- -

Estimación de parámetros biofísicos de la vegetación en praderas y cultivos en Chile mediante fotografía digital hemisférica obtenidas por una cámara GoPro

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Estimación de parámetros biofísicos de la vegetación en praderas y cultivos en Chile mediante fotografía digital hemisférica obtenidas por una cámara GoPro

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: 9315-42792-2-PB.pdf
Tamaño: 3.879Mb
Formato: PDF
Abrir
dc.contributor.author Uribe, D. es_ES
dc.contributor.author Mattar, C. es_ES
dc.contributor.author Camacho, F. es_ES
dc.coverage.spatial east=-71.54296899999997; north=-35.675147; name=Província de Linares, Regió del Maule, Xile es_ES
dc.date.accessioned 2019-01-08T12:12:04Z
dc.date.available 2019-01-08T12:12:04Z
dc.date.issued 2018-12-26
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/114889
dc.description Revista oficial de la Asociación Española de Teledetección
dc.description.abstract [EN] The estimation of the biophysical parameters of vegetation such as LAI (Leaf Area Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) and FCOVER (Fraction of Green Vegetation) have many climatic, hydrologic, ecosystem and silvo-agricultural applications. Despite the various satellite products that estimate these parameters continuously and globally, it’s necessary to continue generating in situ estimations to validate these remote data. It’s in this context where Digital Hemispheric Photography (DHP) technique stands out as being one of the most accurate an adaptable to operate continuously with diverse photographic equipment and field scenarios. The objective of this paper is to estimate effective LAI (LAIeff), true LAI (LAItrue), FAPAR and FCOVER through the DHP method on several agricultural land covers in Chile, between the years 2015 and 2016 using a GoPro camera and the CAN-EYE software to process hemispheric photographs. The results obtained were initially compared with those provided by a CANON EOS 6D camera mounted together with a SIGMA 8mm F3.5-EX DG fisheye lens and subsequently with satellite products provided by the Copernicus Global Land service, derived from PROBA-V mission at 333 m2 spatial resolution. The comparison between the CANON and GoPro shows similar values and R2 over 0,72 for all parameters. The comparison with PROBA-V resulted in values over 0,52 of R2 for the parameters, and similar multitemporal patterns. It’s concluded that it’s possible to estimates LAIeff, FAPAR and FCOVER like other fish eyes cameras. Concerning PROBA-V, except for FAPAR, the estimates with the GoPro do not show much correlation. In both campaigns significant discrepancies were observed in the LAItrue, which could be related to the calculation of CAN-EYE canopy clumping with the characteristics of the camera itself. es_ES
dc.description.abstract [ES] La estimación de los parámetros biofísicos de la vegetación como el LAI (Leaf Area Index, FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) y FCOVER (Fraction of Green Vegetation) tienen una gran cantidad de aplicaciones climáticas, hidrológicas, ecosistémicas y en sistemas silvoagropecuarios. A pesar de los diversos productos satelitales que estiman estos parámetros de forma continua y global, es necesario seguir generando estimaciones in situ para validar estos datos remotos. Es en este escenario en donde la técnica de Fotografía Digital Hemisférica (DHP) destaca por ser una de las más precisas y adaptables para funcionar de forma continua en diversos equipos fotográficos y escenarios de campo. El objetivo de este estudio es estimar el LAI efectivo (LAIeff), LAI verdadero (LAItrue), FAPAR y FCOVER a través del método DHP sobre diversas cubiertas agrícolas de Chile, entre los años 2015 y 2016 utilizando la cámara fotográfica GoPro y el software CAN-EYE para procesar las fotografías hemisféricas. Los resultados obtenidos se compararon inicialmente con los suministrados por una cámara CANON EOS 6D montada junto a un lente ojo de pez SIGMA 8mm F3.5-EX DG y posteriormente con productos satelitales proporcionados por el servicio Copernicus Global