SciELO - Scientific Electronic Library Online

vol.25 número2Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR DataSoil Compaction Due to Wood Harvesting Traffic at Different Extraction Distances índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados



  • nova página do texto(beta)
  • Inglês (pdf)
  • Artigo em XML
  • Como citar este artigo
  • SciELO Analytics
  • Curriculum ScienTI
  • Tradução automática


Links relacionados


Floresta e Ambiente

versão impressa ISSN 1415-0980versão On-line ISSN 2179-8087

Floresta Ambient. vol.25 no.2 Seropédica  2018  Epub 08-Fev-2018 

Original Article

Forest Management

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

Aliny Aparecida dos Reis1  * 

Fausto Weimar Acerbi Júnior1 

José Marcio de Mello1 

Luis Marcelo Tavares de Carvalho1 

Lucas Rezende Gomide1 

1Departamento de Ciências Florestais, Universidade Federal de Lavras – UFLA, Lavras/MG, Brazil


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


Reliable information and knowledge on forest structures is essential to plan sustainable use of forest resources (Mello et al., 2006). This information is obtained through forest inventories (Scolforo & Mello, 2006) that are responsible for a significant amount of forest production costs, due to the time spent allocating and measuring the plots in the field (Silva et al., 2014).

Some studies have demonstrated the potential of geotechnologies to support the traditional forest inventory carried out in the field, since they help with acquisition of information regarding forest growth, mainly through the use of data collected by sensors onboard artificial satellites (Halme & Tomppo, 2001; Mäkelä & Pekkarinen, 2004; Hall et al., 2006; Tomppo et al., 2009; Viana et al., 2012). Consistent, repetitive and systematic observation of forest growth, obtained through remotely sensed images, provides a better characterization of the differences in growth and yield throughout the forest plantation area, since the information can be collected for all pixels in different portions of the electromagnetic spectrum (Ponzoni et al., 2015).

Landsat 5 TM images can be useful in forest management when combined with field measurements (Reis et al., 2015; Silva et al., 2014; Viana et al., 2012; Mäkelä & Pekkarinen, 2004). Spectral information captured by remotely sensed images has a strong relationship with dendrometric variables, both for planted forests (Berra et al., 2012; Canavesi et al., 2010; Watzlawick et al., 2009) and native forests (Reis et al., 2015; Mäkelä & Pekkarinen, 2004; Lu et al., 2004).

However, there is still a need for studies to assess the influence of forest stand age on these relationships, and especially, at what age the images best represent each forest variable and therefore are useful as auxiliary data for the management of planted forest. Thus, the present study aims to contribute to an understanding of the correlations between spectral data and dendrometric variables, regarding the age of Eucalyptus sp. stands. In this sense, the specific objectives of this study were: (a) to assess the correlation 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; (b) to understand how stand age influences these correlations.


The Eucalyptus clonal stands being studied are located in the Lagoa Grande municipality, northwest of Minas Gerais state, Brazil, situated between the geographic coordinates 17º43'00”S - 46° 32'00”W and 17º44'00”S - 46°33'00”W, at an altitude of 560 meters (Figure 1). According to the Köppen climatic classification, the climate in this region is Aw, classified as a tropical savanna climate, with drier months during the winter.

Figure 1 Geographic location of the study area and sampling grid. 

A set of 20 Eucalyptus stands planted in the months of April and May 2004, with initial spacing of 3 × 2 m and 3 × 3 m, was selected, totaling an area of 362.2 hectares. The Continuous Forest Inventory (CFI) was carried out on 35 permanent plots of 400 m2 from 2006 to 2011. CFI consists of a network of permanent plots monitored yearly to verify changes occurring in the forests. Usually, field measurements for Eucalyptus stands in Brazil start at two or three years old (Kanegae et al., 2006).

The sampling procedure adopted was the unencumbered systematic sampling design, allocating approximately one circular plot per 10 ha of forest. The plots were georeferenced in the field with GPS Garmin 60CSx. In all plots, the circumference at 1.3 m above the ground (circumference at breast height) of all stems was measured using a metric tape, as well as the total height of the first fifteen trees with normal stems (without bifurcation or any other defect) and dominant tree height according to Assmann's definition (Assmann, 1970), if these trees were not among the fifteen heights already measured in the plot. To estimate the heights of all trees in the plot, a hypsometric model was used. Next, the volume per plot and age was obtained by fitting a volume model based on scaled trees in the field.

