Open Access
Ciência Téc. Vitiv.
Volume 32, Number 1, 2017
Page(s) 33 - 41
Published online 09 August 2017
  • Asner G., Carlsonm K., Martin R., 2005. Substrate age and precipitation effects on Hawaiian forest canopies from spaceborne imaging spectroscopy. Remote Sens. Environ., 9, 457–467. [CrossRef] [Google Scholar]
  • Asner G.P., Martin R.E., Carlson K.M., Rascher U., Vitousek P.M., 2006. Vegetation-climate interactions among native and invasive species in Hawaiian rainforest. Ecosystems, 9, 1106–1117. [CrossRef] [Google Scholar]
  • Blackburn A.G., 1998. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int. J. Remote Sens., 19, 657–675. [CrossRef] [Google Scholar]
  • Deloire A., 2013. New method to determine optimal ripeness for white wine styles. Practical Winery Journal, Winter 2013, 75–79. [Google Scholar]
  • Dougherty P.H., 2012. Introduction to the geographical study of viticulture and wine production BT - In: The geography of wine: regions, terroir and techniques. 3–36. Dougherty H. P. (ed.). Springer, Netherlands. [CrossRef] [Google Scholar]
  • Gitelson A., 2012. Nondestructive estimation of foliar pigment (chlorophylls, carotenoids, and anthocyanins) contents. In: Hyperspectral remote sensing of vegetation. 141–166. Thenkabail P., Lyon J., Huete A. (eds). CRC Press, Boca Raton. [Google Scholar]
  • Gitelson A.A., Zur Y., Chivkunova O.B., Merzlyak M.N., 2002. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol., 75, 272–281. [CrossRef] [PubMed] [Google Scholar]
  • Hall A., Lamb D.W., Holzapfel B., Louis J., 2002. Optical remote sensing applications in viticulture - a review. Aust. J. Grape Wine Res., 8, 36–47. [CrossRef] [Google Scholar]
  • Johnson L.F., Herwitza, S., Dunagana, S., Lobitza, B., Sullivana D., Slyea R., 2003. Collection of ultra high spatial and spectral resolution image data over California vineyards with a small UAV. In: Proceedings of the International Symposium on Remote Sensing of Environment, Honolulu, HI, USA, 10–14 November 2003; p. 3. [Google Scholar]
  • Keskitalo J., Bergquist G., Gardeström P., Jansson S., 2005. A cellular timetable of Autumn senescence. Plant Physiol., 139, 1635–1648. [CrossRef] [PubMed] [Google Scholar]
  • Lamb D.W., Weedon M.M., Bramley R.G.V., 2004. Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimizing image resolution. Aust. J. Grape Wine Res., 10, 46–54. [CrossRef] [Google Scholar]
  • Matese A., Gennaro S.F. 2015. Technology in precision viticulture: a state of the art review. Intern. J. Wine Res., 7, 69–81. [CrossRef] [Google Scholar]
  • Meggio F., Zarco-Tejada P.J., Núñez L.C., Sepulcre-Cantó G., González M.R., Martín, P. 2010. Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices. Remote Sens. Environ., 114, 1968–1986. [CrossRef] [Google Scholar]
  • Merzlyak M.N., Gitelson A., 1995. Why and what for the leaves are yellow in Autumn? On the interpretation of optical spectra of senescing leaves (Acerplatanoides L.). J. Plant Physiol., 145, 315–320. [CrossRef] [Google Scholar]
  • Munné-Bosch S., Alegre L., 2000. The xanthophyll cycle is induced by light irrespective of water status in field-grown lavender (Lavandula stoechas) plants. Physiol. Plantarum, 108, 147–151. [CrossRef] [Google Scholar]
  • Munné-Bosch S., Penuelas J., 2003. Photo-and antioxidative protection during summer leaf senescence in Pistacia lentiscus L. grown under mediterranean field conditions. Ann. Bot., 92, 385–391. [CrossRef] [PubMed] [Google Scholar]
  • 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 Sens. Environ., 48, 135–46. [CrossRef] [Google Scholar]
  • Peñuelas J., Baret F., Filella I., 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31, 221–230. [Google Scholar]
  • Rakotomalala R., 2005. TANAGRA: un logiciel gratuit pour l'enseignement et la recherche. In: Actes de EGC'2005, RNTI-E-3, vol. 2, pp.697–702. [Google Scholar]
  • Rouse J.W., Deering D.W.Jr, Schell J.A., Harlan J.C., 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC type III final report: Greenbelt, Maryland, NASA, 371 p. [Google Scholar]
  • Sebela D., Olejnickova J., Zupcanova A., Sotolar R., 2012. Response of grapevine leaves to Plasmopara viticola infection by means of measurement of reflectance and fluorescence signals. Acta Univ. Agric. Silvic. Mendelianae Brun., 60, 229–238. [CrossRef] [Google Scholar]
  • 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 Sens. Environ., 81, 337–54. [CrossRef] [Google Scholar]
  • Zartaloudis Z.D., Iatrou M., Savvidis G., Savvidis K., Glavenas D., Kalogeropoulos K., Kyparissi S., 2015. Early and timely detection of Verticillium dahliae in olive growing using remote sensing. El Aceite de Oliva, Actas Simposio Expoliva 2015, Jaen, Espana, 6-8 Mayo. [Google Scholar]

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