Red edge , Índices de vegetación , Reserva Nacional Ralco , Hiperspectral imagery , Spectral response , Vegetation index
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Revista de Teledetección. (issn: 1133-0953 ) (eissn: 1988-8740 )
DOI: 10.4995/raet.2018.10916
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/raet.2018.10916
Description: Revista oficial de la Asociación Española de Teledetección
Thanks:
Este artículo se ha realizado en el contexto de fin de grado del Magíster en Teledetección, Facultad de Ciencias de la Universidad Mayor y en el mar-co del Proyecto “Prospección fitosanitaria para determinar los niveles ...[+]
Type: Artículo

References

Adamczyk, J., Osberger, A. 2015. Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests. International Journal of Applied Earth Observation and Geoinformation, 37, 90-99. https://doi.org/10.1016/j.jag.2014.10.013

Alonzo, M., Bookhagen, B., Roberts, D. A. 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148, 70-83. https://doi.org/10.1016/J.RSE.2014.03.018

Ángel, Y. 2012. Metodología para identificar cultivos de coca mediante análisis de parámetros red edge y espectroscopia de imágenes. Tesis magister, Universidad Nacional de Colombia, Colombia. [+]
Adamczyk, J., Osberger, A. 2015. Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests. International Journal of Applied Earth Observation and Geoinformation, 37, 90-99. https://doi.org/10.1016/j.jag.2014.10.013

Alonzo, M., Bookhagen, B., Roberts, D. A. 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148, 70-83. https://doi.org/10.1016/J.RSE.2014.03.018

Ángel, Y. 2012. Metodología para identificar cultivos de coca mediante análisis de parámetros red edge y espectroscopia de imágenes. Tesis magister, Universidad Nacional de Colombia, Colombia.

Armesto, J., Villagrán, C., Arroyo, M. 1996. Ecología de los bosques nativos de Chile (Vol. 1). Santiago de Chile: Editorial Universitaria.

Awad, M. M. 2018. Forest mapping: a comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research, 29(5), 1395-1405 https://doi.org/10.1007/s11676-017- 0528-y

Baldeck, C. A., Asner, G. P., Martin, R. E., Anderson, C. B., Knapp, D. E., Kellner, J. R., Wright, S. J. 2015. Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy. PLOS ONE, 10(7), e0118403. https://doi.org/10.1371/journal.pone.0118403

Birth, G., McVey, G. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6), 640-643. https://doi. org/10.2134/agronj1968.00021962006000060016x

Borràs, J., Delegido, J., Pezzola, A., Pereira, M., Morassi, G., Camps-Valls, G. 2017. Clasificación de usos del suelo a partir de imágenes Sentinel-2. Revista de Teledetección, 48, 55-66. https://doi.org/10.4995/raet.2017.7133

Centro del Clima y la Resiliencia (CR2). 2018. Explorador Climático. http://explorador.cr2.cl/ Último acceso: 28 de noviembre, 2018.

Chen, J. M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229-242. https://doi.org/10.1080/07038992.1996.10855178

Cho, M. A., Skidmore, A. K. 2006. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote sensing of environment, 101(2), 181-193. https://doi.org/10.1016/j.rse.2005.12.011

Cho, M. A., Debba, P., Mutanga, O., Dudeni-Tlhone, N., Magadla, T., Khuluse, S. A. 2012. Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health. International Journal of Applied Earth Observation and Geoinformation, 16, 85-93.

Clark, M. L., Roberts, D. A. 2012. Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier. Remote Sensing, 4(6), 1820-1855. https:// doi.org/10.3390/rs4061820

CONAF (Corporación Nacional Forestal, CL). 2008. Catastro de los Recursos Vegetacionales Nativos de Chile, Región del Bíobio, Chile.

Dalponte, M., Bruzzone, L., Gianelle, D. 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment, 123, 258-270. https://doi.org/10.1016/J.RSE.2012.03.013

Dalponte, M., Orka, H. O., Gobakken, T., Gianelle, D., Naesset, E. 2013. Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2632- 2645. https://doi.org/10.1109/TGRS.2012.2216272

Dawson, T. P., Curran, P. J. 1998. A new technique for interpolating red edge position. International Journal of Remote Sensing, 19(11), 2133−2139.https://doi. org/10.1080/014311698214910

Drake, F. 2004. Uso sostenible en bosques de Araucaria araucana (Mol.) K. Koch; aplicación de modelos de gestión. Tesis doctoral, Escuela Técnica Superior de Ingenieros Agrónomos y de Montes, Universidad de Córdoba, Córdoba, España.

Fassnacht, F. E., Latifi, H., Ghosh, A., Joshi, P. K., Koch, B. 2014. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sensing of Environment, 140, 533-548.https:// doi.org/10.1016/j.rse.2013.09.014

Fassnacht, F. E., Stenzel, S., Gitelson, A. A. 2015. Non-destructive estimation of foliar carotenoid content of tree species using merged vegetation indices. Journal of Plant Physiology, 176, 210-217. https://doi.org/10.1016/J.JPLPH.2014.11.003

Gholizadeh, A., Mišurec, J., Kopačková, V., Mielke, C., Rogass, C. 2016. Assessment of Red-Edge Position Extraction Techniques: A Case Study for Norway Spruce Forests Using HyMap and Simulated Sentinel-2 Data. Forests, 7(226), 1-17. https://doi.org/10.3390/f7100226

Guyot, G., Baret, F., Major, D. 1988. High spectral resolution: Determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing, 11(750-760).

Hakkenberg, C. R., Peet, R. K., Urban, D. L., Song, C. 2018. Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing. Ecological Applications, 28(1), 177- 190. https://doi.org/10.1002/eap.1638

Hall, M. A. 1998. Correlation-based feature subset selection for machine learning. Thesis degree of doctor, University of Waikato, New Zealand.

Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W. 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158, 220-234. https://doi.org/10.1016/j.rse.2014.11.005

Horler, D., Dockray, M., Barber, J. 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273-288. https://doi.org/10.1080/01431168308948546

Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295- 309. https://doi.org/10.1016/0034-4257(88)90106-X

Jeffrey, A. 1985. Mathematics for Engineers and Scientists. Wokingham, UK: Van Nostrand Reinhold.

Kemerer, A., Mari, N., Di Bella, C., Rebella, C. 2008. Comparación de técnicas de clasificación de cultivos a partir de información Multi E Hyperespectral. Revista de Teledetección, 29, 67-72. Accesible en: http:// www.aet.org.es/revistas/revista29/Revista-AET-29-7. pdf Último acceso: 28 de noviembre, 2018.

Kokaly, R., Despain, D., Clark, R., Livo, K. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote sensing of environment, 84(3), 437-456. https://doi.org/10.1016/S0034-4257(02)00133-5

Landis, J., Koch, G. 1977. The measurement of observeragreement for categorical data. Biometrics. 33, 159-174. https://doi.org/10.2307/2529310

Liang S. 2005. Quantitative Remote Sensing of Land Surfaces. New Jersey, A John Wiley & Sons.

Liu, L., Coops, N. C., Aven, N. W, Pang, Y. 2017. Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data. Remote Sensing of Environment, 200, 170-182. https://doi.org/10.1016/J.RSE.2017.08.010

Melendo-Vega, J. R., Martín, M. P., Vilar del Hoyo, L., Pacheco-Labrador, J., Echavarría, P., Martínez-Vega, J. 2017. Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectroradiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección, 48, 13-28. https://doi.org/10.4995/raet.2017.7481

Ministerio del Medio Ambiente. 2008. Ficha de especie: Araucaria araucana (Molina) K. Koch. Inventario nacional de especies de Chile. http://especies. mma.gob.cl/CNMWeb/Web/WebCiudadana/ficha_ indepen.aspx?EspecieId=240&Version=1 Último acceso:20 de Mayo, 2017.

