Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG)
ilustraciones, gráficas, tablas
- Autores:
-
Montero Espinosa, Jhon Eder
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81270
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación
Elaeis guineensis
Radiómetros
Palma de aceite
Nitrógeno
Potasio
Índices espectrales
PLSR
UAV
Espectrorradiómetro
Oil palm
Nitrogen
Potassium
Spectral indices
PLSR
UAV
spectroradiometer
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
| id |
UNACIONAL2_b404287cad939b49f04610556c72016b |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/81270 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| dc.title.translated.eng.fl_str_mv |
Relationship between spectral responses and nitrogen and potassium fertilization in oil palm crop (OxG hybrid) |
| title |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| spellingShingle |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación Elaeis guineensis Radiómetros Palma de aceite Nitrógeno Potasio Índices espectrales PLSR UAV Espectrorradiómetro Oil palm Nitrogen Potassium Spectral indices PLSR UAV spectroradiometer |
| title_short |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| title_full |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| title_fullStr |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| title_full_unstemmed |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| title_sort |
Relación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG) |
| dc.creator.fl_str_mv |
Montero Espinosa, Jhon Eder |
| dc.contributor.advisor.none.fl_str_mv |
Martínez Martínez, Luis Joel Torres León, Jorge Luis |
| dc.contributor.author.none.fl_str_mv |
Montero Espinosa, Jhon Eder |
| dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación |
| topic |
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación Elaeis guineensis Radiómetros Palma de aceite Nitrógeno Potasio Índices espectrales PLSR UAV Espectrorradiómetro Oil palm Nitrogen Potassium Spectral indices PLSR UAV spectroradiometer |
| dc.subject.agrovocuri.none.fl_str_mv |
Elaeis guineensis Radiómetros |
| dc.subject.proposal.spa.fl_str_mv |
Palma de aceite Nitrógeno Potasio Índices espectrales PLSR UAV Espectrorradiómetro |
| dc.subject.proposal.eng.fl_str_mv |
Oil palm Nitrogen Potassium Spectral indices PLSR UAV spectroradiometer |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-17T15:14:49Z |
| dc.date.available.none.fl_str_mv |
2022-03-17T15:14:49Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/81270 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81270 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Ahamed, T., Tian, L., Zhang, Y., & Ting, K. C. (2011). A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35(7), 2455–2469. https://doi.org/10.1016/j.biombioe.2011.02.028 Ammawath, W., Man, Y. B. C., Rahman, R. B. A., & Baharin, B. S. (2006). A Fourier Transform Infrared Spectroscopic Method for Determining Butylated Hydroxytoluene in Palm Olein and Palm Oil. Spectroscopy, 83(3), 187–191. Apan, A., Held, A., Phinn, S., & Markley, J. (2004). Detecting sugarcane “orange rust” disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing, 25(2), 489–498. https://doi.org/10.1080/01431160310001618031 ASDinc. (2012). Chemometrics Training Manual. June. Ataga, D. O. (1978). Soil phosphorus status and response of oil palm to phosphate on some acid soils. Nigerian Inst. For Oil Palm Research, 5, 25– 36. Bagchi, T. B., Sharma, S., & Chattopadhyay, K. (2016). Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran. Food Chemistry, 191, 21–27. https://doi.org/10.1016/j.foodchem.2015.05.038 Balasundram, siva k, Memarian, H., & Khosla, R. (2013). Estimating Oil Palm Yields using Vegetation Indices Derived from Quickbird. Life Science Journal, 10(4). Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273–285. https://doi.org/10.1016/S00344257(98)00059-5 Boochs, F., Kupfer, G., Dockter, K., & Kuhbaüch, W. (1990). Shape of the red edge as vitality indicator for plants. International Journal of Remote Sensing, 11(10), 1741–1753. https://doi.org/10.1080/01431169008955127 Botero Herrera, J. M., Parra Sánchez, L. N., & Cabrera Torres, K. R. (2009). Determinación del nivel de nutrición foliar en banano por espectrometría de reflectancia. Revista Facultad Nacional de Agronomía Medellín, 62(2), 5089– 5098. https://doi.org/https://revistas.unal.edu.co/index.php/refame/article/view/2491 9 Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156–172. https://doi.org/10.1016/S0034-4257(00)00197-8 Buchanan, G. M., Butchart, S. H. M., Dutson, G., Pilgrim, J. D., Steininger, M. K., Bishop, K. D., & Mayaux, P. (2008). Using remote sensing to inform conservation status assessment: Estimates of recent deforestation rates on New Britain and the impacts upon endemic birds. Biological Conservation, 141(1), 56–66. https://doi.org/10.1016/j.biocon.2007.08.023 Camo Software SA. (2006). The Unscrambler Methods. 288. Carter, G.A., Dell, T. R., & Cibula, W. G. (1996). Spectral reflectance characteristics and digital imagery of a pine needle blight in the southeastern United States. Can. J. Forest Res., 26, 402–407. Carter, Gregory A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 517–520. https://doi.org/10.1080/01431169408954109 Castaño Vidriales, J. L. (1991). Espectrometria Derivada En Quimica Clinica. Revista de La Sociedad Espanola de Quimica Clinica, 10(5), 347–359. Castro Franco, H. E. (1998). Fundamentos para el conocimiento y manejo de suelos agrícolas. Manual técnico. Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment, 39(3), 239–247. https://doi.org/10.1016/0034-4257(92)90089-3 Che Man, Y. B., & Mirghani, M. E. S. (2000). Rapid method for determining moisture content in crude palm oil by Fourier transform infrared spectroscopy. Journal of the American Oil Chemists’ Society, 77(6), 631–637. https://doi.org/10.1007/s11746-000-0102-9 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 Chong, K. L., Kanniah, K. D., Pohl, C., & Tan, K. P. (2017). A review of remote sensing applications for oil palm studies. Geo-Spatial Information Science, 20(2), 184–200. https://doi.org/10.1080/10095020.2017.1337317 Clarke, T. R., Moran, M. S., Barnes, E. M., Pinter, P. J., & Qi, J. (2001). Planar domain indices: A method for measuring a quality of a single component in two-component pixels. International Geoscience and Remote Sensing Symposium (IGARSS), 3(C), 1279–1281. https://doi.org/10.1109/igarss.2001.976818 Cloutis, E. A., Connery, D. R., Dover, F. J., & Major, D. J. (1996). Airborne multispectral monitoring of agricultural crop status: Effect of time of year, crop type and crop condition parameter. International Journal of Remote Sensing, 17(13), 2579–2601. https://doi.org/10.1080/01431169608949094 Cozzolino, D., & Moron, A. (2004). Exploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes. Animal Feed Science and Technology, 111(1–4), 161–173. https://doi.org/10.1016/j.anifeedsci.2003.08.001 Crespo Gonzalez, J. J., Ruiz Villadiego, O. S., & Ospino Villalba, K. S. (2020). Determinación de nitrógeno foliar en palma de aceite con espectroscopía en el infrarrojo medio (mir) y cercano (nir) por el método de regresión de mínimos cuadrados parciales de componentes principales (pls). Revista de Investigación Agraria y Ambiental, 11(2), 43–57. https://doi.org/10.22490/21456453.3206 Curran, P. J., Dungan, J. L., Macler, B. A., & Plummer, S. E. (1991). The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment, 35(1), 69–76. https://doi.org/10.1016/0034-4257(91)90066-F Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25(23), 5403–5413. https://doi.org/10.1080/0143116042000274015 Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741– 2759. https://doi.org/10.1080/014311699211778 Datt, Bisun. