Evaluation of the health status of Araucaria araucana trees using hyperspectral images
DOI:
https://doi.org/10.4995/raet.2018.10916Keywords:
Hiperspectral imagery, Araucaria araucana, spectral response, red edge, vegetation index, Reserva Nacional RalcoAbstract
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 in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.
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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. https://doi.org/10.1016/j.jag.2011.12.005
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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