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

  1. Melendo-Vega, J. R.
  2. Martín, M. P.
  3. Vilar del Hoyo, L.
  4. Pacheco-Labrador, J.
  5. Echavarría, P.
  6. Martínez-Vega, J.
Zeitschrift:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Datum der Publikation: 2017

Nummer: 48

Seiten: 13-28

Art: Artikel

DOI: 10.4995/RAET.2017.7481 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Ziele für nachhaltige Entwicklung

Zusammenfassung

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) and AboveGround Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (R2) above 0.91 and RRMSE of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 R2 and RRMSE of 15.5% for the spectral indexes calculated whit CASI data.

Bibliographische Referenzen

  • Casas, A., Riaño, D., Ustin, S.L., Dennison, P., Salas, J. 2014. Estimation of water-related biochemical and byophysical vegetation properties using multitemporal airborne hyperespectral data and its comparison to MODIS spectral response. Remote Sensing of Environment, 148, 28-41. https://doi. org/10.1016/j.rse.2014.03.011
  • Ceccato, P., Flasse, S., Grégoire, J.M. 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sensing of Environment, 82(2-3), 198-207. https://doi.org/10.1016/S00344257(02)00036-6
  • Cogliati, S., Rossini, M., Julitta, T., Meroni, M., Schickling, A., Burkart, A., Pinto, F., Rascher, U., Colombo, R. 2015. Continuous and longterm measurements of reflectance and suninduced chlorophyll fluorescence by using novel automated field spectroscopy systems. Remote Sensing of Environment, 164, 270-281. https://doi. org/10.1016/j.rse.2015.03.027
  • Chen, J.M. 1996. Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications. Canadian Journal of Remote Sensing, 22(3), 229242. https://doi.org/10.1080/07038992.1996.10855 178
  • Chen, J.M., Cihlar, J. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55(2), 153-162. https://doi.org/10.1016/0034-4257(95)00195-6
  • Cheng, T., Riaño, D., Koltunov, A., Whiting, M.L., Ustin, S.L., Rodriguez, J. 2013. Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data. Remote Sensing of Environment, 132, 1-12. https://doi.org/10.1016/j.rse.2012.12.024
  • Cheng, T., Riaño, D., Ustin, S. 2014. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sensing of Environment, 143, 39-53. https://doi. org/10.1016/j.rse.2013.11.018
  • Cheng, Y.B., Middleton, E.M., Zhang, Q., Corp, L.A., Dandois, J., Kustas, W.P. 2012. The photochemical reflectance index from directional cornfield reflectances: Observations and simulations. Remote Sensing of Environment, 124, 444-453. https://doi. org/10.1016/j.rse.2012.05.030
  • Chuvieco, E., Cocero, D., Riaño, D., Martin, M.P., Martínez-Vega, J., De la Riva, J., Pérez, F. 2004. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322-331. https://doi.org/10.1016/j.rse.2004.01.019
  • Chuvieco, E., Riaño, D., Aguado, I., Cocero, D. 2002. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, 23(11), 2145-2162. https://doi. org/10.1080/01431160110069818
  • Chuvieco, E., Salas, J., Aguado, I., Cocero, D., Riaño, D. 2001. Estimación del estado hídrico de la vegetación a partir de sensores de alta y baja resolución. Geofocus, 1, 1-16.
  • Daughtry, C.S.T., Doraiswamy, P.C., Hunt Jr, E.R., Stern, A.J., McMurtrey III, J.E., Prueger, J.H. 2006. Remote sensing of crop residue cover and soil tillage intensity. Soil and Tillage Research, 91(1-2), 101-108. https://doi.org/10.1016/j. still.2005.11.013
  • Daughtry, C.S.T., Hunt Jr, E.R., McMurtrey III, J.E. 2004. Assessing crop residue cover using shortwave infrared reflectance. Remote Sensing of Environment, 90(1), 126-134. https://doi. org/10.1016/j.rse.2003.10.023
  • Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstoun, E.B., McMurtrey III, J.E. 2000. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74, 229-239. https://doi. org/10.1016/S0034-4257(00)00113-9
  • de Miguel, E., Fernández-Renau, A., Prado, E., Jiménez, M., Gutiérrez de la Cámara, O., Linés, C., Gómez, J.A., Martín, A., Muñoz F. 2014. The processing of CASI-1500I data at INTA PAF. EARSeL e Proceedings, 13, 30-37.
  • Delegido, J., Verrelst, J., Alonso, L., Moreno, J. 2011. Evaluation of Sentinel-2 red edge bands for empirical estimation of green LAI and chorophyll content. Sensors, 11(7), 7063-7081. https://doi. org/10.3390/s110707063
  • Demarty, J., Chevallier, F., Friend, A., Viovy, N., Piao, S., Ciais, P. 2007. Assimilation of global MODIS leaf area index retrieval within a terrestrial biosphere model. Geophysical research letters, 34(15), 1-6. https://doi.org/10.1029/2007GL030014
  • Durá, E., Mendiguren, G., Pacheco-Labrador, J., Martín, M.P., Riaño, D., Iturrate, M., Gimeno, C., Carrara, A. 2013. Validación de productos MODIS relacionados con la estimación de flujos de carbono en un ecosistema de dehesa. Geofocus, 13, 291310.
