Josicleda Domiciano Galvíncio, Rejane MM Pimentel


Insurance and efficiency to monitoring and the use of interacting methods for an accurate diagnosis of the vegetation, mainly those from tropical regions, are essentials to contribute to future development management actions. Remote sensing, biological traits associating structural and biochemical data will permit evaluate the behavioral picture of a dominant specie in vegetation that cover great areas. This study aimed to identify the leaf spectral signature features and structural traits related to dominant specie and photosynthesis implications, relating it with environmental stress consequences and discussing critically monitoring using remote sensing methods. Multispectral analysis and hyperspectral spectroradiometer “FieldSpec Handheld” by Analytical Spectral Devices (ASD) from georeferenced points were made. Perceptible differences were detected for mean values of chlorophyll a and b from field data. The use of Hyperion hyperspectral image showed potential to infer the condition of vegetation regeneration analyzed and different intensities of chlorophyll


Hyperspectral, vegetation stress; pigments,;remote sensing method.

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Ferri, C.P., Formaggio, A.R., Schiavinato, M.A. 2004. Narrow band spectral indexes for chlorophyll determination in soybean canopies [Glycine max (L.) Merril]. Braz. J. Plant Physiol., 16(3):131-136.

Galvíncio, J.D., Pimentel, R.M.M., Fernandes, J.G. 2010. Relação da temperatura do ar e do solo com a quantidade de clorofila a (Chl a) e clorofila b (Chl b) em Jurema Preta (Mimosa tenuiflora (Willd) Poiret) no semiárido do Nordeste do Brasil. Revista Brasileira de Geografia Física, 3(01), 33-40.

Gitelson, A., Merzlyak, M.N., Lichtenthaler, K. 1996. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148, 501-508.

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, 337-352.

Jarocinska, A., Zagajewski, B. 2009. Remote Sensing Tools for Analysis of Vegetation Condition in Extensively Used Agricultural Areas. International journal, 1-6.

Laudien, R., Bareth, G., Doluschitz, R. 2004. Comparison of Remote Sensing Based Analysis of Crop Diseases by Using High Resolution Multispectral and Hyperspectral Data - Case Study: Rhizoctonia solani in Sugar Beet - 1 2 3R. Proc. 12th Int. Conf. on Geoinformatics − Geospatial Information Research: Bridging the Pacific and Atlantic University of Gävle, Sweden, 7-9.

Malenovský, Z., Ufer, C., Lhotáková, Z., Clevers, J.G.P.W., Schaepman, M.E., Albrechtová, J., Cidlín, P. 2006. A new hyperspectral index for chlorophyll estimation of a forest canopy: Area under curve normalised to maximal band depth between 650-725 NM. EARSeL eProceedings 5(2), 161-172.

Wamunyima, S. 2005. Estimating Fresh Grass Biomass at Landscape Level Using Hyperspectral Remote Sensing. Thesis.

Wu, C., Niu, Z., Tang, Q., Huang, W. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148, 1230-1241.

DOI: https://doi.org/10.5935/2237-2202.20120001

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Journal of Hyperspectral Remote Sensing - eISSN: 2237-2202