Análisis de los índices de vegetación NDVI, GNDVI y NDRE para la caracterización del cultivo de café (Coffea arabica)
DOI:
https://doi.org/10.14482/inde.38.2.628Palabras clave:
Agricultura de precisión, Coffea arabica, Imágenes multiespectrales, Índices espectralesResumen
Los índices de vegetación se han usado en los últimos años con el fin de determinar el tipo de cobertura, para evaluar su variación temporal, o para determinar el estado de salud de cultivos a partir de estimaciones de características como vigor vegetal, contenido de clorofila, estado nutricional o estado hídrico. En diversos estudios se han propuesto una variedad de índices de vegetación que usan diferentes bandas en el espectro visible e infrarrojo cercano con el fin de obtener características de interés. En este estudio se evaluaron las diferencias estadísticas entre los índices de vegetación NDVI, GNDVI y NDRE, estimados a partir de imágenes aéreas tomadas a 30 m del dosel de un cultivo experimental de la especie vegetal Coffea arabica. El coeficiente de correlación de Spearman mostró que la correlación es mayor entre los índices NDVI y GNDVI en comparación a la correlación presentada entre cualquiera de ellos y el índice NDRE. Además de ello, se observó que de acuerdo con los valores del coeficiente de variación, y el análisis posterior de los histogramas, el índice NDRE presentó una mayor sensibilidad ante la variación de vigor vegetal, lo que sugeriría un mayor potencial a la hora de caracterizar el estado de desarrollo del cultivo de café, frente a los otros índices estudiados.
Citas
Federación Nacional de Cafeteros. (5 dic. 2018). Informe gerente general [En línea]. Disponible en: https://issuu.com/comitecafeterosvalle/docs/informe_del_gerente_2018_1_
S. Chauhan, R. Darvishzadeh, M. Boschetti, M. Pepe y A. Nelson, “Remote sensing-based crop lodging assessment: current status and perspectives”, ISPRS, vol. 151, pp. 124-140, my. 2019. https://doi.org/10.1016/j.isprsjprs.2019.03.005
L. Galvão, A. Formaggio y D. Tisot, “Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data”, Remote Sensing of Environment, vol. 94, no. 8, pp. 523-534, febr. 2005. https://doi.org/10.1016/j.rse.2004.11.012
Y. E. Shimabukuro y F. J. Ponzoni, “Orbital sensors data applied to vegettion studies”, Revista Brasileira de Cartografía, vol. 64, no. 6, pp. 873-886, dic. 2012.
E. P. Moreira, “Correção radiométrica do efeito de iluminação solar induzido pela topografía”, Tesis de maestría, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, Brasil, 2014.
C. Mattar, B. Franch, J. A. Sobrino, C. Corbari, J. C. Jiménez-Muñoz, L. Olivera-Guerra, D. Skokovic, G. Sória, R. Oltra-Carriò, Y. Julien y M. Mancinie, “Impacts of the broadband albedo on actual evapotranspiration estimated by S-SEBI model over an agricultural área”, Remote Sensing of Environment, vol. 147, pp. 23-42, my. 2014. https://doi.org/10.1016/j.rse.2014.02.011
S. Ediriweera, S. Pathirana, T. Danaher, D. Nichols y T. Moffiet, “Evaluation of different topographic corrections for Landsat TM data by prediction of foliage projective cover (FPC) in topographically complex landscapes”, Remote Sens., vol. 5, no. 12, pp. 6767-6789, 2013. https://doi.org/10.3390/rs5126767
A. R. Petach, M. Toomey, D. M. Aubrecht y A. D. Richardson, “Monitoring vegetation phenology using an infrared-enabled security camera”, Agricultural and Forest Meteorology, vol. 195-196, pp. 143-151, sep. 2014. https://doi.org/10.1016/j.agrformet.2014.05.008
K. Yu, V. Lenz-Wiedemann, X. Chen y G. Bareth, “Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects”, ISPRS, vol. 97, pp. 58-77, nov. 2014. https://doi.org/10.1016/j.isprsjprs.2014.08.005
