Influência de métodos de coleta de dados espectrorradiométricos sob índices de vegetação em eucalipto
DOI:
https://doi.org/10.5902/2236117017745Parole chiave:
Sensoriamento remoto, Reflectância, Padrão espectralAbstract
Objetivou-se verificar a influência de métodos de coleta de folhas em índices de vegetação: posição de coleta na copa, orientação cardeal e estação do ano em Eucalyptus grandis. Os dados foram coletados em dois povoamentos distintos em duas árvores dominantes cada. Foram coletadas 15 folhas em cada posição e em cada orientação, acrescidas de uma amostra de mesmo tamanho no centro. A coleta foi realizada em um único dia, das 13:00 as 15:00 horas. O material foi analisado com o espectrorradiômetro FieldSpec®3 e a resposta espectral de cada orientação e posição foi dada pela média da leitura espectral das folhas. Foram analisados 60 diferentes índices de vegetação de acordo com a literatura. Para avaliar o efeito da estação sobre os índices foram realizadas coletas nas quatro estações do ano, configurando 4 tratamentos. Foram utilizados modelos mistos para analisar a influência nos fatores (posição e estação) e sua interação. Os dados foram considerados aninhados em cada orientação, dentro de cada árvore e de cada área. A análise estatística foi realizada no software R com o pacote nlme. Dos índices de vegetação analisados, seis apresentaram dependência da posição/orientação e estação de coleta, sendo necessário em posteriores estudos a obediência de suas dependências.
Downloads
Riferimenti bibliografici
BARNES, J. D. et al. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environmental and Experimental Botany, v. 32, n. 2, p. 85–100, 1992.
BLACKBURN, G. Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote sensing of environment, v. 4257, n. 98, p. 273–285, 1998.
BROGE, N. H.; LEBLANC, E. 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, v. 76, n. 2, p. 156–172, 2001.
BUSCHMANN, C.; NAGEL, E. In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, v. 14, n. 4, p. 711–722, 1993.
CHAPPELLE, E. W.; KIM, M. S.; MCMURTREY III, J. E. 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, v. 39, n. 3, p. 239–247, 1992.
DALMOLIN, R. S. D.; PEDRON, F. DE A. Solos do município de Santa Maria. Ciência & Ambiente, n. 38, p. 59–78, 2009.
DASH, J.; CURRAN, P. J. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, v.25, n.23, p. 5403-5413, 2004.
DATT, B. Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a +b, and Total Carotenoid Content in Eucalyptus Leaves. Remote Sensing of Environment, v. 66, n. 2, p. 111–121, 1998.
DATT, B. A. A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves. Journal of Plant Physiology, v. 154, n. 1, p. 30–36, 1999.
DAUGHTRY, C.; WALTHALL, C. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, v. 74, n. 2, p. 229–239, 2000.
FAVA, F. et al. Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, v. 11, n. 4, p. 233–243, ago. 2009.
GAMON, J. A.; SERRANO, L.; SURFUS, J. S. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, v. 112, n. 4, p. 492–501, 1997.
GITELSON, A. A. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v. 32, n. 8, p. L08403, 2005.
GITELSON, A.; MERZLYAK, M. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of plant physiology, v. 148, p. 494–500, 1996.
GITELSON, A.; MERZLYAK, M. N. Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoidesL. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology, v. 143, n. 3, p. 286–292, 1994.
GUYOT, G.; BARET, F. Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux Spectral Signatures of Objects in Remote Sensing. In: Proceedins 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France, ESA SP – 287 (Paris), 18-22 January, p. 279-286, 1988.
GUYOT, G.; GUYON, D.; RIOM, J. Factors affecting the spectral response of forest canopies: a review. Geocarto International, v. 4, n. 3, p. 3–18, 1989.
HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, v. 81, n. 2-3, p. 416–426, 2002.
HANSEN, P. M.; SCHJOERRING, J. K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote sensing of environment, v. 86, n. 4, p. 542–553, 2003.
HELDWEIN, A. B.; BURIOL, G. A.; STRECK, N. A. O clima de Santa Maria. Ciência & Ambiente, v. 38, p. 43–58, 2009.
JASPER, J.; REUSCH, S.; LINK, A. Active sensing of the N status of wheat using optimized wavelength combination: impact of seed rate, variety and growth stage. In: E. J. Van Henten, D. Goense, & C. Lokhorst (Eds.), Precision agriculture’09 Proceedings of the 7th European conference on precision agriculture. Wageningen, The Netherlands: Academic Publishers, July 5-8, p. 23-30, 2009.
JORDAN, C. F. Derivation of leaf-area index from quality of light on the forest floor. Ecology, v.50, n.4, p. 663–666, 1969.
LE MAIRE, G. et al. Modeling annual production and carbon fluxes of a large managed temperate forest using forest inventories, satellite data and field measurements. Tree physiology, v. 25, n. 7, p. 859–72, 2005.
MCMURTREY, J. E. et al. Field canopy and leaf level fluorescence for distinguishing plant condition differences due to nitrogen fertilization level Geoscience and Remote Sensing Symposium, 1994. IGARSS’94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International. Anais...IEEE, 1994.
PEÑUELAS, J. et al. Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sensing of Environment, v. 48, n…, p. 135–146, 1994.
PEÑUELAS, J.; BARET, F.; FILELLA, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, v. 31, n. 2, p. 221–230, 1995.
PINHEIRO, J. et al. R Development Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-97, 2010.
R CORE TEAM. R: a language and environment for statistical computing [Internet]. Vienna (Austria): R Foundation for Statistical Computing, 2013.
READ, J. J. et al. Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of environmental quality, v. 31, n. 5, p. 1442–1452, 2002.
RONDEAUX, G.; STEVEN, M.; BARET, F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, v. 55, n. 2, p. 95–107, 1996.
ROUJEAN, J.-L.; BREON, F.-M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, v. 51, n. 3, p. 375–384, 1995.
SCHEPERS, J.S. et al. Transmittance and Reflectance Measurements of CornLeaves from Plants with Different Nitrogen and Water Supply. Journal of plant physiology, v. 148, p. 523–529, 1996.
SERRANO, L.; FILELLA, I.; PENUELAS, J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, v. 40, p. 723–731, 2000.
SIMS, D. A; GAMON, J. A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, v. 81, n. 2-3, p. 337–354, 2002.
SMITH, J. R. Single color extraction and image query. Proceedings., International Conference on Image Processing, v. 3, p. 528–531, 1995.
STEDDOM, K. et al. Remote detection of rhizomania in sugar beets. Phytopathology, v. 93, n. 6, p. 720–726, 2003.
STROPPIANA D. et al. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field crops research, v. 111, n. 1, p. 119–129, 2009.
VOGELMANN, J.; ROCK, B.; MOSS, D. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, v. 14, n. 8, p. 1563–1575, 1993.
ZARCO-TEJADA, P. J. et al. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, v. 39, n. 7, p. 1491–1507, 2001.
