Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy

iForest: Biogeosciences and Forestry

article

Mauro Maesano¹  Giovanni Santopuoli²  Federico Valerio Moresi¹  Giorgio Matteucci³  Bruno Lasserre⁴  Giuseppe Scarascia Mugnozza¹ 

[1] Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia;Department of Agricultural, Environmental and Food Sciences, University of Molise;Institute of Bioeconomy, National Research Council of Italy;Department of Bioscience and Territory, University of Molise

关键词: Above Ground Biomass;    UAV;    Random Forest;    Forest Biomass;    Machine Learning;   

DOI  :  10.3832/ifor3781-015

学科分类：社会科学、人文和艺术（综合）

来源: Societa Italiana di Selvicoltura ed Ecologia Forestale (S I S E F)

【 摘 要 】

Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model.

【 授权许可】

Unknown