Floresta e Ambiente
https://www.floram.org/article/doi/10.1590/2179-8087.017015
Floresta e Ambiente
Original Article Forest Management

Relationship Between Spectral Data and Dendrometric Variables in Eucalyptus sp. Stands

Aliny Aparecida dos Reis; Fausto Weimar Acerbi Júnior; José Marcio de Mello; Luis Marcelo Tavares de Carvalho; Lucas Rezende Gomide

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Abstract

ABSTRACT: The present study aims: (a) to assess the correlations between forest stand characteristics (viz., basal area, dominant height, and volume) and the reflectance values derived from Landsat 5 TM spectral bands as well as from vegetation indices and (b) to understand how Eucalyptus sp. stand age influences these correlations. Sampling data comprised a set of 35 permanent plots from a forest inventory conducted annually between 2006 and 2011. Spectral data were derived from Landsat 5 TM images. The results showed that TM4 and TM5 spectral bands, as well as vegetation indices ND54 and TM5/4, were better correlated with basal area and volume, while the TM2 spectral band was better correlated with dominant height. Eucalyptus sp. stand age directly influenced the correlations between spectral data and forest stand characteristics, and could not be disregarded in the spectral characterization of the forest variables.

Keywords

vegetation indices, spectral bands, volume, basal area, Landsat 5 TM

References

Almeida AQ, Mello AA, Dória AL No, Ferraz RC. Relações empíricas entre características dendrométricas da Caatinga brasileira e dados TM Landsat 5. Pesquisa Agropecuária Brasileira 2014; 49(4): 306-315. http://dx.doi.org/10.1590/S0100-204X2014000400009.

Assmann E. The principles of forest yield study. Oxford: Pergamon Press; 1970.

Berra EF, Brandelero C, Pereira RS, Sebem E, Goergen LCG, Benedetti ACP et al. Estimativa do volume total de madeira em espécies de eucalipto a partir de imagens de satélite Landsat. Ciência Florestal 2012; 22(4): 853-864. http://dx.doi.org/10.5902/198050987566.

Bolfe EL, Batistella M, Ferreira MC. Correlação de variáveis espectrais e estoque de carbono da biomassa aérea de sistemas agroflorestais. Pesquisa Agropecuária Brasileira 2012; 47(9): 1261-1269. http://dx.doi.org/10.1590/S0100-204X2012000900011.

Canavesi V, Ponzoni FJ, Valeriano MM. Estimativa de volume de madeira em plantios de Eucalyptus spp. utilizando dados hiperespectrais e dados topográficos. Revista Árvore 2010; 34(3): 539-549. http://dx.doi.org/10.1590/S0100-67622010000300018.

Feng M, Sexton JO, Huang C, Masek JG, Vermote EF, Gao F et al. Global Surface refl ectance products from Landsat: Assessment using coincident MODIS observations. Remote Sensing of Environment 2013; 134: 276-293. http://dx.doi.org/10.1016/j.rse.2013.02.031.

Hall RJ, Skakun RS, Arsenault EJ, Case BS. Modeling forest stand structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. Forest Ecology and Management 2006; 225(1-3): 378-390. http://dx.doi.org/10.1016/j.foreco.2006.01.014.

Halme M, Tomppo E. Improving the accuracy of multisource forest inventory estimates by reducing plot location error: a multicriteria approach. Remote Sensing of Environment 2001; 78(3): 321-327. http://dx.doi.org/10.1016/S0034-4257(01)00227-9.

Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988; 25(3): 295-309. http://dx.doi.org/10.1016/0034-4257(88)90106-X.

Huete AR, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002; 83(1-2): 195-213. http://dx.doi.org/10.1016/S0034-4257(02)00096-2.

Jensen JR. Sensoriamento remoto do ambiente: uma perspective em recursos terrestres. São José dos Campos: Parêntese; 2009.

Jordan CF. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969; 50(4): 663-666. http://dx.doi.org/10.2307/1936256.

Kanegae H Jr, Scolforo JRS, Mello JM, Oliveira AD. Avaliação de interpoladores estatísticos e determinísticos como instrumento de estratificação de povoamentos clonais de Eucalyptus sp. Cerne 2006; 12(2): 123-136.