Naidoo, L., Cho, M. A., Mathieu, R., Asner, G. 2012. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 167-179. https://doi.org/10.1016/J.ISPRSJPRS.2012.03.005

Ojeda, N., Sandoval, V., Soto, H., Casanova, J., Herrera, M., Morales, L., Espinosa, A., San Martín, J. 2011. Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM. Bosque (Valdivia), 32(2), 113-125. https://doi.org/10.4067/S0717-92002011000200002

Peñuelas, J., Filella, I., Biel, C., Serrano, L., Save, R. 1993. The reflectance at the 950-970 nm region as an indicator of plant water status. International journal of remote sensing, 14(10), 1887-1905. https://doi.org/10.1080/01431169308954010

Premoli, A., Quiroga, P., Gardner, M. 2013. Araucaria araucana. The IUCN Red List of Threatened Species 2013: e.T31355A2805113. Último acceso: 15 de Marzo, 2017, de https://doi.org/10.2305/IUCN. UK.2013-1.RLTS.T31355A2805113.en

Roig, M. 2010. Identificación y clasificación de formaciones forestales mediante imágenes hiperespectrales aéreas. Tesis Escuela de ingeniería forestal. Universidad Mayor de Chile, 76 p.

Roujean, J., Breon, M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote sensing of Environment, 51(3), 375-384. https://doi.org/10.1016/0034- 4257(94)00114-3

Rouse, W., Haas, H., Schell, J., Deering, D. 1974. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351 I: 309-317.

Shafri, H., Hamdan, N. 2009. Hyperspectral Imagery for Mapping Disease Infection in Oil Palm Plantation Using Vegetation Indices and Red Edge Techniques. American Journal of Applied Sciences, 6(6), 1031. https://doi.org/10.3844/ajassp.2009.1031.1035

Shafri, H., Salleh, M., Ghiyamat, A. 2006. Hyperspectral remote sensing of vegetation using red edge position techniques. American Journal of Applied Sciences, 3(6), 1864-1871. https://doi.org/10.3844/ajassp.2006.1864.1871

Shi, Y., Skidmore, A. K., Wang, T., Holzwarth, S., Heiden, U., Pinnel, N., Zhu, X., Heurich, M. 2018. Tree species classification using plant functional traits from LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 73, 207-219. https://doi.org/10.1016/J.JAG.2018.06.018

Sims, D., Gamon, J. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote sensing of environment, 81(2), 337-354. https://doi.org/10.1016/S0034-4257(02)00010-X

Smith, K., Steven, M., Colls, J. 2004. Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote sensing of environment, 92(2), 207-217. https://doi.org/10.1016/j.rse.2004.06.002

Somers, B., Verbesselt, J., Ampe, E. M., Sims, N., Verstraeten, W. W., Coppin, P. 2010. Spectral mixture analysis to monitor defoliation in mixedaged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO1Hyperion data. International Journal of Applied Earth Observation and Geoinformation, 12(4), 270- 277. https://doi.org/10.1016/J.JAG.2010.03.005

Torralba, J. 2012. Generación de algoritmo para la identificación de alerce (Fitzroya cupressoides) mediante análisis de imágenes hiperespectrales en el lago Tagua-Tagua, X Región, Chile. Proyecto final de Grado en Ingeniería Forestal y del Medio Natural, Universidad Castilla-La Mancha, 95 p.

Vogelmann, J., Rock, B., Moss, D. 1993. Red edge spectral measurements from sugar maple leaves. Remote sensing, 14(8), 1563-1575. https://doi. org/10.1080/01431169308953986

Willis, K. 2015. Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233-242. https://doi.org/10.1016/j.biocon.2014.12.006

Wright, C., Gallant, A. 2007. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107(4), 582-605. https://doi.org/10.1016/j.rse.2006.10.019

Zarco-Tejada, P. J., Hornero, A., Hernández-Clemente, R., Beck, P. S. A. 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 134- 148. https://doi.org/10.1016/j.isprsjprs.2018.01.017

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