Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Authors

  • Marcos Cicarini Hott Embrapa Gado de Leite, Rua Eugênio do Nascimento, no 610, Dom Bosco, CEP 36083‑330 Juiz de Fora, MG, Brazil.
  • Luis Marcelo Tavares de Carvalho Universidade Federal de Lavras, Campus Universitário, Caixa Postal 3037, CEP 37200‑000 Lavras, MG, Brazil
  • Mauro Antonio Homem Antunes Universidade Federal Rural do Rio de Janeiro, BR 465, Km 7, CEP 23890‑000 Seropédica, RJ, Brazil.
  • Polyanne Aguiar Santos Universidade Federal de Lavras, Campus Universitário, Caixa Postal 3037, CEP 37200‑000 Lavras, MG, Brazil
  • Tássia Borges Arantes Universidade Federal de Lavras, Campus Universitário, Caixa Postal 3037, CEP 37200‑000 Lavras, MG, Brazil
  • João Cesar de Resende Embrapa Gado de Leite, Rua Eugênio do Nascimento, no 610, Dom Bosco, CEP 36083‑330 Juiz de Fora, MG, Brazil.
  • Wadson Sebastião Duarte da Rocha Embrapa Gado de Leite, Rua Eugênio do Nascimento, no 610, Dom Bosco, CEP 36083‑330 Juiz de Fora, MG, Brazil.

DOI:

https://doi.org/10.1590/S1678-3921.pab2016.v51.22829

Keywords:

growth index, pastures, remote sensing, time series, Zona da Mata

Abstract

The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16‑day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

Author Biographies

Marcos Cicarini Hott, Embrapa Gado de Leite, Rua Eugênio do Nascimento, no 610, Dom Bosco, CEP 36083‑330 Juiz de Fora, MG, Brazil.

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4760763H4

Luis Marcelo Tavares de Carvalho, Universidade Federal de Lavras, Campus Universitário, Caixa Postal 3037, CEP 37200‑000 Lavras, MG, Brazil

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4794466E6

Mauro Antonio Homem Antunes, Universidade Federal Rural do Rio de Janeiro, BR 465, Km 7, CEP 23890‑000 Seropédica, RJ, Brazil.

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4723058E5

Polyanne Aguiar Santos, Universidade Federal de Lavras, Campus Universitário, Caixa Postal 3037, CEP 37200‑000 Lavras, MG, Brazil

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4423831U8

Tássia Borges Arantes, Universidade Federal de Lavras, Campus Universitário, Caixa Postal 3037, CEP 37200‑000 Lavras, MG, Brazil

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4435183U4

João Cesar de Resende, Embrapa Gado de Leite, Rua Eugênio do Nascimento, no 610, Dom Bosco, CEP 36083‑330 Juiz de Fora, MG, Brazil.

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4783308U6

Wadson Sebastião Duarte da Rocha, Embrapa Gado de Leite, Rua Eugênio do Nascimento, no 610, Dom Bosco, CEP 36083‑330 Juiz de Fora, MG, Brazil.

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4794528P2

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Additional Files

Published

2016-09-26

How to Cite

Hott, M. C., de Carvalho, L. M. T., Antunes, M. A. H., Santos, P. A., Arantes, T. B., de Resende, J. C., & da Rocha, W. S. D. (2016). Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor. Pesquisa Agropecuaria Brasileira, 51(7), 858–868. https://doi.org/10.1590/S1678-3921.pab2016.v51.22829

Issue

v.51, n.7, jul. 2016

Section

REMOTE SENSING