  • Efron, B. 1979. Bootstrap Methods: Another Look at the Jackknife. The annals of statistics, 7(1), 1-26. https://doi.org/10.1214/aos/1176344552
  • Elvidge, C.D. 1990. Visible and near infrared reflectance characteristics of dry plant materials. International Journal of Remote Sensing, 11(10), 1775-1795. https://doi.org/10.1080/01431169008955129
  • Faurtyot, T., Baret, F. 1997. Vegetation water and dry matter contents estimated from top-of-theatmosphere reflectance data: A simulation study. Remote Sensing of Environment, 61(1), 34-45. https://doi.org/10.1016/S0034-4257(96)00238-6
  • Feret, J.B., François, C., Asner, G.P., Gitelson, A.A., Martin, R.E., Bidel, L.P.R., Ustin, S.L., le Maire, G., Jacquemoud, S. 2008. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6), 3030-3043. https://doi. org/10.1016/j.rse.2008.02.012
  • Fernández, D., Martin, M.P., Vilar, L., PachecoLabrador, J. 2015. Estimación del contenido de humedad de la vegetación herbácea en una zona de dehesa a partir de imágenes hiperespectrales adquiridas por el sensor aerportado CASI. Geofocus, 16, 177-204.
  • Ferrier, G., Trahair, N.S. 1995. Evaluation of apparent surface reflectance estimation methodologies. International Journal of Remote Sensing, 16(12), 2291-2297. https://doi. org/10.1080/01431169508954557
  • Frampton, W.J., Dash, J., Watmough, G., Milton, E.J., 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of byophysical variables in vegetation. Journal of Photogrammetry and Remote Sensing, 82, 83-92. https://doi.org/10.1016/j. isprsjprs.2013.04.007
  • Gamon, J.A., Peñuelas, J., Field, C.B. 1992. A narrowwaveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41, 35-44. https://doi. org/10.1016/0034-4257(92)90059-S
  • Gao, B. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
  • Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76-87. https://doi.org/10.1016/S00344257(01)00289-9
  • González-Cascón, R., Pacheco-Labrador, J., Martín, M.P. 2016. Evolution of spectral behavior and chemical composition in the tree canopy of a dehesa ecosystem. Revista de Teledetección, 46, 31-43. https://doi.org/10.4995/raet.2016.5688
  • Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B. 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. https://doi. org/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., Kolari, P., Porcar-Castell, A., Korhonen, L., Mõttus, M. 2016. Tracking the Seasonal Dynamics of Boreal Forest Photosynthesis Using EO-1 Hyperion Reflectance: Sensitivity to Structural and Illumination Effects. IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5105-5116. https://doi.org/10.1109/ TGRS.2016.2554466
  • 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. https://doi. org/10.1016/j.rse.2011.04.036
  • Herrmann, I., Pimstein, A., Karnieli, A., Cogen, 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), 21412151. https://doi.org/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. https://doi. org/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., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213. https://doi.org/10.1016/S00344257(02)00096-2
  • Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295309. https://doi.org/10.1016/0034-4257(88)90106-X
  • Kuusk, A. 1995. A fast, invertible canopy reflectance model. Remote Sensing of Environment, 51(3), 342350. https://doi.org/10.1016/0034-4257(94)00059-V
  • le Maire, G., François, C., Dufrêne, E. 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1), 1-28. https://doi.org/10.1016/j. rse.2003.09.004
  • Lee, K.S., Cohen, W., Kennedy, R., Maiersperger, T., Gower, S. 2004. Hyperspectral versus multiespectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3-4), 508520. https://doi.org/10.1016/j.rse.2004.04.010
  • Li, W., Niu, Z., Liang, X., Li, Z., Huan, N., Gao, S., Wang, C., Muhammad, S. 2015. Geostatistical modeling using LiDAR-derived prior knowledge with SPOT6 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. https:// doi.org/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
  • Mendiguren, G., Martín, M.P., Nieto, G., PachecoLabrador, J., Jurdao, S. 2015. Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences, 12, 5523-5535. https://doi. org/10.5194/bg-12-5523-2015
  • Migliavacca, M., Perez-Priego, O., Rossini, M., ElMadany, T., Moreno, G., Van der Tol, C., Rascher, U., Berninger, A., Bessenbacher, V., Burkart, A., Carrara, A., Fava, F., Guan, J., Hammer, T., Henkel, K., JuarezAlcalde, E., Julitta, T., Kolle, O., Martin, M.P., Musavi, T., Pacheco-Labrador, J., Pérez-Burgueño, A., Wutzler, T., Zaehle, S., Reichstein, M. 2017. Plant functional traits and canopy structure control the relationship between photosynthetic CO2 uptake and far-red sun induced fluorescence in a Mediterranean grassland under different nutrient availability. New Phytologist, 214(3), 1078-1091. https://doi.org/10.1111/nph.14437
  • Nagler, P.L., Daughtry, C.S.T., Goward, S.N. 2000. Plant Litter and Soil Reflectance. Remote Sensing of Environment, 71(2), 207-215. https://doi.org/10.1016/ S0034-4257(99)00082-6
  • Nagler, P.L., Inoue, Y., Glenn, E.P., Russ, A.L., Daughtry, C.S.T. 2003. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sensing of Environment, 87(2-3), 310-325. https://doi. org/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. https://doi.org/10.1016/j.jag.2013.05.013
  • Perez-Priego, O., Guan, J., Rossini, M., Fava, F., Wutzler, T., Moreno, G., Carvalhais, N., Carrara, A., Kolle, O., Julitta, T., Schrumpf, M., Reichstein, M., Migliavacca, M. 2015. Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remotesensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12, 6351-6367. https://doi.org/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. https://doi.org/10.1007/BF00031911
  • Privette, J.L., Myneni, R.B., Knyazikhin, Y., Mukelabai, M., Roberts, G., Tian, Y., Wang, Y., Leblanc, S.G. 2002. Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment, 83(1-2), 232-243. https://doi. org/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. https://doi.org/10.1016/0034-4257(94)90134-1
  • Reichstein, M., Tenhunen, J., Roupsard, O., Ourcival, J., Rambal, S., Miglietta, F., Peressotti, A., Pecchiari, M., Tirone, G., Valentini, R. 2003. Inverse modeling of seasonal drought effects on canopy CO2/H2O exchange in three Mediterranean ecosystems. Journal of Geophysical Research, 108(D23), 1-16-. https://doi. org/10.1029/2003JD003430
  • Riaño, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P., 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 Transaction on Geoscience and Remote Sensing, 43, 819-826. https:// doi.org/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), 1-23. https:// doi.org/10.1117/1.JRS.6.063557
  • Roujean, J.L., Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375-384. https://doi.org/10.1016/00344257(94)00114-3
  • 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., Sassin, J. 2004. Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92(1), 126-138. https://doi.org/10.1016/j.rse.2004.05.004
  • Scholes, R.J., Archer, S.R. 1997. Tree-grass interactions in savannas. Annual Review of Ecology and Systematics, 28, 517-544. https://doi.org/10.1146/ annurev.ecolsys.28.1.517
  • Schucknecht, A., Meroni, M., Kayitakire, F., Rembold, F., Boureima, A. 2015. Biomass estimation to support pasture management in Niger. 36th International Symposium on Remote Sensing of Environment. Berlín, Germany, 109-114. https://doi.org/10.5194/ isprsarchives-xl-7-w3-109-2015
  • Serrano, L., Peñuelas, J., Ustin, S.L. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment, 81(2-3), 355-364. https://doi. org/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. h t tps : / /doi .org /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, 2653-2662. https://doi. org/10.1080/014311699211994
  • Stagakis, S., González-Dugo, V., Cid, P., GuillénCliment, M., Zarco-Tejada, P. 2012. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-namd strutural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 47-61. https://doi.org/10.1016/j. isprsjprs.2012.05.003
  • Ustin, S.L., Roberts, D.A., Gamon, J.A., Asner, G.P., Green, R.O. 2004. Using Imaging Spectroscopy to Study Ecosystem Processes and Properties. BioScience, 54, 523-534. https://doi. org/10.1641/0006-3568(2004)054[0523:UISTSE]2 .0.CO;2
  • Ustin, S.L., Roberts, D.A., Pinzón, J., Jacquemoud, S., Gardner, M., Scheer, G., Castañeda, C.M., PalaciosOrueta, A. 1998. Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods. Remote Sensing of Environment, 65(3), 280-291. https://doi. org/10.1016/S0034-4257(98)00038-8
  • 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. https://doi. org/10.1016/j.rse.2016.07.025
  • Wilcoxon, F. 1945. Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1, 80-83. https://doi. org/10.2307/3001968
  • Yebra, M., Chuvieco, E., Riaño, D. 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and forest meteorology, 148, 523-536. https://doi.org/10.1016/j. agrformet.2007.12.005
  • 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. https://doi. org/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.J., Rueda, C.A., Ustin, S.L. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109-124. https://doi.org/10.1016/S0034-4257(02)00197-9
  • Zarco-Tejada, P.J., Ustin, S.L. 2001. Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites. IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, 1, 342344. https://doi.org/10.1109/igarss.2001.976152
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