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Book (stand-alone)Integrating surveys with geospatial data through small area estimation to disaggregate SDG indicators at subnational level
Case study on SDG Indicators 2.3.1 and 2.3.2
2023Also available in:
No results found.The present technical report illustrates a case study on the adoption of small area estimation techniques to produce granular sub-national estimates of SDG Indicators 2.3.1 and 2.3.2, by integrating survey microdata with auxiliary information retrieved from various trustworthy geospatial information systems. The technical report provides practical guidance to national statistical offices and other institutions wanting to implement small area estimation techniques on SDG Indicators 2.3.1 and 2.3.2 or similar indicators based on surveys microdata. -
DocumentBiomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.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 -
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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