Lu D, Mausel P, Brondízio E, Moran E. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management 2004; 198(1-3): 149-167. http://dx.doi.org/10.1016/j.foreco.2004.03.048.

Mäkelä H, Pekkarinen A. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management 2004; 196(2-3): 245-255. http://dx.doi.org/10.1016/j.foreco.2004.02.049.

Mello JM, Oliveira MS, Batista JLF, Justiniano PR Jr, Kanegae H Jr. Uso do estimador geoestatístico para predição volumétrica por talhão. Floresta 2006; 36(2): 251-260. http://dx.doi.org/10.5380/rf.v36i2.6454.

Meng Q, Cieszewski C, Madden M. Large area forest inventory using Landsat ETM+: a geostatistical approach. ISPRS Journal of Photogrammetry and Remote Sensing 2009; 64(1): 27-36. http://dx.doi.org/10.1016/j.isprsjprs.2008.06.006.

Pacheco LRF, Ponzoni FJ, Santos SB, Andrades CO Fo, Mello MP, Campos RC. Structural characterization of canopies of Eucalyptus spp. using radiometric data from TM/Landsat 5. Cerne 2012; 18(1): 105-116. http://dx.doi.org/10.1590/S0104-77602012000100013.

Ponzoni FJ, Pacheco LRF, Santos SB, Andrades CO Fo. Caracterização espectro-temporal de dosséis de Eucalyptus spp. mediante dados radiométricos TM/Landsat 5. Cerne 2015; 21(2): 267-275. http://dx.doi.org/10.1590/01047760201521021457.

Ponzoni FJ, Shimabukuro YE, Kuplich TM. Sensoriamento remoto da vegetação. 2. ed. São Paulo: Oficina de Textos; 2012.

Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. A modified soil adjusted vegetation index. Remote Sensing of Environment 1994; 48(2): 119-126. http://dx.doi.org/10.1016/0034-4257(94)90134-1.

R Core Team. R: a language and environment for statistical computing [software]. Vienna: R Foundation for Statistical Computing; 2015 [cited 2016 Apr 14]. Available from: http://www.R-project.org

Reis AA, Mello JM, Acerbi FW Jr, Carvalho LMT. Estratificação em cerrado sensu stricto a partir de imagens de sensoriamento remoto e técnicas geoestatísticas. Scientia Forestalis 2015; 43(106): 377-386.

Rouse J, Haas R, Schell J, Deering D, Harlan J. Monitoring the vernal advancements and retrogradation (greenwave effect) of nature vegetation. Greenbelt: NASA; 1973. NASA/GSFC Final Report.

Scolforo JRS, Mello JM. Inventário florestal. Lavras: UFLA/FAEPE; 2006.

Silva ST, Mello JM, Acerbi FW Jr, Reis AA, Raimundo MR, Silva ILG et al. Uso de imagens de sensoriamento remoto para estratificação do cerrado em inventários florestais. Pesquisa Florestal Brasileira 2014; 34(80): 337-343. http://dx.doi.org/10.4336/2014.pfb.34.80.742.

Thenkabail PS, Hall J, Lin T, Ashton MS, Harris D, Enclona EA. Detecting floristic structure and pattern across topographic and moisture gradients in a mixed species Central African forest using IKONOS and Landsat-7 ETM+ images. International Journal of Applied Earth Observation and Geoinformation 2003; 4(3): 255-270. http://dx.doi.org/10.1016/S0303-2434(03)00006-0.

Tomppo EO, Gagliano C, Natele F, Katila M, McRoberts RE. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. Remote Sensing of Environment 2009; 113(3): 500-517. http://dx.doi.org/10.1016/j.rse.2008.05.021.

Viana H, Aranha J, Lopes D, Cohen WB. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecological Modelling 2012; 226: 22-35. http://dx.doi.org/10.1016/j.ecolmodel.2011.11.027.

Watzlawick LF, Kirchner FF, Sanquetta CR. Estimativa de biomassa e carbono em floresta com araucária utilizando imagens do satélite IKONOS II. Ciência Florestal 2009; 19(2): 169-181. http://dx.doi.org/10.5902/19805098408.
 

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