Determination of relationship between data obtained from biomass derived from field and remote sensing ndvi along the arroyo Chucul (Pcia. Córdoba)

  • V. Santa Universidad Nacional de Rio Cuarto, Cátedra de Ecología Vegetal, Fac. de Agronomía y Veterinaria.
  • M. J. Rosa Universidad Nacional de Rio Cuarto, Cátedra de Ecología Vegetal, Fac. de Agronomía y Veterinaria.
  • N. Mónaco Universidad Nacional de Rio Cuarto, Cátedra de Ecología Vegetal, Fac. de Agronomía y Veterinaria.
  • A. Heguiabehere Universidad Nacional de Rio Cuarto, Cátedra de Ecología Vegetal, Fac. de Agronomía y Veterinaria.

Keywords:

NDVI, Green biomasa, Grassland, Correlation

Abstract

The values of green biomass (Bv) measured in a grassland were associated with index values of normalized difference vegetation index (NDVI) from satellite data in three relict natural grassland on stream Chucul, from his beginning site 1 (32° 49'21, 0” S and 64° 24`07.0”W) until its demise in plain area: site 3 (33° 06` 25.5`` S and 63° 32`49.1`` W). Under the hypothesis of correspondence of Bv and data obtained by images, the aim of this work is to determine the relationship between measured field data and satellite data in natural grasslands. Seasonally during the 2009-2011 cycle were sampled at random with 10 replicates of 0.25 m2 recording floristic list. To determine Bv in each plot the biomass was cut and separated in green and dry compartments and dryed to constant weight. For digital analysis bands 3 and 4 of Landsat 5 TM image (Path 228 Row 083) were used for

each site close to the sampling date. The highest values of green biomass were determined for site 3, in December 2011: 189.6 g/m2 and in March for sites 1 and 2: 105.74 and 115.22 g/m2. Among all the observed values of biomass and NDVI estimated the correlation coefficient was highest at site 3 (R= 0.50). The results of the work for site 3 validate the hypothesis and indicats of the aptitude of digital images for study the status and changes in vegetation.

Downloads

Download data is not yet available.

References

Asrar G., M. Fuchus, E.T. Kanemasu & J.L. hatfield. 1984. Estimation absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76: 300-306.

Baret F. & G. Guyot. 1989. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 35: 161-173.

Boyd W. 1986. Correlation of rangelands brush canopy cover with Landsat MSS data. J. Range Manage. 39: 268- 271.

Chen J. 1995. Integrating AVHRR derived NDVI with Ecological Modelling. Middle States Goegrapher. Vol. 28.

Chuvieco E. 1996. Fundamentos de Teledetección Espacial. Eds RIALP.S.A. Madrid, España.

Curran P.J. 1983. Multispectral remote sensing for the estimation of green leaf area index. Philosophical Transactions of the Royal Society of London A. 309: 257-270.

Gerberman A.J., J.A. Cuellar & H.W. Gausman, 1984. Relationship of sorghum canopy variables to reflected infrared radiation for 2 wavelengths and 2 wavebands. Photogramm. Eng. Rem.S. 50: 209-214.

Mársico L. & A. Altesor. 2011. Relación entre la riqueza de especies vegetales y la productividad en pastizales naturales. Ecol. Austral 21: 101-109.

Paruelo J.M., H.E. Epstein, W.K Lauenroth & I.C. Burke. 1997. A NPP estimates from NDVI for the Central Grassland Region of the US. Ecology 78: 953-958.

Prince S.D. 1991. A model of regional primary production for use with coarse resolution satellite data. Int. J.Remote S. 12: 1313-1330.

Pucheta E., E. Ferrero, L. Heil & C. Schneide. 2004. Modelos de regresión para la estimación de la biomasa aérea en un pastizal de montaña de Pampa de Achala (Córdoba, Argentina). Agriscientia 21(1): 23-30.

Pueyo J.M., L. Lacopini, Y. Bonini, J. Fonseca, R. Ludi & R. Grancell. 2003. Productividad del campo natural. Publicaciones. EEA INTA Paraná, Entre Ríos.

Pueyo J.M., L. Lacopini, Y. Bonini, J. Fonseca R. Ludi & R. Grancell. 2005. Productividad del Pastizal Natural. EEA Concepción del Uruguay.

Sánchez Rodríguez E, M.á. Torres Crespo, A. Fernández Palacios Carmona, M. Aguilar Alba, I. Pino Serrato & L. Granado Ruiz. 2000. Comparación del NDVI con el PVI y el SAVI como Indicadores para la Asignación de Modelos de Combustible para la Estimación del Riesgo de Incendios en Andalucía. Tecnologías Geográficas para el Desarrollo Sostenible. Departamento de Geografía. Universidad de Alcalá. pp. 164-174.

Sellers P.J. 1985. Canopy reflectance, photosynthesis, and transpiration. Int. J. Remote S. 6: 1335-1372.

Rueter B. & M. Bertolani 2005. Evaluación de la Productividad, Degradación y Ritmos Bioclimáticos en Ecosistemas áridos del Distrito Central, Mediante la Utilización de Percepción Remota. Nat. Patagón.1: 66-72.

Tucker C.J. 1977. Resolution of grass canopy biomass classes. Photogramm. Eng. Rem. S. 43: 1059-1067.

Published

2020-03-21

How to Cite

Santa, V., Rosa, M. J., Mónaco, N., & Heguiabehere, A. (2020). Determination of relationship between data obtained from biomass derived from field and remote sensing ndvi along the arroyo Chucul (Pcia. Córdoba). Semiárida, 22, 157–162. Retrieved from https://cerac.unlpam.edu.ar/ojs/index.php/semiarida/article/view/4474