Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

编号 010035605

推送时间 20220815

研究领域 森林生态 

年份 2022 

类型 期刊 

语种 英语

标题 Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

来源期刊 forest

期 第356期

发表时间 20220801

关键词 biomass;  carbon stock;  combination of ALS-UAV and TLS data;  segmentation tree;  Mediterranean region;  allometric equations;  Cork oak forest; 

摘要 The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock. View Full-Text

服务人员 王璐

服务院士 蒋有绪

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