Thumbnail Image

Biomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information

XV World Forestry Congress, 2-6 May 2022

Year of publication 2022

Place of publication Rome, Italy ;

Pages 1p.

Author Rahman, M. S., Donoghue, D. N. M., Bracken, L. J., Mahmood, H.

Publisher FAO ;

Product type Document

Synopsis (short abstract) Improved estimates of aboveground biomass are required to improve our understanding of the productivity of mangrove forests to support the long-term conservation of these fragile ecosystems which are under threat from many natural and anthropogenic pressures. To understand how individual species affects biomass estimates in mangrove forests, five species-specific and four genus-specific allometric models were developed. Independent tree inventory data were collected from 140 sample plots to compare the aboveground biomass (AGB) among the species-specific models and seven existing frequently used pan-tropical and Sundarbans-specific generic models. The effect of individual tree species was also evaluated using model parameters for wood densities (from individual trees to the whole Sundarbans) and tree heights (individual, plot average and plot top height). All nine species-specific models explained a high percentage of the variance in tree AGB (R2 = 0.97 to 0.99) with the diameter at breast height (DBH) and total height (H). At the individual tree level, the generic allometric models overestimated AGB from 22% to 167% compared to the species-specific models. At the plot level, mean AGB varied from 111.36 Mg ha-1 to 299.48 Mg ha-1, where AGB significantly differed in all generic models compared to the species-specific models (p < 0.05). Using measured species wood density (WD) in the allometric model showed 4.5% to 9.7% less biomass than WD from a published database and other sources. When using plot top height and plot average height rather than measured individual tree height, the AGB was overestimated by 19.5 % and underestimated by 8.3% (p < 0.05). The study demonstrates that species-specific allometric models and individual tree measurements benefit biomass estimation in mangrove forests. Tree level measurement from the inventory plots, if available, should be included in allometric models to improve the accuracy of forest biomass estimates, particularly when upscaling individual trees up to the ecosystem level. Keywords: Climate change, Monitoring and data collection, Sustainable forest management ID: 3621710

Statutory body / Meeting title World Forestry Congress (WFC)

Language English

Also available in:
No results found.

Related items

Showing items related by metadata.

