- -

Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas

Show full item record

Melendo-Vega, JR.; Martín, MP.; 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 espectro-radiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección. (48):13-28. https://doi.org/10.4995/raet.2017.7481

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/83600

Files in this item

Item Metadata

Title: Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas
Secondary Title: Estimation of grassland biophysical parameters in a “dehesa” ecosystem from field spectroscopy and airborne hyperspectral imagery
Author: Melendo-Vega, J. R. Martín, M. P. Vilar del Hoyo, L. Pacheco-Labrador, J. Echavarría, P. Martínez-Vega, J.
Issued date:
Abstract:
[EN] The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (FMC), Canopy Water Content (CWC), Leaf Area Index (LAI), dry matter (Cm) ...[+]


[ES] Este trabajo aborda la estimación de variables biofísicas de un pastizal de dehesa a partir de información óptica generada por sensores próximos y remotos. Las variables de contenido de humedad del combustible (FMC), ...[+]
Subjects: Espectro-radiometría de campo , Imágenes hiperespectrales aeroportadas , Variables biofísicas , Pastizal , CASI , Índices espectrales , Field spectroscopy , Airbones hyperspectal imagery , Biophysical parameters , Spectral indices
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Revista de Teledetección. (issn: 1133-0953 ) (eissn: 1988-8740 )
DOI: 10.4995/raet.2017.7481
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/raet.2017.7481
Project ID:
info:eu-repo/grantAgreement/MICINN//CGL2008-02301/ES/OBTENCION DE INFORMACION ESPECTRAL A DIVERSAS ESCALAS PARA LA ESTIMACION DE PARAMETROS BIOFISICOS DE LA VEGETACION MEDITERRANEA EN EL CONTEXTO DEL CAMBIO GLOBAL/
info:eu-repo/grantAgreement/MINECO//CGL2012-34383/ES/SEGUIMIENTO DE FLUJOS DE AGUA Y CARBONO MEDIANTE TELEDETECCION EN ECOSISTEMAS MEDITERRANEOS DE DEHESA/
Thanks:
Este trabajo se ha realizado en el contexto de los proyectos BIOSPEC (CGL2008-02301/CLI) financiado por el Ministerio e Innovación y FLUχPEC (CGL2012-34383) financiado por el Ministerio de Economía y Competitividad. ...[+]
Type: Artículo

Location


 

References

Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337-352. doi:10.1016/j.rse.2003.12.013

Hardisky, M.A., Klemas, V., Smart, R.M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrametry Engineering and Remote Sensing, 49, 77-83

Hernández-Clemente, R., Navarro-Cerrillo, R. M., Suárez, L., Morales, F., & Zarco-Tejada, P. J. (2011). Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 115(9), 2360-2375. doi:10.1016/j.rse.2011.04.036 [+]
Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337-352. doi:10.1016/j.rse.2003.12.013

Hardisky, M.A., Klemas, V., Smart, R.M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrametry Engineering and Remote Sensing, 49, 77-83

Hernández-Clemente, R., Navarro-Cerrillo, R. M., Suárez, L., Morales, F., & Zarco-Tejada, P. J. (2011). Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 115(9), 2360-2375. doi:10.1016/j.rse.2011.04.036

Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141-2151. doi:10.1016/j.rse.2011.04.018

Hilker, T., Coops, N. C., Hall, F. G., Black, T. A., Wulder, M. A., Nesic, Z., & Krishnan, P. (2008). Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sensing of Environment, 112(6), 2777-2788. doi:10.1016/j.rse.2008.01.011

Hill, M.J., Hanan, N.P., Hoffmann, W., Scholes, R., Prince, S., Ferwerda, J., Lucas, R.M., Baker, I., Arneth, A., Higgings, S.I., Barret, D.J., Disney, M., Hutley, L. 2011. Remote sensing and modeling of savannas: The state of the dis-union. 34th International Symposium on Remote Sensing of Environment. Sydney, 1-6.

