Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 10953 - 10963
• Main Authors: Jin, Decai, Qi, Jianbo, Huang, Huaguo, Li, Linyuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Artificial neural networks; Biological system modeling; Canopies; Canopy; convolutional neural network (CNN); Convolutional neural networks; Data models; Datasets; FCC; forest canopy cover (FCC); Forestry; High resolution; Hydrology; Image resolution; Learning; Neural networks; Plant cover; Radiative transfer; Remote sensing; Resolution; Satellite imagery; Simulation; Solid modeling; Spaceborne remote sensing; Three dimensional models; Three-dimensional (3-D) radiative transfer model; Three-dimensional displays; Training; Transfer learning

Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this article, we proposed...