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111–121. https://doi.org/10.1016/S0034-4257(98)000467 Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9 Eitel, J. U. H., Long, D. S., Gessler, P. E., & Smith, A. M. S. (2007). Using in-situ measurements to evaluate the new RapidEye TM satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, 28(18), 4183–4190. https://doi.org/10.1080/01431160701422213 Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54(1), 38–48. https://doi.org/10.1016/0034-4257(95)00132-K Escadafal, R., Belghith, A., & Ben Moussa, H. (1994a). Indices spectraux pour la dégradation des milieux naturels en Tunisie aride. 6ème Symp. Int. Mesures Physiques et Signatures En Télédétection, ISPRS-CNES, 253–259. Escadafal, R., Belghith, A., & Ben Moussa, H. (1994b). Indices spectraux pour la dégradation des milieux naturels en Tunisie aride. 6ème Symp. Int. Mesures Physiques et Signatures En Télédétection, ISPRS-CNES, 253– 259. Filella, I., & Peñuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7), 1459–1470. https://doi.org/10.1080/01431169408954177 Foody, G. M., Cutler, M. E., Mcmorrow, J., Pelz, D., Tangki, H., Boyd, D. S., & Douglas, I. A. N. (2001). Mapping the biomass of Bornean tropical rain forest from remotely sensed data ESTIMATION AND MAPPING. 379–387. Frampton, W. J., Dash, J., Watmough, G., & Milton, E. J. (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83– 92. https://doi.org/10.1016/j.isprsjprs.2013.04.007 Galvão, L. S., Formaggio, A. R., & Tisot, D. A. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94(4), 523–534. https://doi.org/10.1016/j.rse.2004.11.012 Gamon, J., Nuelas, J. P., & Field, C. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35–44. https://doi.org/10.1088/0305-4470/24/13/001 Gandia, S., Fernández, G., García, J. C., & Moreno, J. (2004). Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaing. European Space Agency, (Special Publication) ESA SP, 578, 40– 48. Gao, B.-C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. Garrity, S. R., Eitel, J. U. H., & Vierling, L. A. (2011). Disentangling the relationships between plant pigments and the photochemical reflectance index reveals a new approach for remote estimation of carotenoid content. Remote Sensing of Environment, 115(2), 628–635. https://doi.org/10.1016/j.rse.2010.10.007 Gitelson, A. A, & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 1812(July 2015), 2691–2697. https://doi.org/10.1080/014311697217558 Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology, B: Biology, 22(3), 247– 252. https://doi.org/10.1016/1011-1344(93)06963-4 Gitelson, Anatoly A., Buschmann, C., & Lichtenthaler, H. K. (1999). The chlorophyll fluorescence ratio F735F700 as an accurate measure of the chlorophyll content in plants. Remote Sensing of Environment, 69(3), 296– 302. https://doi.org/10.1016/S0034-4257(99)00023-1 Gitelson, Anatoly A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282. https://doi.org/10.1078/0176-1617-00887 Gitelson, Anatoly A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S00344257(96)00072-7 Gitelson, Anatoly A., Keydan, G. P., & Merzlyak, M. N. (2006). Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33(11), 2–6. https://doi.org/10.1029/2006GL026457 Gitelson, Anatoly A., Peng, Y., & Huemmrich, K. F. (2014). Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250m resolution data. Remote Sensing of Environment, 147, 108– 120. https://doi.org/10.1016/j.rse.2014.02.014 Gitelson, Anatoly A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), n/a-n/a. https://doi.org/10.1029/2002gl016450 Guyot, G., & Frederic, B. (1988). Utilisation de la Haute Resolution Spectrale pour Suivre L’etat des Couverts Vegetaux. Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing Aussios, France, 18-22 January 1988, 287, 279. 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 Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. https://doi.org/10.1016/S00344257(02)00018-4 He, L., Zhang, H. Y., Zhang, Y. S., Song, X., Feng, W., Kang, G. Z., Wang, C. Y., & Guo, T. C. (2016). Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. European Journal of Agronomy, 73, 170–185. https://doi.org/10.1016/j.eja.2015.11.017 Henrich, V., Krauss, G., Götze, C., & Sandow, C. (2011). The IndexDatabase. 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 Hernández-Clemente, R., Navarro-Cerrillo, R. M., & Zarco-Tejada, P. J. (2012). Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+DART simulations. Remote Sensing of Environment, 127, 298–315. https://doi.org/10.1016/j.rse.2012.09.014 Hruschka, E. (2001). Data analysis: wavelength selection methods. Chapter 3. Near Infrared Technology in Agriculture and Food Industries, 2nd Edn (Eds P. Williams \& K. Norris), 39–160. Huete, A. (1998). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(0), 295–309. Huete, A. R., Liu, H. Q., Batchily, K., & VanLeeuwen, W. (1997). A comparison of vegetation indices global set of TM images for EOS–MODIS. Remote Sensing of Environment, 59(3), 440–451. https://doi.org/doi:10.1016/s00344257(96)00112-5 Hunt, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., Perry, E. M., & Akhmedov, B. (2012). A visible band index for remote sensing leaf chlorophyll content at the Canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21(1), 103–112. https://doi.org/10.1016/j.jag.2012.07.020 Hunt, E. R., & Rock, B. N. (1989). Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing of Environment, 30(1), 43–54. https://doi.org/10.1016/0034-4257(89)90046-1 Infrasoft International. (2005). WinISI III Manual. James, H., Nawi, N. M., Wan, W. I., Mohamed, A., Juva, V., & Arulandoo, X. (2017). Application of Spectroscopy for Nutrient Prediction of Oil Palm. Journal of Experimental Agriculture International, 15(3), 1–9. https://doi.org/10.9734/JEAI/2017/31502 Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. In Geocarto International (Vol. 2, Issue 1). https://doi.org/10.1080/10106048709354084 J Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on forest floor. Ecology, 50(4), 663–666. https://doi.org/10.1155/2012/651039 Kim, Moon S., Dougtry, C. S. ., Chappelle, E., Mcmurtrey, J. ., & Walthall, C. . (1993). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar). 299–306. Kim, Moon Sung. (1994). The use of narrow spectral bands for improving remote sensing estimations of fractionally absorbed photosynthetically active radiation (fapar). 75. Kokaly, Raymond, I., & Clark, R. N. (1999). Spectroscopic Determination of Leaf Biochemistry: Use of Normalized Band-Depths and Laboratory Measurements and Possible Extension to Remote Sensing Measurements. Remote Sensing of Environment, 67, 267–287. http://aviris.jpl.nasa.gov/proceedings/workshops/98_docs/30.pdf Lamb, D. W., Steyn-ross, M., Schaares, P., Hanna, M. M., Silvester, W., & SteynRoss, A. (2002). Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations. International Journal of Remote Sensing, 23(18), 3619–3648. https://doi.org/10.1080/01431160110114529 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 le Maire, Guerric, François, C., Soudani, K., Berveiller, D., Pontailler, J. Y., Bréda, N., Genet, H., Davi, H., & Dufrêne, E. (2008). Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112(10), 3846–3864. https://doi.org/10.1016/j.rse.2008.06.005 Levin, N., Kidron, G. J., & Ben-Dor, E. (2007). Surface properties of stabilizing coastal dunes: Combining spectral and field analyses. Sedimentology, 54(4), 771–788. https://doi.org/10.1111/j.1365-3091.2007.00859.x Liaghat, S., Ehsani, R., Mansor, S., & Helmi, Z. M. (2014). International Journal of Remote Early detection of basal stem rot disease ( Ganoderma ) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. June, 37–41. https://doi.org/10.1080/01431161.2014.903353 Liang, S. (2004). Quantitative remote sensing for land surface characterization. Lichtenthaler, H. K., Lang, M., Sowinska, M., Heisel, F., & Miehé, J. A. (1996). Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 148(5), 599–612. https://doi.org/10.1016/S0176-1617(96)80081-2 Lobell, D. B., Asner, G. P., Law, B. E., & Treuhaft, R. N. (2001). Subpixel canopy cover estimation of coniferous forests in Oregon using SWIR imaging spectrometry. Journal of Geophysical Research Atmospheres, 106(D6), 5151–5160. https://doi.org/10.1029/2000JD900739 Maccioni, A., Agati, G., & Mazzinghi, P. (2001). New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology B: Biology, 61(1–2), 52–61. https://doi.org/10.1016/S1011-1344(01)00145-2 Marten, G., Shenk, J., & Barton, F. (1989). Near infrared reflectance spectroscopy (NIRS): Analysis of forage quality. Agriculture Handbook No. 63, 643, 1–110. Martens, H., & Næs, T. (1984). Multivariate Calibration. Chemometrics, 147–156. https://doi.org/10.1007/978-94-017-1026-8_5 Martínez, L. J. (2017). Relationship between crop nutritional status , spectral measurements and Sentinel 2 images Relación entre el estado nutricional de los cultivos , las mediciones espectrales y las imágenes Sentinel 2. 35(2), 205–215. https://doi.org/10.15446/agron.colomb.v35n2.62857 McMorrow, J. (2001). Linear regression modelling for the estimation of oil palm age from Landsat TM. International Journal of Remote Sensing, 22(12), 2243–2264. https://doi.org/10.1080/01431160117188 McMurtrey, J. E., Chappelle, E. W., Kim, M. S., Meisinger, J. J., & Corp, L. A. (1994). Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment, 47(1), 36–44. https://doi.org/10.1016/0034- 4257(94)90125-2 Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non- destructive optical detection of leaf senescence and fruit ripening. Physiologia Plantarum, 106(1), 135–141. Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24(14), 2855–2877. https://doi.org/10.1080/01431160210163074 Miller, J. R., Hare, E. W., & Wu, J. (1990). Quantitative characterization of the vegetation red edge reflectance 1. An inverted-gaussian reflectance model. International Journal of Remote Sensing, 11(10), 1755–1773. https://doi.org/10.1080/01431169008955128 Mirik, M., Michels, G. J., Kassymzhanova-Mirik, S., & Elliott, N. C. (2007). Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture, 57(2), 123–134. https://doi.org/10.1016/j.compag.2007.03.002 Mulyadi, T., Hariyandi, Sudradjat, & Kustiyo. (2017). Nitrogen Content and Carbon Stock Prediction in Oil Palm using Satellite Image Analysis. 05(04), 677–683. Munévar, F. (2001). Palmas. Revista Palmas, 22(4), 9–17. https://publicaciones.fedepalma.org/index.php/palmas/article/view/888 Munevar, F., Franco, P., & Arias, N. (2008). Guía general para el muestreo foliar y de suelos en palma de aceite (Issue 37). 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 Ng, P. H. C., Totok, S., S.H., T., & K.J., G. (2013). Remote Sensing and Digital Technologies for Plantation Management. 34, 259–279. Ng, S. (2002). Nutrition and nutrient management of oil palm-New thrust for the future perspective. … of Potassium in India New Delhi. International Potash …, 415–429. http://www.ipipotash.org/udocs/Nutrition and Nutrient Management of the Oil Palm.pdf Nooni, I. K., Duker, A. A., Van Duren, I., Addae-Wireko, L., & Osei Jnr, E. M. (2014). Support vector machine to map oil palm in a heterogeneous environment. International Journal of Remote Sensing, 35(13), 4778–4794. https://doi.org/10.1080/01431161.2014.930201 Ollagnier, M., Ochis, R., & Martin, G. (1970). El abonamiento de la palma de aceite en el mundo. Fertilite, 36, 30–61. Oppelt, N., & Mauser, W. (2004). Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing, 25(1), 145–159. https://doi.org/10.1080/0143116031000115300 Osco, L. P., Junior, J. M., Ramos, A. P. M., Furuya, D. E. G., Santana, D. C., Teodoro, L. P. R., Gonçalves, W. N., Baio, F. H. R., Pistori, H., Junior, C. A. da S., & Teodoro, P. E. (2020). Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques. Remote Sensing, 12(19), 1–17. https://doi.org/10.3390/rs12193237 Owen, E. (1992). Fertilización de la palma africana (Elaeis Guineensis Jacq.) en Colombia. Palmas, 13(2), 39–64. http://www.embase.com/search/results?subaction=viewrecord&from=export&i d=L127279692 Papeş, M., Tupayachi, R., Martínez, P., Peterson, A. T., & Powell, G. V. N. (2010). Using hyperspectral satellite imagery for regional inventories: A test with tropical emergent trees in the Amazon Basin. Journal of Vegetation Science, 21(2), 342–354. https://doi.org/10.1111/j.1654-1103.2009.01147.x Peng, Y., Nguy-Robertson, A., Arkebauer, T., & Gitelson, A. A. (2017). Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms. Remote Sensing, 9(3). https://doi.org/10.3390/rs9030226 Penuelas, J., Frederic, B., & Filella, I. (1995). Semi-Empirical Indices to Assess Carotenoids/Chlorophyll-a Ratio from Leaf Spectral Reflectance. Photosynthetica, 31, 221–230. Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48(2), 135– 146. https://doi.org/10.1016/0034-4257(94)90136-8 Peñuelas, J., Piñol, J., Ogaya, R., & Filella, I. (1997). International Journal of Remote Estimation of plant water concentration by the reflectance Water Index WI ( R900 / R970 ). International Journal of Remote, 18(13), 2869– 2875. Pereda, J. (2016). Calibración para determinar composición proximal de la quinua (Chenopodium quinoa W.) usando la espectroscopía de transmitancia en el infrarrojo cercano. Universidad Nacional Agraria La Molina. Perry, C. R., & Lautenschlager, L. F. (1984). Functional equivalence of spectral vegetation indices. Remote Sensing of Environment, 14(1–3), 169–182. https://doi.org/10.1016/0034-4257(84)90013-0 Pu, R., Gong, P., & Yu, Q. (2008). Comparative analysis of EO-1 ALI and Hyperion, and Landsat ETM+ data for mapping forest crown closure and leaf area index. Sensors, 8(6), 3744–3766. https://doi.org/10.3390/s8063744 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 Rao, N. R., Garg, P. K., & Ghosh, S. K. (2007). Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture, 8(4–5), 173–185. https://doi.org/10.1007/s11119-007-9037-x Rendana, M., Abdul, S., Lihan, T., Mohd, W., & Ali, Z. (2015). A Review of Methods for Detecting Nutrient Stress of Oil Palm in Malaysia. J. Appl. Environ. Biol. Sci. Journal of Applied Environmental and Biological Sciences, 5(6), 60–64. www.textroad.com Rey, L., Ayala, I., Gómez, P., & Ruiz, R. (2006a). Selección de palmas élite en plantaciones comerciales. Boletín Técnico, 20, 30. Rey, L., Ayala, I., Gómez, P., & Ruiz, R. (2006b). Selección de palmas élite en plantaciones comerciales. Boletín Técnico, 20, 30. Reyniers, M., & Vrindts, E. (2006). Measuring wheat nitrogen status from space and ground-based platform. International Journal of Remote Sensing, 27(3), 549–567. https://doi.org/10.1080/01431160500117907 Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7 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/10.1016/0034-4257(94)00114-3 Santana, D. C., Cotrim, M. F., Flores, M. S., Rojo Baio, F. H., Shiratsuchi, L. S., Silva Junior, C. A. da, Teodoro, L. P. R., & Teodoro, P. E. (2021). UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing. Remote Sensing Applications: Society and Environment, 23(April), 100534. https://doi.org/10.1016/j.rsase.2021.100534 Schlemmera, M., Gitelson, A., Schepersa, J., Fergusona, R., Peng, Y., Shanahana, J., & Rundquist, D. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25(1), 47–54. https://doi.org/10.1016/j.jag.2013.04.003 Selvaraja, S., Balasundram, S. K., Vadamalai, G., & Husni, M. H. A. (2013). Use of spectral reflectance to discriminate between potassium deficiency and orange spotting symptoms in oil palm (Elaeis guineensis). Life Science Journal, 10(4), 947–951. 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. https://doi.org/10.1016/s00344257(02)00011-1 Shafri, H. Z. M., & Hamdan, N. (2011). Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery. 32(8), 2095– 2115. https://doi.org/10.1080/01431161003662928 Shapiro, A. S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality ( Complete Samples ) Published by : Biometrika Trust Stable URL : http://www.jstor.org/stable/2333709. Biometrika, 52(3/4), 591–611. Sims, D. A., & Gamon, J. A. (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(2–3), 337–354. https://doi.org/10.1016/S0034-4257(02)00010-X Smith, R. C. G., Adams, J., Stephens, D. J., & Hick, P. T. (1995). Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Australian Journal of Agricultural Research, 46(1), 113–125. https://doi.org/10.1071/AR9950113 Tamburini, E., Ferrari, G., Marchetti, M. G., Pedrini, P., & Ferro, S. (2015). Development of FT-NIR models for the simultaneous estimation of chlorophyll and nitrogen content in fresh apple (Malus Domestica) leaves. Sensors (Switzerland), 15(2), 2662–2679. https://doi.org/10.3390/s150202662 Teoh, K. C., & Chew, P. S. (1980). Fertiliser responses of oil palm on coastal clay soils in Peninsular Malaysia. Conference on Soil Science and Agricultural Development in Malaysia. Kuala Lumpur (Malaysia). 12-14 May 1980. Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Wiegand and Richardson, † International Center for Agricultural Research in the Dry Areas 1990), natural vegetation (Friedl et al., 1994), and in (ICARDA). Environ, 71(99), 158–182. Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering and Remote Sensing, 68(6), 607–621. Tucker, C. J., Elgin, J. H., McMurtrey, J. E., & Fan, C. J. (1979). Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sensing of Environment, 8(3), 237–248. https://doi.org/10.1016/0034-4257(79)90004-X Tucker, Compton J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0 Vincini, M., Frazzi, E., & D’Alessio, P. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, 9(5), 303–319. https://doi.org/10.1007/s11119-008-9075-z Vincini, Massimo, & Frazzi, E. (2006). Angular dependence of maize and sugar beet VIs from directional CHRIS/Proba data. Cuore, January, 5–9. Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8), 1563–1575. https://doi.org/10.1080/01431169308953986 Wang, F., Huang, J., Tang, Y., & Wang, X. (2007). New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Rice Science, 14(3), 195– 203. https://doi.org/10.1016/s1672-6308(07)60027-4 Williams, P. C., & Sobering, D. C. (1996). How do we do it: a brief summary of the methods we use in developing near infrared calibrations. Near Infrared Spectroscopy: The Future Waves, 185–188. Witt, C., Fairhurst, T., & Griffiths, W. (2005). Key Principles of Crop and Nutrient Management in Oil Palm. Better Crops, 89(3), 27–31. http://www.oilpalm.net/ppiweb/bcrops.nsf/$webindex/DF49AA8010785C14852570490074C 097/$file/05-3p27.pdf Wu. (2014). The Generalized Difference Vegetation Index (GDVI) for dryland characterization. Remote Sensing, 6(2), 1211–1233. https://doi.org/10.3390/rs6021211 Wu, C., Niu, Z., Tang, Q., & Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148(8–9), 1230–1241. https://doi.org/10.1016/j.agrformet.2008.03.005 Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., & Feng, J. (2009). Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, 149(6–7), 1015– 1021. https://doi.org/10.1016/j.agrformet.2008.12.007 Zarco-Tejada, P. J., González-Dugo, V., Williams, L. E., Suárez, L., Berni, J. A. J., Goldhamer, D., & Fereres, E. (2013). A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sensing of Environment, 138, 38–50. https://doi.org/10.1016/j.rse.2013.07.024 Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steadystate chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84(2), 283–294. https://doi.org/10.1016/S0034-4257(02)00113-X Zarco-Tejada, Pablo J., & Miller, J. R. (1999). Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery. Journal of Geophysical Research Atmospheres, 104(D22), 27921–27933. https://doi.org/10.1029/1999JD900161 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
xv, 215 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias Agrarias - Maestría en Geomática |
| dc.publisher.department.spa.fl_str_mv |
Departamento de Agronomía |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias Agrarias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/81270/3/TESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdf https://repositorio.unal.edu.co/bitstream/unal/81270/4/license.txt https://repositorio.unal.edu.co/bitstream/unal/81270/5/TESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdf.jpg |
| bitstream.checksum.fl_str_mv |
7012b71536bf66db0ccdb370cbe1dc93 8153f7789df02f0a4c9e079953658ab2 a90a258d939dd13b2b4aa3f2f41f0da0 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814090143716343808 |
| spelling |
Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Martínez Martínez, Luis Joel94d011bd9a7f169197ab0a1837a443b9600Torres León, Jorge Luis9bf4f5fe3560fcfe7bfb8bc05ee20e09Montero Espinosa, Jhon Eder0c62392dd25591880cbe662b8c0350e62022-03-17T15:14:49Z2022-03-17T15:14:49Z2021https://repositorio.unal.edu.co/handle/unal/81270Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa presente investigación se realizó en la plantación Guaicaramo ubicada en el municipio de Cabuyaro departamento del Meta, con el objetivo de determinar la relación entre las respuestas espectrales y el contenido nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG, Coarí x LaMé). Se utilizó un diseño completamente al azar, con seis tratamientos de diferentes dosis de nitrógeno y potasio. Se efectuaron 3 muestreos, para cada uno se muestrearon siete palmas por tratamiento para un total de 42 palmas por muestreo y de cada palma se analizaron los datos de 6 foliolos. En cada uno de los muestreos se tomaron fotografías aéreas con UAV y una cámara multiespectral MicaSense de 5 bandas (rojo, azul, verde, RedEdge y NIR); se realizaron análisis de contenidos foliares en laboratorio; se midió la reflectancia de 6 foliolos por palma con el espectroradiómetro FieldSpect4 y para el segundo muestreo se realizó un análisis edáfico. Se encontró que, a