Allometric models for aboveground biomass estimation of the mangrove Avicennia schaueriana

Estrada, Gustavo Calderucio Duque; Soares, Mário Luiz Gomes; Santos, Daniel Medina Corrêa; Fernandez, Viviane; Almeida, Paula Maria Moura de; Estevam, Marciel Rocha de Medeiros; Machado, Maria Rita Olyntho

doi:10.1007/s10750-014-1878-5

Cited by 22 publications

(9 citation statements)

References 49 publications

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“…SCHAEFFER-NOVELLI (2005) and ESTRADA et al (2014), who found similar results for these compartments. For the reproductive parts, it should also be noted the seasonal behavior of this species with respect to its reproduction, as discussed earlier.…”

Section: Discussionsupporting

confidence: 57%

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

et al. 2017

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Santos et al.: Models for estimating the biomass of mangroves 44Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangleThe development of species-specific allometric models is critical to the improvement of aboveground biomass estimates, as well as to the estimation of carbon stock and sequestration in mangrove forests. This study developed allometric equations for estimating aboveground biomass of Rhizophora mangle in the mangroves of the estuary of the São Francisco River, in northeastern Brazil. Using a sample of 74 trees, simple linear regression analysis was used to test the dependence of biomass (total and per plant part) on size, considering both transformed (ln) and not-transformed data. Best equations were considered as those with the lowest standard error of estimation (SEE) and highest adjusted coefficient of determination (R values, probably attributed to the seasonal nature of this compartment. "Basal Area² × Height" showed to be the best predictor, present in most of the bestfitted equations. The models presented here can be considered reliable predictors of the aboveground biomass of R. mangle in the NE-Brazilian mangroves as well as in any site were this widely distributed species present similar architecture to the trees used in the present study. , o que pode ser atribuído ao caráter sazonal deste compartimento. "Área basal²×Altura" demonstrou ser o melhor preditor, presente na maioria das equações melhor ajustadas. Os modelos aqui apresentados podem ser considerados preditores confiáveis da biomassa aérea de R. mangle no manguezal do Nordeste brasileiro, bem como em qualquer local onde esta espécie de ampla distribuição assemelhe-se à arquitetura das árvores utilizadas no presente estudo. AbstrAct resumoDescritores: Equações alométricas, Biomassa aérea, Manguezal, Análises de regressão, Rhizophora mangle.

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Section: Discussionsupporting

confidence: 57%

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

et al. 2017

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“…The eight most important variables for the M2sp.rf model were p99, p50, max, d06, p95, d08, avg, and qav, in that order. Including species dominance information as an additional variable did not improve the model prediction capability (not shown); although, wood density among mangrove species varies from 0.53 g·cm −3 (Laguncularia racemosa) to 0.68 g·cm −3 (Rhizophora mangle) [45,54]. The mangrove aboveground biomass map ( Figure 7) was generated using the best performing M2sp.autopls model.…”

Section: Resultsmentioning

confidence: 99%

“…Plot sizes varied between 121 and 560 m 2 , depending on the density of individuals and their homogeneity in terms of structural features and species composition. The following tree measures were obtained during field surveys in 2010-2011, according to the methods proposed by [45,53] and described in [54,55]: (i) individual tree height; (ii) stem diameter at breast height (DBH); (iii) identification of the plant at the species level; (iv) living status of the stems (alive or dead). DBH of all stems >1 m tall were obtained using a measuring tape.…”

Section: Ground Datamentioning

confidence: 99%

“…The measurement of total tree height from the base of the tree to the top of the canopy was obtained with an optical rangefinder or a measuring pole. Mangrove biomass in the study area was calculated using two approaches: (a) using species and local-specific allometric equations (referred to as "species-specific" throughout the text) developed for Rio de Janeiro mangrove forests [45,54], and (b) using pantropical mangrove equations provided by Komiyama et al [44] and Chave et al [56] ( Table 1). Note that for all the equations in Table 1, we used the species-specific wood density value from [45,54] (i.e, A. schaueriana = 0.6751 g·cm −3 , L. racemosa = 0.5292 g·cm −3 , and R. mangle = 0.6868 g·cm −3 ).…”

Section: Ground Datamentioning

confidence: 99%

“…In each case, the predicted model was tested with the lidar metrics derived from the original polygon of each plot (without buffer, named Model 1sp) for species-specific AGB estimate, and the extended polygons (with the 5-m buffer) for all types of AGB estimates: species-specific AGB estimate [45,54] (Model 2sp), Komiyama et al [44] (Model 3K), and Chave et al [56] (Model 4C). The accuracy of the different models was evaluated in terms of root-mean-square error (RMSE) and coefficient of determination between prediction and observation (R 2 ).…”

Section: Biomass Models: Predicting Biomass On a Plot Level From Lidamentioning

confidence: 99%

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Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

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Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km 2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R 2 (calibration) = 0.89, R 2 (validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha −1 , and RMSE(validation) = 14.80 t·ha −1 ). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

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Nitrogen dynamics in subtropical fringe and basin mangrove forests inferred from stable isotopes

et al. 2016

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Mangroves exhibit low species richness compared to other tropical forests, but great structural and functional diversity. Aiming to contribute to a better understanding of the functioning of mangrove forests, we investigated nitrogen (N) dynamics in two physiographic types of mangroves (fringe and basin forests) in southeastern Brazil. Because fringe forests are under great influence of tidal flushing we hypothesized that these forests would exhibit higher N cycling rates in sediment and higher N losses to the atmosphere compared to basin forests. We quantified net N mineralization and nitrification rates in sediment and natural abundance of N stable isotopes (δN) in the sediment-plant-litter system. The fringe forest exhibited higher net N mineralization rates and δN in the sediment-plant-litter system, but net nitrification rates were similar to those of the basin forest. The results of the present study suggest that fringe forests exhibit higher N availability and N cycling in sediment compared to basin forests.

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Allometric models for aboveground biomass estimation of the mangrove Avicennia schaueriana

Cited by 22 publications

References 49 publications

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Nitrogen dynamics in subtropical fringe and basin mangrove forests inferred from stable isotopes

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