Land, derivado de la misión PROBA-V a 333 m2 de resolución espacial. La comparación entre las cámaras CANON y GoPro muestra estimaciones similares y valores de R2 sobre 0,72 para todos los parámetros. La comparación con PROBA-V dio lugar a valores sobre 0,52 de R2 para los parámetros y estimaciones multitemporales con patrones similares. Se concluye que con la cámara GoPro, es posible generar estimaciones de LAIeff, FAPAR y FCOVER de forma similar a otras cámaras ojos de pez. Respecto a PROBA-V, a excepción de FAPAR, las estimaciones con la GoPro no muestran mucha correlación. En ambas campañas se observaron discrepancias significativas del LAItrue lo que se podría relacionar al cálculo del agrupamiento de la canopia de CAN-EYE sobre las características propia cámara. es_ES
dc.description.sponsorship Los autores agradecen el financiamiento parcial del proyecto Conicyt – Fondecyt Iniciación 11130359 “Estimating the Surface soil moisture at regional scale by using a synergic optical-passive microwave approach and remote sensing data”. Al Earth Observation Laboratory (EOLAB) y al proyecto IMAGINES, junto con la libre entrega de datos PROBA-V Copernicus es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València
dc.relation.ispartof Revista de Teledetección
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject GoPro es_ES
dc.subject Parámetros Biofísicos de la Vegetación es_ES
dc.subject DHP es_ES
dc.subject Vegetation Biophysical Parameters es_ES
dc.title Estimación de parámetros biofísicos de la vegetación en praderas y cultivos en Chile mediante fotografía digital hemisférica obtenidas por una cámara GoPro es_ES
dc.title.alternative Estimation of vegetation biophysical parameters in grasslands and crops in Chile through hemispheric digital photography by a GoPro camera es_ES
dc.type Artículo es_ES
dc.date.updated 2019-01-08T12:03:33Z
dc.identifier.doi 10.4995/raet.2018.9315
dc.relation.projectID info:eu-repo/grantAgreement/CONICYT//11130359/
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Uribe, D.; Mattar, C.; Camacho, F. (2018). Estimación de parámetros biofísicos de la vegetación en praderas y cultivos en Chile mediante fotografía digital hemisférica obtenidas por una cámara GoPro. Revista de Teledetección. (52):1-15. https://doi.org/10.4995/raet.2018.9315 es_ES
dc.description.accrualMethod SWORD es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2018.9315 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 52
dc.identifier.eissn 1988-8740
dc.contributor.funder Comisión Nacional de Investigación Científica y Tecnológica, Chile
dc.contributor.funder Fondo Nacional de Desarrollo Científico y Tecnológico, Chile
dc.contributor.funder Earth Observation Laboratory
dc.description.references Baret, F., Camacho, F., Cernicharo, J., Lacaze, R., Weiss, M. 2013. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 310-329. https://doi.org/10.1016/j.rse.2012.12.027 es_ES
dc.description.references Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F.,Weiss, M., Samain, O., Roujean, J.L., Leroy, M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sensing of Environment, 110(3), 275-286. https://doi.org/10.1016/j.rse.2007.02.018 es_ES
dc.description.references Baret, F., Weiss, M. 2018. Gio Global Land Component - Lot I "Operation of the Global Land Component" Algorithm Theoretical Basis Document, 1-41. es_ES
dc.description.references Baret, F., Weiss, M., Allard, D., Garrigues, S., Leroy, M., Jeanjean, H., et al., 2005. VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products. Remote Sensing of Environment, 76(3), 36-39. es_ES
dc.description.references Baret, F., Weiss, M., Verger, A., Smets, B. 2016. Gio Global Land Component - ATBD. Bréda, N.J.J. 2003. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany, 54(392), 2403-2417. https://doi.org/10.1093/jxb/erg263 es_ES
dc.description.references Bréda, N.J.J. 2003. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany, 54(392), 2403-2417. https://doi.org/10.1093/jxb/erg263 es_ES
dc.description.references Casanova, M., Salazar, O., Seguel, O., Luzio, W. 2013. The Soils of Chile, Springer. https://doi.org/10.1007/978-94-007-5949-7 es_ES
dc.description.references Cescatti, A. 2007. Indirect estimates of canopy gap fraction based on the linear conversion of hemispherical photographs. Methodology and comparison with standard thresholding techniques. Agricultural and Forest Meteorology, 143(1-2), 1-12. https://doi.org/10.1016/j.agrformet.2006.04.009 es_ES
dc.description.references Chen, J.M., Black, T.A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell & Environment, 15(4), 421-429. https://doi.org/10.1111/j.1365-3040.1992.tb00992.x es_ES
dc.description.references Confalonieri, R., Foi, M., Casa, R., Aquaro, S., Tona, E., Peterle, M., et al. 2013. Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Computers and Electronics in Agriculture, 96, 67- 74. https://doi.org/10.1016/j.compag.2013.04.019 es_ES
dc.description.references De Bei, R., Fuentes, S., Gilliham, M., Tyerman, S., Edwards, E., Bianchini, N., Smith, J., Collins, C. 2016. Viticanopy: A free computer app to estimate canopy vigor and porosity for grapevine. Sensors, 16(4). https://doi.org/10.3390/s16040585 es_ES
dc.description.references Demarez, V., Duthoit, S., Baret, F., Weiss, M., Dedieu, G. 2008. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agricultural and Forest Meteorology, 148(4), 644-655. https://doi.org/10.1016/j.agrformet.2007.11.015 es_ES
dc.description.references Fang, H., Liang, S., Hoogenboom, G. 2011. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International Journal of Remote Sensing, 32(4), 1039-1065. https://doi.org/10.1080/01431160903505310 es_ES
dc.description.references Fournier, R.A., Landry, R., August, N.M., Fedosejevs, G., Gauthier, R.P. 1996. Modelling light obstruction in three conifer forests using hemispherical photography and fine tree architecture. Agricultural and Forest Meteorology, 82(1-4), 47-72. https://doi.org/10.1016/0168-1923(96)02345-3 es_ES
dc.description.references Garrigues, S., Shabanov, N. V, Swanson, K., Morisette, J.T., Baret, F., Myneni, R.B. 2008. Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands. Agricultural and Forest Meteorology, 148(8-9), 1193-1209. https://doi.org/10.1016/j.agrformet.2008.02.014 es_ES
dc.description.references Gower, S.T., Kucharik, C.J., Norman, J.M. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1), 29-51. https://doi.org/10.1016/S0034-4257(99)00056-5 es_ES
dc.description.references Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., Baret, F. 2004. Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2), 19- 35. https://doi.org/10.1016/j.agrformet.2003.08.027 es_ES
dc.description.references Kross, A., McNairn, H., Lapen, D., Sunohara, M., Champagne, C. 2015. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34(1), 235-248. https://doi.org/10.1016/j.jag.2014.08.002 es_ES
dc.description.references Lang, M., Kuusk, A., Mõttus, M., Rautiainen, M., Nilson, T. 2010. Canopy gap fraction estimation from digital hemispherical images using sky radiance models and a linear conversion method. Agricultural and Forest Meteorology, 150(1), 20-29. https://doi.org/10.1016/j.agrformet.2009.08.001 es_ES
dc.description.references Latorre, C., Camacho, F., Mattar, C., Santamaría-Artigas, A., Leiva-Büchi, N., Lacaze, R. 2016. Obtención de mapas verdad-terreno de LAI, FAPAR y cobertura vegetal a partir de imágenes del satélite chileno FASat-C y medidas in-situ en la zona agrícola de Chimbarongo, Chile, para la validación de productos de satélite. Revista de Teledeteccion, 2016(47), 51- 64. https://doi.org/10.4995/raet.2016.5691 es_ES
dc.description.references Li, W., Weiss, M., Waldner, F., Defourny, P., Demarez, V., Morin, D., Hagolle, O., Baret, F. 2015. A generic algorithm to estimate LAI, FAPAR and FCOVER variables from SPOT4_HRVIR and landsat sensors: Evaluation of the consistency and comparison with ground measurements. Remote Sensing, 7(11), 15494- 15516. https://doi.org/10.3390/rs71115494 es_ES
dc.description.references López-Lozano, R., Baret, F., García de Cortázar-Atauri, I., Bertrand, N., Casterad, M.A. 2009. Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies: The case of vineyards. Agricultural and Forest Meteorology, 149(8), 1307- 1316. https://doi.org/10.1016/j.agrformet.2009.03.001 es_ES