Spectral data were obtained from Landsat 5 TM images, with spatial resolution of 30 m, on the dates of 06/17/2006, 07/22/2007, 06/06/2008, 06/25/2009, 06/12/2010 and 03/27/2011, corresponding with field data collection, in orbit 220, point 072, in bands TM1 (0.45-0.52 µm), TM2 (0.52-0.60 µm), TM3 (0.63-0.69 µm), TM4 (0.76-0.90 µm), TM5 (1.55-1.75 µm) and TM7 (2.18-2.35 µm).

The Landsat 5 TM images were obtained from the USGS database (United States Geological Survey), already presenting radiometric calibration, and geometric and atmospheric corrections. The USGS, agency responsible for making the Landsat time series available, uses the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) application, which calculates surface reflectance by compensating atmospheric absorption and scattering effects, thereby generating the correct reflectance of the top of the atmosphere and surface (Feng et al., 2013). In addition, vegetation indices were calculated from the spectral bands of Landsat 5 TM images (Table 1).

Table 1 Vegetation indices used in this study. 

Vegetation indices Formulation Reference
TM4/3 TM4/TM3 Jordan (1969)
TM5/3 TM5/TM3 Jordan (1969)
TM5/4 TM5/TM4 Jordan (1969)
TM5/7 TM5/TM7 Jordan (1969)
NDVI (TM4 - TM3)/(TM4 + TM3) Rouse et al. (1973)
ND53 (TM5 - TM3)/(TM5 + TM3) Huete et al. (2002)
ND54 (TM5 - TM4)/(TM5 + TM4) Huete et al. (2002)
ND57 (TM5 - TM7)/(TM5 + TM7) Huete et al. (2002)
ND32 (TM3 - TM2)/(TM3 + TM2) Huete et al. (2002)
SAVI [(TM4 - TM3)/(TM4 + TM3 + L)].(1.5) Huete (1988)
MSAVI [(2TM4+1 )- (2TM4+1)2-8(TM4-2TM3) ]/2 Qi et al. (1994)

Where: TM= Thematic Mapper; ND= Normalized Difference; NDVI= Normalized Difference Vegetation Index; SAVI= Soil-Adjusted Vegetation Index; MSAVI = Modified Soil-Adjusted Vegetation Index.

We used the pixels located in the centroid of each forest inventory plot to extract the reflectance band values and the vegetation indices for each year of measurement. After that, for each year of measurement the Pearson correlation (r) analysis was carried out between basal area (G), dominant height (HD), volume (V), and values of spectral bands and vegetation indices. From these correlations, the relationship between the dendrometric characteristics of Eucalyptus stands and their spectral response in Landsat 5 TM images was investigated at different ages. Pearson coefficients, as well as the p-values associated with the test statistics were generated with R software, using the stats package and the cor.test function (R Core Team, 2015).


Dendrometric variables presented direct and inverse correlations with Landsat 5 TM reflectance bands and such a relationship varied according to forest age. Table 2 summarizes the correlation coefficients between dendrometric variables and Landsat 5 TM reflectance bands for all measurement ages.

Table 2 Pearson’s coefficients (r) between dendrometric variables and Landsat 5 TM spectral bands. 

Age (years) Variables TM1 TM2 TM3 TM4 TM5 TM7
2 G 0.297ns 0.168ns -0.166ns 0.236ns -0.611* -0.412*
HD -0.181ns -0.444* -0.090ns -0.409* -0.028ns -0.078ns
V 0.204ns -0.057ns -0.159ns 0.025ns -0.478* -0.360*
3 G -0.129ns 0.158ns -0.380* 0.570* -0.515* -0.461*
HD -0.151ns -0.632* -0.349* -0.482* -0.145ns -0.133ns
V -0.191ns -0.320ns -0.484* 0.052ns -0.432* -0.380*
4 G -0.114ns 0.097ns -0.332ns 0.539* -0.502* -0.447*
HD -0.325ns -0.620* -0.253ns -0.218ns 0.003ns -0.027ns
V -0.366* -0.433* -0.456* 0.231ns -0.375* -0.349*
5 G -0.117ns 0.162ns -0.186ns 0.799* -0.666* -0.684*
HD 0.256ns -0.133ns -0.109ns -0.241ns 0.038ns 0.013ns
V 0.095ns -0.063ns -0.206ns 0.380* -0.448* -0.505*
6 G -0.112ns 0.138ns -0.429* 0.646* -0.608* -0.647*
HD -0.313ns -0.703* -0.309ns -0.537* 0.139ns 0.185ns
V -0.288ns -0.400* -0.444* 0.001ns -0.251ns -0.228ns
7 G -0.303ns -0.176ns -0.602* 0.459* -0.655* -0.653*
HD -0.291ns -0.735* 0.032ns -0.692* 0.316ns 0.298ns
V -0.388* -0.495* -0.514* 0.060ns -0.401* -0.414*

Where: TM1, TM2, TM3, TM4, TM5, and TM7 = reflectance in the bands of blue, green, red, near infrared and mid infrared, respectively; G = basal area (m2.ha-1); HD = dominant height (m); V = volume (m3.ha-1); ns= not significant at 5% and *significant at 5%.

Basal area (G) presented significant correlations with TM4, TM5 and TM7 bands across all ages, with the exception of the TM4 band at age 2 years. At 2 years old, eucalyptus stands are in the initial growth stage, and therefore, there is a greater increase in height in comparison to what is observed for the diameter and crowns. Thus, as canopy closure has not yet occurred, the reflectance values are contaminated by the reflectance of exposed soil, resulting in an absence of significant correlations between basal area and spectral bands in this age. On the other hand, at ages 3, 6 and 7 years, basal area presented significant correlation with the TM3 band. In the case of healthy vegetation, the presence of pigments in the leaves such as chlorophyll, carotenes and xanthophylls are responsible for a greater absorption of electromagnetic radiation in the visible region, mainly in the red region (Ponzoni et al., 2012). Other studies found similar results with high correlations between basal area and TM3, TM4, TM5 and TM7 bands (Berra et al., 2012; Bolfe et al., 2012; Almeida et al., 2014).

The volume (V) presented significant correlation values with TM5 and TM7 bands for all ages, except at age 6 years. These bands were able to efficiently detect the relationship between forest canopy reflectance and the dendrometric variables such as volume and basal area, as also observed by Hall et al. (2006) and Thenkabail et al. (2003). Basal area derives from tree diameter, which is directly related to canopy size, which in turn will determine canopy reflectance. Therefore, this relationship is captured by satellite images that detect the electromagnetic energy reflected by tree crowns that formed the forest canopy. Lu et al. (2004) explored the relationships between forest parameters and their spectral responses in Landsat 5 TM images from three areas in the Eastern Amazon basin. The authors observed that the TM5 band is the spectral band that is most strongly correlated with forest variables, corroborating the results found in this study.

Dominant height (HD) presented the best correlations with the TM2 band at all ages, with the exception of the measurement at age 5 years. This fact is due to a severe drought which occurred in that year, according to data recorded in the field, that caused mortality and loss of vigor of the remaining individuals, thereby affecting the correlations with dominant height at this age. Hall et al. (2006) used Landsat 7 ETM+ bands to study a native forest dominated by Pinus species in Canada and found negative correlations between tree height and spectral response. These results corroborate the negative correlations found in this study between dominant height and spectral bands. According to Meng et al. (2009) the TM2 band is usually applied for assessments of vegetation vigor, and since more productive sites tend to have more vigorous trees and higher values of dominant height, this explains the higher correlations presented by TM2 band and dominant height.

The low correlations found between dendrometric variables and the TM1 band can be explained by the low contribution of this band to vegetation analysis, since as vegetation cover increases, little change is observed in the reflectance values of TM1 band (Meng et al., 2009).

Correlation coefficients between dendrometric variables and vegetation indices are shown in Tables 3 and 4.

Table 3 Pearson’s coefficients (r) between dendrometric variables and vegetation indices (normalized reasons). 