R. Main, M. A. Cho, R. Mathieu, M. M. O’Kennedy, A. Ramoelo y S. Koch, “An investigation into robust spectral indices for leaf chlorophyll estimation”, ISPRS, vol. 66, no. 6, pp. 751-761, nov. 2011. https://doi.org/10.1016/j.isprsjprs.2011.08.001
B. Zhao, A. Duan, S. Ata-Ul-Karim, Z. Liu, Z. Chen, Z. Gong, J. Zhang, J. Xiao, Z. Liu, A. Qin y D. Ning, “Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize”, European Journal of Agronomy, vol. 93, pp. 113-125, febr. 2018. https://doi.org/10.1016/j.eja.2017.12.006
J. Abdulridha, Y. Ampatzidis, R. Ehsani y A. I. de Castro, “Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado”, Computers and Electronics in Agriculture, vol. 155, pp. 203-2011, dic. 2018. https://doi.org/10.1016/j.compag.2018.10.016
M. Rossini, F. Fava, S. Cogliati, M. Meroni, A. Marchesi, C. Panigada, C. Giardino, L. Busseto, M. Migliavacca, S. Amaducci y R. Colombo, “Assessing canopy PRI from airborne imagery to map water stress in maize”, ISPRS, vol. 86, pp. 168-177, dic. 2013. https://doi.org/10.1016/j.isprsjprs.2013.10.002
A. Ramoelo, S. Dzikiti, H. van Deventer, A. Maherry, M. Cho y M. Gush, “Potential to monitor plant stress using remote sensing tools”, Journal of Arid Environments, vol. 113, pp. 134-144, febr. 2015. doi: 10.1016/j.jaridenv.2014.09.003
R.S.NLima, I.García-Tejero, T.S. Lopes, J. M. Costa, M. Vaz, V. H. Durán-Zuazo, M. Chaves, D. M. Glenn y E. Campostrinid, “Linking thermal imaging to physiological indicators in Carica papaya L. under different watering regimes”, Agricultural Water Management, vol. 164, p. 148-157, en. 2016. https://doi.org/10.1016/j.agwat.2015.07.017
J. Farifteh, R. R. Struthers, R. Swennen y P. Coppin, “Plant spectral and thermal response to water stress induced by regulated deficit irrigation”, International Journal of Geosciences and Geomatics, vol. 1, no. 1, pp. 17-22, 2013.
Y. Ge, G. Bai, V. Stoerger y J. Schnable, “Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging”, Computers and Electronics in Agriculture, vol. 127, pp. 625-632, sep. 2016. https://doi.org/10.1016/j.compag.2016.07.028
A. Viña, A. A. Gitelson, A. L. Nguy-Robertson y Y. Peng, “Comparison of different vegetation indices for the remote assessment of green leaf are index crops”, Remote Sensing of Environment, vol. 115, no. 12, pp. 3468-3478, dic. 2011. https://doi.org/10.1016/j.rse.2011.08.010
V. M. Rodríguez-Moreno y S. H. Bullock, “Comparación espacial y temporal de índices de la vegetación para verdor y humedad y aplicación para estimar LAI en el desierto sonorense”, Rev. Mex. Cienc. Agríc, vol. 4, no. 16, pp. 611-623, my.-jun. 2013.
J. Qiu, W. T. Crow, W. Wagner y T. Zhao, “Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing”, International Journal of Applied Earth Observation and Geoinformation, vol. 80, pp. 47-57, ag. 2019. https://doi.org/10.1016/j.jag.2019.03.015
V. Kumar, A. Sharma, R. Bhardwaj y A. K. Thukral, “Comparison of different reflectance indices for vegetation analysis using Landsat-TM data”, Remote Sensing Applications: Society and Environment, vol. 12, pp. 70-77, nov. 2018. https://doi.org/10.1016/j.rsase.2018.10.013
A. Kross, H. McNairn, D. Lapen, M. Sunohara y C. Champagne, “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, vol. 34, p. 235-248, febr. 2015. https://doi.org/10.1016/j.jag.2014.08.002 2
M. Xu, R. Liu, J. M. Chen, Y. Liu, R. Shang, W. Ju, C. Wu y W. Huang, “Retrieving leaf chlorophyll content using a matrix based vegetation index combination approach”, Remote Sensing of Environment, vol. 224, pp. 60-73, abr. 2019. https://doi.org/10.1016/j.rse.2019.01.039
S. Jay, F. Maupas, R. Bendoula y N. Gorretta, “Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping”, Field Crops Research, vol. 210, pp. 33-46, ag. 2017. https://doi.org/10.1016/j.fcr.2017.05.005