menor contenido de nitrógeno foliar, la reflectancia en el rango visible fue mayor; por otra parte, las mediciones de la reflectancia con el espectroradiómetro para el primer muestreo tuvieron la mayor cantidad de índices con correlaciones significativas para el nitrógeno foliar, destacando el índice de vegetación Datt, como el de mejor desempeño en estas pruebas. Para potasio y fósforo foliar, en ninguno de los tres muestreos se presentó correlaciones mayores a 0.5 entre los índices espectrales y la concentración de potasio y fósforo foliar. Para la construcción de los modelos PLSR la longitud de onda de mayor influencia, fue cercana a los 718 nm, donde el modelo generado para nitrógeno puede ser usado para realizar predicciones cuantitativas. Por otra parte, se encontró que las correlaciones para los índices espectrales y el contenido de Nitrógeno a partir de las fotografías aéreas fueron mejores que las obtenidas a partir de las mediciones de reflectancia con el espectroradiómetro para los tres muestreos. (Texto tomado de la fuente)The present investigation was carried out in the Guaicaramo plantation located in the municipality of Cabuyaro department of Meta, with the objective of determining the relationship between the spectral responses and the nitrogen and potassium content in the oil palm cultivation (Hybrid OxG, Coarí x LaMé) for non-destructive nutritional diagnostic purposes. For the above, a completely randomized design was used, with six treatments of different doses of nitrogen and potassium. 3 samplings were carried out, for each one seven palms were sampled per treatment for a total of 42 palms per sample and from each palm the data of 6 leaflets were analyzed. In each of the samplings aerial photographs were taken with UAV integrating the MicaSense multispectral camera with 5 bands (red, blue, green, RedEdge and NIR); leaf content analyzes were carried out in the laboratory; The reflectance of 6 leaflets was measured with the FieldSpect4 spectroradiometer; edaphic analysis where done for the second sampling. It was found that for a lower nitrogen content, the reflectance was greater in the visible range; Based on the reflectance measurements with the spectroradiometer, for the first sampling the highest number of indices with significant correlations for foliar nitrogen were obtained, highlighting the Datt vegetation index, as the one with the best performance in these tests; for potassium and foliar phosphorus, in none of the three samplings there were correlations greater than 0.5 between the spectral indices from reflectance measurements with the spectroradiometer and the concentration of potassium and foliar phosphorus; for the construction of the PLSR models, the wavelength of greatest influence is close to 718 nm, where the model generated for nitrogen can make quantitative predictions; It was found that the correlations for the spectral indices and the Nitrogen content from the aerial photographs were better than those obtained from the reflectance measurements with the spectroradiometer for the three samplings.MaestríaMagíster en GeomáticaGeoinformación para el uso sostenible de los recursos naturalesxv, 215 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaDepartamento de AgronomíaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantaciónElaeis guineensisRadiómetrosPalma de aceiteNitrógenoPotasioÍndices espectralesPLSRUAVEspectrorradiómetroOil palmNitrogenPotassiumSpectral indicesPLSRUAVspectroradiometerRelación entre las respuestas espectrales y la fertilización con nitrógeno y potasio en el cultivo de palma de aceite (Híbrido OxG)Relationship between spectral responses and nitrogen and potassium fertilization in oil palm crop (OxG hybrid)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAhamed, T., Tian, L., Zhang, Y., & Ting, K. C. (2011). A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35(7), 2455–2469. https://doi.org/10.1016/j.biombioe.2011.02.028Ammawath, W., Man, Y. B. C., Rahman, R. B. A., & Baharin, B. S. (2006). A Fourier Transform Infrared Spectroscopic Method for Determining Butylated Hydroxytoluene in Palm Olein and Palm Oil. Spectroscopy, 83(3), 187–191.Apan, A., Held, A., Phinn, S., & Markley, J. (2004). Detecting sugarcane “orange rust” disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing, 25(2), 489–498. https://doi.org/10.1080/01431160310001618031ASDinc. (2012). Chemometrics Training Manual. June.Ataga, D. O. (1978). Soil phosphorus status and response of oil palm to phosphate on some acid soils. Nigerian Inst. For Oil Palm Research, 5, 25– 36.Bagchi, T. B., Sharma, S., & Chattopadhyay, K. (2016). Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran. Food Chemistry, 191, 21–27. https://doi.org/10.1016/j.foodchem.2015.05.038Balasundram, siva k, Memarian, H., & Khosla, R. (2013). Estimating Oil Palm Yields using Vegetation Indices Derived from Quickbird. Life Science Journal, 10(4).Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273–285. https://doi.org/10.1016/S00344257(98)00059-5Boochs, F., Kupfer, G., Dockter, K., & Kuhbaüch, W. (1990). Shape of the red edge as vitality indicator for plants. International Journal of Remote Sensing, 11(10), 1741–1753. https://doi.org/10.1080/01431169008955127Botero Herrera, J. M., Parra Sánchez, L. N., & Cabrera Torres, K. R. (2009). Determinación del nivel de nutrición foliar en banano por espectrometría de reflectancia. Revista Facultad Nacional de Agronomía Medellín, 62(2), 5089– 5098. https://doi.org/https://revistas.unal.edu.co/index.php/refame/article/view/2491 9Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156–172. https://doi.org/10.1016/S0034-4257(00)00197-8Buchanan, G. M., Butchart, S. H. M., Dutson, G., Pilgrim, J. D., Steininger, M. K., Bishop, K. D., & Mayaux, P. (2008). Using remote sensing to inform conservation status assessment: Estimates of recent deforestation rates on New Britain and the impacts upon endemic birds. Biological Conservation, 141(1), 56–66. https://doi.org/10.1016/j.biocon.2007.08.023Camo Software SA. (2006). The Unscrambler Methods. 288.Carter, G.A., Dell, T. R., & Cibula, W. G. (1996). Spectral reflectance characteristics and digital imagery of a pine needle blight in the southeastern United States. Can. J. Forest Res., 26, 402–407.Carter, Gregory A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 517–520. https://doi.org/10.1080/01431169408954109Castaño Vidriales, J. L. (1991). Espectrometria Derivada En Quimica Clinica. Revista de La Sociedad Espanola de Quimica Clinica, 10(5), 347–359.Castro Franco, H. E. (1998). Fundamentos para el conocimiento y manejo de suelos agrícolas. Manual técnico.Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment, 39(3), 239–247. https://doi.org/10.1016/0034-4257(92)90089-3Che Man, Y. B., & Mirghani, M. E. S. (2000). Rapid method for determining moisture content in crude palm oil by Fourier transform infrared spectroscopy. Journal of the American Oil Chemists’ Society, 77(6), 631–637. https://doi.org/10.1007/s11746-000-0102-9Chen, 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/10.1080/07038992.1996.10855178Cho, 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.2005.12.011Chong, K. L., Kanniah, K. D., Pohl, C., & Tan, K. P. (2017). A review of remote sensing applications for oil palm studies. Geo-Spatial Information Science, 20(2), 184–200. https://doi.org/10.1080/10095020.2017.1337317Clarke, T. R., Moran, M. S., Barnes, E. M., Pinter, P. J., & Qi, J. (2001). Planar domain indices: A method for measuring a quality of a single component in two-component pixels. International Geoscience and Remote Sensing Symposium (IGARSS), 3(C), 1279–1281. https://doi.org/10.1109/igarss.2001.976818Cloutis, E. A., Connery, D. R., Dover, F. J., & Major, D. J. (1996). Airborne multispectral monitoring of agricultural crop status: Effect of time of year, crop type and crop condition parameter. International Journal of Remote Sensing, 17(13), 2579–2601. https://doi.org/10.1080/01431169608949094Cozzolino, D., & Moron, A. (2004). Exploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes. Animal Feed Science and Technology, 111(1–4), 161–173. https://doi.org/10.1016/j.anifeedsci.2003.08.001Crespo Gonzalez, J. J., Ruiz Villadiego, O. S., & Ospino Villalba, K. S. (2020). Determinación de nitrógeno foliar en palma de aceite con espectroscopía en el infrarrojo medio (mir) y cercano (nir) por el método de regresión de mínimos cuadrados parciales de componentes principales (pls). Revista de Investigación Agraria y Ambiental, 11(2), 43–57. https://doi.org/10.22490/21456453.3206Curran, P. J., Dungan, J. L., Macler, B. A., & Plummer, S. E. (1991). The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment, 35(1), 69–76. https://doi.org/10.1016/0034-4257(91)90066-FDash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25(23), 5403–5413. https://doi.org/10.1080/0143116042000274015Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741– 2759. https://doi.org/10.1080/014311699211778Datt, Bisun. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111–121. https://doi.org/10.1016/S0034-4257(98)000467Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9Eitel, J. U. H., Long, D. S., Gessler, P. E., & Smith, A. M. S. (2007). Using in-situ measurements to evaluate the new RapidEye TM satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, 28(18), 4183–4190. https://doi.org/10.1080/01431160701422213Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54(1), 38–48. https://doi.org/10.1016/0034-4257(95)00132-KEscadafal, R., Belghith, A., & Ben Moussa, H. (1994a). Indices spectraux pour la dégradation des milieux naturels en Tunisie aride. 6ème Symp. Int. Mesures Physiques et Signatures En Télédétection, ISPRS-CNES, 253–259.Escadafal, R., Belghith, A., & Ben Moussa, H. (1994b). Indices spectraux pour la dégradation des milieux naturels en Tunisie aride. 6ème Symp. Int. Mesures Physiques et Signatures En Télédétection, ISPRS-CNES, 253– 259.Filella, I., & Peñuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7), 1459–1470. https://doi.org/10.1080/01431169408954177Foody, G. M., Cutler, M. E., Mcmorrow, J., Pelz, D., Tangki, H., Boyd, D. S., & Douglas, I. A. N. (2001). Mapping the biomass of Bornean tropical rain forest from remotely sensed data ESTIMATION AND MAPPING. 379–387.Frampton, W. J., Dash, J., Watmough, G., & Milton, E. J. (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83– 92. https://doi.org/10.1016/j.isprsjprs.2013.04.007Galvão, L. S., Formaggio, A. R., & Tisot, D. A. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94(4), 523–534. https://doi.org/10.1016/j.rse.2004.11.012Gamon, J., Nuelas, J. P., & Field, C. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35–44. https://doi.org/10.1088/0305-4470/24/13/001Gandia, S., Fernández, G., García, J. C., & Moreno, J. (2004). Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaing. European Space Agency, (Special Publication) ESA SP, 578, 40– 48.Gao, B.-C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.Garrity, S. R., Eitel, J. U. H., & Vierling, L. A. (2011). Disentangling the relationships between plant pigments and the photochemical reflectance index reveals a new approach for remote estimation of carotenoid content. Remote Sensing of Environment, 115(2), 628–635. https://doi.org/10.1016/j.rse.2010.10.007Gitelson, A. A, & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 1812(July 2015), 2691–2697. https://doi.org/10.1080/014311697217558Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology, B: Biology, 22(3), 247– 252. https://doi.org/10.1016/1011-1344(93)06963-4Gitelson, Anatoly A., Buschmann, C., & Lichtenthaler, H. K. (1999). The chlorophyll fluorescence ratio F735F700 as an accurate measure of the chlorophyll content in plants. Remote Sensing of Environment, 69(3), 296– 302. https://doi.org/10.1016/S0034-4257(99)00023-1Gitelson, Anatoly A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282. https://doi.org/10.1078/0176-1617-00887Gitelson, Anatoly A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S00344257(96)00072-7Gitelson, Anatoly A., Keydan, G. P., & Merzlyak, M. N. (2006). Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33(11), 2–6. https://doi.org/10.1029/2006GL026457Gitelson, Anatoly A., Peng, Y., & Huemmrich, K. F. (2014). Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250m resolution data. Remote Sensing of Environment, 147, 108– 120. https://doi.org/10.1016/j.rse.2014.02.014Gitelson, Anatoly A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), n/a-n/a. https://doi.org/10.1029/2002gl016450Guyot, G., & Frederic, B. (1988). Utilisation de la Haute Resolution Spectrale pour Suivre L’etat des Couverts Vegetaux. Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing Aussios, France, 18-22 January 1988, 287, 279.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.2003.12.013Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. https://doi.org/10.1016/S00344257(02)00018-4He, L., Zhang, H. Y., Zhang, Y. S., Song, X., Feng, W., Kang, G. Z., Wang, C. Y., & Guo, T. C. (2016). Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. European Journal of Agronomy, 73, 170–185. https://doi.org/10.1016/j.eja.2015.11.017Henrich, V., Krauss, G., Götze, C., & Sandow, C. (2011). The IndexDatabase.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.2011.04.036Hernández-Clemente, R., Navarro-Cerrillo, R. M., & Zarco-Tejada, P. J. (2012). Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+DART simulations. Remote Sensing of Environment, 127, 298–315. https://doi.org/10.1016/j.rse.2012.09.014Hruschka, E. (2001). Data analysis: wavelength selection methods. Chapter 3. Near Infrared Technology in Agriculture and Food Industries, 2nd Edn (Eds P. Williams \& K. Norris), 39–160.Huete, A. (1998). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(0), 295–309.Huete, A. R., Liu, H. Q., Batchily, K., & VanLeeuwen, W. (1997). A comparison of vegetation indices global set of TM images for EOS–MODIS. Remote Sensing of Environment, 59(3), 440–451. https://doi.org/doi:10.1016/s00344257(96)00112-5Hunt, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., Perry, E. M., & Akhmedov, B. (2012). A visible band index for remote sensing leaf chlorophyll content at the Canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21(1), 103–112. https://doi.org/10.1016/j.jag.2012.07.020Hunt, E. R., & Rock, B. N. (1989). Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing of Environment, 30(1), 43–54. https://doi.org/10.1016/0034-4257(89)90046-1Infrasoft International. (2005). WinISI III Manual.James, H., Nawi, N. M., Wan, W. I., Mohamed, A., Juva, V., & Arulandoo, X. (2017). Application of Spectroscopy for Nutrient Prediction of Oil Palm. Journal of Experimental Agriculture International, 15(3), 1–9. https://doi.org/10.9734/JEAI/2017/31502Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. In Geocarto International (Vol. 2, Issue 1). https://doi.org/10.1080/10106048709354084 JJordan, C. F. (1969). Derivation of leaf-area index from quality of light on forest floor. Ecology, 50(4), 663–666. https://doi.org/10.1155/2012/651039Kim, Moon S., Dougtry, C. S. ., Chappelle, E., Mcmurtrey, J. ., & Walthall, C. . (1993). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar). 299–306.Kim, Moon Sung. (1994). The use of narrow spectral bands for improving remote sensing estimations of fractionally absorbed photosynthetically active radiation (fapar). 75.Kokaly, Raymond, I., & Clark, R. N. (1999). Spectroscopic Determination of Leaf Biochemistry: Use of Normalized Band-Depths and Laboratory Measurements and Possible Extension to Remote Sensing Measurements. Remote Sensing of Environment, 67, 267–287. http://aviris.jpl.nasa.gov/proceedings/workshops/98_docs/30.pdfLamb, D. W., Steyn-ross, M., Schaares, P., Hanna, M. M., Silvester, W., & SteynRoss, A. (2002). Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations. International Journal of Remote Sensing, 23(18), 3619–3648. https://doi.org/10.1080/01431160110114529Le 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.2003.09.004le Maire, Guerric, François, C., Soudani, K., Berveiller, D., Pontailler, J. Y., Bréda, N., Genet, H., Davi, H., & Dufrêne, E. (2008). Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112(10), 3846–3864. https://doi.org/10.1016/j.rse.2008.06.005Levin, N., Kidron, G. J., & Ben-Dor, E. (2007). Surface properties of stabilizing coastal dunes: Combining spectral and field analyses. Sedimentology, 54(4), 771–788. https://doi.org/10.1111/j.1365-3091.2007.00859.xLiaghat, S., Ehsani, R., Mansor, S., & Helmi, Z. M. (2014). International Journal of Remote Early detection of basal stem rot disease ( Ganoderma ) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. June, 37–41. https://doi.org/10.1080/01431161.2014.903353Liang, S. (2004). Quantitative remote sensing for land surface characterization.Lichtenthaler, H. K., Lang, M., Sowinska, M., Heisel, F., & Miehé, J. A. (1996). Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 148(5), 599–612. https://doi.org/10.1016/S0176-1617(96)80081-2Lobell, D. B., Asner, G. P., Law, B. E., & Treuhaft, R. N. (2001). Subpixel canopy cover estimation of coniferous forests in Oregon using SWIR imaging spectrometry. Journal of Geophysical Research Atmospheres, 106(D6), 5151–5160. https://doi.org/10.1029/2000JD900739Maccioni, A., Agati, G., & Mazzinghi, P. (2001). New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology B: Biology, 61(1–2), 52–61. https://doi.org/10.1016/S1011-1344(01)00145-2Marten, G., Shenk, J., & Barton, F. (1989). Near infrared reflectance spectroscopy (NIRS): Analysis of forage quality. Agriculture Handbook No. 63, 643, 1–110.Martens, H., & Næs, T. (1984). Multivariate Calibration. Chemometrics, 147–156. https://doi.org/10.1007/978-94-017-1026-8_5Martínez, L. J. (2017). Relationship between crop nutritional status , spectral measurements and Sentinel 2 images Relación entre el estado nutricional de los cultivos , las mediciones espectrales y las imágenes Sentinel 2. 35(2), 205–215. https://doi.org/10.15446/agron.colomb.v35n2.62857McMorrow, J. (2001). Linear regression modelling for the estimation of oil palm age from Landsat TM. International Journal of Remote Sensing, 22(12), 2243–2264. https://doi.org/10.1080/01431160117188McMurtrey, J. E., Chappelle, E. W., Kim, M. S., Meisinger, J. J., & Corp, L. A. (1994). Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment, 47(1), 36–44. https://doi.org/10.1016/0034- 4257(94)90125-2Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non- destructive optical detection of leaf senescence and fruit ripening. Physiologia Plantarum, 106(1), 135–141.Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24(14), 2855–2877. https://doi.org/10.1080/01431160210163074Miller, J. R., Hare, E. W., & Wu, J. (1990). Quantitative characterization of the vegetation red edge reflectance 1. An inverted-gaussian reflectance model. International Journal of Remote Sensing, 11(10), 1755–1773. https://doi.org/10.1080/01431169008955128Mirik, M., Michels, G. J., Kassymzhanova-Mirik, S., & Elliott, N. C. (2007). Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture, 57(2), 123–134. https://doi.org/10.1016/j.compag.2007.03.002Mulyadi, T., Hariyandi, Sudradjat, & Kustiyo. (2017). Nitrogen Content and Carbon Stock Prediction in Oil Palm using Satellite Image Analysis. 05(04), 677–683.Munévar, F. (2001). Palmas. Revista Palmas, 22(4), 9–17. https://publicaciones.fedepalma.org/index.php/palmas/article/view/888Munevar, F., Franco, P., & Arias, N. (2008). Guía general para el muestreo foliar y de suelos en palma de aceite (Issue 37).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.2003.06.001Ng, P. H. C., Totok, S., S.H., T., & K.J., G. (2013). Remote Sensing and Digital Technologies for Plantation Management. 34, 259–279.Ng, S. (2002). Nutrition and nutrient management of oil palm-New thrust for the future perspective. … of Potassium in India New Delhi. International Potash …, 415–429. http://www.ipipotash.org/udocs/Nutrition and Nutrient Management of the Oil Palm.pdfNooni, I. K., Duker, A. A., Van Duren, I., Addae-Wireko, L., & Osei Jnr, E. M. (2014). Support vector machine to map oil palm in a heterogeneous environment. International Journal of Remote Sensing, 35(13), 4778–4794. https://doi.org/10.1080/01431161.2014.930201Ollagnier, M., Ochis, R., & Martin, G. (1970). El abonamiento de la palma de aceite en el mundo. Fertilite, 36, 30–61.Oppelt, N., & Mauser, W. (2004). Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing, 25(1), 145–159. https://doi.org/10.1080/0143116031000115300Osco, L. P., Junior, J. M., Ramos, A. P. M., Furuya, D. E. G., Santana, D. C., Teodoro, L. P. R., Gonçalves, W. N., Baio, F. H. R., Pistori, H., Junior, C. A. da S., & Teodoro, P. E. (2020). Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques. Remote Sensing, 12(19), 1–17. https://doi.org/10.3390/rs12193237Owen, E. (1992). Fertilización de la palma africana (Elaeis Guineensis Jacq.) en Colombia. Palmas, 13(2), 39–64. http://www.embase.com/search/results?subaction=viewrecord&from=export&i d=L127279692Papeş, M., Tupayachi, R., Martínez, P., Peterson, A. T., & Powell, G. V. N. (2010). Using hyperspectral satellite imagery for regional inventories: A test with tropical emergent trees in the Amazon Basin. Journal of Vegetation Science, 21(2), 342–354. https://doi.org/10.1111/j.1654-1103.2009.01147.xPeng, Y., Nguy-Robertson, A., Arkebauer, T., & Gitelson, A. A. (2017). Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms. Remote Sensing, 9(3). https://doi.org/10.3390/rs9030226Penuelas, J., Frederic, B., & Filella, I. (1995). Semi-Empirical Indices to Assess Carotenoids/Chlorophyll-a Ratio from Leaf Spectral Reflectance. Photosynthetica, 31, 221–230.Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48(2), 135– 146. https://doi.org/10.1016/0034-4257(94)90136-8Peñuelas, J., Piñol, J., Ogaya, R., & Filella, I. (1997). International Journal of Remote Estimation of plant water concentration by the reflectance Water Index WI ( R900 / R970 ). International Journal of Remote, 18(13), 2869– 2875.Pereda, J. (2016). Calibración para determinar composición proximal de la quinua (Chenopodium quinoa W.) usando la espectroscopía de transmitancia en el infrarrojo cercano. Universidad Nacional Agraria La Molina.Perry, C. R., & Lautenschlager, L. F. (1984). Functional equivalence of spectral vegetation indices. Remote Sensing of Environment, 14(1–3), 169–182. https://doi.org/10.1016/0034-4257(84)90013-0Pu, R., Gong, P., & Yu, Q. (2008). Comparative analysis of EO-1 ALI and Hyperion, and Landsat ETM+ data for mapping forest crown closure and leaf area index. Sensors, 8(6), 3744–3766. https://doi.org/10.3390/s8063744Qi, 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)90134-1Rao, N. R., Garg, P. K., & Ghosh, S. K. (2007). Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture, 8(4–5), 173–185. https://doi.org/10.1007/s11119-007-9037-xRendana, M., Abdul, S., Lihan, T., Mohd, W., & Ali, Z. (2015). A Review of Methods for Detecting Nutrient Stress of Oil Palm in Malaysia. J. Appl. Environ. Biol. Sci. Journal of Applied Environmental and Biological Sciences, 5(6), 60–64. www.textroad.comRey, L., Ayala, I., Gómez, P., & Ruiz, R. (2006a). Selección de palmas élite en plantaciones comerciales. Boletín Técnico, 20, 30.Rey, L., Ayala, I., Gómez, P., & Ruiz, R. (2006b). Selección de palmas élite en plantaciones comerciales. Boletín Técnico, 20, 30.Reyniers, M., & Vrindts, E. (2006). Measuring wheat nitrogen status from space and ground-based platform. International Journal of Remote Sensing, 27(3), 549–567. https://doi.org/10.1080/01431160500117907Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7Roujean, 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/10.1016/0034-4257(94)00114-3Santana, D. C., Cotrim, M. F., Flores, M. S., Rojo Baio, F. H., Shiratsuchi, L. S., Silva Junior, C. A. da, Teodoro, L. P. R., & Teodoro, P. E. (2021). UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing. Remote Sensing Applications: Society and Environment, 23(April), 100534. https://doi.org/10.1016/j.rsase.2021.100534Schlemmera, M., Gitelson, A., Schepersa, J., Fergusona, R., Peng, Y., Shanahana, J., & Rundquist, D. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25(1), 47–54. https://doi.org/10.1016/j.jag.2013.04.003Selvaraja, S., Balasundram, S. K., Vadamalai, G., & Husni, M. H. A. (2013). Use of spectral reflectance to discriminate between potassium deficiency and orange spotting symptoms in oil palm (Elaeis guineensis). Life Science Journal, 10(4), 947–951.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. https://doi.org/10.1016/s00344257(02)00011-1Shafri, H. Z. M., & Hamdan, N. (2011). Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery. 32(8), 2095– 2115. https://doi.org/10.1080/01431161003662928Shapiro, A. S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality ( Complete Samples ) Published by : Biometrika Trust Stable URL : http://www.jstor.org/stable/2333709. Biometrika, 52(3/4), 591–611.Sims, D. A., & Gamon, J. A. (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(2–3), 337–354. https://doi.org/10.1016/S0034-4257(02)00010-XSmith, R. C. G., Adams, J., Stephens, D. J., & Hick, P. T. (1995). Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Australian Journal of Agricultural Research, 46(1), 113–125. https://doi.org/10.1071/AR9950113Tamburini, E., Ferrari, G., Marchetti, M. G., Pedrini, P., & Ferro, S. (2015). Development of FT-NIR models for the simultaneous estimation of chlorophyll and nitrogen content in fresh apple (Malus Domestica) leaves. Sensors (Switzerland), 15(2), 2662–2679. https://doi.org/10.3390/s150202662Teoh, K. C., & Chew, P. S. (1980). Fertiliser responses of oil palm on coastal clay soils in Peninsular Malaysia. Conference on Soil Science and Agricultural Development in Malaysia. Kuala Lumpur (Malaysia). 12-14 May 1980.Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Wiegand and Richardson, † International Center for Agricultural Research in the Dry Areas 1990), natural vegetation (Friedl et al., 1994), and in (ICARDA). Environ, 71(99), 158–182.Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering and Remote Sensing, 68(6), 607–621.Tucker, C. J., Elgin, J. H., McMurtrey, J. E., & Fan, C. J. (1979). Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sensing of Environment, 8(3), 237–248. https://doi.org/10.1016/0034-4257(79)90004-XTucker, Compton J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0Vincini, M., Frazzi, E., & D’Alessio, P. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, 9(5), 303–319. https://doi.org/10.1007/s11119-008-9075-zVincini, Massimo, & Frazzi, E. (2006). Angular dependence of maize and sugar beet VIs from directional CHRIS/Proba data. Cuore, January, 5–9.Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8), 1563–1575. https://doi.org/10.1080/01431169308953986Wang, F., Huang, J., Tang, Y., & Wang, X. (2007). New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Rice Science, 14(3), 195– 203. https://doi.org/10.1016/s1672-6308(07)60027-4Williams, P. C., & Sobering, D. C. (1996). How do we do it: a brief summary of the methods we use in developing near infrared calibrations. Near Infrared Spectroscopy: The Future Waves, 185–188.Witt, C., Fairhurst, T., & Griffiths, W. (2005). Key Principles of Crop and Nutrient Management in Oil Palm. Better Crops, 89(3), 27–31. http://www.oilpalm.net/ppiweb/bcrops.nsf/$webindex/DF49AA8010785C14852570490074C 097/$file/05-3p27.pdfWu. (2014). The Generalized Difference Vegetation Index (GDVI) for dryland characterization. Remote Sensing, 6(2), 1211–1233. https://doi.org/10.3390/rs6021211Wu, C., Niu, Z., Tang, Q., & Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148(8–9), 1230–1241. https://doi.org/10.1016/j.agrformet.2008.03.005Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., & Feng, J. (2009). Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, 149(6–7), 1015– 1021. https://doi.org/10.1016/j.agrformet.2008.12.007Zarco-Tejada, P. J., González-Dugo, V., Williams, L. E., Suárez, L., Berni, J. A. J., Goldhamer, D., & Fereres, E. (2013). A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sensing of Environment, 138, 38–50. https://doi.org/10.1016/j.rse.2013.07.024Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steadystate chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84(2), 283–294. https://doi.org/10.1016/S0034-4257(02)00113-XZarco-Tejada, Pablo J., & Miller, J. R. (1999). Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery. Journal of Geophysical Research Atmospheres, 104(D22), 27921–27933. https://doi.org/10.1029/1999JD900161CenipalmaUniversidad Nacional de ColombiaInvestigadoresORIGINALTESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdfTESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdfTesis de Maestría en Geomáticaapplication/pdf10162770https://repositorio.unal.edu.co/bitstream/unal/81270/3/TESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdf7012b71536bf66db0ccdb370cbe1dc93MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81270/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAILTESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdf.jpgTESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdf.jpgGenerated Thumbnailimage/jpeg5301https://repositorio.unal.edu.co/bitstream/unal/81270/5/TESIS_MAESTRIA_GEOMATICA_JHON_MONTERO.pdf.jpga90a258d939dd13b2b4aa3f2f41f0da0MD55unal/81270oai:repositorio.unal.edu.co:unal/812702023-08-03 23:03:38.542Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |