Structural characterization of canopies of Eucalyptus spp. using radiometric data from TM/Landsat 5

Pacheco, Ludmila Roque Ferraz; Ponzoni, Flávio Jorge; Santos, Sandra Benfica dos; Filho, Clódis de Oliveira Andrades; Mello, Márcio Pupin; Campos, Rogério Costa

doi:10.1590/s0104-77602012000100013

Cited by 3 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O comportamento espectral de uma vegetação sadia demonstra picos de maior reflectância na faixa do verde e infravermelho próximo, enquanto que nas demais faixas a energia é absorvida em maiores quantidades (PONZONI et al, 2012;JENSEN, 2009).…”

Section: Resultsunclassified

“…O aumento do volume relaciona-se diretamente com o crescimento em diâmetro e altura dos indivíduos, e para povoamentos jovens, esse fator culmina na maior produção de folhas verdes, com elevada atividade fotossintética (Berra, 2013). Esse processo, segundo Ponzoni et al (2012) faz com que povoamentos jovens apresentem valores elevados de reflectância na região do infravermelho próximo e menor na região do visível.…”

Section: Resultsunclassified

“…Esses resultados são consistentes com os encontrados por Berra (2013), Goergen (2014) e Pacheco et al (2012). A faixa espectral compreendida pela banda B5 apresenta alta reflectância para a vegetação, o que proporciona maior sensibilidade às mudanças ocorridas na cobertura florestal, exibindo maior relação com as variáveis biofísicas.…”

Section: Resultsunclassified

“…Goergen (2014) ao modelar o volume com imagens TM obteve R² de 0,48 para povoamentos de E. dunnii aos 5 anos, já para E. urograndis aos 2,2 anos obteve um R² de 0,57 e 0,45 aos 4,2 anos. Pacheco et al (2012) ao utilizar dados espectrais do satélite Landsat 5/TM na caracterização do dossel de Eucalyptus spp obteve melhores respostas com a banda B5. Assim, essa variável compôs o modelo de estimativa do DAP, altura total e densidade de plantas, sendo que esses modelos explicaram 61%, 50% e 68% da variabilidade dos dados, respectivamente.…”

Section: Resultsunclassified

“…A caracterização dos dosséis baseou-se nestas faixas espectrais devido a maior interação com a vegetação conforme descrito na literatura (PONZONI, et al 2012;JENSEN, 2009).…”

Section: Methodsunclassified

See 4 more Smart Citations

USO DE IMAGENS DE MÉDIA RESOLUÇÃO ESPACIAL PARA O MONITORAMENTO DE DOSSÉIS DE Eucalyptus grandis

Alba¹,

Marchesan²,

Tramontina³

et al. 2017

RSA

View full text Add to dashboard Cite

Resumo -O objetivo desse estudo foi identificar plantios comerciais de Eucalyptus grandis com diferentesidades e estimar o volume total de madeira utilizando dados radiométricos do sensor OLI/Landsat 8. Demarcaram-se parcelas em povoamentos aos 4, 18 e 23 anos de idade, nas quais foi mensurado o volume total (m³/ha). A imagem OLI/Landsat 8 foi convertida para reflectância de superfície utilizando a correção radiométrica e, posteriormente gerados os índices de vegetação ARVI, DVI, GNDVI, MVI, NDVI, SAVIL=0,25, SAVIL=0,50 e SR. A diferenciação da idade dos dosséis foi estabelecida baseando-se na reflectância das bandas B4 e B5. Os dados espectrais e o volume total de madeira foram submetidos à análise de correlação e regressão múltipla baseada no método Stepwise. Os povoamentos foram diferenciados uma vez que o aumento da idade ocasionou a redução da reflectância na banda B5 e aumento na banda B4. Para a estimativa do volume total, apenas os povoamentos aos 4 anos de idade apresentaram bom ajuste, sendo o modelo composto pelo índice SAVI, explicando 74% da variabilidade do volume de madeira. Os dados espectrais extraídos de imagens OLI/Landsat 8 mostraram-se sensível às variações das características dos indivíduos nas diferentes fases de crescimento dos povoamentos, assim como na estimativa das variações do volume total.Palavras-chave -Sensoriamento remoto, índices de vegetação, volume estimado. Abstract -The objective of this study was to identify commercial Eucalyptus grandis plantations with differentages and estimate the total wood volume using OLI/Landsat 8 radiometric data. Plots were demarcated in stands at 4, 18 and 23 years of age, in which it was measured total volume (m³/ha). The OLI/Landsat 8 image was converted to surface reflectance using the radiometric calibration and, subsequently generated the vegetation indices ARVI, NDVI, NDVI, MVI, NDVI, SAVIL=0.25 SAVIL=0.50 and SR. The differentiation of the age of the canopies was established based on the reflectance of the B4 and B5 bands. The spectral and total volume of wood were subjected to correlation analysis and adjustment of the data to the regression models, leading to selection of variables through the Stepwise -Forward method. The stands were differentiated since the increase of the age caused the reduction in reflectance on B5 band and increase in band B4. For the estimation of total volume, only the stands at 4 years of age presented good adjustment, being the model composed by the SAVI index, explaining 74% of the volume variability of wood. The spectral data extracted from OLI/Landsat 8 images showed to be sensitive to variations in the characteristics of individuals in different growth stages of the stands, as well as in the estimation of changes in the total volume.

