Correlação de variáveis espectrais e estoque de carbono da biomassa aérea de sistemas agroflorestais

Bolfe, É. L.; Batistella, M.; Ferreira, Marcos César

doi:10.1590/s0100-204x2012000900011

Cited by 19 publications

(15 citation statements)

References 16 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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).…”

Section: Resultsmentioning

confidence: 68%

“…According to Ponzoni et al (2015) the reflectance of eucalyptus stands older than 6 years is influenced by Where: ND= Normalized Difference; NDVI= Normalized Difference Vegetation Index; G = basal area (m 2 .ha -1 ); HD = dominant height (m); V = volume (m 3 .ha -1 ); ns = not significant at 5% and *significant at 5%. 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).…”

Section: /8mentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Resultsmentioning

confidence: 68%

Section: /8mentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As demais variáveis (SR, NDVI, EVI 2 e Savi), todas provenientes de sensoriamento remoto, apresentaram correlações que variaram de fracas a moderadas (Tabela 1). Bolfe et al (2012) Almeida et al (2014) e Ferraz et al (2014. A superioridade destas estatísticas pode estar relacionada tanto à alta intensidade amostral (18% da área foi amostrada) quanto às características da resolução espacial e espectral do sensor LISS-III e às características da vegetação estudada.…”

Section: Resultsunclassified

Redes neurais artificiais para a modelagem do volume de madeira e biomassa do cerradão com dados de satélite

Miguel

Rezende

Leal

et al. 2015

Pesq. agropec. bras.

View full text Add to dashboard Cite

Resumo -O objetivo deste trabalho foi avaliar a eficácia da aplicação de modelos de análise de regressão e redes neurais artificiais (RNAs) na predição do volume de madeira e da biomassa acima do solo, da vegetação arbórea em área de cerradão. Volume de madeira e biomassa foram estimados com equações alométricas desenvolvidas para a área de estudo. Os índices de vegetação, como variáveis preditoras, foram estimados a partir de imagens do sensor LISS-III, e a área basal foi determinada por medições na floresta. A precisão das equações foi verificada pela correlação entre os valores estimados e observados (r), erro-padrão da estimativa (Syx) e gráfico residual. As equações de regressão para o volume de madeira total e do fuste (0,96 e 0,97 para r, e 11,92 e 9,72% para Syx, respectivamente) e para a biomassa (0,91 e 0,92 para r, e 22,73 e 16,80% para Syx, respectivamente) apresentaram bons ajustes. As redes neurais também apresentaram bom ajuste com o volume de madeira (0,99 e 0,99 para r, e 4,93 e 4,83% para Syx) e a biomassa (0,97 e 0,98 r, e 8,92 e 7,96% para Syx, respectivamente). A área basal e os índices de vegetação foram eficazes na estimativa do volume de madeira e biomassa para o cerradão. Os valores reais de volume de madeira e biomassa não diferiram estatisticamente dos valores estimados pelos modelos de regressão e redes neurais (χ² ns ); contudo, as RNAs são mais acuradas.Termos para indexação: índice de vegetação, inventário florestal, produção, regressão, sensoriamento remoto. Artificial neural networks for modeling wood volume and aboveground biomass of tall Cerrado using satellite dataAbstract -The objective of this work was to evaluate the effectiveness of regression models and artificial neural networks (ANNs) in predicting wood volume and aboveground biomass of arboreal vegetation in area of tall Cerrado (a forest, savanna-like vegetation). Wood volume and biomass were estimated with allometric equations developed for the studied area. The vegetation indices, as predictor variables, were estimated from LISS-III sensor imagery, and the basal area was determined from field measurements. Equation precision was verified by the correlation between estimated and observed values (r), standard error of estimate (Syx), and by the residual plot. The regression equations for total wood volume and bole volume (0.96 and 0.97 for r, and 11.92 and 9.72% for Syx, respectively), as well as for aboveground biomass (0.91 and 0.92 for r, and 22.73 and 16.80% for Syx, respectively) showed good adjustments. The neural networks also showed good adjustments for both wood volume (0.99 and 0,99 for r, and 4.93 and 4.83% for Syx) and biomass (0.97 and 0.98 for r, and 8.92 and 7.96% for Syx, respectively). Basal area and vegetation indices were effective in estimating wood volume and biomass for the tall cerrado vegetation. Measured wood volume and aboveground biomass did not differ statistically from the predicted values by both the regression models and neural networks (χ² ns ); however, the ANNs are more accurate.

show abstract

“…When this occurs, NDVI becomes insensitive to biomass changes, which may have reflexes on productivity (POVH et al, 2008). For this reason, the simple index ratio (RVI, RNIR/RR) and inverse ratio (IRVI, RR/RNIR) have also been studied to predict the biomass in areas with higher intensity of vegetation, since these indexes are less susceptible to saturation (HATFIELD & PRUEGER, 2010;LI et al, 2010;BOLFE et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Use of active canopy sensors to discriminate wheat response to nitrogen fertilization under no-tillage

2016

View full text Add to dashboard Cite

Spectral reflectance sensors may be useful in discriminating biomass and nitrogen status in plants. A field experiment was carried out on a loamy Typic Hapludox under no-tillage in Ponta Grossa, Parana State, Brazil, aiming to evaluate the efficiency of active canopy sensors (GreenSeeker 505 and Crop Circle ACS-470) to discriminate wheat response to nitrogen fertilization by determining the dry mass, nitrogen status, and grain yield. A randomized complete block design was used and four treatments were replicated 12 times. The treatments consisted of nitrogen application, as urea, at the rates of 0, 50, 100, and 150 kg ha -1 . The nitrogen rates were applied in topdressing at tillering of wheat crop. The readings from the sensors were sensitive to changes caused by nitrogen rates application and yet had close and significant correlations with the dry biomass production and nitrogen uptake by wheat plants. GreenSeeker 505 and Crop Circle ACS-470 sensors showed similar efficiency in discriminating biomass production and the nutritional status in the wheat crop related to nitrogen. Wheat grain yield was high and it did not follow the gains in the production of dry mass in the shoot.

show abstract

Correlação de variáveis espectrais e estoque de carbono da biomassa aérea de sistemas agroflorestais

Cited by 19 publications

References 16 publications

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

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

Redes neurais artificiais para a modelagem do volume de madeira e biomassa do cerradão com dados de satélite

Use of active canopy sensors to discriminate wheat response to nitrogen fertilization under no-tillage

Contact Info

Product

Resources

About