A. Tong y Y. He, “Estimating and mapping chlorophyll content for a heterogeneous grassland: comparing prediction power of a suite of vegetation indices across scales between years”, ISPRS, vol. 126, pp. 146-167, abr. 2017. https://doi.org/10.1016/j.isprsjprs.2017.02.010
Alcaldía Municipal Cajibío Cauca. (2012). Plan de Desarrollo 2012-2015 “Propósito de todos [En línea]. Disponible en: http://www.cajibio-cauca.gov.co/planes/plan-de-desarrollo-vigencia-20122015
C. Qiu, G. Liao, H. Tang, F. Liu, X. Liao, R. Zhang y Z. Zhao, “Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index”, Appl. Sci., vol 8, no. 8, p. 1300, 2018, https://doi.org/10.3390/app8081300
E. Raymond Hunt Jr., C. S. T. Daughtry, J. U. H. Eitel y D. S. Long, “Remote sensing leaf chlorophyll content using a visible band index”, Agronomy Journal, vol. 103, no. 4, pp. 1090-1099, jul. 2011. https://doi.org/10.2134/agronj2010.0395
R. Sonobe, T. Sano y H. Horie, “Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments”, Biosystems Engineering, vol. 175, pp. 168-182, nov. 2018. https://doi.org/10.1016/j.biosystemseng.2018.09.018
A. de la Casa, G. Ovando, L. Bressanini, J. Martínez, G. Díaz y C. Miranda, “Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot”, ISPRS vol. 146, pp. 531-547, dic. 2018. https://doi.org/10.1016/j.isprsjprs.2018.10.018
R. R. Fern, E. A. Foxley, A. Bruno y M. L. Morrison, “Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland”, Ecological Indicators, vol. 94, pp. 16-21, nov. 2018. https://doi.org/10.1016/j.ecolind.2018.06.029
G. Zhijia, D. Xingwu, S. Yandong, L. Ya y P. Xi, “Spatiotemporal variation in vegetation coverage and its response to climatic factors in the Red River Basin, China”, Ecological Indicators, vol. 93, pp. 54-64, oct. 2018. https://doi.org/10.1016/j.ecolind.2018.04.033
Y. Zhang, F. Ling, G. Foody, Y. Ge, D. Boyd, X. Li, Y. Du y M. Atkinson, “Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007-2016”, Remote Sensing of Environment, vol. 224, pp. 74-91, abr. 2019. https://doi.org/10.1016/j.rse.2019.01.038
G. Wang, J. Wang, X. Zou, G. Chai, M. Wu y Z. Wanga, “Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands”, Int J Appl Earth Obs Geoinformation, vol. 76, pp. 154-166, abr. 2019. https://doi.org/10.1016/j.jag.2018.11.006
C. Wu, Z. Niu, Q. Tang y W. Huang, “Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation”, Agricultural and Forest Meteorology, vol. 148, no. 8-9, pp. 1230-1241, jul. 2018. https://doi.org/10.1016/j.agrformet.2008.03.005
G. Cordon, M. G. Lagorio y J. M. Paruelo, “Chlorophyll fluorescence, photochemical reflective index and normalized difference vegetative index during plant senescence”, Journal of Plant Physiology, vol. 199, pp. 100-110, jul. 2016. https://doi.org/10.1016/j.jplph.2016.05.010
J. G. Clevers y A. A. Gitelson, “Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge”, International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 344-351, ag. 2013. https://doi.org/10.1016/j.jag.2012.10.008
C. Ju, Y. Tian, X. Yao, W. Cao, Y. Zhu y D. Hannaway, “Estimating leaf chlorophyll content using red edge parameters”, Pedosphere, vol. 20, no. 5, pp. 633-644, 2010. doi: 10.1016/S1002-0160(10)60053-7
T. Zheng, N. Liu, L. Wu, M. Li, H. Sun, Q. Zhang y J. Wu, “Estimation of chlorophyll content in potato leaves based on spectral red edge position”, IFAC-PapersOnLine, vol. 51, no. 17, pp. 602-606, 2018. https://doi.org/10.1016/j.ifacol.2018.08.131
X. Zhou, W. Huang, J. Zhang, W. Kong, R. Casa y Y. Hunag, “A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status”, International Journal of Applied Earth Observation and Geoinformation, vol. 76, pp. 128-142, abr. 2019. https://doi.org/10.1016/j.jag.2018.10.012
V. Ibáñez, Análisis y diseño de experimentos. Perú: Universidad Nacinal del Altiplano Puno, 2009.