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Titre : Modeling of Acacia seyal var seyal Volume, Above-Ground Biomass and Carbon Estimation Using Field-based and RADAR Remotely Sensed Data, Central Sudan

Auteur : Anwar Sid Ahmed Mohamed Abd Alla

Université de soutenance : University of Khartoum

Grade : Ph.D. in Environmental Studies 2021

Résumé partiel
Acacia seyal is the most dominant tree species in Sudan with significant economic, social and environmental values. Development of methodologies to estimate the species wood volume, biomass and carbon contents is important for sustainable management in the context of reducing emission from deforestation and forest degradation. This study investigated the relationships of A. seyal trees volume with trees ; biomass and carbon content using field-based measurements and RADAR backscatter, and to estimate tree form factor, wood density and carbon ratios. The field data was collected from Wad-Elbasir and Okalma forests in Elgadrif and Sinnar States, respectively. Compartments in Wad-Elbashir forest contains of trees with deferent age-groups and stocking density. In the two forests, Systematic sample plots were determined and tree height (Ht), stump diameter (STD), diameter at breast height (DBH) were measured. In addition, RADAR backscatter of Sentinel 1A C band (VV, VH polarization), PALSAR and ALOS 2 L band data (HH, HV polarization) was obtained and analyzed to test their sensitivity to the tree volume, DBH and Ht. Then land cover maps were created using Sentinel 1A. In Okalma Forest trees volume was retrieved from ALOS2 data using water Cloud Model. Furthermore, twenty representative sample plots representing groups of A. seyal tree ages (range of 5–29 years) in Wad-Elbashir Forest were determined. A representative tree per plot was felled, sectioned into logs and measured, and then form factor and tree volume were calculated. Wood sections per log and branches were taken to the laboratory to estimate wood density and carbon fraction (CF) in the laboratory, and then dry above-ground wood biomass and carbon content (CC) were calculated. The statistical analysis of the data included Analysis of variance and regression to test differences interrelationships based on adjusted coefficient of determination (R2) and P values.

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