Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management. (15th August 2021)

• Record Type: Journal Article
• Authors: Liu, Tiedong; Sun, Yuxin; Wang, Cai; Zhang, Yangyang; Qiu, Zixuan; Gong, Wenfeng; Lei, Shuhan; Tong, Xinyu; Duan, Xuanyu
• Is Part Of: Journal of cleaner production. Volume 311(2021)
• Publication Date: 2021-08-15
• Subjects: Unmanned aerial vehicle -- Artificial intelligence -- Tropical forest -- Forest ecological monitoring -- Sustainable forest management; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.127546
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The ecological value of tropical forests in water conservation district has been of great interest because of their rich vegetation types and higher biomass density than any other land cover types, it is urgent to evaluate the ecological value of tropical forests in water conservation district. However, the monitoring of tropical forests in water conservation district is faced with many problems, such as high forest density, complexity and diversity of the forest structure, complex topography and climate conditions, and the difficulty of access for investigators. In order to solve the above difficulties, this study combined 3D point cloud reconstruction based on Unmanned Aerial Vehicle - Structure from Motion (UAV-SfM) technology with forest type classification based on the Convolutional Neural Network (CNN) method, combined with a small amount of forest permanent sample plot survey data, to accurately evaluate the forest biomass distribution and forest biodiversity in water conservation district. The results show that the overall classification accuracy of the 20 forest types in water conservation district based on the CNN method is 0.61, the overall Kappa coefficient is 0.59, and the conditional Kappa coefficient is concentrated in the range of 0.43–0.85. The Root Mean Square Error (RMSE) of the plane measurement of UAV-SfM technology is 0.432 m, and the RMSE of the elevation measurement is 0.989 m, the effect of this UAV technology in tropical forest monitoring is superior. Using the techniques mentioned above, this study can effectively and accurately monitor and evaluate the biomass distribution and biodiversity of tropical forests in the water conservation district. Based on the precision forest ecological monitoring data, this study can develop a scientific and reasonable sustainable forest management plan for the water conservation district according to the distribution of forest biomass and biodiversity. The combination of UAV-SfM technology and the CNN method is an innovative attempt, and the integration of UAV and artificial intelligence technology solves practical problems faced by sustainable forest management. UAV and artificial intelligence will also provide an important foundation for forest ecological environment sustainability assessment research. Highlights: UAV and AI technology bring innovation to sustainable forest management. UAV and AI technology improve efficiency of forest ecological monitoring. UAV and AI technology accurately analyze forest biomass and biodiversity. Sustainable forest management based on forest biomass and biodiversity.