Age (years) Variable NDVI ND53 ND54 ND57 ND32
2 G 0.250ns -0.398* -0.656* -0.026ns -0.296ns
HD -0.069ns 0.094ns 0.188ns 0.047ns 0.375*
V 0.165ns -0.271ns -0.430* 0.026ns -0.065ns
3 G 0.608* -0.037ns -0.720* 0.281ns -0.572*
HD 0.113ns 0.346* 0.124ns 0.101ns 0.408*
V 0.483* 0.210ns -0.392* 0.233ns -0.102ns
4 G 0.443* -0.229ns -0.538* 0.299ns -0.485*
HD 0.115ns 0.584* 0.070ns 0.021ns 0.141ns
V 0.427* 0.302* -0.350* 0.219ns -0.252ns
5 G 0.812* -0.611* -0.878* 0.457* -0.480*
HD -0.026ns 0.224ns 0.108ns -0.019ns 0.053ns
V 0.543* -0.280ns -0.538* 0.373* -0.178
6 G 0.771* -0.172ns -0.733* 0.515* -0.538*
HD -0.083ns 0.542* 0.361* -0.255ns 0.287ns
V 0.382* 0.258ns -0.174ns 0.100ns -0.102ns
7 G 0.637* -0.278ns -0.596* 0.559* -0.405*
HD -0.451* 0.560* 0.550* -0.383* 0.574*
V 0.337* 0.058ns -0.242ns 0.300ns -0.088ns

Where: ND= Normalized Difference; NDVI= Normalized Difference Vegetation Index; G = basal area (m2.ha-1); HD = dominant height (m); V = volume (m3.ha-1); ns= not significant at 5% and *significant at 5%.

Table 4 Pearson’s coefficients (r) between dendrometric variables and vegetation indices (simple reasons and complex vegetation indices). 

Age (years) Variable TM4/3 TM5/3 TM5/4 TM5/7 SAVI MSAVI
2 G 0.199ns -0.393* -0.660* 0.010ns 0.316ns 0.318ns
HD -0.135ns 0.097ns 0.164ns 0.042ns -0.355* -0.296ns
V 0.098ns -0.261ns -0.444* 0.052ns 0.108ns 0.130ns
3 G 0.608* -0.015ns -0.712* 0.327ns 0.698* 0.706*
HD 0.104ns 0.350* 0.114ns 0.116ns -0.287ns -0.161ns
V 0.463* 0.229ns -0.397* 0.271ns 0.270ns 0.357*
4 G 0.436* -0.223ns -0.494* 0.273ns 0.529* 0.528*
HD 0.126ns 0.594* 0.023ns 0.015ns -0.088ns -0.054ns
V 0.438* 0.317ns -0.353* 0.188ns 0.328ns 0.355*
5 G 0.763* -0.625* -0.872* 0.430* 0.857* 0.858*
HD -0.124ns 0.202ns 0.040ns -0.048ns -0.176ns -0.180ns
V 0.438* -0.305 -0.583* 0.336* 0.466* 0.463*
6 G 0.675* -0.145ns -0.755* 0.506* 0.724* 0.734*
HD -0.177ns 0.550* 0.315ns -0.281ns -0.434* -0.387*
V 0.255ns 0.284ns -0.218ns 0.082ns 0.115ns 0.150ns
7 G 0.564* -0.264ns -0.623* 0.523* 0.525* 0.546*
HD -0.493* 0.542* 0.510* -0.421* -0.641* -0.610*
V 0.250ns 0.066ns -0.282ns 0.250ns 0.144ns 0.177ns

Where: TM= Thematic Mapper; SAVI= Soil-Adjusted Vegetation Index; MSAVI = Modified Soil-Adjusted Vegetation Index; G = basal area (m2.ha-1); HD = dominant height (m); V = volume (m3.ha-1); ns= not significant at 5% and *significant at 5%.

Vegetation indices that include TM4 and TM5 bands in their composition, such as ND54 and TM5/4, had significant correlations with basal area at all forest ages. With the exception of the measurement at age 2 years, the NDVI, TM4/3, SAVI and MSAVI indices also presented significant correlations with basal area.