HongRui, R., GuangSheng, Z., Feng, Z., XinShi, Z. 2011. Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia. Chinese Science Bulletin, 57, 1716-1722.

Huete, A. . (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:10.1016/0034-4257(88)90106-x

Kuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment, 51(3), 342-350. doi:10.1016/0034-4257(94)00059-v

Lee, K.-S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3-4), 508-520. doi:10.1016/j.rse.2004.04.010

Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., … Muhammad, S. (2015). Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. International Journal of Applied Earth Observation and Geoinformation, 41, 88-98. doi:10.1016/j.jag.2015.04.020

Liu, J., Miller, J.R., Haboudane, D., Pattey, E. 2004. Exploring the relationship between red edge parameters and crop variables for precision agriculture. 2004 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Anchorage, 1276-1279.

Mahalanobis, P.C. 1936. On the generalised distance in statistics. Proceedings National Institute of Science, India, 49-55

Nagler, P. L., Inoue, Y., Glenn, E. ., Russ, A. ., & Daughtry, C. S. . (2003). Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87(2-3), 310-325. doi:10.1016/j.rse.2003.06.001

Pacheco-Labrador, J., González-Cascón, R., Martín, M. P., & Riaño, D. (2014). Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 26, 105-118. doi:10.1016/j.jag.2013.05.013

Perez-Priego, O., Guan, J., Rossini, M., Fava, F., Wutzler, T., Moreno, G., … Migliavacca, M. (2015). Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12(21), 6351-6367. doi:10.5194/bg-12-6351-2015

Pinty, B., & Verstraete, M. M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1), 15-20. doi:10.1007/bf00031911

Privette, J. ., Myneni, R. ., Knyazikhin, Y., Mukelabai, M., Roberts, G., Tian, Y., … Leblanc, S. . (2002). Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83(1-2), 232-243. doi:10.1016/s0034-4257(02)00075-5

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126. doi:10.1016/0034-4257(94)90134-1

Riano, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P. J., & Ustin, S. L. (2005). Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 819-826. doi:10.1109/tgrs.2005.843316

Richter, K., Atzberger, C., Hank, T. B., & Mauser, W. (2012). Derivation of biophysical variables from Earth observation data: validation and statistical measures. Journal of Applied Remote Sensing, 6(1), 063557-1. doi:10.1117/1.jrs.6.063557

Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring Vegetation Systems in the Great Plains whit ERTS. Proceeding, 3rd Earth Resource Technology Satellite (ERTS) Symposium, NASA, Washington DC, 1, 48-62

SCHMIDTLEIN, S. (2004). Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92(1), 126-138. doi:10.1016/j.rse.2004.05.004

Serrano, L., Peñuelas, J., & Ustin, S. L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data. Remote Sensing of Environment, 81(2-3), 355-364. doi:10.1016/s0034-4257(02)00011-1

SHAPIRO, S. S., & WILK, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611. doi:10.1093/biomet/52.3-4.591

Smith, G. M., & Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20(13), 2653-2662. doi:10.1080/014311699211994

Wieneke, S., Ahrends, H., Damm, A., Pinto, F., Stadler, A., Rossini, M., & Rascher, U. (2016). Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity. Remote Sensing of Environment, 184, 654-667. doi:10.1016/j.rse.2016.07.025

Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80. doi:10.2307/3001968

Yi, Q., Wang, F., Bao, A., & Jiapaer, G. (2014). Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models. International Journal of Applied Earth Observation and Geoinformation, 33, 67-75. doi:10.1016/j.jag.2014.04.019

Zarco-Tejada, P., Miller, J.R., Mohammed, G.H., Noland, T.L., & Sampson, P.H. 1999. Índices ópticos obtenidos mediante datos hiperespectrales del sensor CASI como indicadores de estrés en zonas forestales. VIII Congreso Nacional de Teledetección. Albacete, 1-5

Zarco-Tejada, P. ., Rueda, C. ., & Ustin, S. . (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109-124. doi:10.1016/s0034-4257(02)00197-9

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record