  • Thumbnail Image
    Article
    Allometric models for estimating above ground biomass of Bambusa tulda Roxb. and Melocanna baccifera (Roxb.) Kurz
    XV World Forestry Congress, 2-6 May 2022
    2022
    Also available in:
    No results found.
    Allometric equations are used to estimate the biomass and carbon stock of forests. There is a dearth of species- specific allometric equations for bamboos growing in Bangladesh. Bambusa tulda and Melocanna baccifera are the two most common bamboo species of commercial importance in Bangladesh. This study reports allometric equations for estimating biomass of bamboo compartments (leaf, branches, and stem) and total above-ground biomass. Data was collected from natural bamboo forests of different locations of Khagrachhari district. A total of 50 bamboos (25 B. tulda and 25 M. baccifera) were sampled following the destructive method. Bamboo leaf, branch, and stem were measured for fresh weight in the field. Sub-samples were collected in sufficient amounts and processed in the laboratory for density and oven-dry weight to derive fresh to oven-dry weight ratio. Commonly used 10 candidate equations were tested using Diameter at Breast Height (DBH), diameter at base (D5), and height (H) as explanatory variables to find the best fitted allometric equation. In total, the study developed 60 models with 10 for each component of the two species. Applying the goodness-of-fit statistics, 4 best-fitted models were selected for estimating stem and total above-ground biomass (TAGB) of the two bamboo species. The best fit allometric biomass models for M. baccifera were, Ys = 0.398*DBH1.542 and Yt = 0.627*DBH1.382, where, Ys = stem biomass and Yt = total above-ground biomass. On the other hand, best fit allometric biomass models for B. tulda were, Ys = 0.041*DBH1.0658*H1.2311, and Yt = 0.235*D5 1.867, where, D5 is diameter at the base (5 cm above the ground). The relationship between the biomass and dendrometric variables in the form of best-fitted models was statistically significant at p < 0.05 levels. The allometric models developed by this study will be useful for better estimation of biomass and sequestered carbon in the plain land homestead forests of Bangladesh. Keywords: Khagrachhari, Bamboo, Carbon sequestration, Bambusa tulda and Melocanna baccifera ID: 3623846
  • Thumbnail Image
    Article
    Allometric equation for estimating tree above ground biomass modified by ecological environmental factors in tropical dipterocarp forests
    XV World Forestry Congress, 2-6 May 2022
    2022
    Also available in:
    No results found.
    Tropical Dipterocarp Forest (DF) plays an important role in mitigating climate change thanks to its carbon sequestration capacity. In order to estimate the CO2 absorption capacity of DF as a basis for the development of forest ecological services, a system of biomass equations is needed; while very few models for estimating biomass in DF have been published and have not yet reflected the impact of ecological environmental factors. The purpose of the study was to validate and select the best model for estimating tree above ground biomass (AGB, kg) in DF under the influence of ecological environmental factors, thereby improving the reliability. Twenty-eight 0.25 ha plots in the Central Highlands and one 1 ha plot in the Southeast ecoregion in Viet Nam were measured. A total of 329 trees were destructively sampled to obtain a dataset of AGB; Methods for developing equations were weighted nonlinear fixed/mixed models with/without random effects fit by Maximum Likelihood; Using K-fold cross validation with K = 10, we compared and selected the best model with and without ecological environmental factors. As a result, separate ecological environmental factors did not affect AGB, while the combination of the factors influences the AGB model through the form: AGB = AVERAGE × MODIFIER, AGB = a × Db ×WDd × exp (e2 × (P - 1502) + e3 × (BA - 12.62)) that was significantly more reliable than a model without these factors involved; where D (cm), WD (g / cm3), P (mm year-1) and BA (m2 ha-1) are the diameter at breast height, wood density, averaged annual rainfall and total basal area of forest stand, respectively. Keywords: above ground biomass, dipterocarp forest, ecological factor ID: 3473259
  • Thumbnail Image
    Article
    The use of Normalized Difference Vegetation Index (NDVI) to assess urban forests dynamics in West Africa: A case study of Mbao Classified Forest, Dakar (Senegal)
    XV World Forestry Congress, 2-6 May 2022
    2022
    Also available in:
    No results found.
    Mbao Classified Forest is the largest urban forest in Dakar. It covers an area of 720 hectares and is the most important green lung of the city. This forest plays a key role in terms of carbon storage and sequestration, air pollution removal, and more generally in ecosystem services provision. Hence it is urgent to monitor the dynamic of this forest over the past twenty years (1998-2018) because a lot of infrastructures including a water pumping station and a highway were established inside during this period. These installations make it subject to encroachments and the risk of depletion that could compromise its existence. The aim of this this paper is to assess urban forest dynamics using artificial intelligence and vegetation indices. To achieve this goal the first step is to perform a forest inventory. We opted for a sampling rate of 0.5%. The area of a plot in the i-Tree Eco inventory is 391 m2 with a radius of 11.16 m, which resulted in a total number of 90 plots. The variables measured for each tree are D.B.H, total height, crown width. The allometric equations were used to compute the above-ground biomass. The NDVI of every plot was computed from Landsat datasets followed by the development of a linear regression model with NDVI as the independent variable and biomass as the dependent variable. Landsat imagery enables the NDVI computation of each plot during the twenty past years and using the regression model, the biomass was determined over this period. Our results provide a sound basis to advocate the safeguarding of Mbao Classified Forest. Keywords: Urban forest, biomass, NDVI, inventory. ID: 3621874
Show more

Users also downloaded

Showing related downloaded files

No results found.