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ArticleTree-biomass-carbon estimation in the coastal afforestation sites of Chittagong, Bangladesh
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Global climate is changing relentlessly due to anthropogenic greenhouse gas emissions into the atmosphere. Its impacts are globally visible now. Bangladesh is the worst-affected country in the world due to this climate change. Coastal afforestation, among several forestry options, is critical to climate change mitigation and adaptation. This study estimated the tree biomass growth and its carbon in the Kattoli and Parki beach under the Chittagong coastal forest division. The study estimated that the total biomass density of Acacia auriculiformis, Acacia nilotica, Avicennia officinalis, Casuarina equisetifolia, Samanea saman, Sonneratia apetala and Terminalia arjuna were 131.57±6.77, 116.96±6.41, 350.64±7.99, 296.47±9.46, 119.27±7.45, 154.86±4.78 and 117.11±9.68 tha-1, respectively, with the mean annual increment of 65.79±3.38, 58.48±3.20, 15.25±0.35, 33.15±1.60, 59.63±3.73, 6.45±0.11 and 58.55±4.84 tha-1 yr-1, respectively. Furthermore, the total biomass-carbon of each species was also estimated, which were 65.79±3.38, 58.48±3.2, 175.32±3.10, 148.23±4.73, 59.63±3.73, 77.43±2.39 and 58.55±4.84 tCha-1 for the respective species, respectively, with the mean annual increment of 32.89±1.69, 29.24±1.60, 7.62±0.17, 16.57±0.80, 29.82±1.86, 3.23±0.10, 29.28±2.42 tCha-1 yr-1, respectively. All the findings of the study indicate that afforestation with both mangrove and non-mangrove species along with the coastal belts in Chittagong has the potential to mitigate climate change. The results can be useful for climate change mitigation practitioners, researchers, and policymakers on a native and broad scale. Keywords: Tree species; Coastal plantation; Carbon sequestration; Aboveground biomass; Belowground biomass ID: 3474035 -
ArticleThe tropical biomass & carbon project– An app for forest biomass and carbon estimates
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.This article introduces the project called Tropical Biomass & Carbon – TB&C, available on the permanent link www.tropicalbiomass.com. The App requires input attributes of the forest stand or diameter class easily obtained, being: smallest and largest diameters, number of trees ha-1, and parameters of the diameter distribution. The output attributes are at the stand and tree levels. At stand level, the App delivers mean aboveground biomass (AGB) and carbon (AGC), in Mg ha-1, as well as their confidence intervals (CIs) and uncertainties. The tree-level outputs are AGB and diameter for every tree in the stand. The project TB&C comprises four Brazilian forest (and non-forest) formations: Campinarana, Floresta estacional, Floresta ombrofila, and Savana. This article aims to disclose the algorithm written for the TB&C App. This phase counts on a standardized database of 1,428 trees with dry AGB destructively measured. Model uncertainties were incorporated into the modeling process. In addition to its reliability, we cite as great advantages of the TB&C App; (i) simplicity and a user-friendly layout, (ii) AGB and AGC estimates provided along with robust CIs, and (iii) estimates at the stand and tree levels with consistent totals. As a secondary product, the project TB&C delivers a dataset of 64,000 simulated plots, informing dry AGB, tree density, basal area, Lorey’s height, and shape of the diameter distribution. Keywords: Tropical Forest, Aboveground biomass, Uncertainty analysis, Stand- and tree-level, estimates, Web application ID: 3623771 -
DocumentThe effects of ignoring clustered data structure in allometric biomass models on large forest area biomass estimation
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The aim of this study was to assess the effects of ignoring the clustered data structure on large area biomass estimation, when model uncertainty is included or not in the biomass prediction process. We used a Monte Carlo error propagation procedure to combine the uncertainty from allometric model predictions with the uncertainty from plot-to-plot variation, to produce estimates of mean AGB per hectare and standard error of the mean. An alternative procedure that ignores model prediction uncertainty was also used, therefore, showing only uncertainty due to differences between plots. Ignoring the clustered data structure, (i.e., fitting allometric models using ordinary least squares), the estimates of mean biomass per hectare were approximately 11% less than the estimates based on mixed effects models (that accounted for the clustered data structure), regardless of including or not the model prediction uncertainty. The estimates of uncertainty were also affected by ignoring the clustered data structure. When including model prediction uncertainty, ignoring the clustered data structure resulted in underestimation of standard error by 30%, whereas when model uncertainty was not included, the underestimation was 13%. Therefore, ignoring the clustered data structure, may affect both, the accuracy and the precision of biomass estimations over large forest areas. Keywords: Monitoring and data collection, Climate change ID: 3616826
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