Dominant height presented closer correlation with vegetation indices in the measurements at ages 4, 6, and 7 years. At ages 4 and 6 years, dominant height was more closely correlated with ND53 and TM5/3 indices. At age 7 years, dominant height presented significant correlations with all vegetation indices, but mainly with SAVI (r = -0.641) and MSAVI (r = -0.610) indices. Since the canopy reflectance of Eucalyptus stands at ages 4 and 6 years is mostly composed by the reflectance of green leaves with little participation of soil background and shadows, the dominant height presented high correlations with TM3 and TM5 bands and vegetation indices that include these bands in their composition. According to Ponzoni et al. (2015) the reflectance of eucalyptus stands older than 6 years is influenced by green leaf loss and the greater participation of litter (including dry branches deposited on the ground), therefore explaining the highest correlations with the SAVI and MSAVI indices at age 7 years, since these indices are constructed to attenuate soil influence on vegetation reflectance (Bolfe et al., 2012).

Volume presented the best correlations with NDVI, ND54 and TM5/4 indices at age 5 years. However, at ages 6 and 7 years, volume presented significant correlations only with NDVI index, but of low magnitudes (r = 0.382 and r = 0.377, respectively). Berra et al. (2012), studying the correlations between dendrometric variables of Eucalyptus sp. stands and vegetation indices obtained from Landsat 5 TM images, found a positive correlation of 0.79 between NDVI and wood volume (m3.ha-1) and of 0.82 between TM4/3 and wood volume (m3.ha-1). In our study, NDVI and TM4/3 indices were also positively related to wood volume (m3.ha-1) but to a lesser degree. Vegetation indices that use the infrared spectral bands suffer less atmospheric dispersion (Jensen, 2009) and this fact can explain the higher correlations presented by these indices and dendrometric variables in comparison with indices that use the visible region bands in their composition. Moreover, these indices present a non-linear relationship with biophysical characteristics such as volume (Ponzoni et al., 2012), which means that these indices reach saturation under medium and high productivity conditions, which is the case of eucalyptus stands at more advanced ages. This fact explains the lower magnitude of correlations found in our study between volume and the NDVI and TM4/3 indices at ages 6 and 7 years.

Berra et al. (2012) and Pacheco et al. (2012) applied correlation analysis between dendrometric variables and spectral data to develop prediction models and concluded that remote sensing data have great potential to explain the forest dendrometric variables. However, these authors only analyzed one occasion of forest stand measurements, therefore not considering the temporal changes that may occur between the correlations during forest stand growth, as observed in our study.

For the estimation of Eucalyptus sp. stand variables such as basal area and volume at different ages, we recommend the use of spectral data obtained from TM4 and TM5 bands of Landsat 5 TM images as well as the vegetation indices that are composed by these bands (i.e. ND54 and TM5/4) as auxiliary data for the traditional forest inventory. For dominant height estimation, we recommend the use of spectral data from the TM2 band of Landsat 5 TM images, independent of eucalyptus stand age.


TM4 and TM5 bands of Landsat 5 TM images and vegetation indices that include these bands in their composition, such as ND54 and TM5/4, presented the closest correlations with the dendrometric variables analyzed.

Eucalyptus sp. stand age directly influences the correlations between spectral data and dendrometric variables, and cannot be disregarded in the spectral characterization of the forest variables. Basal area and volume correlated better with spectral data when measured at age 5 years, while dominant height correlated more closely with spectral data when measured at age 7 years.

TM4 and TM5 bands and ND54 and TM5/4 indices have the potential to be used as auxiliary data to manage Eucalyptus sp. stands, at all stages of forest stand growth.


We thank CAPES (Coordenadoria de Aperfeiçoamento do Pessoal do Ensino Superior) for the scholarship provided to the first author.


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. [ Links ]

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

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988; 25(3): 295-309. [ Links ]

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. [ Links ]

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

Jordan CF. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969; 50(4): 663-666. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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

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. [ Links ]

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.orgLinks ]

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. [ Links ]

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. [ Links ]

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

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

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. [ Links ]

Received: April 14, 2016; Accepted: March 03, 2017

*Aliny Aparecida dos ReisDepartamento de Ciências Florestais, Universidade Federal de Lavras – UFLA, Câmpus Universitário, CP 3037, CEP 37200-000, Lavras, MG, Brazil e-mail:

Creative Commons License This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.