show abstract

Section: Resultsunclassified

“…A caracterização dos dosséis baseou-se nestas faixas espectrais devido a maior interação com a vegetação conforme descrito na literatura (PONZONI, et al 2012;JENSEN, 2009).…”

Section: Methodsunclassified

See 3 more Smart Citations

USO DE IMAGENS DE MÉDIA RESOLUÇÃO ESPACIAL PARA O MONITORAMENTO DE DOSSÉIS DE Eucalyptus grandis

Alba¹,

Marchesan²,

Tramontina³

et al. 2017

RSA

View full text Add to dashboard Cite

show abstract

Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Reis

Carvalho

Mello

et al. 2018

N.Z. j. of For. Sci.

View full text Add to dashboard Cite

Background: In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and ensure the efficiency of the wood supply chain. In the past decade, advances in remote sensing and computational methods have yielded new tools, techniques, and technologies that have led to improvements in forest management and forest productivity assessments. Our aim was to estimate and map the basal area and volume of Eucalyptus stands through the integration of forest inventory, remote sensing, parametric, and nonparametric methods of spatial prediction. Methods: This study was conducted in 20 5-year-old clonal stands (362 ha) of Eucalyptus urophylla S.T.Blake x Eucalyptus camaldulensis Dehnh. The stands are located in the northwest region of Minas Gerais state, Brazil. Basal area and volume data were obtained from forest inventory operations carried out in the field. Spectral data were collected from a Landsat 5 TM satellite image, composed of spectral bands and vegetation indices. Multiple linear regression (MLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) methods were used for basal area and volume estimation. Using ordinary kriging, we spatialised the residuals generated by the spatial prediction methods for the correction of trends in the estimates and more detailing of the spatial behaviour of basal area and volume. Results: The ND54 index was the spectral variable that had the best correlation values with basal area (r = − 0.91) and volume (r = − 0.52) and was also the variable that most contributed to basal area and volume estimates by the MLR and RF methods. The RF algorithm presented smaller basal area and volume errors when compared to other machine learning algorithms and MLR. The addition of residual kriging in spatial prediction methods did not necessarily result in relative improvements in the estimations of these methods. Conclusions: Random forest was the best method of spatial prediction and mapping of basal area and volume in the study area. The combination of spatial prediction methods with residual kriging did not result in relative improvement of spatial prediction accuracy of basal area and volume in all methods assessed in this study, and there is not always a spatial dependency structure in the residuals of a spatial prediction method. The approaches used in this study provide a framework for integrating field and multispectral data, highlighting methods that greatly improve spatial prediction of basal area and volume estimation in Eucalyptus stands. This has potential to support fast growth plantation monitoring, offering options for a robust analysis of high-dimensional data.

show abstract

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Structural characterization of canopies of Eucalyptus spp. using radiometric data from TM/Landsat 5

Cited by 3 publications

References 10 publications

USO DE IMAGENS DE MÉDIA RESOLUÇÃO ESPACIAL PARA O MONITORAMENTO DE DOSSÉIS DE Eucalyptus grandis

USO DE IMAGENS DE MÉDIA RESOLUÇÃO ESPACIAL PARA O MONITORAMENTO DE DOSSÉIS DE Eucalyptus grandis

Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

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

Contact Info

Product

Resources

About