dc.description.references Martínez, B., Camacho-de Coca, F., García-Haro, F. 2005a. Estimación de parámetros biofísicos de la cubierta vegetal a alta resolución a partir de medidas in-situ obtenidas en SPARC'03. XI Congreso Nacional de Teledetección, 21-23. es_ES
dc.description.references Martínez, B., García-haro, F., Camacho-de Coca, F. 2005b. Estimación de parámetros biofísicos de vegetación utilizando el método de la cámara hemisférica. Revista de Teledetección, 23, 13-26. es_ES
dc.description.references Mattar, C., Hernández, J., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L., Inzunza, M., Tapia, D., Escobar-lavín, E. 2014. A first in-flight absolute calibration of the Chilean Earth Observation Satellite. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 16-25. https://doi.org/10.1016/j.isprsjprs.2014.02.017 es_ES
dc.description.references Mougin, E., Demarez, V., Diawara, M., Hiernaux, P., Soumaguel, N., Berg, A. 2014. Estimation of LAI, fAPAR and fCover of Sahel rangelands (Gourma, Mali). Agricultural and Forest Meteorology, 198, 155- 167. https://doi.org/10.1016/j.agrformet.2014.08.006 es_ES
dc.description.references Nestola, E., Sánchez-Zapero, J., Latorre, C., Mazzenga, F., Matteucci, G., Calfapietra, C., Camacho, F. 2017. Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy. Remote Sensing, 9(2), 126. https://doi.org/10.3390/rs9020126 es_ES
dc.description.references Olivera-Guerra, L., Mattar, C., Galleguillos, M. 2014. Estimation of real evapotranspiration and its variation in Mediterranean landscapes of central-southern Chile. International Journal of Applied Earth Observation and Geoinformation, 28(1), 160-169. https://doi.org/10.1016/j.jag.2013.11.012 es_ES
dc.description.references Olivera-Guerra, L., Merlin, O., Mattar, C., Duran-Alarcon, C., Santamaria-Artigas, A., Stefan, V. 2015. Combining meteorological and lysimeter data to evaluate energy and water fluxes over a row crop for remote sensing applications. International Geoscience and Remote Sensing Symposium (IGARSS), 2015-Novem, 4649- 4651. https://doi.org/10.1109/IGARSS.2015.7326865 es_ES
dc.description.references Paul M. Rich, 1990. Characterizing Plant Canopies with Hemispherical Photograph s. Remote Sensing Reviews, 5(November 2012), 37-41. https://doi.org/10.1080/02757259009532119 es_ES
dc.description.references Rigon, J.P.G., Capuani, S., Fernandes, D.M., Guimarães, T.M. 2016. A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica, 54(4), 559-566. https://doi.org/10.1007/s11099-016-0214-x es_ES
dc.description.references Sarricolea, P., Herrera-Ossandon, M., MeseguerRuiz, Ó. 2017. Climatic regionalisation of continental Chile. Journal of Maps, 13(2), 66-73. https://doi.org/10.1080/17445647.2016.1259592 es_ES
dc.description.references Sellers, P.J., Dickinson, R.E., Randall, D.A., Betts, A.K., Hall, F.G., Berry, J.A., et al. 1997. Modeling the Exchanges of Energy, Water, and Carbon between Continents and the Atmosphere. Science , 275(5299), 502-509. https://doi.org/10.1126/science.275.5299.502 es_ES
dc.description.references Tarnavsky, E., Garrigues, S., Brown, M.E. 2008. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing of Environment, 112(2), 535-549. https://doi.org/10.1016/j.rse.2007.05.008 es_ES
dc.description.references Verger, A., Baret, F., Camacho, F. 2011. Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations. Remote Sensing of Environment, 115(2), 415-426. https://doi.org/10.1016/j.rse.2010.09.012 es_ES
dc.description.references Weiss, M., Baret, F. 2016. Can Eye User Manual. es_ES
dc.description.references Weiss, M., Baret, F., Smith, G.J., Jonckheere, I., Coppin, P. 2004. Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology, 121(1-2), 37-53. https://doi.org/10.1016/j.agrformet.2003.08.001 es_ES
dc.description.references Zheng, G., Moskal, L.MX009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4), 2719-2745. https://doi.org/10.3390/s90402719 es_ES


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

Mostrar el registro sencillo del ítem