Aboveground Biomass Allometric Equations for the Miombo Forests of the Democratic Republic of the Congo Based on Terrestrial Lidar Data
SSRN, ISSN: 1556-5068
2024
- 111Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Accurate assessment of aboveground biomass (AGB) stocks and stock changes in extensive Miombo forests are difficult to make due to a lack of specific allometric equations (AE). Terrestrial Laser Scanning (TLS) is a non-destructive method allowing to calibrate AEs that has been recently validated by the IPCC’s guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the dry Miombo forest of Katanga, in the DR Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented high performance criteria (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between the sampled sites and plot. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from -19.97 to 35.04%was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga.
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